From 1f924f34249d4db456f83b306f34a4354594414a Mon Sep 17 00:00:00 2001 From: Ufuk <59481549+ufukguenes@users.noreply.github.com> Date: Fri, 11 Aug 2023 14:43:18 +0200 Subject: [PATCH] Revert "Doe categorical (#243)" This reverts commit 126393eb0e5edb5225539a4e19e1b2bf0d6b9698. --- bofire/data_models/constraints/nchoosek.py | 1 - bofire/data_models/constraints/nonlinear.py | 4 +- bofire/data_models/domain/domain.py | 2 +- bofire/data_models/strategies/doe.py | 9 +- .../strategies/samplers/polytope.py | 6 - bofire/strategies/doe/branch_and_bound.py | 232 - bofire/strategies/doe/design.py | 422 +- bofire/strategies/doe/objective.py | 6 - bofire/strategies/doe/utils.py | 6 +- .../doe/utils_categorical_discrete.py | 563 - bofire/strategies/doe/utils_features.py | 147 - bofire/strategies/doe_strategy.py | 148 +- tests/bofire/data_models/specs/strategies.py | 2 - tests/bofire/strategies/doe/test_design.py | 80 +- tests/bofire/strategies/doe/test_objective.py | 6 +- tests/bofire/strategies/test_doe.py | 86 +- .../Reaction_Optimization_Example.ipynb | 764 +- .../doe/design_with_explicit_formula.ipynb | 244 +- tutorials/doe/optimality_criteria.ipynb | 26 +- tutorials/getting_started.ipynb | 11849 +++++++++++++++- tutorials/models_serial.ipynb | 548 +- tutorials/strategies_serial.ipynb | 106 +- 22 files changed, 13413 insertions(+), 1844 deletions(-) delete mode 100644 bofire/strategies/doe/branch_and_bound.py delete mode 100644 bofire/strategies/doe/utils_categorical_discrete.py delete mode 100644 bofire/strategies/doe/utils_features.py diff --git a/bofire/data_models/constraints/nchoosek.py b/bofire/data_models/constraints/nchoosek.py index be7283892..86282527d 100644 --- a/bofire/data_models/constraints/nchoosek.py +++ b/bofire/data_models/constraints/nchoosek.py @@ -93,7 +93,6 @@ def is_fulfilled(self, experiments: pd.DataFrame, tol: float = 1e-6) -> pd.Serie Returns: bool: True if fulfilled else False. """ - cols = self.features sums = (np.abs(experiments[cols]) > tol).sum(axis=1) diff --git a/bofire/data_models/constraints/nonlinear.py b/bofire/data_models/constraints/nonlinear.py index c7815f2e4..598b97cd6 100644 --- a/bofire/data_models/constraints/nonlinear.py +++ b/bofire/data_models/constraints/nonlinear.py @@ -73,7 +73,7 @@ def jacobian(self, experiments: pd.DataFrame) -> pd.DataFrame: class NonlinearEqualityConstraint(NonlinearConstraint): - """Nonlinear equality constraint of the form 'expression == 0'. + """Nonlinear inequality constraint of the form 'expression <= 0'. Attributes: expression: Mathematical expression that can be evaluated by `pandas.eval`. @@ -91,7 +91,7 @@ def __str__(self): class NonlinearInequalityConstraint(NonlinearConstraint): - """Nonlinear inequality constraint of the form 'expression <= 0'. + """Linear inequality constraint of the form 'expression == 0'. Attributes: expression: Mathematical expression that can be evaluated by `pandas.eval`. diff --git a/bofire/data_models/domain/domain.py b/bofire/data_models/domain/domain.py index 279580d9c..edadf379a 100644 --- a/bofire/data_models/domain/domain.py +++ b/bofire/data_models/domain/domain.py @@ -179,7 +179,7 @@ def validate_linear_constraints(cls, v, values): # gather continuous inputs in dictionary continuous_inputs_dict = {} for f in values["inputs"]: - if isinstance(f, ContinuousInput): + if type(f) is ContinuousInput: continuous_inputs_dict[f.key] = f # check if non continuous input features appear in linear constraints diff --git a/bofire/data_models/strategies/doe.py b/bofire/data_models/strategies/doe.py index 031007e3e..4b4444aac 100644 --- a/bofire/data_models/strategies/doe.py +++ b/bofire/data_models/strategies/doe.py @@ -2,6 +2,8 @@ from bofire.data_models.constraints.api import Constraint from bofire.data_models.features.api import ( + CategoricalInput, + DiscreteInput, Feature, MolecularInput, ) @@ -20,11 +22,6 @@ class DoEStrategy(Strategy): ], str, ] - optimization_strategy: Literal[ - "default", "exhaustive", "branch-and-bound", "partially-random", "relaxed" - ] = "default" - - verbose: bool = False @classmethod def is_constraint_implemented(cls, my_type: Type[Constraint]) -> bool: @@ -32,7 +29,7 @@ def is_constraint_implemented(cls, my_type: Type[Constraint]) -> bool: @classmethod def is_feature_implemented(cls, my_type: Type[Feature]) -> bool: - if my_type in [MolecularInput]: + if my_type in [CategoricalInput, DiscreteInput, MolecularInput]: return False return True diff --git a/bofire/data_models/strategies/samplers/polytope.py b/bofire/data_models/strategies/samplers/polytope.py index d69ef5db1..7c26323f5 100644 --- a/bofire/data_models/strategies/samplers/polytope.py +++ b/bofire/data_models/strategies/samplers/polytope.py @@ -15,10 +15,6 @@ Feature, ) from bofire.data_models.strategies.samplers.sampler import SamplerStrategy -from bofire.strategies.doe.utils_features import ( - RelaxableBinaryInput, - RelaxableDiscreteInput, -) class PolytopeSampler(SamplerStrategy): @@ -49,6 +45,4 @@ def is_feature_implemented(cls, my_type: Type[Feature]) -> bool: CategoricalInput, DiscreteInput, CategoricalDescriptorInput, - RelaxableBinaryInput, - RelaxableDiscreteInput, ] diff --git a/bofire/strategies/doe/branch_and_bound.py b/bofire/strategies/doe/branch_and_bound.py deleted file mode 100644 index 1c7d597ca..000000000 --- a/bofire/strategies/doe/branch_and_bound.py +++ /dev/null @@ -1,232 +0,0 @@ -from __future__ import annotations - -from functools import total_ordering -from queue import PriorityQueue -from typing import List - -import numpy as np -import pandas as pd - -from bofire.data_models.constraints.api import ConstraintNotFulfilledError -from bofire.data_models.domain.domain import Domain -from bofire.strategies.doe.design import find_local_max_ipopt -from bofire.strategies.doe.objective import get_objective_class -from bofire.strategies.doe.utils import get_formula_from_string -from bofire.strategies.doe.utils_features import ( - RelaxableBinaryInput, - RelaxableDiscreteInput, -) - - -@total_ordering -class NodeExperiment: - def __init__( - self, - partially_fixed_experiments: pd.DataFrame, - design_matrix: pd.DataFrame, - value: float, - categorical_groups: List[List[RelaxableBinaryInput]], - discrete_vars: List[RelaxableDiscreteInput], - ): - """ - - Args: - partially_fixed_experiments: dataframe containing (some) fixed variables for experiments. - design_matrix: optimal design for given the fixed and partially fixed experiments - value: value of the objective function evaluated with the design_matrix - categorical_groups: Represents the different groups of the categorical variables - discrete_vars: List of discrete variables in the optimization problem - """ - self.partially_fixed_experiments = partially_fixed_experiments - self.design_matrix = design_matrix - self.value = value - self.categorical_groups = categorical_groups - self.discrete_vars = discrete_vars - - def get_next_fixed_experiments(self) -> List[pd.DataFrame]: - """ - Based on the current partially_fixed_experiment DataFrame the next branches are determined. One variable will - be fixed more than before. - Returns: List of the next possible branches where only one variable more is fixed - - """ - # branching for the binary/ categorical variables - for group in self.categorical_groups: - for row_index, _exp in self.partially_fixed_experiments.iterrows(): - if ( - self.partially_fixed_experiments.iloc[row_index][group[0].key] - is None - ): - current_keys = [elem.key for elem in group] - allowed_fixations = np.eye(len(group)) - branches = [ - self.partially_fixed_experiments.copy() - for i in range(len(allowed_fixations)) - ] - for k, elem in enumerate(branches): - elem.loc[row_index, current_keys] = allowed_fixations[k] - return branches - - # branching for the discrete variables - for var in self.discrete_vars: - for row_index, _exp in self.partially_fixed_experiments.iterrows(): - current_fixation = self.partially_fixed_experiments.iloc[row_index][ - var.key - ] - first_fixation, second_fixation = None, None - if current_fixation is None: - lower_split, upper_split = var.equal_count_split( - var.lower_bound, var.upper_bound - ) - first_fixation = (var.lower_bound, lower_split) - second_fixation = (upper_split, var.upper_bound) - - elif current_fixation[0] != current_fixation[1]: - lower_split, upper_split = var.equal_count_split( - current_fixation[0], current_fixation[1] - ) - first_fixation = (current_fixation[0], lower_split) - second_fixation = (upper_split, current_fixation[1]) - - if first_fixation is not None: - first_branch = self.partially_fixed_experiments.copy() - second_branch = self.partially_fixed_experiments.copy() - - first_branch.loc[row_index, var.key] = first_fixation - second_branch.loc[row_index, var.key] = second_fixation - - return [first_branch, second_branch] - - return [] - - def __eq__(self, other: NodeExperiment) -> bool: - return self.value == other.value - - def __ne__(self, other: NodeExperiment) -> bool: - return self.value != other.value - - def __lt__(self, other: NodeExperiment) -> bool: - return self.value < other.value - - def __str__(self): - return ( - "\n ================ Branch-and-Bound Node ================ \n" - + f"objective value: {self.value} \n" - + f"design matrix: \n{self.design_matrix.round(4)} \n" - + f"current fixations: \n{self.partially_fixed_experiments.round(4)} \n" - ) - - -def is_valid( - design_matrix: pd.DataFrame, domain: Domain, tolerance: float = 1e-2 -) -> bool: - """ - test if a design is a valid solution. i.e. binary and discrete variables are valid - Args: - design_matrix (pd.DataFrame): the design to test - domain (Domain): the domain for which the design should be tested - tolerance: absolute tolerance between valid values and values in the design - - Returns: True if the design is valid, else False - - """ - categorical_vars = domain.get_features(includes=RelaxableBinaryInput) - for var in categorical_vars: - value = design_matrix.get(var.key) - if not ( - np.logical_or( - np.isclose(value, 0, atol=tolerance), - np.isclose(value, 1, atol=tolerance), - ).all() - ): - return False - - discrete_vars = domain.get_features(includes=RelaxableDiscreteInput) - for var in discrete_vars: - value = design_matrix.get(var.key) - if False in [True in np.isclose(v, var.values, atol=tolerance) for v in value]: - return False - return True - - -def bnb( - priority_queue: PriorityQueue, - verbose: bool = False, - num_explored: int = 0, - **kwargs, -) -> NodeExperiment: - """ - branch-and-bound algorithm for solving optimization problems containing binary and discrete variables - Args: - num_explored: keeping track of how many branches have been explored - priority_queue (PriorityQueue): initial nodes of the branching tree - verbose (bool): if true, print information during the optimization process - **kwargs: parameters for the actual optimization / find_local_max_ipopt - - Returns: a branching Node containing the best design found - - """ - if priority_queue.empty(): - raise RuntimeError("Queue empty before feasible solution was found") - - domain = kwargs["domain"] - n_experiments = kwargs["n_experiments"] - - # get objective function - model_formula = get_formula_from_string( - model_type=kwargs["model_type"], rhs_only=True, domain=domain - ) - objective_class = get_objective_class(kwargs["objective"]) - objective_class = objective_class( - domain=domain, model=model_formula, n_experiments=n_experiments - ) - - pre_size = priority_queue.qsize() - current_branch = priority_queue.get() - # test if current solution is already valid - if is_valid(current_branch.design_matrix, domain): - return current_branch - - # branch current solutions in sub-problems - next_branches = current_branch.get_next_fixed_experiments() - - if verbose: - print( - f"current length of branching queue (+ new branches): {pre_size} + {len(next_branches)} currently " - f"explored branches: {num_explored}, current best value: {current_branch.value}" - ) - # solve branched problems - for _i, branch in enumerate(next_branches): - initial_sample = branch.where( - ~pd.isnull(branch), current_branch.design_matrix.values - ) - initial_sample = initial_sample.astype("float64") - kwargs["sampling"] = initial_sample - try: - design = find_local_max_ipopt(partially_fixed_experiments=branch, **kwargs) - value = objective_class.evaluate(design.to_numpy().flatten()) - new_node = NodeExperiment( - branch, - design, - value, - current_branch.categorical_groups, - current_branch.discrete_vars, - ) - domain.validate_candidates( - candidates=design.apply(lambda x: np.round(x, 8)), - only_inputs=True, - tol=1e-4, - raise_validation_error=True, - ) - - priority_queue.put(new_node) - except ConstraintNotFulfilledError: - if verbose: - print("skipping branch because of not fulfilling constraints") - - return bnb( - priority_queue, - verbose=verbose, - num_explored=num_explored + len(next_branches), - **kwargs, - ) diff --git a/bofire/strategies/doe/design.py b/bofire/strategies/doe/design.py index 90bd93f4c..1086b4f39 100644 --- a/bofire/strategies/doe/design.py +++ b/bofire/strategies/doe/design.py @@ -1,8 +1,5 @@ -import time import warnings -from itertools import combinations_with_replacement, product -from queue import PriorityQueue -from typing import Dict, List, Optional, Tuple, Union +from typing import Dict, Optional, Union import numpy as np import pandas as pd @@ -16,9 +13,6 @@ ) from bofire.data_models.domain.api import Domain from bofire.data_models.enum import SamplingMethodEnum -from bofire.data_models.features.api import ( - Input, -) from bofire.data_models.strategies.api import ( PolytopeSampler as PolytopeSamplerDataModel, ) @@ -29,319 +23,10 @@ metrics, nchoosek_constraints_as_bounds, ) -from bofire.strategies.doe.utils_features import ( - RelaxableBinaryInput, - RelaxableDiscreteInput, -) from bofire.strategies.enum import OptimalityCriterionEnum from bofire.strategies.samplers.polytope import PolytopeSampler -def find_local_max_ipopt_BaB( - domain: Domain, - model_type: Union[str, Formula], - n_experiments: Optional[int] = None, - delta: float = 1e-7, - ipopt_options: Optional[Dict] = None, - sampling: Optional[pd.DataFrame] = None, - fixed_experiments: Optional[pd.DataFrame] = None, - partially_fixed_experiments: Optional[pd.DataFrame] = None, - objective: OptimalityCriterionEnum = OptimalityCriterionEnum.D_OPTIMALITY, - categorical_groups: Optional[List[List[RelaxableBinaryInput]]] = None, - verbose: bool = False, -) -> pd.DataFrame: - """Function computing a d-optimal design" for a given domain and model. - It allows for the problem to have categorical values which is solved by Branch-and-Bound - Args: - domain (Domain): domain containing the inputs and constraints. - model_type (str, Formula): keyword or formulaic Formula describing the model. Known keywords - are "linear", "linear-and-interactions", "linear-and-quadratic", "fully-quadratic". - n_experiments (int): Number of experiments. By default the value corresponds to - the number of model terms - dimension of ker() + 3. - delta (float): Regularization parameter. Default value is 1e-3. - ipopt_options (Dict, optional): options for IPOPT. For more information see [this link](https://coin-or.github.io/Ipopt/OPTIONS.html) - sampling (pd.DataFrame): dataframe containing the initial guess. - fixed_experiments (pd.DataFrame): dataframe containing experiments that will be definitely part of the design. - Values are set before the optimization. - partially_fixed_experiments (pd.DataFrame): dataframe containing (some) fixed variables for experiments. - Values are set before the optimization. Within one experiment not all variables need to be fixed. - Variables can be fixed to one value or can be set to a range by setting a tuple with lower and upper bound - Non-fixed variables have to be set to None or nan. - objective (OptimalityCriterionEnum): OptimalityCriterionEnum object indicating which objective function to use. - categorical_groups (List[List[ContinuousBinaryInput]], optional). Represents the different groups of the - categorical variables. Defaults to []. - verbose (bool): if true, print information during the optimization process - Returns: - A pd.DataFrame object containing the best found input for the experiments. In general, this is only a - local optimum. - """ - from bofire.strategies.doe.branch_and_bound import NodeExperiment, bnb - - if categorical_groups is None: - categorical_groups = [] - - n_experiments = get_n_experiments( - domain=domain, model_type=model_type, n_experiments=n_experiments - ) - - # get objective function - model_formula = get_formula_from_string( - model_type=model_type, rhs_only=True, domain=domain - ) - objective_class = get_objective_class(objective) - objective_class = objective_class( - domain=domain, model=model_formula, n_experiments=n_experiments, delta=delta - ) - - # setting up initial node in the branch-and-bound tree - column_keys = domain.inputs.get_keys() - - subtract = 0 - if fixed_experiments is not None: - subtract = len(fixed_experiments) - initial_branch = pd.DataFrame( - np.full((n_experiments - subtract, len(column_keys)), None), - columns=column_keys, - ) - - if partially_fixed_experiments is not None: - partially_fixed_experiments = pd.concat( - [ - partially_fixed_experiments, - pd.DataFrame( - np.full( - ( - n_experiments - len(partially_fixed_experiments), - len(domain.inputs), - ), - None, - ), - columns=domain.get_feature_keys(includes=Input), - ), - ] - ).reset_index(drop=True) - - initial_branch.mask( - partially_fixed_experiments.notnull(), - other=partially_fixed_experiments, - inplace=True, - ) - - initial_design = find_local_max_ipopt( - domain, - model_type, - n_experiments, - delta, - ipopt_options, - sampling, - fixed_experiments, - partially_fixed_experiments=initial_branch, - objective=objective, - ) - initial_value = objective_class.evaluate( - initial_design.to_numpy().flatten(), - ) - - discrete_vars = domain.inputs.get(includes=RelaxableDiscreteInput) - initial_node = NodeExperiment( - initial_branch, - initial_design, - initial_value, - categorical_groups, - discrete_vars, - ) - - # initializing branch-and-bound queue - initial_queue = PriorityQueue() - initial_queue.put(initial_node) - - # starting branch-and-bound - result_node = bnb( - initial_queue, - domain=domain, - model_type=model_type, - n_experiments=n_experiments, - delta=delta, - ipopt_options=ipopt_options, - sampling=sampling, - fixed_experiments=fixed_experiments, - objective=objective, - verbose=verbose, - ) - - return result_node.design_matrix - - -def find_local_max_ipopt_exhaustive( - domain: Domain, - model_type: Union[str, Formula], - n_experiments: Optional[int] = None, - delta: float = 1e-7, - ipopt_options: Optional[Dict] = None, - sampling: Optional[pd.DataFrame] = None, - fixed_experiments: Optional[pd.DataFrame] = None, - objective: OptimalityCriterionEnum = OptimalityCriterionEnum.D_OPTIMALITY, - partially_fixed_experiments: Optional[pd.DataFrame] = None, - categorical_groups: Optional[List[List[RelaxableBinaryInput]]] = None, - verbose: bool = False, -) -> pd.DataFrame: - """Function computing a d-optimal design" for a given domain and model. - It allows for the problem to have categorical values which is solved by exhaustive search - Args: - domain (Domain): domain containing the inputs and constraints. - model_type (str, Formula): keyword or formulaic Formula describing the model. Known keywords - are "linear", "linear-and-interactions", "linear-and-quadratic", "fully-quadratic". - n_experiments (int): Number of experiments. By default the value corresponds to - the number of model terms - dimension of ker() + 3. - delta (float): Regularization parameter. Default value is 1e-3. - ipopt_options (Dict, optional): options for IPOPT. For more information see [this link](https://coin-or.github.io/Ipopt/OPTIONS.html) - sampling (pd.DataFrame): dataframe containing the initial guess. - fixed_experiments (pd.DataFrame): dataframe containing experiments that will be definitely part of the design. - Values are set before the optimization. - objective (OptimalityCriterionEnum): OptimalityCriterionEnum object indicating which objective function to use. - partially_fixed_experiments (pd.DataFrame): dataframe containing (some) fixed variables for experiments. - Values are set before the optimization. Within one experiment not all variables need to be fixed. - Variables can be fixed to one value or can be set to a range by setting a tuple with lower and upper bound - Non-fixed variables have to be set to None or nan. - categorical_groups (List[List[ContinuousBinaryInput]], optional). Represents the different groups of the - categorical variables. Defaults to []. - verbose (bool): if true, print information during the optimization process - Returns: - A pd.DataFrame object containing the best found input for the experiments. In general, this is only a - local optimum. - """ - - if categorical_groups is None: - categorical_groups = [] - - if len(domain.get_features(includes=RelaxableDiscreteInput)) > 0: - raise NotImplementedError( - "Exhaustive search for discrete variables is not implemented yet." - ) - - # get objective function - model_formula = get_formula_from_string( - model_type=model_type, rhs_only=True, domain=domain - ) - objective_class = get_objective_class(objective) - objective_class = objective_class( - domain=domain, model=model_formula, n_experiments=n_experiments, delta=delta - ) - - # get binary variables - binary_vars = domain.get_features(RelaxableBinaryInput) - list_keys = binary_vars.get_keys() - - # determine possible fixations of the different categories - allowed_fixations = [] - for group in categorical_groups: - allowed_fixations.append(np.eye(len(group))) - - n_non_fixed_experiments = n_experiments - if fixed_experiments is not None: - n_non_fixed_experiments -= len(fixed_experiments) - - allowed_fixations = product(*allowed_fixations) - all_n_fixed_experiments = combinations_with_replacement( - allowed_fixations, n_non_fixed_experiments - ) - - if partially_fixed_experiments is not None: - partially_fixed_experiments = pd.concat( - [ - partially_fixed_experiments, - pd.DataFrame( - np.full( - ( - n_non_fixed_experiments - len(partially_fixed_experiments), - len(domain.inputs), - ), - None, - ), - columns=domain.get_feature_keys(includes=Input), - ), - ] - ).reset_index(drop=True) - - # testing all different fixations - column_keys = domain.inputs.get_keys() - group_keys = [var.key for group in categorical_groups for var in group] - minimum = float("inf") - optimal_design = pd.DataFrame() - len(domain.inputs) - len(binary_vars) - all_n_fixed_experiments = list(all_n_fixed_experiments) - for i, binary_fixed_experiments in enumerate(all_n_fixed_experiments): - if verbose: - start_time = time.time() - # setting up the pd.Dataframe for the partially fixed experiment - binary_fixed_experiments = np.array( - [ - var - for experiment in binary_fixed_experiments - for group in experiment - for var in group - ] - ).reshape(n_non_fixed_experiments, len(binary_vars)) - - binary_fixed_experiments = pd.DataFrame( - binary_fixed_experiments, columns=group_keys - ) - one_set_of_experiments = pd.DataFrame( - np.full((n_non_fixed_experiments, len(domain.inputs)), None), - columns=column_keys, - ) - - one_set_of_experiments.mask( - binary_fixed_experiments.notnull(), - other=binary_fixed_experiments, - inplace=True, - ) - - if partially_fixed_experiments is not None: - one_set_of_experiments.mask( - partially_fixed_experiments.notnull(), - other=partially_fixed_experiments, - inplace=True, - ) - - if sampling is not None: - sampling.loc[:, list_keys] = one_set_of_experiments[list_keys].to_numpy() - - # minimizing with the current fixation - try: - current_design = find_local_max_ipopt( - domain, - model_type, - n_experiments, - delta, - ipopt_options, - sampling, - fixed_experiments, - one_set_of_experiments, - objective, - ) - domain.validate_candidates( - candidates=current_design.apply(lambda x: np.round(x, 8)), - only_inputs=True, - tol=1e-4, - raise_validation_error=True, - ) - temp_value = objective_class.evaluate( - current_design.to_numpy().flatten(), - ) - if minimum is None or minimum > temp_value: - minimum = temp_value - optimal_design = current_design - if verbose: - print( - f"branch: {i} / {len(all_n_fixed_experiments)}, time: {time.time() - start_time} solution: {temp_value}, minimum after run {minimum}, difference: {temp_value - minimum}" - ) - except ConstraintNotFulfilledError: - if verbose: - print("skipping branch because of not fulfilling constraints") - return optimal_design - - def find_local_max_ipopt( domain: Domain, model_type: Union[str, Formula], @@ -350,7 +35,6 @@ def find_local_max_ipopt( ipopt_options: Optional[Dict] = None, sampling: Optional[pd.DataFrame] = None, fixed_experiments: Optional[pd.DataFrame] = None, - partially_fixed_experiments: Optional[pd.DataFrame] = None, objective: OptimalityCriterionEnum = OptimalityCriterionEnum.D_OPTIMALITY, ) -> pd.DataFrame: """Function computing an optimal design for a given domain and model. @@ -362,13 +46,9 @@ def find_local_max_ipopt( the number of model terms - dimension of ker() + 3. delta (float): Regularization parameter. Default value is 1e-3. ipopt_options (Dict, optional): options for IPOPT. For more information see [this link](https://coin-or.github.io/Ipopt/OPTIONS.html) - sampling (pd.DataFrame): dataframe containing the initial guess. + sampling (Sampling, np.ndarray): Sampling class or a np.ndarray object containing the initial guess. fixed_experiments (pd.DataFrame): dataframe containing experiments that will be definitely part of the design. Values are set before the optimization. - partially_fixed_experiments (pd.DataFrame): dataframe containing (some) fixed variables for experiments. - Values are set before the optimization. Within one experiment not all variables need to be fixed. - Variables can be fixed to one value or can be set to a range by setting a tuple with lower and upper bound - Non-fixed variables have to be set to None or nan. objective (OptimalityCriterionEnum): OptimalityCriterionEnum object indicating which objective function to use. Returns: A pd.DataFrame object containing the best found input for the experiments. In general, this is only a @@ -415,8 +95,8 @@ def find_local_max_ipopt( # if sampling is not None: - sampling.sort_index(axis=1, inplace=True) - x0 = sampling.values.flatten() + domain.validate_candidates(sampling, only_inputs=True) + x0 = sampling.values else: if len(domain.constraints.get(NonlinearConstraint)) == 0: sampler = PolytopeSampler( @@ -455,17 +135,12 @@ def find_local_max_ipopt( # fix experiments if any are given if fixed_experiments is not None: - fixed_experiments.sort_index(axis=1, inplace=True) domain.validate_candidates(fixed_experiments, only_inputs=True) - for i, val in enumerate(fixed_experiments.values.flatten()): + fixed_experiments = np.array(fixed_experiments.values) + for i, val in enumerate(fixed_experiments.flatten()): bounds[i] = (val, val) x0[i] = val - # partially fix experiments if any are given - bounds, x0 = partially_fix_experiment( - bounds, fixed_experiments, n_experiments, partially_fixed_experiments, x0 - ) - # set ipopt options if ipopt_options is None: ipopt_options = {} @@ -519,56 +194,6 @@ def find_local_max_ipopt( return design -def partially_fix_experiment( - bounds: list, - fixed_experiments: Union[pd.DataFrame, None], - n_experiments: int, - partially_fixed_experiments: Union[pd.DataFrame, None], - x0: np.ndarray, -) -> Tuple[List, np.ndarray]: - """ - fixes some variables for experiments. Within one experiment not all variables need to be fixed. - Variables can be fixed to one value or can be set to a range by setting a tuple with lower and upper bound - Non-fixed variables have to be set to None or nan. - - Args: - bounds (list): current bounds - fixed_experiments (pd.Dataframe): experiments which are guaranteed to be part of the design and are fully fixed - n_experiments (int): number of experiments - partially_fixed_experiments (pd.Dataframe): experiments which are partially fixed - x0: initial guess - - Returns: Tuple of list and pd.Dataframe containing the new bounds for each variable and an adapted initial guess - which comply with the bounds - - """ - - shift = 0 - if partially_fixed_experiments is not None: - partially_fixed_experiments.sort_index(axis=1, inplace=True) - if fixed_experiments is not None: - if ( - len(fixed_experiments) + len(partially_fixed_experiments) - > n_experiments - ): - raise AttributeError( - "Number of fixed experiments and partially fixed experiments exceeds the number of total " - "experiments" - ) - shift = len(fixed_experiments) - - shift = shift * len(partially_fixed_experiments.columns) - for i, val in enumerate(np.array(partially_fixed_experiments.values).flatten()): - index = shift + i - if type(val) is tuple: - bounds[index] = (val[0], val[1]) - x0[index] = val[0] - elif val is not None and not np.isnan(val): - bounds[index] = (val, val) - x0[index] = val - return bounds, x0 - - def check_fixed_experiments( domain: Domain, n_experiments: int, fixed_experiments: np.ndarray ) -> None: @@ -592,41 +217,6 @@ def check_fixed_experiments( ) -def check_partially_and_fully_fixed_experiments( - domain: Domain, - n_experiments: int, - fixed_experiments: np.ndarray, - paritally_fixed_experiments: np.ndarray, -) -> None: - """Checks if the shape of the fixed experiments is correct and if the number of fixed experiments is valid - Args: - domain (Domain): domain defining the input variables used for the check. - n_experiments (int): total number of experiments in the design that fixed_experiments are part of. - fixed_experiments (np.ndarray): fixed experiment proposals to be checked. - paritally_fixed_experiments (np.ndarray): partially fixed experiment proposals to be checked. - """ - - check_fixed_experiments(domain, n_experiments, fixed_experiments) - n_fixed_experiments, dim = np.array(fixed_experiments).shape - - n_partially_fixed_experiments, partially_dim = np.array( - paritally_fixed_experiments - ).shape - - if partially_dim != len(domain.inputs): - raise ValueError( - f"Invalid shape of partially_fixed_experiments. Length along axis 1 is {partially_dim}, but must be {len(domain.inputs)}" - ) - - if n_fixed_experiments + n_partially_fixed_experiments > n_experiments: - warnings.warn( - UserWarning( - "The number of fixed experiments and partially fixed experiments exceeds the amount " - "of the overall count of experiments. Partially fixed experiments may be cut of" - ) - ) - - def get_n_experiments( domain: Domain, model_type: Union[str, Formula], n_experiments: Optional[int] = None ): diff --git a/bofire/strategies/doe/objective.py b/bofire/strategies/doe/objective.py index 503605652..3348932ba 100644 --- a/bofire/strategies/doe/objective.py +++ b/bofire/strategies/doe/objective.py @@ -69,12 +69,6 @@ def evaluate_jacobian(self, x: np.ndarray) -> np.ndarray: def _convert_input_to_model_tensor( self, x: np.ndarray, requires_grad: bool = True ) -> Tensor: - """ - - Args: - x: x (np.ndarray): values of design variables a 1d array. - """ - assert x.ndim == 1, "values of design should be 1d array" X = pd.DataFrame( x.reshape(len(x.flatten()) // self.n_vars, self.n_vars), columns=self.vars ) diff --git a/bofire/strategies/doe/utils.py b/bofire/strategies/doe/utils.py index af960f771..3e554f143 100644 --- a/bofire/strategies/doe/utils.py +++ b/bofire/strategies/doe/utils.py @@ -12,10 +12,10 @@ LinearInequalityConstraint, NChooseKConstraint, NonlinearEqualityConstraint, + NonlinearInequalityConstraint, ) -from bofire.data_models.constraints.nonlinear import NonlinearInequalityConstraint -from bofire.data_models.domain.domain import Domain -from bofire.data_models.features.continuous import ContinuousInput +from bofire.data_models.domain.api import Domain +from bofire.data_models.features.api import ContinuousInput from bofire.data_models.strategies.api import ( PolytopeSampler as PolytopeSamplerDataModel, ) diff --git a/bofire/strategies/doe/utils_categorical_discrete.py b/bofire/strategies/doe/utils_categorical_discrete.py deleted file mode 100644 index 9f3683381..000000000 --- a/bofire/strategies/doe/utils_categorical_discrete.py +++ /dev/null @@ -1,563 +0,0 @@ -from itertools import combinations -from typing import List, Optional, Tuple, Union - -import numpy as np -import pandas as pd - -from bofire.data_models.constraints.linear import ( - LinearEqualityConstraint, - LinearInequalityConstraint, -) -from bofire.data_models.constraints.nchoosek import NChooseKConstraint -from bofire.data_models.constraints.nonlinear import NonlinearInequalityConstraint -from bofire.data_models.domain.domain import Domain -from bofire.data_models.features.categorical import CategoricalInput -from bofire.data_models.features.continuous import ContinuousInput -from bofire.data_models.features.discrete import DiscreteInput -from bofire.data_models.features.feature import Feature, Output -from bofire.strategies.doe.utils_features import ( - RelaxableBinaryInput, - RelaxableDiscreteInput, -) - - -def discrete_to_relaxable_domain_mapper( - domain: Domain, -) -> Tuple[Domain, List[List[RelaxableBinaryInput]]]: - """Converts a domain with discrete and categorical inputs to a domain with relaxable inputs. - - Args: - domain (Domain): Domain with discrete and categorical inputs. - """ - - # get all discrete and categorical inputs - kept_inputs = domain.get_features( - excludes=[CategoricalInput, DiscreteInput, Output] - ).features - discrete_inputs: List[DiscreteInput] = domain.inputs.get(DiscreteInput) - categorical_inputs: List[CategoricalInput] = domain.inputs.get(CategoricalInput) - - # convert discrete inputs to continuous inputs - relaxable_discrete_inputs = [ - RelaxableDiscreteInput(key=d_input.key, values=d_input.values) - for d_input in discrete_inputs - ] - - # convert categorical inputs to continuous inputs - relaxable_categorical_inputs = [] - new_constraints: List[LinearEqualityConstraint] = [] - categorical_groups = [] - for c_input in categorical_inputs: - current_group_keys = list(c_input.categories) - pick_1_constraint, group_vars = generate_mixture_constraints(current_group_keys) - categorical_groups.append(group_vars) - relaxable_categorical_inputs.extend(group_vars) - new_constraints.append(pick_1_constraint) - - # create new domain with continuous inputs - new_domain = Domain( - inputs=kept_inputs + relaxable_discrete_inputs + relaxable_categorical_inputs, - outputs=domain.outputs.features, - constraints=domain.constraints + new_constraints, - ) - - return new_domain, categorical_groups - - -def nchoosek_to_relaxable_domain_mapper( - domain: Domain, -) -> Tuple[Domain, List[List[RelaxableBinaryInput]]]: - var_occuring_in_nchoosek = [] - new_categories = [] - new_constraints = [] - n_choose_k_constraints = domain.constraints.get(includes=NChooseKConstraint) - - for constr in n_choose_k_constraints: - var_occuring_in_nchoosek.extend(constr.features) - - current_features: List[Feature] = [ - domain.get_feature(k) for k in constr.features - ] - new_relaxable_categorical_vars, new_nchoosek_constraints = NChooseKGroup( - current_features, constr.min_count, constr.max_count, constr.none_also_valid - ) - new_categories.append(new_relaxable_categorical_vars) - new_constraints.extend(new_nchoosek_constraints) - - # allow vars to be set to 0 - for var in var_occuring_in_nchoosek: - current_var = domain.inputs.get_by_key(var) - if current_var.lower_bound > 0: - current_var.bounds = (0, current_var.upper_bound) - elif current_var.upper_bound < 0: - current_var.bounds = (current_var.lower_bound, 0) - - new_domain = Domain( - inputs=domain.inputs.features - + [var for group in new_categories for var in group], - outputs=domain.outputs, - constraints=domain.constraints.get(excludes=NChooseKConstraint) - + new_constraints, - ) - return new_domain, new_categories - - -def NChooseKGroup_with_quantity( - unique_group_identifier: str, - keys: List[str], - pick_at_least: int, - pick_at_most: int, - quantity_if_picked: Optional[ - Union[Tuple[float, float], List[Tuple[float, float]]] - ] = None, - combined_quantity_limit: Optional[float] = None, - combined_quantity_is_equal_or_less_than: bool = False, - use_non_relaxable_category_and_non_linear_constraint: bool = False, -) -> tuple[ - Union[List[CategoricalInput], List[RelaxableBinaryInput]], - List[ContinuousInput], - List[LinearEqualityConstraint], -]: - """ - helper function to generate an N choose K problem with categorical variables, with an option to connect each - element of a category to a corresponding quantity of how much that category should be used. - - Args: - unique_group_identifier (str): unique ID for the category/group which will be used to mark all variables - containing to this group - keys (List[str]): defines the names and the amount of the elements within the category - pick_at_least (int): minimum number of elements to be picked from the category. >=0 - pick_at_most (int): maximum number of elements to be picked from the category. >=pick_at_least - quantity_if_picked (Optional[Union[Tuple[float, float], List[Tuple[float, float]]]): If provided, specifies - the lower and upper bound of the quantity, for each element in the category. List of bounds to specify the - allowed quantity for each element separately or one single bound to set the same bounds for all elements. - combined_quantity_limit (Optional[float]): If provided, sets an upper bound on what the sum of all the - quantities of all elements should be - combined_quantity_is_equal_or_less_than (bool): If True, the combined_quantity_limit describes the exact amount - of the sum of all quantities. If False, it is a upper bound, i.e. the sum of the quantities can be lower. - Default is False - use_non_relaxable_category_and_non_linear_constraint (bool): Default is False. - If False, RelaxableCategoricalInput is used in combination with LinearConstraints. - If True, CategoricalInput used in combination with NonlinearConstraints, as CategoricalInput can not be - used within LinearConstraints - Returns: - Either one CategoricalInput wrapped in a List or List of RelaxableBinaryInput describing the group, - If quantities are provided, List of ContinuousInput describing the quantity of each element of the group - otherwise empty List, - List of either LinearConstraints or mix of Linear- and NonlinearConstraints, which enforce the quantities - and group restrictions. - """ - if quantity_if_picked is not None: - if type(quantity_if_picked) is list and len(keys) != len(quantity_if_picked): - raise ValueError( - f"number of keys must be the same as corresponding quantities. Received {len(keys)} keys " - f"and {len(quantity_if_picked)} quantities" - ) - - if type(quantity_if_picked) is list and True in [ - 0 in q for q in quantity_if_picked - ]: - raise ValueError( - "If an element out of the group is chosen, the quantity with which it is used must be " - "larger than 0" - ) - - if pick_at_least > pick_at_most: - raise ValueError( - f"your upper bound to pick an element should be larger your lower bound. " - f"Currently: pick_at_least {pick_at_least} > pick_at_most {pick_at_most}" - ) - - if pick_at_least < 0: - raise ValueError( - f"you should at least pick 0 elements. Currently pick_at_least = {pick_at_least}" - ) - - if pick_at_most > len(keys): - raise ValueError( - f"you can not pick more elements than are available. " - f"Received pick_at_most {pick_at_most} > number of keys {len(keys)}" - ) - - if "pick_none" in keys: - raise ValueError("pick_none is not allowed as a key") - - if True in ["_" in k for k in keys]: - raise ValueError('"_" is not allowed as an character in the keys') - - if quantity_if_picked is not None and type(quantity_if_picked) != list: - quantity_if_picked = [quantity_if_picked for k in keys] - - quantity_var, all_new_constraints = [], [] - quantity_constraints_lb, quantity_constraints_ub = [], [] - max_quantity_constraint = None - - # creating possible combination of n choose k - combined_keys_as_tuple = [] - if pick_at_most > 1: - for i in range(max(2, pick_at_least), pick_at_most + 1): - combined_keys_as_tuple.extend(list(combinations(keys, i))) - if pick_at_least <= 1: - combined_keys_as_tuple.extend([[k] for k in keys]) - - combined_keys = ["_".join(w) for w in combined_keys_as_tuple] - - # generating the quantity variables and corresponding constraints - if quantity_if_picked: - ( - quantity_var, - quantity_constraints_lb, - quantity_constraints_ub, - max_quantity_constraint, - ) = _generate_quantity_var_constr( - unique_group_identifier, - keys, - quantity_if_picked, - combined_keys, - combined_keys_as_tuple, - use_non_relaxable_category_and_non_linear_constraint, - combined_quantity_limit, - combined_quantity_is_equal_or_less_than, - ) - - # allowing to pick none - if pick_at_least == 0: - combined_keys.append(unique_group_identifier + "_pick_none") - - # adding the new possible combinations to the list of keys - keys = [unique_group_identifier + "_" + k for k in combined_keys] - - # choosing between CategoricalInput and RelaxableBinaryInput - if use_non_relaxable_category_and_non_linear_constraint: - category = [CategoricalInput(key=unique_group_identifier, categories=keys)] - # if we use_legacy_class is true this constraint will be added by the discrete_to_relaxable_domain_mapper function - pick_exactly_one_of_group_const = [] - else: - category = [RelaxableBinaryInput(key=k) for k in keys] - pick_exactly_one_of_group_const = [ - LinearEqualityConstraint( - features=list(keys), coefficients=[1 for k in keys], rhs=1 - ) - ] - - all_new_constraints = ( - pick_exactly_one_of_group_const - + quantity_constraints_lb - + quantity_constraints_ub - ) - if max_quantity_constraint is not None: - all_new_constraints.append(max_quantity_constraint) - return category, quantity_var, all_new_constraints - - -def _generate_quantity_var_constr( - unique_group_identifier, - keys, - quantity_if_picked, - combined_keys, - combined_keys_as_tuple, - use_non_relaxable_category_and_non_linear_constraint, - combined_quantity_limit, - combined_quantity_is_equal_or_less_than, -) -> Tuple[ - List[ContinuousInput], - Union[List[NonlinearInequalityConstraint], List[LinearInequalityConstraint]], - Union[List[NonlinearInequalityConstraint], List[LinearInequalityConstraint]], - Optional[Union[LinearEqualityConstraint, LinearInequalityConstraint]], -]: - """ - Internal helper function just to create the quantity variables and the corresponding constraints. - """ - quantity_var = [ - ContinuousInput( - key=unique_group_identifier + "_" + k + "_quantity", bounds=(0, q[1]) - ) - for k, q in zip(keys, quantity_if_picked) - ] - - all_quantity_features = [] - for k in keys: - all_quantity_features.append( - [ - unique_group_identifier + "_" + state_key - for state_key, state_tuple in zip(combined_keys, combined_keys_as_tuple) - if k in state_tuple - ] - ) - - if use_non_relaxable_category_and_non_linear_constraint: - quantity_constraints_lb = [ - NonlinearInequalityConstraint( - expression="".join( - ["-" + unique_group_identifier + "_" + k + "_quantity"] - + [f" + {q[0]} * {key_c}" for key_c in combi] - ), - features=[unique_group_identifier + "_" + k + "_quantity"] + combi, - ) - for combi, k, q in zip(all_quantity_features, keys, quantity_if_picked) - if len(combi) >= 1 - ] - - quantity_constraints_ub = [ - NonlinearInequalityConstraint( - expression="".join( - [unique_group_identifier + "_" + k + "_quantity"] - + [f" - {q[1]} * {key_c}" for key_c in combi] - ), - features=[unique_group_identifier + "_" + k + "_quantity"] + combi, - ) - for combi, k, q in zip(all_quantity_features, keys, quantity_if_picked) - if len(combi) >= 1 - ] - else: - quantity_constraints_lb = [ - LinearInequalityConstraint( - features=[unique_group_identifier + "_" + k + "_quantity"] + combi, - coefficients=[-1] + [q[0] for i in range(len(combi))], - rhs=0, - ) - for combi, k, q in zip(all_quantity_features, keys, quantity_if_picked) - if len(combi) >= 1 - ] - - quantity_constraints_ub = [ - LinearInequalityConstraint( - features=[unique_group_identifier + "_" + k + "_quantity"] + combi, - coefficients=[1] + [-q[1] for i in range(len(combi))], - rhs=0, - ) - for combi, k, q in zip(all_quantity_features, keys, quantity_if_picked) - if len(combi) >= 1 - ] - - max_quantity_constraint = None - if combined_quantity_limit is not None: - if combined_quantity_is_equal_or_less_than: - max_quantity_constraint = LinearEqualityConstraint( - features=[q.key for q in quantity_var], - coefficients=[1 for q in quantity_var], - rhs=combined_quantity_limit, - ) - else: - max_quantity_constraint = LinearInequalityConstraint( - features=[q.key for q in quantity_var], - coefficients=[1 for q in quantity_var], - rhs=combined_quantity_limit, - ) - - return ( - quantity_var, - quantity_constraints_lb, - quantity_constraints_ub, - max_quantity_constraint, - ) - - -def NChooseKGroup( - variables: List[Feature], - pick_at_least: int, - pick_at_most: int, - none_also_valid: bool, -) -> Tuple[ - List[RelaxableBinaryInput], - List[Union[LinearEqualityConstraint, LinearInequalityConstraint]], -]: - """ - helper function to generate an N choose K problem with categorical variables, with an option to connect each - element of a category to a corresponding quantity of how much that category should be used. - - Args: - variables (List[ContinuousInput]): variables to pick from - pick_at_least (int): minimum number of elements to be picked from the category. >=0 - pick_at_most (int): maximum number of elements to be picked from the category. >=pick_at_least - none_also_valid (bool): defines if also none of the elements can be picked - Returns: - List of RelaxableBinaryInput describing the group, - List of either LinearConstraints, which enforce the quantities - and group restrictions. - """ - - keys = [var.key for var in variables] - if pick_at_least > pick_at_most: - raise ValueError( - f"your upper bound to pick an element should be larger your lower bound. " - f"Currently: pick_at_least {pick_at_least} > pick_at_most {pick_at_most}" - ) - - if pick_at_least < 0: - raise ValueError( - f"you should at least pick 0 elements. Currently pick_at_least = {pick_at_least}" - ) - - if pick_at_most > len(keys): - raise ValueError( - f"you can not pick more elements than are available. " - f"Received pick_at_most {pick_at_most} > number of keys {len(keys)}" - ) - - if "pick_none" in keys: - raise ValueError("pick_none is not allowed as a key") - - # creating possible combination of n choose k - combined_keys_as_tuple = [] - if pick_at_most > 1: - for i in range(max(2, pick_at_least), pick_at_most + 1): - combined_keys_as_tuple.extend(list(combinations(keys, i))) - if pick_at_least <= 1: - combined_keys_as_tuple.extend([[k] for k in keys]) - - combined_keys = ["_".join(w) for w in combined_keys_as_tuple] - combined_keys = ["categorical_helper" + "_" + k for k in combined_keys] - - # generating the corresponding constraints - valid_states = [] - for k in keys: - valid_states.append( - [ - state_key - for state_key, state_tuple in zip(combined_keys, combined_keys_as_tuple) - if k in state_tuple - ] - ) - - quantity_constraints_lb = [ - LinearInequalityConstraint( - features=[var.key] + combi, - coefficients=[-1] + [var.lower_bound for i in range(len(combi))], - rhs=0, - ) - for combi, var in zip(valid_states, variables) - if len(combi) >= 1 - ] - - quantity_constraints_ub = [ - LinearInequalityConstraint( - features=[var.key] + combi, - coefficients=[1] + [-var.upper_bound for i in range(len(combi))], - rhs=0, - ) - for combi, var in zip(valid_states, variables) - if len(combi) >= 1 - ] - - # allowing to pick none - if pick_at_least == 0 or none_also_valid: - combined_keys.append("categorical_helper_pick_none_of_" + "".join(keys)) - - # adding the new possible combinations to the list of keys - keys = combined_keys - - category = [RelaxableBinaryInput(key=k) for k in keys] - pick_exactly_one_of_group_const = [ - LinearEqualityConstraint( - features=list(keys), coefficients=[1 for k in keys], rhs=1 - ) - ] - - all_new_constraints = [] - all_new_constraints.extend(pick_exactly_one_of_group_const) - all_new_constraints.extend(quantity_constraints_lb) - all_new_constraints.extend(quantity_constraints_ub) - - return category, all_new_constraints - - -def generate_mixture_constraints( - keys: List[str], -) -> Tuple[LinearEqualityConstraint, List[RelaxableBinaryInput]]: - binary_vars = (RelaxableBinaryInput(key=x) for x in keys) - - mixture_constraint = LinearEqualityConstraint( - features=keys, coefficients=[1 for x in range(len(keys))], rhs=1 - ) - - return mixture_constraint, list(binary_vars) - - -def validate_categorical_groups( - categorical_group: List[List[RelaxableBinaryInput]], domain: Domain -): - """Validate if features given as the categorical groups are also features in the domain and if each feature - is in exactly one group - - Args: categorical_group (List[List[RelaxableBinaryInput]]) : groups of the different categories - domain (Domain): Domain to test against - - Raises - ValueError: Feature key not registered in any group or registered too often. - - Returns: - List[List[RelaxableBinaryInput]]: groups of the different categories - """ - - bin_vars = domain.inputs.get_keys(includes=RelaxableBinaryInput) - - if len(bin_vars) == 0: - return categorical_group - - simplified_groups = [[f.key for f in group] for group in categorical_group] - groups_flattened = [var.key for group in categorical_group for var in group] - for k in bin_vars: - if groups_flattened.count(k) < 1: - raise ValueError( - f"feature {k} is not registered in any of the categorical groups {simplified_groups}." - ) - elif groups_flattened.count(k) > 1: - raise ValueError( - f"feature {k} is registered to often in the categorical groups {simplified_groups}." - ) - return categorical_group - - -def design_from_original_to_new_domain( - original_domain: Domain, new_domain: Domain, design: pd.DataFrame -) -> pd.DataFrame: - raise NotImplementedError( - "mapping a design to a new domain is not implemented yet." - ) - - -def design_from_new_to_original_domain( - original_domain: Domain, design: pd.DataFrame -) -> pd.DataFrame: - # map the RelaxableBinaryInputs to the corresponding CategoricalInputs, choose random if for multiple solutions - transformed_design = design[ - original_domain.get_feature_keys(excludes=[CategoricalInput, Output]) - ] - - for group in original_domain.get_features(includes=CategoricalInput): - categorical_columns = design[group.categories] - mask = ~np.isclose(categorical_columns.to_numpy(), 0) - - for i, row in enumerate(mask): - index_to_keep = np.random.choice(np.argwhere(row).flatten()) - mask[i] = np.zeros_like(row, dtype=bool) - mask[i][index_to_keep] = True - - categorical_columns = categorical_columns.where( - np.invert(mask), - pd.DataFrame( - np.full( - (len(categorical_columns), len(group.categories)), - group.categories, - ), - columns=categorical_columns.columns, - index=categorical_columns.index, - ), - ) - categorical_columns = categorical_columns.where( - mask, - pd.DataFrame( - np.full((len(categorical_columns), len(group.categories)), ""), - columns=categorical_columns.columns, - index=categorical_columns.index, - ), - ) - transformed_design[group.key] = categorical_columns.apply("".join, axis=1) - - # map the RelaxableDiscreteInput to the closest valid value - for var in original_domain.get_features(includes=DiscreteInput): - closest_solution = var.from_continuous(transformed_design) - transformed_design[var.key] = closest_solution - - return transformed_design diff --git a/bofire/strategies/doe/utils_features.py b/bofire/strategies/doe/utils_features.py deleted file mode 100644 index f4eee870e..000000000 --- a/bofire/strategies/doe/utils_features.py +++ /dev/null @@ -1,147 +0,0 @@ -import math -import warnings -from typing import List, Tuple - -import numpy as np -import pandas as pd -from pydantic import root_validator - -from bofire.data_models.features.continuous import ContinuousInput - - -class RelaxableBinaryInput(ContinuousInput): - """Base class for all binary input features. It behaves like a continuous inputs to allow for easy relaxation. - It is not a true binary variable as it does not allow the variable to be either 0 or 1 while prohibiting all values - in between. - - """ - - def __init__(self, **kwargs): - if "bounds" in kwargs and kwargs["bounds"] != (0, 1): - warnings.warn("bounds must be set to (0, 1). Readjusting the bounds...") - kwargs["bounds"] = (0, 1) - super().__init__(**kwargs) - else: - bounds = (0, 1) - super().__init__(bounds=bounds, **kwargs) - - @root_validator(pre=False, skip_on_failure=True) - def validate_lower_upper(cls, values): - """Validates that lower bound is set to 0 and upper bound set to 1 - - Args: - values (Dict): Dictionary with attributes key, lower and upper bound - - Raises: - ValueError: when the lower bound is higher than the upper bound - - Returns: - Dict: The attributes as dictionary - """ - - if values["bounds"][0] != values["bounds"][1] and ( - values["bounds"][0] != 0 or values["bounds"][1] != 1 - ): - raise ValueError( - f'if variable is relaxed, lower bound must be 0 and upper bound 1, got {values["bounds"][0]}, {values["bounds"][1]}' - ) - elif values["bounds"][0] == values["bounds"][1] and not ( - values["bounds"][0] == 0 or values["bounds"][0] == 1 - ): - raise ValueError( - f"if variable is fixed, lower and upper bound must be equal and both either 0 or 1," - f' got {values["bounds"][0]}, {values["bounds"][1]}' - ) - - return values - - -class RelaxableDiscreteInput(ContinuousInput): - """Feature with discrete ordinal values allowed in the optimization. - - Attributes: - key(str): key of the feature. - values(List[float]): the allowed discrete values during the optimization. - """ - - values: List[float] - - def __init__(self, **kwargs): - super().__init__(bounds=(0, 1), **kwargs) - self.bounds = (self.lower_bound, self.upper_bound) - - @property - def lower_bound(self) -> float: - """Lower bound of the set of allowed values""" - return min(self.values) - - @property - def upper_bound(self) -> float: - """Upper bound of the set of allowed values""" - return max(self.values) - - @lower_bound.setter - def lower_bound(self, lb: float): - self.values = [val for val in self.values if val >= lb] - - @upper_bound.setter - def upper_bound(self, ub: float): - self.values = [val for val in self.values if val <= ub] - - def sample(self, n: int) -> pd.Series: - """Draw random samples from the feature. - - Args: - n (int): number of samples. - - Returns: - pd.Series: drawn samples. - """ - return pd.Series(name=self.key, data=np.random.choice(self.values, n)) - - def equal_range_split( - self, lower_bound: float, upper_bound: float - ) -> Tuple[float, float]: - """ - Determines the two identical elements x such that the intervals (lower_bound, x) and (x, upper_bound) - are of equal length - Args: - lower_bound: inclusive lower bound - upper_bound: inclusive upper bound - - Returns: tuple of floats which split the interval in half - - """ - x = (upper_bound - lower_bound) / 2 + lower_bound - return x, x - - def equal_count_split( - self, lower_bound: float, upper_bound: float - ) -> Tuple[float, float]: - """ - Determines the two elements x and y such that the intervals (lower_bound, x) and (y, upper_bound) - have the same number of elements regarding the values of the discrete variable - Args: - lower_bound: inclusive lower bound - upper_bound: inclusive upper bound - - Returns: tuple of floats which split the interval in half - - """ - self.values.sort() - sub_list = [elem for elem in self.values if lower_bound <= elem <= upper_bound] - - size = len(sub_list) - if size % 2 == 0: - lower_index = size / 2 - 1 - upper_index = size / 2 - elif size == 1: - return sub_list[0], sub_list[0] - else: - lower_index = math.floor(size / 2) - upper_index = math.ceil(size / 2) - - lower_index = int(lower_index) - upper_index = int(upper_index) - - return sub_list[lower_index], sub_list[upper_index] diff --git a/bofire/strategies/doe_strategy.py b/bofire/strategies/doe_strategy.py index 315dadf92..acf406a0c 100644 --- a/bofire/strategies/doe_strategy.py +++ b/bofire/strategies/doe_strategy.py @@ -2,24 +2,7 @@ from pydantic.types import PositiveInt import bofire.data_models.strategies.api as data_models -from bofire.data_models.features.api import ( - Input, -) -from bofire.strategies.doe.design import ( - find_local_max_ipopt, - find_local_max_ipopt_BaB, - find_local_max_ipopt_exhaustive, -) -from bofire.strategies.doe.utils_categorical_discrete import ( - design_from_new_to_original_domain, - discrete_to_relaxable_domain_mapper, - nchoosek_to_relaxable_domain_mapper, - validate_categorical_groups, -) -from bofire.strategies.doe.utils_features import ( - RelaxableBinaryInput, - RelaxableDiscreteInput, -) +from bofire.strategies.doe.design import find_local_max_ipopt from bofire.strategies.strategy import Strategy @@ -37,74 +20,8 @@ def __init__( ): super().__init__(data_model=data_model, **kwargs) self.formula = data_model.formula - self.data_model = data_model - self._partially_fixed_experiments_for_next_design = None - - def tell( - self, - experiments: pd.DataFrame, - replace: bool = False, - ) -> None: - """This function passes new experimental data to the optimizer - - Args: - experiments (pd.DataFrame): DataFrame with experimental data - replace (bool, optional): Boolean to decide if the experimental data should replace the former DataFrame or if the new experiments should be attached. Defaults to False. - """ - - self._partially_fixed_experiments_for_next_design = experiments[ - experiments.isnull().any(axis=1) - ][self.domain.get_feature_keys(includes=Input)] - experiments = experiments[experiments.notnull().all(axis=1)] - - if len(experiments) == 0: - return - if replace: - self.set_experiments(experiments=experiments) - else: - self.add_experiments(experiments=experiments) - # we check here that the experiments do not have completely fixed columns - cleaned_experiments = ( - self.domain.outputs.preprocess_experiments_all_valid_outputs( - experiments=experiments - ) - ) - for feature in self.domain.inputs.get_fixed(): - if (cleaned_experiments[feature.key] == feature.fixed_value()[0]).all(): # type: ignore - raise ValueError( - f"No variance in experiments for fixed feature {feature.key}" - ) - self._tell() - - def _tell(self) -> None: - self.set_candidates( - self.experiments[self.domain.get_feature_keys(includes=Input)] - ) - raise NotImplementedError( - "For the DoEStrategy, the tell method is not implemented yet" - ) def _ask(self, candidate_count: PositiveInt) -> pd.DataFrame: - all_new_categories = [] - - # map categorical/ discrete Domain to a relaxable Domain - new_domain, new_categories = discrete_to_relaxable_domain_mapper(self.domain) - all_new_categories.extend(new_categories) - - # check for NchooseK constraint and solve the problem differently depending on the strategy - if self.data_model.optimization_strategy != "partially-random": - ( - new_domain, - new_categories, - ) = nchoosek_to_relaxable_domain_mapper(new_domain) - all_new_categories.extend(new_categories) - - # check categorical_groups - validate_categorical_groups(all_new_categories, new_domain) - - # here we adapt the (partially) fixed experiments to the new domain - # todo - if self.candidates is not None: _fixed_experiments_count = len(self.candidates) _candidate_count = candidate_count + len(self.candidates) @@ -112,64 +29,13 @@ def _ask(self, candidate_count: PositiveInt) -> pd.DataFrame: _fixed_experiments_count = 0 _candidate_count = candidate_count - num_binary_vars = len(new_domain.get_features(includes=[RelaxableBinaryInput])) - num_discrete_vars = len( - new_domain.get_features(includes=[RelaxableDiscreteInput]) + design = find_local_max_ipopt( + self.domain, + self.formula, + n_experiments=_candidate_count, + fixed_experiments=self.candidates, ) - - if ( - self.data_model.optimization_strategy == "relaxed" - or (num_binary_vars == 0 and num_discrete_vars == 0) - or ( - self.data_model.optimization_strategy == "partially-random" - and num_binary_vars == 0 - and num_discrete_vars == 0 - ) - ): - design = find_local_max_ipopt( - new_domain, - self.formula, - n_experiments=_candidate_count, - fixed_experiments=self.candidates, - partially_fixed_experiments=self._partially_fixed_experiments_for_next_design, - ) - # todo adapt to when exhaustive search accepts discrete variables - elif ( - self.data_model.optimization_strategy == "exhaustive" - and num_discrete_vars == 0 - ): - design = find_local_max_ipopt_exhaustive( - domain=new_domain, - model_type=self.formula, - n_experiments=_candidate_count, - fixed_experiments=self.candidates, - verbose=self.data_model.verbose, - partially_fixed_experiments=self._partially_fixed_experiments_for_next_design, - categorical_groups=all_new_categories, - ) - elif self.data_model.optimization_strategy in [ - "branch-and-bound", - "default", - "partially-random", - ]: - design = find_local_max_ipopt_BaB( - domain=new_domain, - model_type=self.formula, - n_experiments=_candidate_count, - fixed_experiments=self.candidates, - verbose=self.data_model.verbose, - partially_fixed_experiments=self._partially_fixed_experiments_for_next_design, - categorical_groups=all_new_categories, - ) - else: - raise RuntimeError("Could not find suitable optimization strategy") - - # mapping the solution to the variables from the original domain - transformed_design = design_from_new_to_original_domain(self.domain, design) - - # restart the partially fixed experiments - self._partially_fixed_experiments_for_next_design = None - return transformed_design.iloc[_fixed_experiments_count:, :] # type: ignore + return design.iloc[_fixed_experiments_count:, :] # type: ignore def has_sufficient_experiments( self, diff --git a/tests/bofire/data_models/specs/strategies.py b/tests/bofire/data_models/specs/strategies.py index 6f76ecd42..14a08939a 100644 --- a/tests/bofire/data_models/specs/strategies.py +++ b/tests/bofire/data_models/specs/strategies.py @@ -125,8 +125,6 @@ lambda: { "domain": domain.valid().obj().dict(), "formula": "linear", - "optimization_strategy": "default", - "verbose": False, "seed": 42, }, ) diff --git a/tests/bofire/strategies/doe/test_design.py b/tests/bofire/strategies/doe/test_design.py index 35ddebd36..ce5feca39 100644 --- a/tests/bofire/strategies/doe/test_design.py +++ b/tests/bofire/strategies/doe/test_design.py @@ -11,18 +11,13 @@ NonlinearInequalityConstraint, ) from bofire.data_models.domain.api import Domain -from bofire.data_models.features.api import ( - ContinuousInput, - ContinuousOutput, -) +from bofire.data_models.features.api import ContinuousInput, ContinuousOutput from bofire.strategies.doe.design import ( check_fixed_experiments, - check_partially_and_fully_fixed_experiments, find_local_max_ipopt, get_n_experiments, ) from bofire.strategies.doe.utils import get_formula_from_string, n_zero_eigvals -from bofire.strategies.doe.utils_features import RelaxableBinaryInput CYIPOPT_AVAILABLE = importlib.util.find_spec("cyipopt") is not None @@ -478,76 +473,3 @@ def test_get_n_experiments(): # user provided n_experiment with pytest.warns(UserWarning): assert get_n_experiments(domain, "linear", 4) == 4 - - -@pytest.mark.skipif(not CYIPOPT_AVAILABLE, reason="requires cyipopt") -def test_partially_fixed_experiments(): - domain = Domain( - inputs=[ - ContinuousInput(key="x1", bounds=(0, 5)), - ContinuousInput(key="x2", bounds=(0, 15)), - RelaxableBinaryInput(key="a1"), - RelaxableBinaryInput(key="a2"), - ], - outputs=[ContinuousOutput(key="y")], - constraints=[ - # Case 1: a and b are active - LinearInequalityConstraint( - features=["x1", "x2", "a1", "a2"], coefficients=[1, 1, 10, -10], rhs=15 - ), - LinearInequalityConstraint( - features=["x1", "x2", "a1", "a2"], coefficients=[1, 0.2, 2, -2], rhs=5 - ), - LinearInequalityConstraint( - features=["x1", "x2", "a1", "a2"], coefficients=[1, -1, -3, 3], rhs=5 - ), - # Case 2: a and c are active - LinearInequalityConstraint( - features=["x1", "x2", "a1", "a2"], coefficients=[1, 1, -10, -10], rhs=5 - ), - LinearInequalityConstraint( - features=["x1", "x2", "a1", "a2"], coefficients=[1, 0.2, 2, 2], rhs=7 - ), - LinearInequalityConstraint( - features=["x1", "x2", "a1", "a2"], coefficients=[1, -1, -3, -3], rhs=2 - ), - # Case 3: c and b are active - LinearInequalityConstraint( - features=["x1", "x2", "a1", "a2"], coefficients=[1, 1, 0, -10], rhs=5 - ), - LinearInequalityConstraint( - features=["x1", "x2", "a1", "a2"], coefficients=[1, 0.2, 0, 2], rhs=5 - ), - LinearInequalityConstraint( - features=["x1", "x2", "a1", "a2"], coefficients=[1, -1, 0, 3], rhs=5 - ), - ], - ) - fixed_experiments = pd.DataFrame( - np.array([[1, 0, 0, 0], [0, 1, 0, 0]]), columns=domain.inputs.get_keys() - ) - partially_fixed_experiments = pd.DataFrame( - np.array([[1, None, None, None], [0, 1, 0, 0]]), - columns=domain.inputs.get_keys(), - ) - # all fine - check_partially_and_fully_fixed_experiments( - domain, 10, fixed_experiments, partially_fixed_experiments - ) - - # all fine - check_partially_and_fully_fixed_experiments( - domain, 4, fixed_experiments, partially_fixed_experiments - ) - - # partially fixed will be cut of - with pytest.warns(UserWarning): - check_partially_and_fully_fixed_experiments( - domain, 3, fixed_experiments, partially_fixed_experiments - ) - - # to few experiments - with pytest.raises(ValueError): - check_partially_and_fully_fixed_experiments( - domain, 2, fixed_experiments, partially_fixed_experiments - ) diff --git a/tests/bofire/strategies/doe/test_objective.py b/tests/bofire/strategies/doe/test_objective.py index 4a1761509..383992b19 100644 --- a/tests/bofire/strategies/doe/test_objective.py +++ b/tests/bofire/strategies/doe/test_objective.py @@ -430,7 +430,7 @@ def test_DOptimality_instantiation(): assert np.allclose(B, d_optimality._model_jacobian_t(x)) assert np.shape( - d_optimality.evaluate_jacobian(np.array([[1, 1, 1], [2, 2, 2]]).flatten()) + d_optimality.evaluate_jacobian(np.array([[1, 1, 1], [2, 2, 2]])) ) == (6,) # 5th order model @@ -459,9 +459,7 @@ def test_DOptimality_instantiation(): assert np.allclose(B, d_optimality._model_jacobian_t(x)) assert np.shape( - d_optimality.evaluate_jacobian( - np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]).flatten() - ) + d_optimality.evaluate_jacobian(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])) ) == (9,) diff --git a/tests/bofire/strategies/test_doe.py b/tests/bofire/strategies/test_doe.py index fe83c714d..026e7d8f4 100644 --- a/tests/bofire/strategies/test_doe.py +++ b/tests/bofire/strategies/test_doe.py @@ -2,6 +2,7 @@ import numpy as np import pandas as pd +import pytest import bofire.data_models.strategies.api as data_models from bofire.data_models.constraints.api import ( @@ -45,7 +46,7 @@ outputs=[ContinuousOutput(key="y")], constraints=[ LinearEqualityConstraint( - features=[f"x{i + 1}" for i in range(3)], coefficients=[1, 1, 1], rhs=1 + features=[f"x{i+1}" for i in range(3)], coefficients=[1, 1, 1], rhs=1 ), LinearInequalityConstraint(features=["x1", "x2"], coefficients=[5, 4], rhs=3.9), LinearInequalityConstraint( @@ -80,21 +81,43 @@ def test_doe_strategy_ask_with_candidates(): assert candidates.shape == (12, 3) +def test_doe_categoricals_not_implemented(): + categorical_inputs = [ + CategoricalInput(key=f"x{i+1}", categories=["a", "b", "c"]) for i in range(3) + ] + domain = Domain.from_lists( + inputs=categorical_inputs, + outputs=[ContinuousOutput(key="y")], + constraints=[], + ) + with pytest.raises(Exception): + data_models.DoEStrategy(domain=domain, formula="linear") + + +def test_doe_discrete_not_implemented(): + discrete_inputs = [DiscreteInput(key=f"x{i+1}", values=[1, 2, 3]) for i in range(3)] + domain = Domain.from_lists( + inputs=discrete_inputs, + outputs=[ContinuousOutput(key="y")], + constraints=[], + ) + with pytest.raises(Exception): + data_models.DoEStrategy(domain=domain, formula="linear") + + def test_nchoosek_implemented(): nchoosek_constraint = NChooseKConstraint( - features=[f"x{i + 1}" for i in range(3)], + features=[f"x{i+1}" for i in range(3)], min_count=0, max_count=2, none_also_valid=True, ) domain = Domain.from_lists( - inputs=[ContinuousInput(key=f"x{i + 1}", bounds=(0.0, 1.0)) for i in range(3)], + inputs=[ContinuousInput(key=f"x{i+1}", bounds=(0.0, 1.0)) for i in range(3)], outputs=[ContinuousOutput(key="y")], constraints=[nchoosek_constraint], ) - data_model = data_models.DoEStrategy( - domain=domain, formula="linear", optimization_strategy="partially-random" - ) + data_model = data_models.DoEStrategy(domain=domain, formula="linear") strategy = DoEStrategy(data_model=data_model) candidates = strategy.ask(candidate_count=12) assert candidates.shape == (12, 3) @@ -150,57 +173,6 @@ def test_doe_strategy_amount_of_candidates(): assert len(candidates) == num_candidates_expected -def test_categorical_discrete_doe(): - quantity_a = [ - ContinuousInput(key=f"quantity_a_{i}", bounds=(0, 100)) for i in range(3) - ] - quantity_b = [ - ContinuousInput(key=f"quantity_b_{i}", bounds=(0, 15)) for i in range(3) - ] - all_inputs = [ - CategoricalInput(key="animals", categories=["Whale", "Turtle", "Sloth"]), - DiscreteInput(key="discrete", values=[0.1, 0.2, 0.3, 1.6, 2]), - ContinuousInput(key="independent", bounds=(3, 10)), - ] - all_inputs.extend(quantity_a) - all_inputs.extend(quantity_b) - - all_constraints = [ - NChooseKConstraint( - features=[var.key for var in quantity_a], - min_count=0, - max_count=1, - none_also_valid=True, - ), - NChooseKConstraint( - features=[var.key for var in quantity_b], - min_count=0, - max_count=2, - none_also_valid=True, - ), - LinearEqualityConstraint( - features=[var.key for var in quantity_b], - coefficients=[1 for var in quantity_b], - rhs=15, - ), - ] - - n_experiments = 10 - domain = Domain( - inputs=all_inputs, - outputs=[ContinuousOutput(key="y")], - constraints=all_constraints, - ) - - data_model = data_models.DoEStrategy( - domain=domain, formula="linear", optimization_strategy="partially-random" - ) - strategy = DoEStrategy(data_model=data_model) - candidates = strategy.ask(candidate_count=n_experiments) - - assert candidates.shape == (10, 9) - - # if __name__ == "__main__": # test_doe_strategy_ask() # test_doe_strategy_ask_with_candidates() diff --git a/tutorials/basic_examples/Reaction_Optimization_Example.ipynb b/tutorials/basic_examples/Reaction_Optimization_Example.ipynb index bbbce3814..7bfba68f7 100644 --- a/tutorials/basic_examples/Reaction_Optimization_Example.ipynb +++ b/tutorials/basic_examples/Reaction_Optimization_Example.ipynb @@ -40,10 +40,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "e129aecf-a983-4da7-af73-9e3614142cb6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SMOKE_TEST: None\n" + ] + } + ], "source": [ "# python imports we'll need in this notebook\n", "from pprint import pprint as pp\n", @@ -67,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "eb8fc687-219e-4ddf-b82e-64df06cf3cbf", "metadata": {}, "outputs": [], @@ -79,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "2ce468a7-b6dd-478b-a1f7-d0c9a51ecdef", "metadata": {}, "outputs": [], @@ -106,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "3ace0feb-9051-4074-b361-dc6424f57d80", "metadata": {}, "outputs": [], @@ -125,20 +133,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "cfcc28e9-9e3e-4ec4-a095-e759db19c68e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "MaximizeObjective(type='MaximizeObjective', w=1.0, bounds=(0, 1))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "objective" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "4380f5d7-bd61-41c5-a3a5-0c28a369b12a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input_features: type='Inputs' features=[ContinuousInput(type='ContinuousInput', key='Temperature', unit='°C', bounds=(0.0, 60.0), stepsize=None), CategoricalInput(type='CategoricalInput', key='Solvent Type', categories=['MeOH', 'THF', 'Dioxane'], allowed=[True, True, True]), ContinuousInput(type='ContinuousInput', key='Solvent Volume', unit=None, bounds=(20.0, 90.0), stepsize=None)]\n", + "output_features: type='Outputs' features=[ContinuousOutput(type='ContinuousOutput', key='Yield', unit=None, objective=MaximizeObjective(type='MaximizeObjective', w=1.0, bounds=(0, 1)))]\n" + ] + } + ], "source": [ "# we now have\n", "print('input_features:', input_features)\n", @@ -147,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "b2752d5a-8eba-4378-9de0-b1610dd678fb", "metadata": {}, "outputs": [], @@ -161,10 +189,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "8dd9ab91-d84e-4bc2-bbd4-176d2a5d73de", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TypeDescription
Solvent VolumeContinuousInput[20.0,90.0]
TemperatureContinuousInput[0.0,60.0]
Solvent TypeCategoricalInput3 categories
YieldContinuousOutputContinuousOutputFeature
\n", + "
" + ], + "text/plain": [ + " Type Description\n", + "Solvent Volume ContinuousInput [20.0,90.0]\n", + "Temperature ContinuousInput [0.0,60.0]\n", + "Solvent Type CategoricalInput 3 categories\n", + "Yield ContinuousOutput ContinuousOutputFeature" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# you can now have a pretty printout of your domain via\n", "domain.get_feature_reps_df()" @@ -172,10 +263,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "3ec6a44b-6a89-4700-bead-6a5cbe7a071c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solvent Volume | [20.0,90.0]\n", + "Temperature | [0.0,60.0]\n", + "Solvent Type | 3 categories\n" + ] + } + ], "source": [ "# and you can access your domain features via \n", "for feature_key in domain.inputs.get_keys(): # this will get all the feature names and loop over them\n", @@ -185,10 +286,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "b1338ead-3f90-4fa6-973c-41088c3a04b3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Yield | ContinuousOutput(type='ContinuousOutput', key='Yield', unit=None, objective=MaximizeObjective(type='MaximizeObjective', w=1.0, bounds=(0, 1)))\n" + ] + } + ], "source": [ "# as well as the output features as\n", "# and you can access your domain features via \n", @@ -199,10 +308,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "26ff44cd-db3f-465c-8181-47617b363360", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TypeDescription
Solvent VolumeContinuousInput[20.0,90.0]
TemperatureContinuousInput[0.0,60.0]
Solvent TypeCategoricalInput3 categories
YieldContinuousOutputContinuousOutputFeature
\n", + "
" + ], + "text/plain": [ + " Type Description\n", + "Solvent Volume ContinuousInput [20.0,90.0]\n", + "Temperature ContinuousInput [0.0,60.0]\n", + "Solvent Type CategoricalInput 3 categories\n", + "Yield ContinuousOutput ContinuousOutputFeature" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "domain.get_feature_reps_df()" ] @@ -218,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "3b0e3708-e8aa-480e-8414-c5a577322e06", "metadata": {}, "outputs": [], @@ -228,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "d06f13a7-f1d4-413d-9460-04b3fbe9ff6b", "metadata": {}, "outputs": [], @@ -239,17 +411,85 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "6444f3bb-ccf5-4871-a960-03cdd153e246", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TemperatureSolvent VolumeSolvent Type
052.32918130.065721MeOH
18.20077838.290215MeOH
211.11399659.208722THF
37.97873255.514603THF
\n", + "
" + ], + "text/plain": [ + " Temperature Solvent Volume Solvent Type\n", + "0 52.329181 30.065721 MeOH\n", + "1 8.200778 38.290215 MeOH\n", + "2 11.113996 59.208722 THF\n", + "3 7.978732 55.514603 THF" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "candidates" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "d1d5c9c2-13ac-4839-b544-d85bccc5a448", "metadata": {}, "outputs": [], @@ -260,10 +500,88 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "757ec10d-14f3-48c6-a96d-2eff2c474b1b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TemperatureSolvent VolumeYieldSolvent Typevalid_Yield
052.32918130.06572142.341612MeOH1.0
18.20077838.29021513.330654MeOH1.0
211.11399659.20872212.621553THF1.0
37.97873255.51460311.741233THF1.0
\n", + "
" + ], + "text/plain": [ + " Temperature Solvent Volume Yield Solvent Type valid_Yield\n", + "0 52.329181 30.065721 42.341612 MeOH 1.0\n", + "1 8.200778 38.290215 13.330654 MeOH 1.0\n", + "2 11.113996 59.208722 12.621553 THF 1.0\n", + "3 7.978732 55.514603 11.741233 THF 1.0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "experiments" ] @@ -281,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "efc381df-750a-472b-a5c0-2b7fa62ba48e", "metadata": {}, "outputs": [], @@ -294,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "c45a62f3-2423-4bfb-addf-2aba2c9f08dd", "metadata": {}, "outputs": [], @@ -313,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "642c30b6-8ee4-416d-9f1f-270f24212f84", "metadata": {}, "outputs": [], @@ -333,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "ca0317a8-4bdb-42e5-b216-5cb2a4e15c92", "metadata": {}, "outputs": [], @@ -354,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "40eae194-3486-4bcd-a69f-c2f37d944325", "metadata": {}, "outputs": [], @@ -368,17 +686,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "9670bdbb-9d79-4695-bb14-d7be9bbe24b9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Domain(type='Domain', inputs=Inputs(type='Inputs', features=[ContinuousInput(type='ContinuousInput', key='Temperature', unit='°C', bounds=(0.0, 60.0), stepsize=None), CategoricalInput(type='CategoricalInput', key='Solvent Type', categories=['MeOH', 'THF', 'Dioxane'], allowed=[True, True, True]), ContinuousInput(type='ContinuousInput', key='Solvent Volume', unit=None, bounds=(20.0, 90.0), stepsize=None)]), outputs=Outputs(type='Outputs', features=[ContinuousOutput(type='ContinuousOutput', key='Yield', unit=None, objective=MaximizeObjective(type='MaximizeObjective', w=1.0, bounds=(0, 1)))]), constraints=Constraints(type='Constraints', constraints=[]))" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "domain" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "5d8bfd4f-390f-4ef0-81f4-1d17b9e67c7f", "metadata": {}, "outputs": [], @@ -397,10 +726,85 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "89b0052c-194e-4c0d-8b6e-d874c8f1f8da", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Solvent VolumeTemperatureSolvent Type
025.38862517.921848THF
156.17452340.051222MeOH
273.15731910.913488Dioxane
355.81570415.347848Dioxane
470.77095335.500173Dioxane
\n", + "
" + ], + "text/plain": [ + " Solvent Volume Temperature Solvent Type\n", + "0 25.388625 17.921848 THF\n", + "1 56.174523 40.051222 MeOH\n", + "2 73.157319 10.913488 Dioxane\n", + "3 55.815704 15.347848 Dioxane\n", + "4 70.770953 35.500173 Dioxane" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "candidates" ] @@ -415,7 +819,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "1e246d1b-9ccc-4cb1-a397-9082661bd305", "metadata": {}, "outputs": [], @@ -425,10 +829,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "f444423c-4360-4446-9133-34e4d6d4e73d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TemperatureSolvent VolumeYieldSolvent Typevalid_Yield
017.92184825.38862514.846252THF1.0
140.05122256.17452328.623439MeOH1.0
210.91348873.15731910.673204Dioxane1.0
315.34784855.81570410.776096Dioxane1.0
435.50017370.77095320.057475Dioxane1.0
\n", + "
" + ], + "text/plain": [ + " Temperature Solvent Volume Yield Solvent Type valid_Yield\n", + "0 17.921848 25.388625 14.846252 THF 1.0\n", + "1 40.051222 56.174523 28.623439 MeOH 1.0\n", + "2 10.913488 73.157319 10.673204 Dioxane 1.0\n", + "3 15.347848 55.815704 10.776096 Dioxane 1.0\n", + "4 35.500173 70.770953 20.057475 Dioxane 1.0" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "experiments" ] @@ -451,10 +942,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "8b88124b-4359-4ffe-a082-872f50e04ff3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fit took 0.62 seconds\n" + ] + } + ], "source": [ "t1 = time.time()\n", "sobo_strategy.tell(experiments, replace=True, retrain=True) \n", @@ -471,10 +970,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "4d67eb95-c797-4787-95d4-7cfafc6c769f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SOBO step took 16.03 seconds\n" + ] + } + ], "source": [ "t1 = time.time()\n", "new_candidate = sobo_strategy.ask(1)\n", @@ -491,10 +998,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "b4497026-cc6a-4d85-aec0-90a4b2f5a0dd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Solvent VolumeTemperatureSolvent TypeYield_predYield_sdYield_des
090.060.0THF67.2149070.77789567.214907
\n", + "
" + ], + "text/plain": [ + " Solvent Volume Temperature Solvent Type Yield_pred Yield_sd Yield_des\n", + "0 90.0 60.0 THF 67.214907 0.777895 67.214907" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "new_candidate" ] @@ -510,7 +1070,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "302ad31a-320c-4bab-bf3f-fedb3c3341bb", "metadata": {}, "outputs": [], @@ -547,7 +1107,94 @@ "execution_count": null, "id": "a1092c38-dfe4-4e5e-9675-796f8a4fc8fb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TemperatureSolvent VolumeYieldSolvent Typevalid_Yield
017.92184825.38862514.846252THF1.0
140.05122256.17452328.623439MeOH1.0
210.91348873.15731910.673204Dioxane1.0
315.34784855.81570410.776096Dioxane1.0
435.50017370.77095320.057475Dioxane1.0
\n", + "
" + ], + "text/plain": [ + " Temperature Solvent Volume Yield Solvent Type valid_Yield\n", + "0 17.921848 25.388625 14.846252 THF 1.0\n", + "1 40.051222 56.174523 28.623439 MeOH 1.0\n", + "2 10.913488 73.157319 10.673204 Dioxane 1.0\n", + "3 15.347848 55.815704 10.776096 Dioxane 1.0\n", + "4 35.500173 70.770953 20.057475 Dioxane 1.0" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# you have access to the experiments here\n", "sobo_strategy.experiments " @@ -555,10 +1202,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "1e073b2c-6672-4471-8201-e23b764d57d7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# quick plot of yield vs. Iteration\n", "sobo_strategy.experiments['Yield'].plot()" diff --git a/tutorials/doe/design_with_explicit_formula.ipynb b/tutorials/doe/design_with_explicit_formula.ipynb index 3de85b4dc..8f9bc6a79 100644 --- a/tutorials/doe/design_with_explicit_formula.ipynb +++ b/tutorials/doe/design_with_explicit_formula.ipynb @@ -22,9 +22,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/miniforge/base/envs/bofire/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "from bofire.data_models.api import Domain, Inputs\n", "from bofire.data_models.features.api import ContinuousInput\n", @@ -46,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -71,9 +80,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1 + a + a**2 + b + c + d + a:b + a:c + a:d + b:c + b:d + c:d" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model_type = Formula(\"a + {a**2} + b + c + d + a:b + a:c + a:d + b:c + b:d + c:d\")\n", "model_type\n" @@ -89,9 +109,199 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit https://github.com/coin-or/Ipopt\n", + "******************************************************************************\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
abcd
exp05.000000e+0040.000000180.000002199.999998
exp12.500000e+00800.00000879.999999800.000008
exp2-9.972222e-09800.000008180.000002199.999998
exp35.000000e+00800.000008180.000002800.000008
exp4-9.975610e-0940.000000180.000002199.999998
exp5-9.975610e-09800.000008180.000002800.000008
exp62.500000e+00800.000008180.000002199.999998
exp75.000000e+0040.00000079.999999800.000008
exp85.000000e+00800.00000879.999999199.999998
exp9-9.750000e-0940.00000079.999999199.999998
exp10-9.975610e-09800.00000879.999999199.999998
exp11-9.975610e-0940.00000079.999999800.000008
exp125.000000e+00800.00000879.999999800.000008
exp132.500000e+0040.000000180.000002800.000008
exp145.000000e+0040.00000079.999999199.999998
exp15-9.972222e-09800.00000879.999999800.000008
exp165.000000e+00800.000008180.000002199.999998
\n", + "
" + ], + "text/plain": [ + " a b c d\n", + "exp0 5.000000e+00 40.000000 180.000002 199.999998\n", + "exp1 2.500000e+00 800.000008 79.999999 800.000008\n", + "exp2 -9.972222e-09 800.000008 180.000002 199.999998\n", + "exp3 5.000000e+00 800.000008 180.000002 800.000008\n", + "exp4 -9.975610e-09 40.000000 180.000002 199.999998\n", + "exp5 -9.975610e-09 800.000008 180.000002 800.000008\n", + "exp6 2.500000e+00 800.000008 180.000002 199.999998\n", + "exp7 5.000000e+00 40.000000 79.999999 800.000008\n", + "exp8 5.000000e+00 800.000008 79.999999 199.999998\n", + "exp9 -9.750000e-09 40.000000 79.999999 199.999998\n", + "exp10 -9.975610e-09 800.000008 79.999999 199.999998\n", + "exp11 -9.975610e-09 40.000000 79.999999 800.000008\n", + "exp12 5.000000e+00 800.000008 79.999999 800.000008\n", + "exp13 2.500000e+00 40.000000 180.000002 800.000008\n", + "exp14 5.000000e+00 40.000000 79.999999 199.999998\n", + "exp15 -9.972222e-09 800.000008 79.999999 800.000008\n", + "exp16 5.000000e+00 800.000008 180.000002 199.999998" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "design = find_local_max_ipopt(domain=domain, model_type=model_type, n_experiments=17)\n", "design" @@ -107,9 +317,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib\n", @@ -147,8 +368,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" - } + "version": "3.10.9" + }, + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/tutorials/doe/optimality_criteria.ipynb b/tutorials/doe/optimality_criteria.ipynb index 3184c244c..39e8523da 100644 --- a/tutorials/doe/optimality_criteria.ipynb +++ b/tutorials/doe/optimality_criteria.ipynb @@ -33,7 +33,18 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Optimal designs for a quadratic model on the unit square\n", "domain = Domain(\n", @@ -76,7 +87,18 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Space filling design on the unit 2-simplex\n", "domain = Domain(\n", diff --git a/tutorials/getting_started.ipynb b/tutorials/getting_started.ipynb index e864a5fba..0b5d314be 100644 --- a/tutorials/getting_started.ipynb +++ b/tutorials/getting_started.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -105,9 +105,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "CategoricalInput(type='CategoricalInput', key='x5', categories=['A', 'B', 'C'], allowed=[True, True, False])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x5 = input_features.get_by_key('x5')\n", "x5" @@ -123,9 +134,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Inputs(type='Inputs', features=[ContinuousInput(type='ContinuousInput', key='x2', unit=None, bounds=(0.0, 1.0), stepsize=None), CategoricalInput(type='CategoricalInput', key='x5', categories=['A', 'B', 'C'], allowed=[True, True, False])])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "input_features.get_by_keys(['x5', 'x2'])" ] @@ -140,9 +162,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Inputs(type='Inputs', features=[CategoricalDescriptorInput(type='CategoricalDescriptorInput', key='x6', categories=['c1', 'c2', 'c3'], allowed=[True, True, True], descriptors=['d1', 'd2'], values=[[1.0, 2.0], [2.0, 5.0], [1.0, 7.0]]), CategoricalInput(type='CategoricalInput', key='x5', categories=['A', 'B', 'C'], allowed=[True, True, False])])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "input_features.get(CategoricalInput)" ] @@ -157,9 +190,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Inputs(type='Inputs', features=[CategoricalInput(type='CategoricalInput', key='x5', categories=['A', 'B', 'C'], allowed=[True, True, False])])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "input_features.get(CategoricalInput, exact=True)" ] @@ -174,9 +218,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['x6', 'x5']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "input_features.get_keys(CategoricalInput)" ] @@ -191,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -209,9 +264,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 A\n", + "1 A\n", + "Name: x5, dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x5.sample(2)" ] @@ -226,9 +294,152 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
x1x2x3x4x6x5
00.8166290.7011820.1993991.0c2B
10.1258670.6715470.6439042.0c2A
20.0434470.2016670.0236211.0c3A
30.5511770.9904350.7609105.0c2A
40.7408290.8069600.4603555.0c3A
50.9014420.5033590.8441095.0c1B
60.3476340.4338130.2139382.0c2B
70.4871460.3468770.9273072.0c1B
80.6795400.0625510.5549067.5c1B
90.2619950.1968280.3284677.5c3A
\n", + "
" + ], + "text/plain": [ + " x1 x2 x3 x4 x6 x5\n", + "0 0.816629 0.701182 0.199399 1.0 c2 B\n", + "1 0.125867 0.671547 0.643904 2.0 c2 A\n", + "2 0.043447 0.201667 0.023621 1.0 c3 A\n", + "3 0.551177 0.990435 0.760910 5.0 c2 A\n", + "4 0.740829 0.806960 0.460355 5.0 c3 A\n", + "5 0.901442 0.503359 0.844109 5.0 c1 B\n", + "6 0.347634 0.433813 0.213938 2.0 c2 B\n", + "7 0.487146 0.346877 0.927307 2.0 c1 B\n", + "8 0.679540 0.062551 0.554906 7.5 c1 B\n", + "9 0.261995 0.196828 0.328467 7.5 c3 A" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from bofire.data_models.enum import SamplingMethodEnum\n", "\n", @@ -260,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -299,9 +510,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "NonlinearEqualityConstraint(type='NonlinearEqualityConstraint', expression='x1**2 + x2**2 - 1', features=None, jacobian_expression=None)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from bofire.data_models.constraints.api import NonlinearEqualityConstraint, NonlinearInequalityConstraint\n", "\n", @@ -323,9 +545,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "NChooseKConstraint(type='NChooseKConstraint', features=['x1', 'x2', 'x3'], min_count=2, max_count=3, none_also_valid=True)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from bofire.data_models.constraints.api import NChooseKConstraint\n", "\n", @@ -346,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -366,9 +599,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, True, False, False, False, True, False, False,\n", + " False])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "constr2.is_fulfilled(X).values" ] @@ -383,9 +628,10896 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aaron/anaconda3/envs/bofire/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "x=%{x}
y=%{y}", + "legendgroup": "", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "", + "showlegend": false, + "type": "scattergl", + "x": [ + 0, + 0.00020004000800160032, + 0.00040008001600320064, + 0.000600120024004801, + 0.0008001600320064013, + 0.0010002000400080016, + 0.001200240048009602, + 0.0014002800560112022, + 0.0016003200640128026, + 0.0018003600720144029, + 0.002000400080016003, + 0.0022004400880176033, + 0.002400480096019204, + 0.0026005201040208044, + 0.0028005601120224045, + 0.0030006001200240046, + 0.003200640128025605, + 0.0034006801360272057, + 0.0036007201440288058, + 0.003800760152030406, + 0.004000800160032006, + 0.004200840168033607, + 0.004400880176035207, + 0.004600920184036807, + 0.004800960192038408, + 0.005001000200040008, + 0.005201040208041609, + 0.0054010802160432084, + 0.005601120224044809, + 0.0058011602320464095, + 0.006001200240048009, + 0.00620124024804961, + 0.00640128025605121, + 0.006601320264052811, + 0.006801360272054411, + 0.007001400280056011, + 0.0072014402880576115, + 0.007401480296059212, + 0.007601520304060812, + 0.007801560312062412, + 0.008001600320064013, + 0.008201640328065612, + 0.008401680336067214, + 0.008601720344068814, + 0.008801760352070413, + 0.009001800360072015, + 0.009201840368073614, + 0.009401880376075216, + 0.009601920384076815, + 0.009801960392078415, + 0.010002000400080016, + 0.010202040408081616, + 0.010402080416083218, + 0.010602120424084817, + 0.010802160432086417, + 0.011002200440088018, + 0.011202240448089618, + 0.011402280456091218, + 0.011602320464092819, + 0.011802360472094419, + 0.012002400480096018, + 0.01220244048809762, + 0.01240248049609922, + 0.01260252050410082, + 0.01280256051210242, + 0.01300260052010402, + 0.013202640528105622, + 0.013402680536107221, + 0.013602720544108823, + 0.013802760552110422, + 0.014002800560112022, + 0.014202840568113623, + 0.014402880576115223, + 0.014602920584116823, + 0.014802960592118424, + 0.015003000600120024, + 0.015203040608121624, + 0.015403080616123225, + 0.015603120624124825, + 0.015803160632126424, + 0.016003200640128026, + 0.016203240648129627, + 0.016403280656131225, + 0.016603320664132826, + 0.016803360672134428, + 0.017003400680136026, + 0.017203440688137627, + 0.01740348069613923, + 0.017603520704140826, + 0.017803560712142428, + 0.01800360072014403, + 0.01820364072814563, + 0.01840368073614723, + 0.01860372074414883, + 0.01880376075215043, + 0.01900380076015203, + 0.01920384076815363, + 0.019403880776155232, + 0.01960392078415683, + 0.01980396079215843, + 0.020004000800160033, + 0.02020404080816163, + 0.020404080816163232, + 0.020604120824164834, + 0.020804160832166435, + 0.021004200840168033, + 0.021204240848169634, + 0.021404280856171236, + 0.021604320864172834, + 0.021804360872174435, + 0.022004400880176037, + 0.022204440888177635, + 0.022404480896179236, + 0.022604520904180837, + 0.022804560912182435, + 0.023004600920184037, + 0.023204640928185638, + 0.023404680936187236, + 0.023604720944188837, + 0.02380476095219044, + 0.024004800960192037, + 0.024204840968193638, + 0.02440488097619524, + 0.02460492098419684, + 0.02480496099219844, + 0.02500500100020004, + 0.02520504100820164, + 0.02540508101620324, + 0.02560512102420484, + 0.025805161032206442, + 0.02600520104020804, + 0.026205241048209642, + 0.026405281056211243, + 0.02660532106421284, + 0.026805361072214443, + 0.027005401080216044, + 0.027205441088217645, + 0.027405481096219243, + 0.027605521104220845, + 0.027805561112222446, + 0.028005601120224044, + 0.028205641128225645, + 0.028405681136227247, + 0.028605721144228845, + 0.028805761152230446, + 0.029005801160232048, + 0.029205841168233646, + 0.029405881176235247, + 0.02960592118423685, + 0.029805961192238446, + 0.030006001200240048, + 0.03020604120824165, + 0.030406081216243247, + 0.03060612122424485, + 0.03080616123224645, + 0.03100620124024805, + 0.03120624124824965, + 0.03140628125625125, + 0.03160632126425285, + 0.03180636127225445, + 0.03200640128025605, + 0.03220644128825765, + 0.032406481296259254, + 0.03260652130426085, + 0.03280656131226245, + 0.033006601320264055, + 0.03320664132826565, + 0.03340668133626725, + 0.033606721344268856, + 0.033806761352270454, + 0.03400680136027205, + 0.034206841368273656, + 0.034406881376275254, + 0.03460692138427685, + 0.03480696139227846, + 0.035007001400280055, + 0.03520704140828165, + 0.03540708141628326, + 0.035607121424284856, + 0.03580716143228646, + 0.03600720144028806, + 0.036207241448289657, + 0.03640728145629126, + 0.03660732146429286, + 0.03680736147229446, + 0.03700740148029606, + 0.03720744148829766, + 0.03740748149629926, + 0.03760752150430086, + 0.03780756151230246, + 0.03800760152030406, + 0.038207641528305664, + 0.03840768153630726, + 0.03860772154430886, + 0.038807761552310464, + 0.03900780156031206, + 0.03920784156831366, + 0.039407881576315265, + 0.03960792158431686, + 0.03980796159231846, + 0.040008001600320066, + 0.040208041608321664, + 0.04040808161632326, + 0.04060812162432487, + 0.040808161632326465, + 0.04100820164032806, + 0.04120824164832967, + 0.041408281656331265, + 0.04160832166433287, + 0.04180836167233447, + 0.042008401680336066, + 0.04220844168833767, + 0.04240848169633927, + 0.04260852170434087, + 0.04280856171234247, + 0.04300860172034407, + 0.04320864172834567, + 0.04340868173634727, + 0.04360872174434887, + 0.04380876175235047, + 0.04400880176035207, + 0.04420884176835367, + 0.04440888177635527, + 0.044608921784356874, + 0.04480896179235847, + 0.04500900180036007, + 0.045209041808361675, + 0.04540908181636327, + 0.04560912182436487, + 0.045809161832366475, + 0.04600920184036807, + 0.04620924184836967, + 0.046409281856371276, + 0.046609321864372874, + 0.04680936187237447, + 0.04700940188037608, + 0.047209441888377675, + 0.04740948189637927, + 0.04760952190438088, + 0.047809561912382476, + 0.048009601920384073, + 0.04820964192838568, + 0.048409681936387276, + 0.04860972194438888, + 0.04880976195239048, + 0.04900980196039208, + 0.04920984196839368, + 0.04940988197639528, + 0.04960992198439688, + 0.04980996199239848, + 0.05001000200040008, + 0.05021004200840168, + 0.05041008201640328, + 0.05061012202440488, + 0.05081016203240648, + 0.051010202040408084, + 0.05121024204840968, + 0.05141028205641128, + 0.051610322064412885, + 0.05181036207241448, + 0.05201040208041608, + 0.052210442088417686, + 0.052410482096419284, + 0.05261052210442088, + 0.052810562112422486, + 0.053010602120424084, + 0.05321064212842568, + 0.05341068213642729, + 0.053610722144428885, + 0.05381076215243048, + 0.05401080216043209, + 0.054210842168433686, + 0.05441088217643529, + 0.05461092218443689, + 0.05481096219243849, + 0.05501100220044009, + 0.05521104220844169, + 0.05541108221644329, + 0.05561112222444489, + 0.05581116223244649, + 0.05601120224044809, + 0.05621124224844969, + 0.05641128225645129, + 0.05661132226445289, + 0.056811362272454494, + 0.05701140228045609, + 0.05721144228845769, + 0.057411482296459294, + 0.05761152230446089, + 0.05781156231246249, + 0.058011602320464095, + 0.05821164232846569, + 0.05841168233646729, + 0.058611722344468896, + 0.058811762352470494, + 0.05901180236047209, + 0.0592118423684737, + 0.059411882376475295, + 0.05961192238447689, + 0.0598119623924785, + 0.060012002400480095, + 0.06021204240848169, + 0.0604120824164833, + 0.060612122424484896, + 0.060812162432486494, + 0.0610122024404881, + 0.0612122424484897, + 0.0614122824564913, + 0.0616123224644929, + 0.0618123624724945, + 0.0620124024804961, + 0.0622124424884977, + 0.0624124824964993, + 0.0626125225045009, + 0.0628125625125025, + 0.0630126025205041, + 0.0632126425285057, + 0.0634126825365073, + 0.0636127225445089, + 0.0638127625525105, + 0.0640128025605121, + 0.0642128425685137, + 0.0644128825765153, + 0.06461292258451691, + 0.06481296259251851, + 0.0650130026005201, + 0.0652130426085217, + 0.0654130826165233, + 0.0656131226245249, + 0.06581316263252651, + 0.06601320264052811, + 0.06621324264852971, + 0.0664132826565313, + 0.0666133226645329, + 0.0668133626725345, + 0.06701340268053611, + 0.06721344268853771, + 0.06741348269653931, + 0.06761352270454091, + 0.0678135627125425, + 0.0680136027205441, + 0.06821364272854571, + 0.06841368273654731, + 0.06861372274454891, + 0.06881376275255051, + 0.0690138027605521, + 0.0692138427685537, + 0.06941388277655532, + 0.06961392278455691, + 0.06981396279255851, + 0.07001400280056011, + 0.07021404280856171, + 0.0704140828165633, + 0.07061412282456492, + 0.07081416283256652, + 0.07101420284056811, + 0.07121424284856971, + 0.07141428285657131, + 0.07161432286457292, + 0.07181436287257452, + 0.07201440288057612, + 0.07221444288857772, + 0.07241448289657931, + 0.07261452290458091, + 0.07281456291258252, + 0.07301460292058412, + 0.07321464292858572, + 0.07341468293658732, + 0.07361472294458891, + 0.07381476295259051, + 0.07401480296059212, + 0.07421484296859372, + 0.07441488297659532, + 0.07461492298459692, + 0.07481496299259852, + 0.07501500300060011, + 0.07521504300860173, + 0.07541508301660332, + 0.07561512302460492, + 0.07581516303260652, + 0.07601520304060812, + 0.07621524304860972, + 0.07641528305661133, + 0.07661532306461293, + 0.07681536307261452, + 0.07701540308061612, + 0.07721544308861772, + 0.07741548309661933, + 0.07761552310462093, + 0.07781556311262253, + 0.07801560312062412, + 0.07821564312862572, + 0.07841568313662732, + 0.07861572314462893, + 0.07881576315263053, + 0.07901580316063213, + 0.07921584316863373, + 0.07941588317663532, + 0.07961592318463692, + 0.07981596319263853, + 0.08001600320064013, + 0.08021604320864173, + 0.08041608321664333, + 0.08061612322464493, + 0.08081616323264652, + 0.08101620324064814, + 0.08121624324864973, + 0.08141628325665133, + 0.08161632326465293, + 0.08181636327265453, + 0.08201640328065612, + 0.08221644328865774, + 0.08241648329665933, + 0.08261652330466093, + 0.08281656331266253, + 0.08301660332066413, + 0.08321664332866574, + 0.08341668333666734, + 0.08361672334466894, + 0.08381676335267053, + 0.08401680336067213, + 0.08421684336867373, + 0.08441688337667534, + 0.08461692338467694, + 0.08481696339267854, + 0.08501700340068014, + 0.08521704340868173, + 0.08541708341668333, + 0.08561712342468494, + 0.08581716343268654, + 0.08601720344068814, + 0.08621724344868974, + 0.08641728345669134, + 0.08661732346469293, + 0.08681736347269454, + 0.08701740348069614, + 0.08721744348869774, + 0.08741748349669934, + 0.08761752350470094, + 0.08781756351270253, + 0.08801760352070415, + 0.08821764352870574, + 0.08841768353670734, + 0.08861772354470894, + 0.08881776355271054, + 0.08901780356071214, + 0.08921784356871375, + 0.08941788357671535, + 0.08961792358471694, + 0.08981796359271854, + 0.09001800360072014, + 0.09021804360872175, + 0.09041808361672335, + 0.09061812362472495, + 0.09081816363272655, + 0.09101820364072814, + 0.09121824364872974, + 0.09141828365673135, + 0.09161832366473295, + 0.09181836367273455, + 0.09201840368073615, + 0.09221844368873774, + 0.09241848369673934, + 0.09261852370474095, + 0.09281856371274255, + 0.09301860372074415, + 0.09321864372874575, + 0.09341868373674735, + 0.09361872374474894, + 0.09381876375275056, + 0.09401880376075215, + 0.09421884376875375, + 0.09441888377675535, + 0.09461892378475695, + 0.09481896379275855, + 0.09501900380076016, + 0.09521904380876176, + 0.09541908381676335, + 0.09561912382476495, + 0.09581916383276655, + 0.09601920384076815, + 0.09621924384876976, + 0.09641928385677136, + 0.09661932386477295, + 0.09681936387277455, + 0.09701940388077615, + 0.09721944388877776, + 0.09741948389677936, + 0.09761952390478096, + 0.09781956391278256, + 0.09801960392078415, + 0.09821964392878575, + 0.09841968393678736, + 0.09861972394478896, + 0.09881976395279056, + 0.09901980396079216, + 0.09921984396879376, + 0.09941988397679535, + 0.09961992398479697, + 0.09981996399279856, + 0.10002000400080016, + 0.10022004400880176, + 0.10042008401680336, + 0.10062012402480495, + 0.10082016403280657, + 0.10102020404080816, + 0.10122024404880976, + 0.10142028405681136, + 0.10162032406481296, + 0.10182036407281456, + 0.10202040408081617, + 0.10222044408881777, + 0.10242048409681936, + 0.10262052410482096, + 0.10282056411282256, + 0.10302060412082417, + 0.10322064412882577, + 0.10342068413682737, + 0.10362072414482897, + 0.10382076415283056, + 0.10402080416083216, + 0.10422084416883377, + 0.10442088417683537, + 0.10462092418483697, + 0.10482096419283857, + 0.10502100420084017, + 0.10522104420884176, + 0.10542108421684337, + 0.10562112422484497, + 0.10582116423284657, + 0.10602120424084817, + 0.10622124424884977, + 0.10642128425685136, + 0.10662132426485298, + 0.10682136427285457, + 0.10702140428085617, + 0.10722144428885777, + 0.10742148429685937, + 0.10762152430486097, + 0.10782156431286258, + 0.10802160432086418, + 0.10822164432886577, + 0.10842168433686737, + 0.10862172434486897, + 0.10882176435287058, + 0.10902180436087218, + 0.10922184436887378, + 0.10942188437687538, + 0.10962192438487697, + 0.10982196439287857, + 0.11002200440088018, + 0.11022204440888178, + 0.11042208441688338, + 0.11062212442488498, + 0.11082216443288657, + 0.11102220444088817, + 0.11122224444888978, + 0.11142228445689138, + 0.11162232446489298, + 0.11182236447289458, + 0.11202240448089618, + 0.11222244448889777, + 0.11242248449689939, + 0.11262252450490098, + 0.11282256451290258, + 0.11302260452090418, + 0.11322264452890578, + 0.11342268453690738, + 0.11362272454490899, + 0.11382276455291059, + 0.11402280456091218, + 0.11422284456891378, + 0.11442288457691538, + 0.11462292458491698, + 0.11482296459291859, + 0.11502300460092019, + 0.11522304460892178, + 0.11542308461692338, + 0.11562312462492498, + 0.11582316463292659, + 0.11602320464092819, + 0.11622324464892979, + 0.11642328465693139, + 0.11662332466493298, + 0.11682336467293458, + 0.1170234046809362, + 0.11722344468893779, + 0.11742348469693939, + 0.11762352470494099, + 0.11782356471294259, + 0.11802360472094418, + 0.1182236447289458, + 0.1184236847369474, + 0.11862372474494899, + 0.11882376475295059, + 0.11902380476095219, + 0.11922384476895379, + 0.1194238847769554, + 0.119623924784957, + 0.11982396479295859, + 0.12002400480096019, + 0.12022404480896179, + 0.12042408481696339, + 0.120624124824965, + 0.1208241648329666, + 0.1210242048409682, + 0.12122424484896979, + 0.12142428485697139, + 0.12162432486497299, + 0.1218243648729746, + 0.1220244048809762, + 0.1222244448889778, + 0.1224244848969794, + 0.12262452490498099, + 0.1228245649129826, + 0.1230246049209842, + 0.1232246449289858, + 0.1234246849369874, + 0.123624724944989, + 0.12382476495299059, + 0.1240248049609922, + 0.1242248449689938, + 0.1244248849769954, + 0.124624924984997, + 0.1248249649929986, + 0.1250250050010002, + 0.1252250450090018, + 0.1254250850170034, + 0.125625125025005, + 0.12582516503300661, + 0.1260252050410082, + 0.1262252450490098, + 0.1264252850570114, + 0.126625325065013, + 0.1268253650730146, + 0.1270254050810162, + 0.1272254450890178, + 0.1274254850970194, + 0.127625525105021, + 0.1278255651130226, + 0.1280256051210242, + 0.12822564512902582, + 0.1284256851370274, + 0.128625725145029, + 0.1288257651530306, + 0.1290258051610322, + 0.12922584516903382, + 0.1294258851770354, + 0.12962592518503702, + 0.1298259651930386, + 0.1300260052010402, + 0.1302260452090418, + 0.1304260852170434, + 0.13062612522504502, + 0.1308261652330466, + 0.13102620524104822, + 0.1312262452490498, + 0.1314262852570514, + 0.13162632526505302, + 0.1318263652730546, + 0.13202640528105622, + 0.1322264452890578, + 0.13242648529705942, + 0.132626525305061, + 0.1328265653130626, + 0.13302660532106422, + 0.1332266453290658, + 0.13342668533706742, + 0.133626725345069, + 0.13382676535307061, + 0.13402680536107223, + 0.1342268453690738, + 0.13442688537707542, + 0.134626925385077, + 0.13482696539307862, + 0.13502700540108023, + 0.13522704540908181, + 0.13542708541708343, + 0.135627125425085, + 0.13582716543308662, + 0.1360272054410882, + 0.13622724544908982, + 0.13642728545709143, + 0.136627325465093, + 0.13682736547309463, + 0.1370274054810962, + 0.13722744548909782, + 0.13742748549709943, + 0.13762752550510102, + 0.13782756551310263, + 0.1380276055211042, + 0.13822764552910582, + 0.1384276855371074, + 0.13862772554510902, + 0.13882776555311063, + 0.13902780556111222, + 0.13922784556911383, + 0.1394278855771154, + 0.13962792558511702, + 0.13982796559311864, + 0.14002800560112022, + 0.14022804560912183, + 0.14042808561712342, + 0.14062812562512503, + 0.1408281656331266, + 0.14102820564112822, + 0.14122824564912984, + 0.14142828565713142, + 0.14162832566513303, + 0.14182836567313462, + 0.14202840568113623, + 0.14222844568913784, + 0.14242848569713942, + 0.14262852570514103, + 0.14282856571314262, + 0.14302860572114423, + 0.14322864572914584, + 0.14342868573714743, + 0.14362872574514904, + 0.14382876575315062, + 0.14402880576115223, + 0.14422884576915382, + 0.14442888577715543, + 0.14462892578515704, + 0.14482896579315863, + 0.14502900580116024, + 0.14522904580916182, + 0.14542908581716343, + 0.14562912582516505, + 0.14582916583316663, + 0.14602920584116824, + 0.14622924584916983, + 0.14642928585717144, + 0.14662932586517302, + 0.14682936587317463, + 0.14702940588117624, + 0.14722944588917783, + 0.14742948589717944, + 0.14762952590518102, + 0.14782956591318264, + 0.14802960592118425, + 0.14822964592918583, + 0.14842968593718744, + 0.14862972594518903, + 0.14882976595319064, + 0.14902980596119225, + 0.14922984596919384, + 0.14942988597719545, + 0.14962992598519703, + 0.14982996599319864, + 0.15003000600120023, + 0.15023004600920184, + 0.15043008601720345, + 0.15063012602520504, + 0.15083016603320665, + 0.15103020604120823, + 0.15123024604920984, + 0.15143028605721146, + 0.15163032606521304, + 0.15183036607321465, + 0.15203040608121624, + 0.15223044608921785, + 0.15243048609721943, + 0.15263052610522104, + 0.15283056611322265, + 0.15303060612122424, + 0.15323064612922585, + 0.15343068613722743, + 0.15363072614522905, + 0.15383076615323066, + 0.15403080616123224, + 0.15423084616923385, + 0.15443088617723544, + 0.15463092618523705, + 0.15483096619323866, + 0.15503100620124025, + 0.15523104620924186, + 0.15543108621724344, + 0.15563112622524505, + 0.15583116623324664, + 0.15603120624124825, + 0.15623124624924986, + 0.15643128625725145, + 0.15663132626525306, + 0.15683136627325464, + 0.15703140628125625, + 0.15723144628925786, + 0.15743148629725945, + 0.15763152630526106, + 0.15783156631326264, + 0.15803160632126426, + 0.15823164632926584, + 0.15843168633726745, + 0.15863172634526906, + 0.15883176635327065, + 0.15903180636127226, + 0.15923184636927384, + 0.15943188637727546, + 0.15963192638527707, + 0.15983196639327865, + 0.16003200640128026, + 0.16023204640928185, + 0.16043208641728346, + 0.16063212642528507, + 0.16083216643328666, + 0.16103220644128827, + 0.16123224644928985, + 0.16143228645729146, + 0.16163232646529305, + 0.16183236647329466, + 0.16203240648129627, + 0.16223244648929785, + 0.16243248649729947, + 0.16263252650530105, + 0.16283256651330266, + 0.16303260652130427, + 0.16323264652930586, + 0.16343268653730747, + 0.16363272654530905, + 0.16383276655331067, + 0.16403280656131225, + 0.16423284656931386, + 0.16443288657731547, + 0.16463292658531706, + 0.16483296659331867, + 0.16503300660132025, + 0.16523304660932187, + 0.16543308661732348, + 0.16563312662532506, + 0.16583316663332667, + 0.16603320664132826, + 0.16623324664932987, + 0.16643328665733148, + 0.16663332666533306, + 0.16683336667333468, + 0.16703340668133626, + 0.16723344668933787, + 0.16743348669733946, + 0.16763352670534107, + 0.16783356671334268, + 0.16803360672134426, + 0.16823364672934588, + 0.16843368673734746, + 0.16863372674534907, + 0.16883376675335068, + 0.16903380676135227, + 0.16923384676935388, + 0.16943388677735546, + 0.16963392678535708, + 0.16983396679335866, + 0.17003400680136027, + 0.17023404680936188, + 0.17043408681736347, + 0.17063412682536508, + 0.17083416683336666, + 0.17103420684136827, + 0.1712342468493699, + 0.17143428685737147, + 0.17163432686537308, + 0.17183436687337467, + 0.17203440688137628, + 0.17223444688937786, + 0.17243448689737947, + 0.17263452690538109, + 0.17283456691338267, + 0.17303460692138428, + 0.17323464692938587, + 0.17343468693738748, + 0.1736347269453891, + 0.17383476695339067, + 0.17403480696139229, + 0.17423484696939387, + 0.17443488697739548, + 0.1746349269853971, + 0.17483496699339868, + 0.1750350070014003, + 0.17523504700940187, + 0.17543508701740348, + 0.17563512702540507, + 0.17583516703340668, + 0.1760352070414083, + 0.17623524704940988, + 0.1764352870574115, + 0.17663532706541307, + 0.17683536707341468, + 0.1770354070814163, + 0.17723544708941788, + 0.1774354870974195, + 0.17763552710542108, + 0.1778355671134227, + 0.17803560712142427, + 0.17823564712942588, + 0.1784356871374275, + 0.17863572714542908, + 0.1788357671534307, + 0.17903580716143228, + 0.1792358471694339, + 0.1794358871774355, + 0.17963592718543708, + 0.1798359671934387, + 0.18003600720144028, + 0.1802360472094419, + 0.1804360872174435, + 0.1806361272254451, + 0.1808361672334467, + 0.18103620724144828, + 0.1812362472494499, + 0.18143628725745148, + 0.1816363272654531, + 0.1818363672734547, + 0.1820364072814563, + 0.1822364472894579, + 0.18243648729745948, + 0.1826365273054611, + 0.1828365673134627, + 0.1830366073214643, + 0.1832366473294659, + 0.18343668733746749, + 0.1836367273454691, + 0.18383676735347068, + 0.1840368073614723, + 0.1842368473694739, + 0.1844368873774755, + 0.1846369273854771, + 0.18483696739347868, + 0.1850370074014803, + 0.1852370474094819, + 0.1854370874174835, + 0.1856371274254851, + 0.1858371674334867, + 0.1860372074414883, + 0.1862372474494899, + 0.1864372874574915, + 0.1866373274654931, + 0.1868373674734947, + 0.1870374074814963, + 0.1872374474894979, + 0.1874374874974995, + 0.1876375275055011, + 0.1878375675135027, + 0.1880376075215043, + 0.1882376475295059, + 0.1884376875375075, + 0.18863772754550912, + 0.1888377675535107, + 0.1890378075615123, + 0.1892378475695139, + 0.1894378875775155, + 0.1896379275855171, + 0.1898379675935187, + 0.19003800760152031, + 0.1902380476095219, + 0.1904380876175235, + 0.1906381276255251, + 0.1908381676335267, + 0.19103820764152832, + 0.1912382476495299, + 0.19143828765753151, + 0.1916383276655331, + 0.1918383676735347, + 0.1920384076815363, + 0.1922384476895379, + 0.19243848769753952, + 0.1926385277055411, + 0.1928385677135427, + 0.1930386077215443, + 0.1932386477295459, + 0.19343868773754752, + 0.1936387277455491, + 0.19383876775355072, + 0.1940388077615523, + 0.1942388477695539, + 0.19443888777755552, + 0.1946389277855571, + 0.19483896779355872, + 0.1950390078015603, + 0.19523904780956192, + 0.1954390878175635, + 0.1956391278255651, + 0.19583916783356672, + 0.1960392078415683, + 0.19623924784956992, + 0.1964392878575715, + 0.19663932786557312, + 0.19683936787357473, + 0.1970394078815763, + 0.19723944788957792, + 0.1974394878975795, + 0.19763952790558112, + 0.1978395679135827, + 0.19803960792158432, + 0.19823964792958593, + 0.1984396879375875, + 0.19863972794558912, + 0.1988397679535907, + 0.19903980796159232, + 0.19923984796959393, + 0.19943988797759551, + 0.19963992798559713, + 0.1998399679935987, + 0.20004000800160032, + 0.20024004800960193, + 0.20044008801760352, + 0.20064012802560513, + 0.20084016803360671, + 0.20104020804160833, + 0.2012402480496099, + 0.20144028805761152, + 0.20164032806561313, + 0.20184036807361472, + 0.20204040808161633, + 0.2022404480896179, + 0.20244048809761953, + 0.20264052810562114, + 0.20284056811362272, + 0.20304060812162433, + 0.20324064812962592, + 0.20344068813762753, + 0.2036407281456291, + 0.20384076815363072, + 0.20404080816163234, + 0.20424084816963392, + 0.20444088817763553, + 0.20464092818563712, + 0.20484096819363873, + 0.20504100820164034, + 0.20524104820964192, + 0.20544108821764354, + 0.20564112822564512, + 0.20584116823364673, + 0.20604120824164834, + 0.20624124824964993, + 0.20644128825765154, + 0.20664132826565312, + 0.20684136827365474, + 0.20704140828165632, + 0.20724144828965793, + 0.20744148829765954, + 0.20764152830566113, + 0.20784156831366274, + 0.20804160832166432, + 0.20824164832966593, + 0.20844168833766755, + 0.20864172834566913, + 0.20884176835367074, + 0.20904180836167233, + 0.20924184836967394, + 0.20944188837767552, + 0.20964192838567713, + 0.20984196839367875, + 0.21004200840168033, + 0.21024204840968194, + 0.21044208841768353, + 0.21064212842568514, + 0.21084216843368675, + 0.21104220844168833, + 0.21124224844968995, + 0.21144228845769153, + 0.21164232846569314, + 0.21184236847369475, + 0.21204240848169634, + 0.21224244848969795, + 0.21244248849769953, + 0.21264252850570114, + 0.21284256851370273, + 0.21304260852170434, + 0.21324264852970595, + 0.21344268853770754, + 0.21364272854570915, + 0.21384276855371073, + 0.21404280856171234, + 0.21424284856971396, + 0.21444288857771554, + 0.21464292858571715, + 0.21484296859371874, + 0.21504300860172035, + 0.21524304860972193, + 0.21544308861772354, + 0.21564312862572516, + 0.21584316863372674, + 0.21604320864172835, + 0.21624324864972994, + 0.21644328865773155, + 0.21664332866573316, + 0.21684336867373474, + 0.21704340868173636, + 0.21724344868973794, + 0.21744348869773955, + 0.21764352870574116, + 0.21784356871374275, + 0.21804360872174436, + 0.21824364872974594, + 0.21844368873774755, + 0.21864372874574914, + 0.21884376875375075, + 0.21904380876175236, + 0.21924384876975395, + 0.21944388877775556, + 0.21964392878575714, + 0.21984396879375875, + 0.22004400880176037, + 0.22024404880976195, + 0.22044408881776356, + 0.22064412882576515, + 0.22084416883376676, + 0.22104420884176834, + 0.22124424884976995, + 0.22144428885777157, + 0.22164432886577315, + 0.22184436887377476, + 0.22204440888177635, + 0.22224444888977796, + 0.22244448889777957, + 0.22264452890578115, + 0.22284456891378276, + 0.22304460892178435, + 0.22324464892978596, + 0.22344468893778754, + 0.22364472894578916, + 0.22384476895379077, + 0.22404480896179235, + 0.22424484896979396, + 0.22444488897779555, + 0.22464492898579716, + 0.22484496899379877, + 0.22504500900180036, + 0.22524504900980197, + 0.22544508901780355, + 0.22564512902580516, + 0.22584516903380678, + 0.22604520904180836, + 0.22624524904980997, + 0.22644528905781156, + 0.22664532906581317, + 0.22684536907381475, + 0.22704540908181636, + 0.22724544908981797, + 0.22744548909781956, + 0.22764552910582117, + 0.22784556911382275, + 0.22804560912182437, + 0.22824564912982598, + 0.22844568913782756, + 0.22864572914582917, + 0.22884576915383076, + 0.22904580916183237, + 0.22924584916983395, + 0.22944588917783557, + 0.22964592918583718, + 0.22984596919383876, + 0.23004600920184037, + 0.23024604920984196, + 0.23044608921784357, + 0.23064612922584518, + 0.23084616923384677, + 0.23104620924184838, + 0.23124624924984996, + 0.23144628925785157, + 0.23164632926585318, + 0.23184636927385477, + 0.23204640928185638, + 0.23224644928985796, + 0.23244648929785958, + 0.23264652930586116, + 0.23284656931386277, + 0.23304660932186438, + 0.23324664932986597, + 0.23344668933786758, + 0.23364672934586916, + 0.23384676935387078, + 0.2340468093618724, + 0.23424684936987397, + 0.23444688937787558, + 0.23464692938587717, + 0.23484696939387878, + 0.23504700940188036, + 0.23524704940988198, + 0.2354470894178836, + 0.23564712942588517, + 0.23584716943388678, + 0.23604720944188837, + 0.23624724944988998, + 0.2364472894578916, + 0.23664732946589317, + 0.2368473694738948, + 0.23704740948189637, + 0.23724744948989798, + 0.2374474894978996, + 0.23764752950590118, + 0.2378475695139028, + 0.23804760952190437, + 0.23824764952990599, + 0.23844768953790757, + 0.23864772954590918, + 0.2388477695539108, + 0.23904780956191238, + 0.239247849569914, + 0.23944788957791557, + 0.23964792958591719, + 0.2398479695939188, + 0.24004800960192038, + 0.240248049609922, + 0.24044808961792358, + 0.2406481296259252, + 0.24084816963392677, + 0.24104820964192838, + 0.24124824964993, + 0.24144828965793158, + 0.2416483296659332, + 0.24184836967393478, + 0.2420484096819364, + 0.242248449689938, + 0.24244848969793958, + 0.2426485297059412, + 0.24284856971394278, + 0.2430486097219444, + 0.24324864972994598, + 0.2434486897379476, + 0.2436487297459492, + 0.24384876975395078, + 0.2440488097619524, + 0.24424884976995398, + 0.2444488897779556, + 0.2446489297859572, + 0.2448489697939588, + 0.2450490098019604, + 0.24524904980996198, + 0.2454490898179636, + 0.2456491298259652, + 0.2458491698339668, + 0.2460492098419684, + 0.24624924984997, + 0.2464492898579716, + 0.24664932986597318, + 0.2468493698739748, + 0.2470494098819764, + 0.247249449889978, + 0.2474494898979796, + 0.24764952990598119, + 0.2478495699139828, + 0.2480496099219844, + 0.248249649929986, + 0.2484496899379876, + 0.2486497299459892, + 0.2488497699539908, + 0.24904980996199239, + 0.249249849969994, + 0.2494498899779956, + 0.2496499299859972, + 0.2498499699939988, + 0.2500500100020004, + 0.250250050010002, + 0.2504500900180036, + 0.2506501300260052, + 0.2508501700340068, + 0.2510502100420084, + 0.25125025005001, + 0.2514502900580116, + 0.25165033006601323, + 0.2518503700740148, + 0.2520504100820164, + 0.252250450090018, + 0.2524504900980196, + 0.25265053010602123, + 0.2528505701140228, + 0.2530506101220244, + 0.253250650130026, + 0.2534506901380276, + 0.2536507301460292, + 0.2538507701540308, + 0.2540508101620324, + 0.254250850170034, + 0.2544508901780356, + 0.2546509301860372, + 0.2548509701940388, + 0.2550510102020404, + 0.255251050210042, + 0.25545109021804363, + 0.2556511302260452, + 0.2558511702340468, + 0.2560512102420484, + 0.25625125025005, + 0.25645129025805163, + 0.2566513302660532, + 0.2568513702740548, + 0.2570514102820564, + 0.257251450290058, + 0.25745149029805964, + 0.2576515303060612, + 0.2578515703140628, + 0.2580516103220644, + 0.25825165033006603, + 0.25845169033806764, + 0.2586517303460692, + 0.2588517703540708, + 0.2590518103620724, + 0.25925185037007403, + 0.2594518903780756, + 0.2596519303860772, + 0.2598519703940788, + 0.2600520104020804, + 0.26025205041008204, + 0.2604520904180836, + 0.2606521304260852, + 0.2608521704340868, + 0.26105221044208843, + 0.26125225045009004, + 0.2614522904580916, + 0.2616523304660932, + 0.2618523704740948, + 0.26205241048209643, + 0.26225245049009804, + 0.2624524904980996, + 0.2626525305061012, + 0.2628525705141028, + 0.26305261052210444, + 0.26325265053010605, + 0.2634526905381076, + 0.2636527305461092, + 0.2638527705541108, + 0.26405281056211244, + 0.26425285057011405, + 0.2644528905781156, + 0.2646529305861172, + 0.26485297059411883, + 0.26505301060212044, + 0.265253050610122, + 0.2654530906181236, + 0.2656531306261252, + 0.26585317063412683, + 0.26605321064212845, + 0.26625325065013, + 0.2664532906581316, + 0.2666533306661332, + 0.26685337067413484, + 0.26705341068213645, + 0.267253450690138, + 0.2674534906981396, + 0.26765353070614123, + 0.26785357071414284, + 0.26805361072214445, + 0.268253650730146, + 0.2684536907381476, + 0.26865373074614923, + 0.26885377075415084, + 0.26905381076215246, + 0.269253850770154, + 0.2694538907781556, + 0.26965393078615724, + 0.26985397079415885, + 0.27005401080216046, + 0.270254050810162, + 0.27045409081816363, + 0.27065413082616524, + 0.27085417083416685, + 0.2710542108421684, + 0.27125425085017, + 0.27145429085817163, + 0.27165433086617324, + 0.27185437087417486, + 0.2720544108821764, + 0.272254450890178, + 0.27245449089817964, + 0.27265453090618125, + 0.27285457091418286, + 0.2730546109221844, + 0.273254650930186, + 0.27345469093818764, + 0.27365473094618925, + 0.27385477095419086, + 0.2740548109621924, + 0.27425485097019403, + 0.27445489097819564, + 0.27465493098619725, + 0.27485497099419887, + 0.2750550110022004, + 0.27525505101020203, + 0.27545509101820365, + 0.27565513102620526, + 0.27585517103420687, + 0.2760552110422084, + 0.27625525105021004, + 0.27645529105821165, + 0.27665533106621326, + 0.2768553710742148, + 0.27705541108221643, + 0.27725545109021804, + 0.27745549109821965, + 0.27765553110622126, + 0.2778555711142228, + 0.27805561112222443, + 0.27825565113022604, + 0.27845569113822766, + 0.27865573114622927, + 0.2788557711542308, + 0.27905581116223244, + 0.27925585117023405, + 0.27945589117823566, + 0.27965593118623727, + 0.27985597119423883, + 0.28005601120224044, + 0.28025605121024205, + 0.28045609121824366, + 0.2806561312262453, + 0.28085617123424683, + 0.28105621124224844, + 0.28125625125025006, + 0.28145629125825167, + 0.2816563312662532, + 0.28185637127425484, + 0.28205641128225645, + 0.28225645129025806, + 0.28245649129825967, + 0.2826565313062612, + 0.28285657131426284, + 0.28305661132226445, + 0.28325665133026606, + 0.2834566913382677, + 0.28365673134626923, + 0.28385677135427084, + 0.28405681136227245, + 0.28425685137027407, + 0.2844568913782757, + 0.28465693138627723, + 0.28485697139427885, + 0.28505701140228046, + 0.28525705141028207, + 0.2854570914182837, + 0.28565713142628524, + 0.28585717143428685, + 0.28605721144228846, + 0.2862572514502901, + 0.2864572914582917, + 0.28665733146629324, + 0.28685737147429485, + 0.28705741148229647, + 0.2872574514902981, + 0.28745749149829963, + 0.28765753150630125, + 0.28785757151430286, + 0.28805761152230447, + 0.2882576515303061, + 0.28845769153830764, + 0.28865773154630925, + 0.28885777155431086, + 0.28905781156231247, + 0.2892578515703141, + 0.28945789157831564, + 0.28965793158631725, + 0.28985797159431886, + 0.2900580116023205, + 0.2902580516103221, + 0.29045809161832364, + 0.29065813162632526, + 0.29085817163432687, + 0.2910582116423285, + 0.2912582516503301, + 0.29145829165833165, + 0.29165833166633326, + 0.29185837167433487, + 0.2920584116823365, + 0.2922584516903381, + 0.29245849169833965, + 0.29265853170634126, + 0.2928585717143429, + 0.2930586117223445, + 0.29325865173034604, + 0.29345869173834765, + 0.29365873174634927, + 0.2938587717543509, + 0.2940588117623525, + 0.29425885177035405, + 0.29445889177835566, + 0.29465893178635727, + 0.2948589717943589, + 0.2950590118023605, + 0.29525905181036205, + 0.29545909181836366, + 0.2956591318263653, + 0.2958591718343669, + 0.2960592118423685, + 0.29625925185037005, + 0.29645929185837167, + 0.2966593318663733, + 0.2968593718743749, + 0.2970594118823765, + 0.29725945189037806, + 0.29745949189837967, + 0.2976595319063813, + 0.2978595719143829, + 0.2980596119223845, + 0.29825965193038606, + 0.2984596919383877, + 0.2986597319463893, + 0.2988597719543909, + 0.29905981196239245, + 0.29925985197039406, + 0.2994598919783957, + 0.2996599319863973, + 0.2998599719943989, + 0.30006001200240046, + 0.30026005201040207, + 0.3004600920184037, + 0.3006601320264053, + 0.3008601720344069, + 0.30106021204240846, + 0.30126025205041007, + 0.3014602920584117, + 0.3016603320664133, + 0.3018603720744149, + 0.30206041208241646, + 0.3022604520904181, + 0.3024604920984197, + 0.3026605321064213, + 0.3028605721144229, + 0.30306061212242447, + 0.3032606521304261, + 0.3034606921384277, + 0.3036607321464293, + 0.3038607721544309, + 0.30406081216243247, + 0.3042608521704341, + 0.3044608921784357, + 0.3046609321864373, + 0.30486097219443886, + 0.3050610122024405, + 0.3052610522104421, + 0.3054610922184437, + 0.3056611322264453, + 0.30586117223444687, + 0.3060612122424485, + 0.3062612522504501, + 0.3064612922584517, + 0.3066613322664533, + 0.30686137227445487, + 0.3070614122824565, + 0.3072614522904581, + 0.3074614922984597, + 0.3076615323064613, + 0.3078615723144629, + 0.3080616123224645, + 0.3082616523304661, + 0.3084616923384677, + 0.3086617323464693, + 0.3088617723544709, + 0.3090618123624725, + 0.3092618523704741, + 0.3094618923784757, + 0.3096619323864773, + 0.3098619723944789, + 0.3100620124024805, + 0.3102620524104821, + 0.3104620924184837, + 0.31066213242648527, + 0.3108621724344869, + 0.3110622124424885, + 0.3112622524504901, + 0.3114622924584917, + 0.3116623324664933, + 0.3118623724744949, + 0.3120624124824965, + 0.3122624524904981, + 0.3124624924984997, + 0.3126625325065013, + 0.3128625725145029, + 0.3130626125225045, + 0.3132626525305061, + 0.3134626925385077, + 0.3136627325465093, + 0.3138627725545109, + 0.3140628125625125, + 0.3142628525705141, + 0.31446289257851573, + 0.3146629325865173, + 0.3148629725945189, + 0.3150630126025205, + 0.3152630526105221, + 0.31546309261852373, + 0.3156631326265253, + 0.3158631726345269, + 0.3160632126425285, + 0.3162632526505301, + 0.3164632926585317, + 0.3166633326665333, + 0.3168633726745349, + 0.3170634126825365, + 0.31726345269053813, + 0.3174634926985397, + 0.3176635327065413, + 0.3178635727145429, + 0.3180636127225445, + 0.31826365273054613, + 0.3184636927385477, + 0.3186637327465493, + 0.3188637727545509, + 0.3190638127625525, + 0.31926385277055414, + 0.3194638927785557, + 0.3196639327865573, + 0.3198639727945589, + 0.3200640128025605, + 0.32026405281056214, + 0.3204640928185637, + 0.3206641328265653, + 0.3208641728345669, + 0.32106421284256853, + 0.32126425285057014, + 0.3214642928585717, + 0.3216643328665733, + 0.3218643728745749, + 0.32206441288257653, + 0.3222644528905781, + 0.3224644928985797, + 0.3226645329065813, + 0.3228645729145829, + 0.32306461292258454, + 0.3232646529305861, + 0.3234646929385877, + 0.3236647329465893, + 0.32386477295459093, + 0.32406481296259254, + 0.3242648529705941, + 0.3244648929785957, + 0.3246649329865973, + 0.32486497299459893, + 0.32506501300260054, + 0.3252650530106021, + 0.3254650930186037, + 0.3256651330266053, + 0.32586517303460694, + 0.32606521304260855, + 0.3262652530506101, + 0.3264652930586117, + 0.32666533306661333, + 0.32686537307461494, + 0.32706541308261655, + 0.3272654530906181, + 0.3274654930986197, + 0.32766553310662133, + 0.32786557311462294, + 0.3280656131226245, + 0.3282656531306261, + 0.3284656931386277, + 0.32866573314662934, + 0.32886577315463095, + 0.3290658131626325, + 0.3292658531706341, + 0.3294658931786357, + 0.32966593318663734, + 0.32986597319463895, + 0.3300660132026405, + 0.3302660532106421, + 0.33046609321864373, + 0.33066613322664534, + 0.33086617323464695, + 0.3310662132426485, + 0.3312662532506501, + 0.33146629325865173, + 0.33166633326665335, + 0.33186637327465496, + 0.3320664132826565, + 0.3322664532906581, + 0.33246649329865974, + 0.33266653330666135, + 0.33286657331466296, + 0.3330666133226645, + 0.33326665333066613, + 0.33346669333866774, + 0.33366673334666935, + 0.3338667733546709, + 0.3340668133626725, + 0.33426685337067413, + 0.33446689337867574, + 0.33466693338667736, + 0.3348669733946789, + 0.3350670134026805, + 0.33526705341068214, + 0.33546709341868375, + 0.33566713342668536, + 0.3358671734346869, + 0.33606721344268853, + 0.33626725345069014, + 0.33646729345869175, + 0.33666733346669336, + 0.3368673734746949, + 0.33706741348269653, + 0.33726745349069814, + 0.33746749349869976, + 0.33766753350670137, + 0.3378675735147029, + 0.33806761352270454, + 0.33826765353070615, + 0.33846769353870776, + 0.3386677335467093, + 0.3388677735547109, + 0.33906781356271254, + 0.33926785357071415, + 0.33946789357871576, + 0.3396679335867173, + 0.33986797359471893, + 0.34006801360272054, + 0.34026805361072215, + 0.34046809361872377, + 0.3406681336267253, + 0.34086817363472693, + 0.34106821364272855, + 0.34126825365073016, + 0.34146829365873177, + 0.3416683336667333, + 0.34186837367473494, + 0.34206841368273655, + 0.34226845369073816, + 0.3424684936987398, + 0.34266853370674133, + 0.34286857371474294, + 0.34306861372274455, + 0.34326865373074616, + 0.3434686937387478, + 0.34366873374674933, + 0.34386877375475094, + 0.34406881376275256, + 0.34426885377075417, + 0.3444688937787557, + 0.34466893378675734, + 0.34486897379475895, + 0.34506901380276056, + 0.34526905381076217, + 0.34546909381876373, + 0.34566913382676534, + 0.34586917383476695, + 0.34606921384276856, + 0.3462692538507702, + 0.34646929385877173, + 0.34666933386677334, + 0.34686937387477496, + 0.34706941388277657, + 0.3472694538907782, + 0.34746949389877974, + 0.34766953390678135, + 0.34786957391478296, + 0.34806961392278457, + 0.3482696539307862, + 0.34846969393878774, + 0.34866973394678935, + 0.34886977395479096, + 0.3490698139627926, + 0.3492698539707942, + 0.34946989397879574, + 0.34966993398679735, + 0.34986997399479897, + 0.3500700140028006, + 0.35027005401080213, + 0.35047009401880375, + 0.35067013402680536, + 0.35087017403480697, + 0.3510702140428086, + 0.35127025405081014, + 0.35147029405881175, + 0.35167033406681336, + 0.351870374074815, + 0.3520704140828166, + 0.35227045409081814, + 0.35247049409881975, + 0.35267053410682137, + 0.352870574114823, + 0.3530706141228246, + 0.35327065413082614, + 0.35347069413882776, + 0.35367073414682937, + 0.353870774154831, + 0.3540708141628326, + 0.35427085417083415, + 0.35447089417883576, + 0.35467093418683737, + 0.354870974194839, + 0.3550710142028406, + 0.35527105421084215, + 0.35547109421884376, + 0.3556711342268454, + 0.355871174234847, + 0.35607121424284854, + 0.35627125425085016, + 0.35647129425885177, + 0.3566713342668534, + 0.356871374274855, + 0.35707141428285655, + 0.35727145429085816, + 0.35747149429885977, + 0.3576715343068614, + 0.357871574314863, + 0.35807161432286455, + 0.35827165433086616, + 0.3584716943388678, + 0.3586717343468694, + 0.358871774354871, + 0.35907181436287255, + 0.35927185437087417, + 0.3594718943788758, + 0.3596719343868774, + 0.359871974394879, + 0.36007201440288056, + 0.36027205441088217, + 0.3604720944188838, + 0.3606721344268854, + 0.360872174434887, + 0.36107221444288856, + 0.3612722544508902, + 0.3614722944588918, + 0.3616723344668934, + 0.36187237447489495, + 0.36207241448289657, + 0.3622724544908982, + 0.3624724944988998, + 0.3626725345069014, + 0.36287257451490296, + 0.36307261452290457, + 0.3632726545309062, + 0.3634726945389078, + 0.3636727345469094, + 0.36387277455491096, + 0.3640728145629126, + 0.3642728545709142, + 0.3644728945789158, + 0.3646729345869174, + 0.36487297459491896, + 0.3650730146029206, + 0.3652730546109222, + 0.3654730946189238, + 0.3656731346269254, + 0.36587317463492697, + 0.3660732146429286, + 0.3662732546509302, + 0.3664732946589318, + 0.3666733346669334, + 0.36687337467493497, + 0.3670734146829366, + 0.3672734546909382, + 0.3674734946989398, + 0.36767353470694136, + 0.367873574714943, + 0.3680736147229446, + 0.3682736547309462, + 0.3684736947389478, + 0.36867373474694937, + 0.368873774754951, + 0.3690738147629526, + 0.3692738547709542, + 0.3694738947789558, + 0.36967393478695737, + 0.369873974794959, + 0.3700740148029606, + 0.3702740548109622, + 0.3704740948189638, + 0.3706741348269654, + 0.370874174834967, + 0.3710742148429686, + 0.3712742548509702, + 0.3714742948589718, + 0.3716743348669734, + 0.371874374874975, + 0.3720744148829766, + 0.3722744548909782, + 0.3724744948989798, + 0.3726745349069814, + 0.372874574914983, + 0.3730746149229846, + 0.3732746549309862, + 0.3734746949389878, + 0.3736747349469894, + 0.373874774954991, + 0.3740748149629926, + 0.3742748549709942, + 0.3744748949789958, + 0.3746749349869974, + 0.374874974994999, + 0.3750750150030006, + 0.3752750550110022, + 0.3754750950190038, + 0.3756751350270054, + 0.375875175035007, + 0.3760752150430086, + 0.3762752550510102, + 0.3764752950590118, + 0.3766753350670134, + 0.376875375075015, + 0.3770754150830166, + 0.37727545509101823, + 0.3774754950990198, + 0.3776755351070214, + 0.377875575115023, + 0.3780756151230246, + 0.37827565513102623, + 0.3784756951390278, + 0.3786757351470294, + 0.378875775155031, + 0.3790758151630326, + 0.3792758551710342, + 0.3794758951790358, + 0.3796759351870374, + 0.379875975195039, + 0.38007601520304063, + 0.3802760552110422, + 0.3804760952190438, + 0.3806761352270454, + 0.380876175235047, + 0.38107621524304863, + 0.3812762552510502, + 0.3814762952590518, + 0.3816763352670534, + 0.381876375275055, + 0.38207641528305664, + 0.3822764552910582, + 0.3824764952990598, + 0.3826765353070614, + 0.38287657531506303, + 0.38307661532306464, + 0.3832766553310662, + 0.3834766953390678, + 0.3836767353470694, + 0.38387677535507103, + 0.3840768153630726, + 0.3842768553710742, + 0.3844768953790758, + 0.3846769353870774, + 0.38487697539507904, + 0.3850770154030806, + 0.3852770554110822, + 0.3854770954190838, + 0.3856771354270854, + 0.38587717543508704, + 0.3860772154430886, + 0.3862772554510902, + 0.3864772954590918, + 0.38667733546709343, + 0.38687737547509504, + 0.3870774154830966, + 0.3872774554910982, + 0.3874774954990998, + 0.38767753550710143, + 0.38787757551510305, + 0.3880776155231046, + 0.3882776555311062, + 0.3884776955391078, + 0.38867773554710944, + 0.38887777555511105, + 0.3890778155631126, + 0.3892778555711142, + 0.38947789557911583, + 0.38967793558711744, + 0.389877975595119, + 0.3900780156031206, + 0.3902780556111222, + 0.39047809561912383, + 0.39067813562712544, + 0.390878175635127, + 0.3910782156431286, + 0.3912782556511302, + 0.39147829565913184, + 0.39167833566713345, + 0.391878375675135, + 0.3920784156831366, + 0.39227845569113823, + 0.39247849569913984, + 0.39267853570714145, + 0.392878575715143, + 0.3930786157231446, + 0.39327865573114623, + 0.39347869573914784, + 0.39367873574714946, + 0.393878775755151, + 0.3940788157631526, + 0.39427885577115424, + 0.39447889577915585, + 0.39467893578715746, + 0.394878975795159, + 0.3950790158031606, + 0.39527905581116224, + 0.39547909581916385, + 0.3956791358271654, + 0.395879175835167, + 0.39607921584316863, + 0.39627925585117024, + 0.39647929585917185, + 0.3966793358671734, + 0.396879375875175, + 0.39707941588317663, + 0.39727945589117825, + 0.39747949589917986, + 0.3976795359071814, + 0.397879575915183, + 0.39807961592318464, + 0.39827965593118625, + 0.39847969593918786, + 0.3986797359471894, + 0.39887977595519103, + 0.39907981596319264, + 0.39927985597119425, + 0.39947989597919586, + 0.3996799359871974, + 0.39987997599519903, + 0.40008001600320064, + 0.40028005601120226, + 0.40048009601920387, + 0.4006801360272054, + 0.40088017603520704, + 0.40108021604320865, + 0.40128025605121026, + 0.4014802960592118, + 0.40168033606721343, + 0.40188037607521504, + 0.40208041608321665, + 0.40228045609121826, + 0.4024804960992198, + 0.40268053610722143, + 0.40288057611522304, + 0.40308061612322466, + 0.40328065613122627, + 0.4034806961392278, + 0.40368073614722944, + 0.40388077615523105, + 0.40408081616323266, + 0.40428085617123427, + 0.4044808961792358, + 0.40468093618723744, + 0.40488097619523905, + 0.40508101620324066, + 0.4052810562112423, + 0.40548109621924383, + 0.40568113622724544, + 0.40588117623524705, + 0.40608121624324867, + 0.4062812562512503, + 0.40648129625925183, + 0.40668133626725345, + 0.40688137627525506, + 0.40708141628325667, + 0.4072814562912582, + 0.40748149629925984, + 0.40768153630726145, + 0.40788157631526306, + 0.4080816163232647, + 0.40828165633126623, + 0.40848169633926784, + 0.40868173634726945, + 0.40888177635527106, + 0.4090818163632727, + 0.40928185637127423, + 0.40948189637927584, + 0.40968193638727746, + 0.40988197639527907, + 0.4100820164032807, + 0.41028205641128224, + 0.41048209641928385, + 0.41068213642728546, + 0.41088217643528707, + 0.4110822164432887, + 0.41128225645129024, + 0.41148229645929185, + 0.41168233646729346, + 0.4118823764752951, + 0.4120824164832967, + 0.41228245649129824, + 0.41248249649929986, + 0.41268253650730147, + 0.4128825765153031, + 0.41308261652330464, + 0.41328265653130625, + 0.41348269653930786, + 0.41368273654730947, + 0.4138827765553111, + 0.41408281656331264, + 0.41428285657131425, + 0.41448289657931586, + 0.4146829365873175, + 0.4148829765953191, + 0.41508301660332064, + 0.41528305661132225, + 0.41548309661932387, + 0.4156831366273255, + 0.4158831766353271, + 0.41608321664332865, + 0.41628325665133026, + 0.41648329665933187, + 0.4166833366673335, + 0.4168833766753351, + 0.41708341668333665, + 0.41728345669133826, + 0.4174834966993399, + 0.4176835367073415, + 0.4178835767153431, + 0.41808361672334465, + 0.41828365673134627, + 0.4184836967393479, + 0.4186837367473495, + 0.41888377675535104, + 0.41908381676335266, + 0.41928385677135427, + 0.4194838967793559, + 0.4196839367873575, + 0.41988397679535905, + 0.42008401680336066, + 0.42028405681136227, + 0.4204840968193639, + 0.4206841368273655, + 0.42088417683536705, + 0.42108421684336866, + 0.4212842568513703, + 0.4214842968593719, + 0.4216843368673735, + 0.42188437687537506, + 0.42208441688337667, + 0.4222844568913783, + 0.4224844968993799, + 0.4226845369073815, + 0.42288457691538306, + 0.42308461692338467, + 0.4232846569313863, + 0.4234846969393879, + 0.4236847369473895, + 0.42388477695539106, + 0.4240848169633927, + 0.4242848569713943, + 0.4244848969793959, + 0.42468493698739745, + 0.42488497699539907, + 0.4250850170034007, + 0.4252850570114023, + 0.4254850970194039, + 0.42568513702740546, + 0.42588517703540707, + 0.4260852170434087, + 0.4262852570514103, + 0.4264852970594119, + 0.42668533706741346, + 0.4268853770754151, + 0.4270854170834167, + 0.4272854570914183, + 0.4274854970994199, + 0.42768553710742147, + 0.4278855771154231, + 0.4280856171234247, + 0.4282856571314263, + 0.4284856971394279, + 0.42868573714742947, + 0.4288857771554311, + 0.4290858171634327, + 0.4292858571714343, + 0.4294858971794359, + 0.42968593718743747, + 0.4298859771954391, + 0.4300860172034407, + 0.4302860572114423, + 0.43048609721944386, + 0.4306861372274455, + 0.4308861772354471, + 0.4310862172434487, + 0.4312862572514503, + 0.43148629725945187, + 0.4316863372674535, + 0.4318863772754551, + 0.4320864172834567, + 0.4322864572914583, + 0.43248649729945987, + 0.4326865373074615, + 0.4328865773154631, + 0.4330866173234647, + 0.4332866573314663, + 0.4334866973394679, + 0.4336867373474695, + 0.4338867773554711, + 0.4340868173634727, + 0.4342868573714743, + 0.4344868973794759, + 0.4346869373874775, + 0.4348869773954791, + 0.4350870174034807, + 0.4352870574114823, + 0.4354870974194839, + 0.4356871374274855, + 0.4358871774354871, + 0.4360872174434887, + 0.4362872574514903, + 0.4364872974594919, + 0.4366873374674935, + 0.4368873774754951, + 0.4370874174834967, + 0.4372874574914983, + 0.4374874974994999, + 0.4376875375075015, + 0.4378875775155031, + 0.4380876175235047, + 0.4382876575315063, + 0.4384876975395079, + 0.4386877375475095, + 0.4388877775555111, + 0.43908781756351273, + 0.4392878575715143, + 0.4394878975795159, + 0.4396879375875175, + 0.4398879775955191, + 0.44008801760352073, + 0.4402880576115223, + 0.4404880976195239, + 0.4406881376275255, + 0.4408881776355271, + 0.4410882176435287, + 0.4412882576515303, + 0.4414882976595319, + 0.4416883376675335, + 0.4418883776755351, + 0.4420884176835367, + 0.4422884576915383, + 0.4424884976995399, + 0.4426885377075415, + 0.44288857771554313, + 0.4430886177235447, + 0.4432886577315463, + 0.4434886977395479, + 0.4436887377475495, + 0.44388877775555113, + 0.4440888177635527, + 0.4442888577715543, + 0.4444888977795559, + 0.4446889377875575, + 0.44488897779555914, + 0.4450890178035607, + 0.4452890578115623, + 0.4454890978195639, + 0.44568913782756553, + 0.44588917783556714, + 0.4460892178435687, + 0.4462892578515703, + 0.4464892978595719, + 0.44668933786757353, + 0.4468893778755751, + 0.4470894178835767, + 0.4472894578915783, + 0.4474894978995799, + 0.44768953790758154, + 0.4478895779155831, + 0.4480896179235847, + 0.4482896579315863, + 0.44848969793958793, + 0.44868973794758954, + 0.4488897779555911, + 0.4490898179635927, + 0.4492898579715943, + 0.44948989797959593, + 0.44968993798759754, + 0.4498899779955991, + 0.4500900180036007, + 0.4502900580116023, + 0.45049009801960394, + 0.45069013802760555, + 0.4508901780356071, + 0.4510902180436087, + 0.4512902580516103, + 0.45149029805961194, + 0.45169033806761355, + 0.4518903780756151, + 0.4520904180836167, + 0.45229045809161833, + 0.45249049809961994, + 0.4526905381076215, + 0.4528905781156231, + 0.4530906181236247, + 0.45329065813162633, + 0.45349069813962795, + 0.4536907381476295, + 0.4538907781556311, + 0.4540908181636327, + 0.45429085817163434, + 0.45449089817963595, + 0.4546909381876375, + 0.4548909781956391, + 0.45509101820364073, + 0.45529105821164234, + 0.45549109821964395, + 0.4556911382276455, + 0.4558911782356471, + 0.45609121824364873, + 0.45629125825165034, + 0.45649129825965196, + 0.4566913382676535, + 0.4568913782756551, + 0.45709141828365674, + 0.45729145829165835, + 0.45749149829965996, + 0.4576915383076615, + 0.45789157831566313, + 0.45809161832366474, + 0.45829165833166635, + 0.4584916983396679, + 0.4586917383476695, + 0.45889177835567113, + 0.45909181836367274, + 0.45929185837167436, + 0.4594918983796759, + 0.4596919383876775, + 0.45989197839567914, + 0.46009201840368075, + 0.46029205841168236, + 0.4604920984196839, + 0.4606921384276855, + 0.46089217843568714, + 0.46109221844368875, + 0.46129225845169036, + 0.4614922984596919, + 0.46169233846769353, + 0.46189237847569514, + 0.46209241848369675, + 0.46229245849169837, + 0.4624924984996999, + 0.46269253850770153, + 0.46289257851570315, + 0.46309261852370476, + 0.46329265853170637, + 0.4634926985397079, + 0.46369273854770954, + 0.46389277855571115, + 0.46409281856371276, + 0.4642928585717143, + 0.46449289857971593, + 0.46469293858771754, + 0.46489297859571915, + 0.46509301860372076, + 0.4652930586117223, + 0.46549309861972393, + 0.46569313862772554, + 0.46589317863572716, + 0.46609321864372877, + 0.4662932586517303, + 0.46649329865973194, + 0.46669333866773355, + 0.46689337867573516, + 0.46709341868373677, + 0.46729345869173833, + 0.46749349869973994, + 0.46769353870774155, + 0.46789357871574316, + 0.4680936187237448, + 0.46829365873174633, + 0.46849369873974794, + 0.46869373874774956, + 0.46889377875575117, + 0.4690938187637528, + 0.46929385877175434, + 0.46949389877975595, + 0.46969393878775756, + 0.46989397879575917, + 0.4700940188037607, + 0.47029405881176234, + 0.47049409881976395, + 0.47069413882776556, + 0.4708941788357672, + 0.47109421884376873, + 0.47129425885177034, + 0.47149429885977195, + 0.47169433886777357, + 0.4718943788757752, + 0.47209441888377673, + 0.47229445889177835, + 0.47249449889977996, + 0.47269453890778157, + 0.4728945789157832, + 0.47309461892378474, + 0.47329465893178635, + 0.47349469893978796, + 0.4736947389477896, + 0.4738947789557912, + 0.47409481896379274, + 0.47429485897179435, + 0.47449489897979596, + 0.4746949389877976, + 0.4748949789957992, + 0.47509501900380074, + 0.47529505901180236, + 0.47549509901980397, + 0.4756951390278056, + 0.47589517903580714, + 0.47609521904380875, + 0.47629525905181036, + 0.47649529905981197, + 0.4766953390678136, + 0.47689537907581514, + 0.47709541908381675, + 0.47729545909181836, + 0.47749549909982, + 0.4776955391078216, + 0.47789557911582314, + 0.47809561912382476, + 0.47829565913182637, + 0.478495699139828, + 0.4786957391478296, + 0.47889577915583115, + 0.47909581916383276, + 0.47929585917183437, + 0.479495899179836, + 0.4796959391878376, + 0.47989597919583915, + 0.48009601920384076, + 0.4802960592118424, + 0.480496099219844, + 0.4806961392278456, + 0.48089617923584715, + 0.48109621924384877, + 0.4812962592518504, + 0.481496299259852, + 0.48169633926785355, + 0.48189637927585516, + 0.48209641928385677, + 0.4822964592918584, + 0.48249649929986, + 0.48269653930786155, + 0.48289657931586316, + 0.4830966193238648, + 0.4832966593318664, + 0.483496699339868, + 0.48369673934786955, + 0.48389677935587116, + 0.4840968193638728, + 0.4842968593718744, + 0.484496899379876, + 0.48469693938787756, + 0.48489697939587917, + 0.4850970194038808, + 0.4852970594118824, + 0.485497099419884, + 0.48569713942788556, + 0.48589717943588717, + 0.4860972194438888, + 0.4862972594518904, + 0.48649729945989195, + 0.48669733946789356, + 0.4868973794758952, + 0.4870974194838968, + 0.4872974594918984, + 0.48749749949989996, + 0.48769753950790157, + 0.4878975795159032, + 0.4880976195239048, + 0.4882976595319064, + 0.48849769953990796, + 0.48869773954790957, + 0.4888977795559112, + 0.4890978195639128, + 0.4892978595719144, + 0.48949789957991596, + 0.4896979395879176, + 0.4898979795959192, + 0.4900980196039208, + 0.4902980596119224, + 0.49049809961992397, + 0.4906981396279256, + 0.4908981796359272, + 0.4910982196439288, + 0.4912982596519304, + 0.49149829965993197, + 0.4916983396679336, + 0.4918983796759352, + 0.4920984196839368, + 0.49229845969193836, + 0.49249849969994, + 0.4926985397079416, + 0.4928985797159432, + 0.4930986197239448, + 0.49329865973194637, + 0.493498699739948, + 0.4936987397479496, + 0.4938987797559512, + 0.4940988197639528, + 0.49429885977195437, + 0.494498899779956, + 0.4946989397879576, + 0.4948989797959592, + 0.4950990198039608, + 0.49529905981196237, + 0.495499099819964, + 0.4956991398279656, + 0.4958991798359672, + 0.4960992198439688, + 0.4962992598519704, + 0.496499299859972, + 0.4966993398679736, + 0.4968993798759752, + 0.4970994198839768, + 0.4972994598919784, + 0.49749949989998, + 0.4976995399079816, + 0.4978995799159832, + 0.49809961992398477, + 0.4982996599319864, + 0.498499699939988, + 0.4986997399479896, + 0.4988997799559912, + 0.4990998199639928, + 0.4992998599719944, + 0.499499899979996, + 0.4996999399879976, + 0.4998999799959992, + 0.5001000200040008, + 0.5003000600120024, + 0.500500100020004, + 0.5007001400280056, + 0.5009001800360072, + 0.5011002200440088, + 0.5013002600520104, + 0.501500300060012, + 0.5017003400680136, + 0.5019003800760152, + 0.5021004200840168, + 0.5023004600920185, + 0.50250050010002, + 0.5027005401080216, + 0.5029005801160232, + 0.5031006201240248, + 0.5033006601320265, + 0.503500700140028, + 0.5037007401480296, + 0.5039007801560312, + 0.5041008201640328, + 0.5043008601720345, + 0.504500900180036, + 0.5047009401880376, + 0.5049009801960392, + 0.5051010202040408, + 0.5053010602120425, + 0.505501100220044, + 0.5057011402280456, + 0.5059011802360472, + 0.5061012202440488, + 0.5063012602520505, + 0.506501300260052, + 0.5067013402680536, + 0.5069013802760552, + 0.5071014202840568, + 0.5073014602920584, + 0.50750150030006, + 0.5077015403080616, + 0.5079015803160633, + 0.5081016203240648, + 0.5083016603320664, + 0.508501700340068, + 0.5087017403480696, + 0.5089017803560713, + 0.5091018203640728, + 0.5093018603720744, + 0.509501900380076, + 0.5097019403880776, + 0.5099019803960793, + 0.5101020204040808, + 0.5103020604120824, + 0.510502100420084, + 0.5107021404280856, + 0.5109021804360873, + 0.5111022204440888, + 0.5113022604520904, + 0.511502300460092, + 0.5117023404680936, + 0.5119023804760953, + 0.5121024204840968, + 0.5123024604920984, + 0.5125025005001, + 0.5127025405081016, + 0.5129025805161033, + 0.5131026205241048, + 0.5133026605321064, + 0.513502700540108, + 0.5137027405481096, + 0.5139027805561113, + 0.5141028205641128, + 0.5143028605721144, + 0.514502900580116, + 0.5147029405881176, + 0.5149029805961193, + 0.5151030206041208, + 0.5153030606121224, + 0.515503100620124, + 0.5157031406281256, + 0.5159031806361273, + 0.5161032206441288, + 0.5163032606521304, + 0.5165033006601321, + 0.5167033406681336, + 0.5169033806761353, + 0.5171034206841368, + 0.5173034606921384, + 0.5175035007001401, + 0.5177035407081416, + 0.5179035807161432, + 0.5181036207241448, + 0.5183036607321464, + 0.5185037007401481, + 0.5187037407481496, + 0.5189037807561512, + 0.5191038207641528, + 0.5193038607721544, + 0.5195039007801561, + 0.5197039407881576, + 0.5199039807961592, + 0.5201040208041608, + 0.5203040608121624, + 0.5205041008201641, + 0.5207041408281656, + 0.5209041808361672, + 0.5211042208441689, + 0.5213042608521704, + 0.5215043008601721, + 0.5217043408681736, + 0.5219043808761752, + 0.5221044208841769, + 0.5223044608921784, + 0.5225045009001801, + 0.5227045409081816, + 0.5229045809161832, + 0.5231046209241849, + 0.5233046609321864, + 0.5235047009401881, + 0.5237047409481896, + 0.5239047809561912, + 0.5241048209641929, + 0.5243048609721944, + 0.5245049009801961, + 0.5247049409881976, + 0.5249049809961992, + 0.5251050210042009, + 0.5253050610122024, + 0.5255051010202041, + 0.5257051410282056, + 0.5259051810362072, + 0.5261052210442089, + 0.5263052610522104, + 0.5265053010602121, + 0.5267053410682137, + 0.5269053810762152, + 0.5271054210842169, + 0.5273054610922184, + 0.5275055011002201, + 0.5277055411082217, + 0.5279055811162232, + 0.5281056211242249, + 0.5283056611322264, + 0.5285057011402281, + 0.5287057411482297, + 0.5289057811562312, + 0.5291058211642329, + 0.5293058611722344, + 0.529505901180236, + 0.5297059411882377, + 0.5299059811962392, + 0.5301060212042409, + 0.5303060612122424, + 0.530506101220244, + 0.5307061412282457, + 0.5309061812362472, + 0.5311062212442489, + 0.5313062612522504, + 0.531506301260252, + 0.5317063412682537, + 0.5319063812762552, + 0.5321064212842569, + 0.5323064612922584, + 0.53250650130026, + 0.5327065413082617, + 0.5329065813162632, + 0.5331066213242649, + 0.5333066613322665, + 0.533506701340268, + 0.5337067413482697, + 0.5339067813562712, + 0.5341068213642729, + 0.5343068613722745, + 0.534506901380276, + 0.5347069413882777, + 0.5349069813962792, + 0.5351070214042809, + 0.5353070614122825, + 0.535507101420284, + 0.5357071414282857, + 0.5359071814362872, + 0.5361072214442889, + 0.5363072614522905, + 0.536507301460292, + 0.5367073414682937, + 0.5369073814762952, + 0.5371074214842969, + 0.5373074614922985, + 0.5375075015003, + 0.5377075415083017, + 0.5379075815163032, + 0.5381076215243049, + 0.5383076615323065, + 0.538507701540308, + 0.5387077415483097, + 0.5389077815563112, + 0.5391078215643129, + 0.5393078615723145, + 0.539507901580316, + 0.5397079415883177, + 0.5399079815963193, + 0.5401080216043209, + 0.5403080616123225, + 0.540508101620324, + 0.5407081416283257, + 0.5409081816363273, + 0.5411082216443288, + 0.5413082616523305, + 0.541508301660332, + 0.5417083416683337, + 0.5419083816763353, + 0.5421084216843368, + 0.5423084616923385, + 0.54250850170034, + 0.5427085417083417, + 0.5429085817163433, + 0.5431086217243448, + 0.5433086617323465, + 0.543508701740348, + 0.5437087417483497, + 0.5439087817563513, + 0.5441088217643528, + 0.5443088617723545, + 0.544508901780356, + 0.5447089417883577, + 0.5449089817963593, + 0.5451090218043608, + 0.5453090618123625, + 0.545509101820364, + 0.5457091418283657, + 0.5459091818363673, + 0.5461092218443688, + 0.5463092618523705, + 0.546509301860372, + 0.5467093418683737, + 0.5469093818763753, + 0.5471094218843768, + 0.5473094618923785, + 0.5475095019003801, + 0.5477095419083817, + 0.5479095819163833, + 0.5481096219243848, + 0.5483096619323865, + 0.5485097019403881, + 0.5487097419483897, + 0.5489097819563913, + 0.5491098219643928, + 0.5493098619723945, + 0.5495099019803961, + 0.5497099419883977, + 0.5499099819963993, + 0.5501100220044008, + 0.5503100620124025, + 0.5505101020204041, + 0.5507101420284057, + 0.5509101820364073, + 0.5511102220444088, + 0.5513102620524105, + 0.5515103020604121, + 0.5517103420684137, + 0.5519103820764153, + 0.5521104220844169, + 0.5523104620924185, + 0.5525105021004201, + 0.5527105421084216, + 0.5529105821164233, + 0.5531106221244249, + 0.5533106621324265, + 0.5535107021404281, + 0.5537107421484296, + 0.5539107821564313, + 0.5541108221644329, + 0.5543108621724345, + 0.5545109021804361, + 0.5547109421884376, + 0.5549109821964393, + 0.5551110222044409, + 0.5553110622124425, + 0.5555111022204441, + 0.5557111422284456, + 0.5559111822364473, + 0.5561112222444489, + 0.5563112622524505, + 0.5565113022604521, + 0.5567113422684536, + 0.5569113822764553, + 0.5571114222844569, + 0.5573114622924585, + 0.5575115023004601, + 0.5577115423084616, + 0.5579115823164633, + 0.5581116223244649, + 0.5583116623324665, + 0.5585117023404681, + 0.5587117423484697, + 0.5589117823564713, + 0.5591118223644729, + 0.5593118623724745, + 0.5595119023804761, + 0.5597119423884777, + 0.5599119823964793, + 0.5601120224044809, + 0.5603120624124825, + 0.5605121024204841, + 0.5607121424284857, + 0.5609121824364873, + 0.5611122224444889, + 0.5613122624524906, + 0.5615123024604921, + 0.5617123424684937, + 0.5619123824764953, + 0.5621124224844969, + 0.5623124624924986, + 0.5625125025005001, + 0.5627125425085017, + 0.5629125825165033, + 0.5631126225245049, + 0.5633126625325064, + 0.5635127025405081, + 0.5637127425485097, + 0.5639127825565113, + 0.5641128225645129, + 0.5643128625725145, + 0.5645129025805161, + 0.5647129425885177, + 0.5649129825965193, + 0.5651130226045209, + 0.5653130626125225, + 0.5655131026205241, + 0.5657131426285257, + 0.5659131826365273, + 0.5661132226445289, + 0.5663132626525305, + 0.5665133026605321, + 0.5667133426685337, + 0.5669133826765353, + 0.5671134226845369, + 0.5673134626925385, + 0.5675135027005401, + 0.5677135427085417, + 0.5679135827165434, + 0.5681136227245449, + 0.5683136627325465, + 0.5685137027405481, + 0.5687137427485497, + 0.5689137827565514, + 0.5691138227645529, + 0.5693138627725545, + 0.5695139027805561, + 0.5697139427885577, + 0.5699139827965594, + 0.5701140228045609, + 0.5703140628125625, + 0.5705141028205641, + 0.5707141428285657, + 0.5709141828365674, + 0.5711142228445689, + 0.5713142628525705, + 0.5715143028605721, + 0.5717143428685737, + 0.5719143828765754, + 0.5721144228845769, + 0.5723144628925785, + 0.5725145029005801, + 0.5727145429085817, + 0.5729145829165834, + 0.5731146229245849, + 0.5733146629325865, + 0.5735147029405882, + 0.5737147429485897, + 0.5739147829565914, + 0.5741148229645929, + 0.5743148629725945, + 0.5745149029805962, + 0.5747149429885977, + 0.5749149829965993, + 0.5751150230046009, + 0.5753150630126025, + 0.5755151030206042, + 0.5757151430286057, + 0.5759151830366073, + 0.5761152230446089, + 0.5763152630526105, + 0.5765153030606122, + 0.5767153430686137, + 0.5769153830766153, + 0.5771154230846169, + 0.5773154630926185, + 0.5775155031006202, + 0.5777155431086217, + 0.5779155831166233, + 0.5781156231246249, + 0.5783156631326265, + 0.5785157031406282, + 0.5787157431486297, + 0.5789157831566313, + 0.579115823164633, + 0.5793158631726345, + 0.5795159031806362, + 0.5797159431886377, + 0.5799159831966393, + 0.580116023204641, + 0.5803160632126425, + 0.5805161032206442, + 0.5807161432286457, + 0.5809161832366473, + 0.581116223244649, + 0.5813162632526505, + 0.5815163032606522, + 0.5817163432686537, + 0.5819163832766553, + 0.582116423284657, + 0.5823164632926585, + 0.5825165033006602, + 0.5827165433086617, + 0.5829165833166633, + 0.583116623324665, + 0.5833166633326665, + 0.5835167033406682, + 0.5837167433486697, + 0.5839167833566713, + 0.584116823364673, + 0.5843168633726745, + 0.5845169033806762, + 0.5847169433886777, + 0.5849169833966793, + 0.585117023404681, + 0.5853170634126825, + 0.5855171034206842, + 0.5857171434286857, + 0.5859171834366873, + 0.586117223444689, + 0.5863172634526905, + 0.5865173034606921, + 0.5867173434686938, + 0.5869173834766953, + 0.587117423484697, + 0.5873174634926985, + 0.5875175035007001, + 0.5877175435087018, + 0.5879175835167033, + 0.588117623524705, + 0.5883176635327065, + 0.5885177035407081, + 0.5887177435487098, + 0.5889177835567113, + 0.589117823564713, + 0.5893178635727145, + 0.5895179035807161, + 0.5897179435887178, + 0.5899179835967193, + 0.590118023604721, + 0.5903180636127225, + 0.5905181036207241, + 0.5907181436287258, + 0.5909181836367273, + 0.591118223644729, + 0.5913182636527305, + 0.5915183036607321, + 0.5917183436687338, + 0.5919183836767353, + 0.592118423684737, + 0.5923184636927386, + 0.5925185037007401, + 0.5927185437087418, + 0.5929185837167433, + 0.593118623724745, + 0.5933186637327466, + 0.5935187037407481, + 0.5937187437487498, + 0.5939187837567513, + 0.594118823764753, + 0.5943188637727546, + 0.5945189037807561, + 0.5947189437887578, + 0.5949189837967593, + 0.595119023804761, + 0.5953190638127626, + 0.5955191038207641, + 0.5957191438287658, + 0.5959191838367673, + 0.596119223844769, + 0.5963192638527706, + 0.5965193038607721, + 0.5967193438687738, + 0.5969193838767753, + 0.597119423884777, + 0.5973194638927786, + 0.5975195039007801, + 0.5977195439087818, + 0.5979195839167833, + 0.5981196239247849, + 0.5983196639327866, + 0.5985197039407881, + 0.5987197439487898, + 0.5989197839567914, + 0.5991198239647929, + 0.5993198639727946, + 0.5995199039807961, + 0.5997199439887978, + 0.5999199839967994, + 0.6001200240048009, + 0.6003200640128026, + 0.6005201040208041, + 0.6007201440288058, + 0.6009201840368074, + 0.6011202240448089, + 0.6013202640528106, + 0.6015203040608121, + 0.6017203440688138, + 0.6019203840768154, + 0.6021204240848169, + 0.6023204640928186, + 0.6025205041008201, + 0.6027205441088218, + 0.6029205841168234, + 0.6031206241248249, + 0.6033206641328266, + 0.6035207041408281, + 0.6037207441488298, + 0.6039207841568314, + 0.6041208241648329, + 0.6043208641728346, + 0.6045209041808361, + 0.6047209441888378, + 0.6049209841968394, + 0.6051210242048409, + 0.6053210642128426, + 0.6055211042208442, + 0.6057211442288458, + 0.6059211842368474, + 0.6061212242448489, + 0.6063212642528506, + 0.6065213042608522, + 0.6067213442688538, + 0.6069213842768554, + 0.6071214242848569, + 0.6073214642928586, + 0.6075215043008602, + 0.6077215443088618, + 0.6079215843168634, + 0.6081216243248649, + 0.6083216643328666, + 0.6085217043408682, + 0.6087217443488698, + 0.6089217843568714, + 0.6091218243648729, + 0.6093218643728746, + 0.6095219043808762, + 0.6097219443888777, + 0.6099219843968794, + 0.610122024404881, + 0.6103220644128826, + 0.6105221044208842, + 0.6107221444288857, + 0.6109221844368874, + 0.611122224444889, + 0.6113222644528906, + 0.6115223044608922, + 0.6117223444688937, + 0.6119223844768954, + 0.612122424484897, + 0.6123224644928986, + 0.6125225045009002, + 0.6127225445089017, + 0.6129225845169034, + 0.613122624524905, + 0.6133226645329066, + 0.6135227045409082, + 0.6137227445489097, + 0.6139227845569114, + 0.614122824564913, + 0.6143228645729146, + 0.6145229045809162, + 0.6147229445889177, + 0.6149229845969194, + 0.615123024604921, + 0.6153230646129226, + 0.6155231046209242, + 0.6157231446289257, + 0.6159231846369274, + 0.616123224644929, + 0.6163232646529306, + 0.6165233046609322, + 0.6167233446689337, + 0.6169233846769354, + 0.617123424684937, + 0.6173234646929386, + 0.6175235047009402, + 0.6177235447089418, + 0.6179235847169434, + 0.618123624724945, + 0.6183236647329466, + 0.6185237047409482, + 0.6187237447489498, + 0.6189237847569514, + 0.619123824764953, + 0.6193238647729546, + 0.6195239047809562, + 0.6197239447889578, + 0.6199239847969594, + 0.620124024804961, + 0.6203240648129625, + 0.6205241048209642, + 0.6207241448289658, + 0.6209241848369674, + 0.621124224844969, + 0.6213242648529705, + 0.6215243048609722, + 0.6217243448689738, + 0.6219243848769754, + 0.622124424884977, + 0.6223244648929785, + 0.6225245049009802, + 0.6227245449089818, + 0.6229245849169834, + 0.623124624924985, + 0.6233246649329865, + 0.6235247049409882, + 0.6237247449489898, + 0.6239247849569914, + 0.624124824964993, + 0.6243248649729946, + 0.6245249049809962, + 0.6247249449889978, + 0.6249249849969994, + 0.625125025005001, + 0.6253250650130026, + 0.6255251050210042, + 0.6257251450290058, + 0.6259251850370074, + 0.626125225045009, + 0.6263252650530106, + 0.6265253050610122, + 0.6267253450690138, + 0.6269253850770155, + 0.627125425085017, + 0.6273254650930186, + 0.6275255051010202, + 0.6277255451090218, + 0.6279255851170235, + 0.628125625125025, + 0.6283256651330266, + 0.6285257051410282, + 0.6287257451490298, + 0.6289257851570315, + 0.629125825165033, + 0.6293258651730346, + 0.6295259051810362, + 0.6297259451890378, + 0.6299259851970395, + 0.630126025205041, + 0.6303260652130426, + 0.6305261052210442, + 0.6307261452290458, + 0.6309261852370475, + 0.631126225245049, + 0.6313262652530506, + 0.6315263052610522, + 0.6317263452690538, + 0.6319263852770554, + 0.632126425285057, + 0.6323264652930586, + 0.6325265053010602, + 0.6327265453090618, + 0.6329265853170634, + 0.633126625325065, + 0.6333266653330666, + 0.6335267053410683, + 0.6337267453490698, + 0.6339267853570714, + 0.634126825365073, + 0.6343268653730746, + 0.6345269053810763, + 0.6347269453890778, + 0.6349269853970794, + 0.635127025405081, + 0.6353270654130826, + 0.6355271054210843, + 0.6357271454290858, + 0.6359271854370874, + 0.636127225445089, + 0.6363272654530906, + 0.6365273054610923, + 0.6367273454690938, + 0.6369273854770954, + 0.637127425485097, + 0.6373274654930986, + 0.6375275055011003, + 0.6377275455091018, + 0.6379275855171034, + 0.638127625525105, + 0.6383276655331066, + 0.6385277055411083, + 0.6387277455491098, + 0.6389277855571114, + 0.639127825565113, + 0.6393278655731146, + 0.6395279055811163, + 0.6397279455891178, + 0.6399279855971194, + 0.640128025605121, + 0.6403280656131226, + 0.6405281056211243, + 0.6407281456291258, + 0.6409281856371274, + 0.6411282256451291, + 0.6413282656531306, + 0.6415283056611323, + 0.6417283456691338, + 0.6419283856771354, + 0.6421284256851371, + 0.6423284656931386, + 0.6425285057011403, + 0.6427285457091418, + 0.6429285857171434, + 0.6431286257251451, + 0.6433286657331466, + 0.6435287057411482, + 0.6437287457491498, + 0.6439287857571514, + 0.6441288257651531, + 0.6443288657731546, + 0.6445289057811562, + 0.6447289457891578, + 0.6449289857971594, + 0.6451290258051611, + 0.6453290658131626, + 0.6455291058211642, + 0.6457291458291659, + 0.6459291858371674, + 0.6461292258451691, + 0.6463292658531706, + 0.6465293058611722, + 0.6467293458691739, + 0.6469293858771754, + 0.6471294258851771, + 0.6473294658931786, + 0.6475295059011802, + 0.6477295459091819, + 0.6479295859171834, + 0.6481296259251851, + 0.6483296659331866, + 0.6485297059411882, + 0.6487297459491899, + 0.6489297859571914, + 0.6491298259651931, + 0.6493298659731946, + 0.6495299059811962, + 0.6497299459891979, + 0.6499299859971994, + 0.6501300260052011, + 0.6503300660132026, + 0.6505301060212042, + 0.6507301460292059, + 0.6509301860372074, + 0.6511302260452091, + 0.6513302660532106, + 0.6515303060612122, + 0.6517303460692139, + 0.6519303860772154, + 0.6521304260852171, + 0.6523304660932187, + 0.6525305061012202, + 0.6527305461092219, + 0.6529305861172234, + 0.6531306261252251, + 0.6533306661332267, + 0.6535307061412282, + 0.6537307461492299, + 0.6539307861572314, + 0.6541308261652331, + 0.6543308661732347, + 0.6545309061812362, + 0.6547309461892379, + 0.6549309861972394, + 0.655131026205241, + 0.6553310662132427, + 0.6555311062212442, + 0.6557311462292459, + 0.6559311862372474, + 0.656131226245249, + 0.6563312662532507, + 0.6565313062612522, + 0.6567313462692539, + 0.6569313862772554, + 0.657131426285257, + 0.6573314662932587, + 0.6575315063012602, + 0.6577315463092619, + 0.6579315863172635, + 0.658131626325265, + 0.6583316663332667, + 0.6585317063412682, + 0.6587317463492699, + 0.6589317863572715, + 0.659131826365273, + 0.6593318663732747, + 0.6595319063812762, + 0.6597319463892779, + 0.6599319863972795, + 0.660132026405281, + 0.6603320664132827, + 0.6605321064212842, + 0.6607321464292859, + 0.6609321864372875, + 0.661132226445289, + 0.6613322664532907, + 0.6615323064612922, + 0.6617323464692939, + 0.6619323864772955, + 0.662132426485297, + 0.6623324664932987, + 0.6625325065013002, + 0.6627325465093019, + 0.6629325865173035, + 0.663132626525305, + 0.6633326665333067, + 0.6635327065413082, + 0.6637327465493099, + 0.6639327865573115, + 0.664132826565313, + 0.6643328665733147, + 0.6645329065813163, + 0.6647329465893179, + 0.6649329865973195, + 0.665133026605321, + 0.6653330666133227, + 0.6655331066213243, + 0.6657331466293259, + 0.6659331866373275, + 0.666133226645329, + 0.6663332666533307, + 0.6665333066613323, + 0.6667333466693338, + 0.6669333866773355, + 0.667133426685337, + 0.6673334666933387, + 0.6675335067013403, + 0.6677335467093418, + 0.6679335867173435, + 0.668133626725345, + 0.6683336667333467, + 0.6685337067413483, + 0.6687337467493498, + 0.6689337867573515, + 0.669133826765353, + 0.6693338667733547, + 0.6695339067813563, + 0.6697339467893578, + 0.6699339867973595, + 0.670134026805361, + 0.6703340668133627, + 0.6705341068213643, + 0.6707341468293658, + 0.6709341868373675, + 0.671134226845369, + 0.6713342668533707, + 0.6715343068613723, + 0.6717343468693738, + 0.6719343868773755, + 0.6721344268853771, + 0.6723344668933787, + 0.6725345069013803, + 0.6727345469093818, + 0.6729345869173835, + 0.6731346269253851, + 0.6733346669333867, + 0.6735347069413883, + 0.6737347469493898, + 0.6739347869573915, + 0.6741348269653931, + 0.6743348669733947, + 0.6745349069813963, + 0.6747349469893978, + 0.6749349869973995, + 0.6751350270054011, + 0.6753350670134027, + 0.6755351070214043, + 0.6757351470294058, + 0.6759351870374075, + 0.6761352270454091, + 0.6763352670534107, + 0.6765353070614123, + 0.6767353470694139, + 0.6769353870774155, + 0.6771354270854171, + 0.6773354670934186, + 0.6775355071014203, + 0.6777355471094219, + 0.6779355871174235, + 0.6781356271254251, + 0.6783356671334266, + 0.6785357071414283, + 0.6787357471494299, + 0.6789357871574315, + 0.6791358271654331, + 0.6793358671734346, + 0.6795359071814363, + 0.6797359471894379, + 0.6799359871974395, + 0.6801360272054411, + 0.6803360672134426, + 0.6805361072214443, + 0.6807361472294459, + 0.6809361872374475, + 0.6811362272454491, + 0.6813362672534506, + 0.6815363072614523, + 0.6817363472694539, + 0.6819363872774555, + 0.6821364272854571, + 0.6823364672934586, + 0.6825365073014603, + 0.6827365473094619, + 0.6829365873174635, + 0.6831366273254651, + 0.6833366673334667, + 0.6835367073414683, + 0.6837367473494699, + 0.6839367873574715, + 0.6841368273654731, + 0.6843368673734747, + 0.6845369073814763, + 0.6847369473894779, + 0.6849369873974795, + 0.6851370274054811, + 0.6853370674134827, + 0.6855371074214843, + 0.6857371474294859, + 0.6859371874374875, + 0.6861372274454891, + 0.6863372674534907, + 0.6865373074614923, + 0.6867373474694939, + 0.6869373874774956, + 0.6871374274854971, + 0.6873374674934987, + 0.6875375075015003, + 0.6877375475095019, + 0.6879375875175036, + 0.6881376275255051, + 0.6883376675335067, + 0.6885377075415083, + 0.6887377475495099, + 0.6889377875575114, + 0.6891378275655131, + 0.6893378675735147, + 0.6895379075815163, + 0.6897379475895179, + 0.6899379875975195, + 0.6901380276055211, + 0.6903380676135227, + 0.6905381076215243, + 0.6907381476295259, + 0.6909381876375275, + 0.6911382276455291, + 0.6913382676535307, + 0.6915383076615323, + 0.6917383476695339, + 0.6919383876775355, + 0.6921384276855371, + 0.6923384676935387, + 0.6925385077015404, + 0.6927385477095419, + 0.6929385877175435, + 0.6931386277255451, + 0.6933386677335467, + 0.6935387077415484, + 0.6937387477495499, + 0.6939387877575515, + 0.6941388277655531, + 0.6943388677735547, + 0.6945389077815564, + 0.6947389477895579, + 0.6949389877975595, + 0.6951390278055611, + 0.6953390678135627, + 0.6955391078215644, + 0.6957391478295659, + 0.6959391878375675, + 0.6961392278455691, + 0.6963392678535707, + 0.6965393078615724, + 0.6967393478695739, + 0.6969393878775755, + 0.6971394278855771, + 0.6973394678935787, + 0.6975395079015804, + 0.6977395479095819, + 0.6979395879175835, + 0.6981396279255851, + 0.6983396679335867, + 0.6985397079415884, + 0.6987397479495899, + 0.6989397879575915, + 0.6991398279655932, + 0.6993398679735947, + 0.6995399079815964, + 0.6997399479895979, + 0.6999399879975995, + 0.7001400280056012, + 0.7003400680136027, + 0.7005401080216043, + 0.7007401480296059, + 0.7009401880376075, + 0.7011402280456092, + 0.7013402680536107, + 0.7015403080616123, + 0.7017403480696139, + 0.7019403880776155, + 0.7021404280856172, + 0.7023404680936187, + 0.7025405081016203, + 0.7027405481096219, + 0.7029405881176235, + 0.7031406281256252, + 0.7033406681336267, + 0.7035407081416283, + 0.70374074814963, + 0.7039407881576315, + 0.7041408281656332, + 0.7043408681736347, + 0.7045409081816363, + 0.704740948189638, + 0.7049409881976395, + 0.7051410282056412, + 0.7053410682136427, + 0.7055411082216443, + 0.705741148229646, + 0.7059411882376475, + 0.7061412282456492, + 0.7063412682536507, + 0.7065413082616523, + 0.706741348269654, + 0.7069413882776555, + 0.7071414282856572, + 0.7073414682936587, + 0.7075415083016603, + 0.707741548309662, + 0.7079415883176635, + 0.7081416283256652, + 0.7083416683336667, + 0.7085417083416683, + 0.70874174834967, + 0.7089417883576715, + 0.7091418283656732, + 0.7093418683736747, + 0.7095419083816763, + 0.709741948389678, + 0.7099419883976795, + 0.7101420284056812, + 0.7103420684136827, + 0.7105421084216843, + 0.710742148429686, + 0.7109421884376875, + 0.7111422284456892, + 0.7113422684536908, + 0.7115423084616923, + 0.711742348469694, + 0.7119423884776955, + 0.7121424284856971, + 0.7123424684936988, + 0.7125425085017003, + 0.712742548509702, + 0.7129425885177035, + 0.7131426285257051, + 0.7133426685337068, + 0.7135427085417083, + 0.71374274854971, + 0.7139427885577115, + 0.7141428285657131, + 0.7143428685737148, + 0.7145429085817163, + 0.714742948589718, + 0.7149429885977195, + 0.7151430286057211, + 0.7153430686137228, + 0.7155431086217243, + 0.715743148629726, + 0.7159431886377275, + 0.7161432286457291, + 0.7163432686537308, + 0.7165433086617323, + 0.716743348669734, + 0.7169433886777355, + 0.7171434286857371, + 0.7173434686937388, + 0.7175435087017403, + 0.717743548709742, + 0.7179435887177436, + 0.7181436287257451, + 0.7183436687337468, + 0.7185437087417483, + 0.71874374874975, + 0.7189437887577516, + 0.7191438287657531, + 0.7193438687737548, + 0.7195439087817563, + 0.719743948789758, + 0.7199439887977596, + 0.7201440288057611, + 0.7203440688137628, + 0.7205441088217643, + 0.720744148829766, + 0.7209441888377676, + 0.7211442288457691, + 0.7213442688537708, + 0.7215443088617723, + 0.721744348869774, + 0.7219443888777756, + 0.7221444288857771, + 0.7223444688937788, + 0.7225445089017803, + 0.7227445489097819, + 0.7229445889177836, + 0.7231446289257851, + 0.7233446689337868, + 0.7235447089417884, + 0.7237447489497899, + 0.7239447889577916, + 0.7241448289657931, + 0.7243448689737948, + 0.7245449089817964, + 0.7247449489897979, + 0.7249449889977996, + 0.7251450290058011, + 0.7253450690138028, + 0.7255451090218044, + 0.7257451490298059, + 0.7259451890378076, + 0.7261452290458091, + 0.7263452690538108, + 0.7265453090618124, + 0.7267453490698139, + 0.7269453890778156, + 0.7271454290858171, + 0.7273454690938188, + 0.7275455091018204, + 0.7277455491098219, + 0.7279455891178236, + 0.7281456291258251, + 0.7283456691338268, + 0.7285457091418284, + 0.7287457491498299, + 0.7289457891578316, + 0.7291458291658331, + 0.7293458691738348, + 0.7295459091818364, + 0.7297459491898379, + 0.7299459891978396, + 0.7301460292058412, + 0.7303460692138428, + 0.7305461092218444, + 0.7307461492298459, + 0.7309461892378476, + 0.7311462292458492, + 0.7313462692538508, + 0.7315463092618524, + 0.7317463492698539, + 0.7319463892778556, + 0.7321464292858572, + 0.7323464692938588, + 0.7325465093018604, + 0.7327465493098619, + 0.7329465893178636, + 0.7331466293258652, + 0.7333466693338668, + 0.7335467093418684, + 0.7337467493498699, + 0.7339467893578716, + 0.7341468293658732, + 0.7343468693738747, + 0.7345469093818764, + 0.734746949389878, + 0.7349469893978796, + 0.7351470294058812, + 0.7353470694138827, + 0.7355471094218844, + 0.735747149429886, + 0.7359471894378876, + 0.7361472294458892, + 0.7363472694538907, + 0.7365473094618924, + 0.736747349469894, + 0.7369473894778956, + 0.7371474294858972, + 0.7373474694938987, + 0.7375475095019004, + 0.737747549509902, + 0.7379475895179036, + 0.7381476295259052, + 0.7383476695339067, + 0.7385477095419084, + 0.73874774954991, + 0.7389477895579116, + 0.7391478295659132, + 0.7393478695739147, + 0.7395479095819164, + 0.739747949589918, + 0.7399479895979196, + 0.7401480296059212, + 0.7403480696139227, + 0.7405481096219244, + 0.740748149629926, + 0.7409481896379276, + 0.7411482296459292, + 0.7413482696539307, + 0.7415483096619324, + 0.741748349669934, + 0.7419483896779356, + 0.7421484296859372, + 0.7423484696939388, + 0.7425485097019404, + 0.742748549709942, + 0.7429485897179436, + 0.7431486297259452, + 0.7433486697339468, + 0.7435487097419484, + 0.74374874974995, + 0.7439487897579516, + 0.7441488297659532, + 0.7443488697739548, + 0.7445489097819564, + 0.744748949789958, + 0.7449489897979596, + 0.7451490298059612, + 0.7453490698139628, + 0.7455491098219644, + 0.745749149829966, + 0.7459491898379675, + 0.7461492298459692, + 0.7463492698539708, + 0.7465493098619724, + 0.746749349869974, + 0.7469493898779755, + 0.7471494298859772, + 0.7473494698939788, + 0.7475495099019804, + 0.747749549909982, + 0.7479495899179835, + 0.7481496299259852, + 0.7483496699339868, + 0.7485497099419884, + 0.74874974994999, + 0.7489497899579916, + 0.7491498299659932, + 0.7493498699739948, + 0.7495499099819964, + 0.749749949989998, + 0.7499499899979996, + 0.7501500300060012, + 0.7503500700140028, + 0.7505501100220044, + 0.750750150030006, + 0.7509501900380076, + 0.7511502300460092, + 0.7513502700540108, + 0.7515503100620124, + 0.751750350070014, + 0.7519503900780156, + 0.7521504300860172, + 0.7523504700940188, + 0.7525505101020205, + 0.752750550110022, + 0.7529505901180236, + 0.7531506301260252, + 0.7533506701340268, + 0.7535507101420285, + 0.75375075015003, + 0.7539507901580316, + 0.7541508301660332, + 0.7543508701740348, + 0.7545509101820365, + 0.754750950190038, + 0.7549509901980396, + 0.7551510302060412, + 0.7553510702140428, + 0.7555511102220445, + 0.755751150230046, + 0.7559511902380476, + 0.7561512302460492, + 0.7563512702540508, + 0.7565513102620525, + 0.756751350270054, + 0.7569513902780556, + 0.7571514302860572, + 0.7573514702940588, + 0.7575515103020604, + 0.757751550310062, + 0.7579515903180636, + 0.7581516303260653, + 0.7583516703340668, + 0.7585517103420684, + 0.75875175035007, + 0.7589517903580716, + 0.7591518303660733, + 0.7593518703740748, + 0.7595519103820764, + 0.759751950390078, + 0.7599519903980796, + 0.7601520304060813, + 0.7603520704140828, + 0.7605521104220844, + 0.760752150430086, + 0.7609521904380876, + 0.7611522304460893, + 0.7613522704540908, + 0.7615523104620924, + 0.761752350470094, + 0.7619523904780956, + 0.7621524304860973, + 0.7623524704940988, + 0.7625525105021004, + 0.762752550510102, + 0.7629525905181036, + 0.7631526305261053, + 0.7633526705341068, + 0.7635527105421084, + 0.76375275055011, + 0.7639527905581116, + 0.7641528305661133, + 0.7643528705741148, + 0.7645529105821164, + 0.764752950590118, + 0.7649529905981196, + 0.7651530306061213, + 0.7653530706141228, + 0.7655531106221244, + 0.7657531506301261, + 0.7659531906381276, + 0.7661532306461293, + 0.7663532706541308, + 0.7665533106621324, + 0.7667533506701341, + 0.7669533906781356, + 0.7671534306861373, + 0.7673534706941388, + 0.7675535107021404, + 0.7677535507101421, + 0.7679535907181436, + 0.7681536307261452, + 0.7683536707341468, + 0.7685537107421484, + 0.7687537507501501, + 0.7689537907581516, + 0.7691538307661532, + 0.7693538707741548, + 0.7695539107821564, + 0.7697539507901581, + 0.7699539907981596, + 0.7701540308061612, + 0.7703540708141629, + 0.7705541108221644, + 0.7707541508301661, + 0.7709541908381676, + 0.7711542308461692, + 0.7713542708541709, + 0.7715543108621724, + 0.7717543508701741, + 0.7719543908781756, + 0.7721544308861772, + 0.7723544708941789, + 0.7725545109021804, + 0.7727545509101821, + 0.7729545909181836, + 0.7731546309261852, + 0.7733546709341869, + 0.7735547109421884, + 0.7737547509501901, + 0.7739547909581916, + 0.7741548309661932, + 0.7743548709741949, + 0.7745549109821964, + 0.7747549509901981, + 0.7749549909981996, + 0.7751550310062012, + 0.7753550710142029, + 0.7755551110222044, + 0.7757551510302061, + 0.7759551910382076, + 0.7761552310462092, + 0.7763552710542109, + 0.7765553110622124, + 0.7767553510702141, + 0.7769553910782157, + 0.7771554310862172, + 0.7773554710942189, + 0.7775555111022204, + 0.7777555511102221, + 0.7779555911182237, + 0.7781556311262252, + 0.7783556711342269, + 0.7785557111422284, + 0.7787557511502301, + 0.7789557911582317, + 0.7791558311662332, + 0.7793558711742349, + 0.7795559111822364, + 0.779755951190238, + 0.7799559911982397, + 0.7801560312062412, + 0.7803560712142429, + 0.7805561112222444, + 0.780756151230246, + 0.7809561912382477, + 0.7811562312462492, + 0.7813562712542509, + 0.7815563112622524, + 0.781756351270254, + 0.7819563912782557, + 0.7821564312862572, + 0.7823564712942589, + 0.7825565113022604, + 0.782756551310262, + 0.7829565913182637, + 0.7831566313262652, + 0.7833566713342669, + 0.7835567113422685, + 0.78375675135027, + 0.7839567913582717, + 0.7841568313662732, + 0.7843568713742749, + 0.7845569113822765, + 0.784756951390278, + 0.7849569913982797, + 0.7851570314062812, + 0.7853570714142829, + 0.7855571114222845, + 0.785757151430286, + 0.7859571914382877, + 0.7861572314462892, + 0.7863572714542909, + 0.7865573114622925, + 0.786757351470294, + 0.7869573914782957, + 0.7871574314862972, + 0.7873574714942989, + 0.7875575115023005, + 0.787757551510302, + 0.7879575915183037, + 0.7881576315263052, + 0.7883576715343069, + 0.7885577115423085, + 0.78875775155031, + 0.7889577915583117, + 0.7891578315663133, + 0.7893578715743149, + 0.7895579115823165, + 0.789757951590318, + 0.7899579915983197, + 0.7901580316063213, + 0.7903580716143229, + 0.7905581116223245, + 0.790758151630326, + 0.7909581916383277, + 0.7911582316463293, + 0.7913582716543308, + 0.7915583116623325, + 0.791758351670334, + 0.7919583916783357, + 0.7921584316863373, + 0.7923584716943388, + 0.7925585117023405, + 0.792758551710342, + 0.7929585917183437, + 0.7931586317263453, + 0.7933586717343468, + 0.7935587117423485, + 0.79375875175035, + 0.7939587917583517, + 0.7941588317663533, + 0.7943588717743548, + 0.7945589117823565, + 0.794758951790358, + 0.7949589917983597, + 0.7951590318063613, + 0.7953590718143628, + 0.7955591118223645, + 0.795759151830366, + 0.7959591918383677, + 0.7961592318463693, + 0.7963592718543708, + 0.7965593118623725, + 0.796759351870374, + 0.7969593918783757, + 0.7971594318863773, + 0.7973594718943788, + 0.7975595119023805, + 0.7977595519103821, + 0.7979595919183837, + 0.7981596319263853, + 0.7983596719343868, + 0.7985597119423885, + 0.7987597519503901, + 0.7989597919583917, + 0.7991598319663933, + 0.7993598719743948, + 0.7995599119823965, + 0.7997599519903981, + 0.7999599919983997, + 0.8001600320064013, + 0.8003600720144028, + 0.8005601120224045, + 0.8007601520304061, + 0.8009601920384077, + 0.8011602320464093, + 0.8013602720544108, + 0.8015603120624125, + 0.8017603520704141, + 0.8019603920784157, + 0.8021604320864173, + 0.8023604720944189, + 0.8025605121024205, + 0.8027605521104221, + 0.8029605921184236, + 0.8031606321264253, + 0.8033606721344269, + 0.8035607121424285, + 0.8037607521504301, + 0.8039607921584316, + 0.8041608321664333, + 0.8043608721744349, + 0.8045609121824365, + 0.8047609521904381, + 0.8049609921984396, + 0.8051610322064413, + 0.8053610722144429, + 0.8055611122224445, + 0.8057611522304461, + 0.8059611922384476, + 0.8061612322464493, + 0.8063612722544509, + 0.8065613122624525, + 0.8067613522704541, + 0.8069613922784556, + 0.8071614322864573, + 0.8073614722944589, + 0.8075615123024605, + 0.8077615523104621, + 0.8079615923184637, + 0.8081616323264653, + 0.8083616723344669, + 0.8085617123424685, + 0.8087617523504701, + 0.8089617923584717, + 0.8091618323664733, + 0.8093618723744749, + 0.8095619123824765, + 0.8097619523904781, + 0.8099619923984797, + 0.8101620324064813, + 0.8103620724144829, + 0.8105621124224845, + 0.8107621524304861, + 0.8109621924384877, + 0.8111622324464893, + 0.8113622724544909, + 0.8115623124624926, + 0.8117623524704941, + 0.8119623924784957, + 0.8121624324864973, + 0.8123624724944989, + 0.8125625125025006, + 0.8127625525105021, + 0.8129625925185037, + 0.8131626325265053, + 0.8133626725345069, + 0.8135627125425086, + 0.8137627525505101, + 0.8139627925585117, + 0.8141628325665133, + 0.8143628725745149, + 0.8145629125825165, + 0.8147629525905181, + 0.8149629925985197, + 0.8151630326065213, + 0.8153630726145229, + 0.8155631126225245, + 0.8157631526305261, + 0.8159631926385277, + 0.8161632326465293, + 0.8163632726545309, + 0.8165633126625325, + 0.8167633526705341, + 0.8169633926785357, + 0.8171634326865373, + 0.8173634726945389, + 0.8175635127025405, + 0.8177635527105421, + 0.8179635927185437, + 0.8181636327265454, + 0.8183636727345469, + 0.8185637127425485, + 0.8187637527505501, + 0.8189637927585517, + 0.8191638327665534, + 0.8193638727745549, + 0.8195639127825565, + 0.8197639527905581, + 0.8199639927985597, + 0.8201640328065614, + 0.8203640728145629, + 0.8205641128225645, + 0.8207641528305661, + 0.8209641928385677, + 0.8211642328465694, + 0.8213642728545709, + 0.8215643128625725, + 0.8217643528705741, + 0.8219643928785757, + 0.8221644328865774, + 0.8223644728945789, + 0.8225645129025805, + 0.8227645529105821, + 0.8229645929185837, + 0.8231646329265854, + 0.8233646729345869, + 0.8235647129425885, + 0.8237647529505902, + 0.8239647929585917, + 0.8241648329665934, + 0.8243648729745949, + 0.8245649129825965, + 0.8247649529905982, + 0.8249649929985997, + 0.8251650330066013, + 0.8253650730146029, + 0.8255651130226045, + 0.8257651530306062, + 0.8259651930386077, + 0.8261652330466093, + 0.8263652730546109, + 0.8265653130626125, + 0.8267653530706142, + 0.8269653930786157, + 0.8271654330866173, + 0.8273654730946189, + 0.8275655131026205, + 0.8277655531106222, + 0.8279655931186237, + 0.8281656331266253, + 0.828365673134627, + 0.8285657131426285, + 0.8287657531506302, + 0.8289657931586317, + 0.8291658331666333, + 0.829365873174635, + 0.8295659131826365, + 0.8297659531906382, + 0.8299659931986397, + 0.8301660332066413, + 0.830366073214643, + 0.8305661132226445, + 0.8307661532306462, + 0.8309661932386477, + 0.8311662332466493, + 0.831366273254651, + 0.8315663132626525, + 0.8317663532706542, + 0.8319663932786557, + 0.8321664332866573, + 0.832366473294659, + 0.8325665133026605, + 0.8327665533106622, + 0.8329665933186637, + 0.8331666333266653, + 0.833366673334667, + 0.8335667133426685, + 0.8337667533506702, + 0.8339667933586717, + 0.8341668333666733, + 0.834366873374675, + 0.8345669133826765, + 0.8347669533906782, + 0.8349669933986797, + 0.8351670334066813, + 0.835367073414683, + 0.8355671134226845, + 0.8357671534306862, + 0.8359671934386878, + 0.8361672334466893, + 0.836367273454691, + 0.8365673134626925, + 0.8367673534706941, + 0.8369673934786958, + 0.8371674334866973, + 0.837367473494699, + 0.8375675135027005, + 0.8377675535107021, + 0.8379675935187038, + 0.8381676335267053, + 0.838367673534707, + 0.8385677135427085, + 0.8387677535507101, + 0.8389677935587118, + 0.8391678335667133, + 0.839367873574715, + 0.8395679135827165, + 0.8397679535907181, + 0.8399679935987198, + 0.8401680336067213, + 0.840368073614723, + 0.8405681136227245, + 0.8407681536307261, + 0.8409681936387278, + 0.8411682336467293, + 0.841368273654731, + 0.8415683136627325, + 0.8417683536707341, + 0.8419683936787358, + 0.8421684336867373, + 0.842368473694739, + 0.8425685137027406, + 0.8427685537107421, + 0.8429685937187438, + 0.8431686337267453, + 0.843368673734747, + 0.8435687137427486, + 0.8437687537507501, + 0.8439687937587518, + 0.8441688337667533, + 0.844368873774755, + 0.8445689137827566, + 0.8447689537907581, + 0.8449689937987598, + 0.8451690338067613, + 0.845369073814763, + 0.8455691138227646, + 0.8457691538307661, + 0.8459691938387678, + 0.8461692338467693, + 0.846369273854771, + 0.8465693138627726, + 0.8467693538707741, + 0.8469693938787758, + 0.8471694338867773, + 0.847369473894779, + 0.8475695139027806, + 0.8477695539107821, + 0.8479695939187838, + 0.8481696339267853, + 0.8483696739347869, + 0.8485697139427886, + 0.8487697539507901, + 0.8489697939587918, + 0.8491698339667934, + 0.8493698739747949, + 0.8495699139827966, + 0.8497699539907981, + 0.8499699939987998, + 0.8501700340068014, + 0.8503700740148029, + 0.8505701140228046, + 0.8507701540308061, + 0.8509701940388078, + 0.8511702340468094, + 0.8513702740548109, + 0.8515703140628126, + 0.8517703540708141, + 0.8519703940788158, + 0.8521704340868174, + 0.8523704740948189, + 0.8525705141028206, + 0.8527705541108221, + 0.8529705941188238, + 0.8531706341268254, + 0.8533706741348269, + 0.8535707141428286, + 0.8537707541508301, + 0.8539707941588318, + 0.8541708341668334, + 0.8543708741748349, + 0.8545709141828366, + 0.8547709541908382, + 0.8549709941988398, + 0.8551710342068414, + 0.8553710742148429, + 0.8555711142228446, + 0.8557711542308462, + 0.8559711942388478, + 0.8561712342468494, + 0.8563712742548509, + 0.8565713142628526, + 0.8567713542708542, + 0.8569713942788558, + 0.8571714342868574, + 0.8573714742948589, + 0.8575715143028606, + 0.8577715543108622, + 0.8579715943188638, + 0.8581716343268654, + 0.8583716743348669, + 0.8585717143428686, + 0.8587717543508702, + 0.8589717943588718, + 0.8591718343668734, + 0.8593718743748749, + 0.8595719143828766, + 0.8597719543908782, + 0.8599719943988797, + 0.8601720344068814, + 0.860372074414883, + 0.8605721144228846, + 0.8607721544308862, + 0.8609721944388877, + 0.8611722344468894, + 0.861372274454891, + 0.8615723144628926, + 0.8617723544708942, + 0.8619723944788957, + 0.8621724344868974, + 0.862372474494899, + 0.8625725145029006, + 0.8627725545109022, + 0.8629725945189037, + 0.8631726345269054, + 0.863372674534907, + 0.8635727145429086, + 0.8637727545509102, + 0.8639727945589117, + 0.8641728345669134, + 0.864372874574915, + 0.8645729145829166, + 0.8647729545909182, + 0.8649729945989197, + 0.8651730346069214, + 0.865373074614923, + 0.8655731146229246, + 0.8657731546309262, + 0.8659731946389277, + 0.8661732346469294, + 0.866373274654931, + 0.8665733146629326, + 0.8667733546709342, + 0.8669733946789357, + 0.8671734346869374, + 0.867373474694939, + 0.8675735147029406, + 0.8677735547109422, + 0.8679735947189438, + 0.8681736347269454, + 0.868373674734947, + 0.8685737147429486, + 0.8687737547509502, + 0.8689737947589518, + 0.8691738347669534, + 0.869373874774955, + 0.8695739147829566, + 0.8697739547909582, + 0.8699739947989598, + 0.8701740348069614, + 0.870374074814963, + 0.8705741148229647, + 0.8707741548309662, + 0.8709741948389678, + 0.8711742348469694, + 0.871374274854971, + 0.8715743148629725, + 0.8717743548709742, + 0.8719743948789758, + 0.8721744348869774, + 0.872374474894979, + 0.8725745149029805, + 0.8727745549109822, + 0.8729745949189838, + 0.8731746349269854, + 0.873374674934987, + 0.8735747149429886, + 0.8737747549509902, + 0.8739747949589918, + 0.8741748349669934, + 0.874374874974995, + 0.8745749149829966, + 0.8747749549909982, + 0.8749749949989998, + 0.8751750350070014, + 0.875375075015003, + 0.8755751150230046, + 0.8757751550310062, + 0.8759751950390078, + 0.8761752350470094, + 0.876375275055011, + 0.8765753150630126, + 0.8767753550710142, + 0.8769753950790158, + 0.8771754350870175, + 0.877375475095019, + 0.8775755151030206, + 0.8777755551110222, + 0.8779755951190238, + 0.8781756351270255, + 0.878375675135027, + 0.8785757151430286, + 0.8787757551510302, + 0.8789757951590318, + 0.8791758351670335, + 0.879375875175035, + 0.8795759151830366, + 0.8797759551910382, + 0.8799759951990398, + 0.8801760352070415, + 0.880376075215043, + 0.8805761152230446, + 0.8807761552310462, + 0.8809761952390478, + 0.8811762352470495, + 0.881376275255051, + 0.8815763152630526, + 0.8817763552710542, + 0.8819763952790558, + 0.8821764352870574, + 0.882376475295059, + 0.8825765153030606, + 0.8827765553110622, + 0.8829765953190638, + 0.8831766353270654, + 0.883376675335067, + 0.8835767153430686, + 0.8837767553510703, + 0.8839767953590718, + 0.8841768353670734, + 0.884376875375075, + 0.8845769153830766, + 0.8847769553910783, + 0.8849769953990798, + 0.8851770354070814, + 0.885377075415083, + 0.8855771154230846, + 0.8857771554310863, + 0.8859771954390878, + 0.8861772354470894, + 0.886377275455091, + 0.8865773154630926, + 0.8867773554710943, + 0.8869773954790958, + 0.8871774354870974, + 0.887377475495099, + 0.8875775155031006, + 0.8877775555111023, + 0.8879775955191038, + 0.8881776355271054, + 0.888377675535107, + 0.8885777155431086, + 0.8887777555511103, + 0.8889777955591118, + 0.8891778355671134, + 0.889377875575115, + 0.8895779155831166, + 0.8897779555911183, + 0.8899779955991198, + 0.8901780356071214, + 0.890378075615123, + 0.8905781156231246, + 0.8907781556311263, + 0.8909781956391278, + 0.8911782356471294, + 0.8913782756551311, + 0.8915783156631326, + 0.8917783556711343, + 0.8919783956791358, + 0.8921784356871374, + 0.8923784756951391, + 0.8925785157031406, + 0.8927785557111423, + 0.8929785957191438, + 0.8931786357271454, + 0.8933786757351471, + 0.8935787157431486, + 0.8937787557511502, + 0.8939787957591518, + 0.8941788357671534, + 0.8943788757751551, + 0.8945789157831566, + 0.8947789557911582, + 0.8949789957991598, + 0.8951790358071614, + 0.8953790758151631, + 0.8955791158231646, + 0.8957791558311662, + 0.8959791958391679, + 0.8961792358471694, + 0.8963792758551711, + 0.8965793158631726, + 0.8967793558711742, + 0.8969793958791759, + 0.8971794358871774, + 0.8973794758951791, + 0.8975795159031806, + 0.8977795559111822, + 0.8979795959191839, + 0.8981796359271854, + 0.8983796759351871, + 0.8985797159431886, + 0.8987797559511902, + 0.8989797959591919, + 0.8991798359671934, + 0.8993798759751951, + 0.8995799159831966, + 0.8997799559911982, + 0.8999799959991999, + 0.9001800360072014, + 0.9003800760152031, + 0.9005801160232046, + 0.9007801560312062, + 0.9009801960392079, + 0.9011802360472094, + 0.9013802760552111, + 0.9015803160632127, + 0.9017803560712142, + 0.9019803960792159, + 0.9021804360872174, + 0.9023804760952191, + 0.9025805161032207, + 0.9027805561112222, + 0.9029805961192239, + 0.9031806361272254, + 0.9033806761352271, + 0.9035807161432287, + 0.9037807561512302, + 0.9039807961592319, + 0.9041808361672334, + 0.9043808761752351, + 0.9045809161832367, + 0.9047809561912382, + 0.9049809961992399, + 0.9051810362072414, + 0.905381076215243, + 0.9055811162232447, + 0.9057811562312462, + 0.9059811962392479, + 0.9061812362472494, + 0.906381276255251, + 0.9065813162632527, + 0.9067813562712542, + 0.9069813962792559, + 0.9071814362872574, + 0.907381476295259, + 0.9075815163032607, + 0.9077815563112622, + 0.9079815963192639, + 0.9081816363272655, + 0.908381676335267, + 0.9085817163432687, + 0.9087817563512702, + 0.9089817963592719, + 0.9091818363672735, + 0.909381876375275, + 0.9095819163832767, + 0.9097819563912782, + 0.9099819963992799, + 0.9101820364072815, + 0.910382076415283, + 0.9105821164232847, + 0.9107821564312862, + 0.9109821964392879, + 0.9111822364472895, + 0.911382276455291, + 0.9115823164632927, + 0.9117823564712942, + 0.9119823964792959, + 0.9121824364872975, + 0.912382476495299, + 0.9125825165033007, + 0.9127825565113022, + 0.9129825965193039, + 0.9131826365273055, + 0.913382676535307, + 0.9135827165433087, + 0.9137827565513102, + 0.9139827965593119, + 0.9141828365673135, + 0.914382876575315, + 0.9145829165833167, + 0.9147829565913183, + 0.9149829965993199, + 0.9151830366073215, + 0.915383076615323, + 0.9155831166233247, + 0.9157831566313263, + 0.9159831966393279, + 0.9161832366473295, + 0.916383276655331, + 0.9165833166633327, + 0.9167833566713343, + 0.9169833966793358, + 0.9171834366873375, + 0.917383476695339, + 0.9175835167033407, + 0.9177835567113423, + 0.9179835967193438, + 0.9181836367273455, + 0.918383676735347, + 0.9185837167433487, + 0.9187837567513503, + 0.9189837967593518, + 0.9191838367673535, + 0.919383876775355, + 0.9195839167833567, + 0.9197839567913583, + 0.9199839967993598, + 0.9201840368073615, + 0.920384076815363, + 0.9205841168233647, + 0.9207841568313663, + 0.9209841968393678, + 0.9211842368473695, + 0.921384276855371, + 0.9215843168633727, + 0.9217843568713743, + 0.9219843968793758, + 0.9221844368873775, + 0.9223844768953791, + 0.9225845169033807, + 0.9227845569113823, + 0.9229845969193838, + 0.9231846369273855, + 0.9233846769353871, + 0.9235847169433887, + 0.9237847569513903, + 0.9239847969593918, + 0.9241848369673935, + 0.9243848769753951, + 0.9245849169833967, + 0.9247849569913983, + 0.9249849969993998, + 0.9251850370074015, + 0.9253850770154031, + 0.9255851170234047, + 0.9257851570314063, + 0.9259851970394078, + 0.9261852370474095, + 0.9263852770554111, + 0.9265853170634127, + 0.9267853570714143, + 0.9269853970794159, + 0.9271854370874175, + 0.9273854770954191, + 0.9275855171034206, + 0.9277855571114223, + 0.9279855971194239, + 0.9281856371274255, + 0.9283856771354271, + 0.9285857171434286, + 0.9287857571514303, + 0.9289857971594319, + 0.9291858371674335, + 0.9293858771754351, + 0.9295859171834366, + 0.9297859571914383, + 0.9299859971994399, + 0.9301860372074415, + 0.9303860772154431, + 0.9305861172234446, + 0.9307861572314463, + 0.9309861972394479, + 0.9311862372474495, + 0.9313862772554511, + 0.9315863172634526, + 0.9317863572714543, + 0.9319863972794559, + 0.9321864372874575, + 0.9323864772954591, + 0.9325865173034606, + 0.9327865573114623, + 0.9329865973194639, + 0.9331866373274655, + 0.9333866773354671, + 0.9335867173434687, + 0.9337867573514703, + 0.9339867973594719, + 0.9341868373674735, + 0.9343868773754751, + 0.9345869173834767, + 0.9347869573914783, + 0.9349869973994799, + 0.9351870374074815, + 0.9353870774154831, + 0.9355871174234847, + 0.9357871574314863, + 0.9359871974394879, + 0.9361872374474896, + 0.9363872774554911, + 0.9365873174634927, + 0.9367873574714943, + 0.9369873974794959, + 0.9371874374874976, + 0.9373874774954991, + 0.9375875175035007, + 0.9377875575115023, + 0.9379875975195039, + 0.9381876375275056, + 0.9383876775355071, + 0.9385877175435087, + 0.9387877575515103, + 0.9389877975595119, + 0.9391878375675135, + 0.9393878775755151, + 0.9395879175835167, + 0.9397879575915183, + 0.9399879975995199, + 0.9401880376075215, + 0.9403880776155231, + 0.9405881176235247, + 0.9407881576315263, + 0.9409881976395279, + 0.9411882376475295, + 0.9413882776555311, + 0.9415883176635327, + 0.9417883576715343, + 0.9419883976795359, + 0.9421884376875375, + 0.9423884776955391, + 0.9425885177035407, + 0.9427885577115424, + 0.9429885977195439, + 0.9431886377275455, + 0.9433886777355471, + 0.9435887177435487, + 0.9437887577515504, + 0.9439887977595519, + 0.9441888377675535, + 0.9443888777755551, + 0.9445889177835567, + 0.9447889577915584, + 0.9449889977995599, + 0.9451890378075615, + 0.9453890778155631, + 0.9455891178235647, + 0.9457891578315664, + 0.9459891978395679, + 0.9461892378475695, + 0.9463892778555711, + 0.9465893178635727, + 0.9467893578715744, + 0.9469893978795759, + 0.9471894378875775, + 0.9473894778955791, + 0.9475895179035807, + 0.9477895579115824, + 0.9479895979195839, + 0.9481896379275855, + 0.9483896779355871, + 0.9485897179435887, + 0.9487897579515904, + 0.9489897979595919, + 0.9491898379675935, + 0.9493898779755952, + 0.9495899179835967, + 0.9497899579915984, + 0.9499899979995999, + 0.9501900380076015, + 0.9503900780156032, + 0.9505901180236047, + 0.9507901580316063, + 0.9509901980396079, + 0.9511902380476095, + 0.9513902780556112, + 0.9515903180636127, + 0.9517903580716143, + 0.9519903980796159, + 0.9521904380876175, + 0.9523904780956192, + 0.9525905181036207, + 0.9527905581116223, + 0.9529905981196239, + 0.9531906381276255, + 0.9533906781356272, + 0.9535907181436287, + 0.9537907581516303, + 0.953990798159632, + 0.9541908381676335, + 0.9543908781756352, + 0.9545909181836367, + 0.9547909581916383, + 0.95499099819964, + 0.9551910382076415, + 0.9553910782156432, + 0.9555911182236447, + 0.9557911582316463, + 0.955991198239648, + 0.9561912382476495, + 0.9563912782556512, + 0.9565913182636527, + 0.9567913582716543, + 0.956991398279656, + 0.9571914382876575, + 0.9573914782956592, + 0.9575915183036607, + 0.9577915583116623, + 0.957991598319664, + 0.9581916383276655, + 0.9583916783356672, + 0.9585917183436687, + 0.9587917583516703, + 0.958991798359672, + 0.9591918383676735, + 0.9593918783756752, + 0.9595919183836767, + 0.9597919583916783, + 0.95999199839968, + 0.9601920384076815, + 0.9603920784156832, + 0.9605921184236847, + 0.9607921584316863, + 0.960992198439688, + 0.9611922384476895, + 0.9613922784556912, + 0.9615923184636928, + 0.9617923584716943, + 0.961992398479696, + 0.9621924384876975, + 0.9623924784956991, + 0.9625925185037008, + 0.9627925585117023, + 0.962992598519704, + 0.9631926385277055, + 0.9633926785357071, + 0.9635927185437088, + 0.9637927585517103, + 0.963992798559712, + 0.9641928385677135, + 0.9643928785757151, + 0.9645929185837168, + 0.9647929585917183, + 0.96499299859972, + 0.9651930386077215, + 0.9653930786157231, + 0.9655931186237248, + 0.9657931586317263, + 0.965993198639728, + 0.9661932386477295, + 0.9663932786557311, + 0.9665933186637328, + 0.9667933586717343, + 0.966993398679736, + 0.9671934386877376, + 0.9673934786957391, + 0.9675935187037408, + 0.9677935587117423, + 0.967993598719744, + 0.9681936387277456, + 0.9683936787357471, + 0.9685937187437488, + 0.9687937587517503, + 0.968993798759752, + 0.9691938387677536, + 0.9693938787757551, + 0.9695939187837568, + 0.9697939587917583, + 0.96999399879976, + 0.9701940388077616, + 0.9703940788157631, + 0.9705941188237648, + 0.9707941588317663, + 0.970994198839768, + 0.9711942388477696, + 0.9713942788557711, + 0.9715943188637728, + 0.9717943588717743, + 0.971994398879776, + 0.9721944388877776, + 0.9723944788957791, + 0.9725945189037808, + 0.9727945589117823, + 0.9729945989197839, + 0.9731946389277856, + 0.9733946789357871, + 0.9735947189437888, + 0.9737947589517904, + 0.9739947989597919, + 0.9741948389677936, + 0.9743948789757951, + 0.9745949189837968, + 0.9747949589917984, + 0.9749949989997999, + 0.9751950390078016, + 0.9753950790158031, + 0.9755951190238048, + 0.9757951590318064, + 0.9759951990398079, + 0.9761952390478096, + 0.9763952790558111, + 0.9765953190638128, + 0.9767953590718144, + 0.9769953990798159, + 0.9771954390878176, + 0.9773954790958191, + 0.9775955191038208, + 0.9777955591118224, + 0.9779955991198239, + 0.9781956391278256, + 0.9783956791358271, + 0.9785957191438288, + 0.9787957591518304, + 0.9789957991598319, + 0.9791958391678336, + 0.9793958791758351, + 0.9795959191838368, + 0.9797959591918384, + 0.9799959991998399, + 0.9801960392078416, + 0.9803960792158432, + 0.9805961192238448, + 0.9807961592318464, + 0.9809961992398479, + 0.9811962392478496, + 0.9813962792558512, + 0.9815963192638528, + 0.9817963592718544, + 0.9819963992798559, + 0.9821964392878576, + 0.9823964792958592, + 0.9825965193038608, + 0.9827965593118624, + 0.9829965993198639, + 0.9831966393278656, + 0.9833966793358672, + 0.9835967193438688, + 0.9837967593518704, + 0.9839967993598719, + 0.9841968393678736, + 0.9843968793758752, + 0.9845969193838767, + 0.9847969593918784, + 0.98499699939988, + 0.9851970394078816, + 0.9853970794158832, + 0.9855971194238847, + 0.9857971594318864, + 0.985997199439888, + 0.9861972394478896, + 0.9863972794558912, + 0.9865973194638927, + 0.9867973594718944, + 0.986997399479896, + 0.9871974394878976, + 0.9873974794958992, + 0.9875975195039007, + 0.9877975595119024, + 0.987997599519904, + 0.9881976395279056, + 0.9883976795359072, + 0.9885977195439087, + 0.9887977595519104, + 0.988997799559912, + 0.9891978395679136, + 0.9893978795759152, + 0.9895979195839167, + 0.9897979595919184, + 0.98999799959992, + 0.9901980396079216, + 0.9903980796159232, + 0.9905981196239247, + 0.9907981596319264, + 0.990998199639928, + 0.9911982396479296, + 0.9913982796559312, + 0.9915983196639327, + 0.9917983596719344, + 0.991998399679936, + 0.9921984396879376, + 0.9923984796959392, + 0.9925985197039408, + 0.9927985597119424, + 0.992998599719944, + 0.9931986397279456, + 0.9933986797359472, + 0.9935987197439488, + 0.9937987597519504, + 0.993998799759952, + 0.9941988397679536, + 0.9943988797759552, + 0.9945989197839568, + 0.9947989597919584, + 0.99499899979996, + 0.9951990398079616, + 0.9953990798159632, + 0.9955991198239648, + 0.9957991598319664, + 0.995999199839968, + 0.9961992398479695, + 0.9963992798559712, + 0.9965993198639728, + 0.9967993598719744, + 0.996999399879976, + 0.9971994398879775, + 0.9973994798959792, + 0.9975995199039808, + 0.9977995599119824, + 0.997999599919984, + 0.9981996399279855, + 0.9983996799359872, + 0.9985997199439888, + 0.9987997599519904, + 0.998999799959992, + 0.9991998399679936, + 0.9993998799759952, + 0.9995999199839968, + 0.9997999599919984, + 1 + ], + "xaxis": "x", + "y": [ + 0.9933071490757152, + 0.993293837170715, + 0.9932804989677959, + 0.9932671344157192, + 0.9932537434631491, + 0.9932403260586524, + 0.9932268821506988, + 0.9932134116876604, + 0.9931999146178119, + 0.9931863908893295, + 0.9931728404502921, + 0.9931592632486799, + 0.9931456592323747, + 0.9931320283491604, + 0.9931183705467213, + 0.9931046857726433, + 0.9930909739744131, + 0.9930772350994183, + 0.9930634690949466, + 0.9930496759081868, + 0.9930358554862273, + 0.993022007776057, + 0.9930081327245641, + 0.9929942302785373, + 0.9929803003846639, + 0.9929663429895313, + 0.9929523580396257, + 0.9929383454813321, + 0.9929243052609348, + 0.9929102373246163, + 0.9928961416184576, + 0.9928820180884382, + 0.9928678666804355, + 0.9928536873402247, + 0.9928394800134791, + 0.992825244645769, + 0.9928109811825627, + 0.9927966895692251, + 0.9927823697510184, + 0.9927680216731017, + 0.9927536452805305, + 0.9927392405182568, + 0.9927248073311291, + 0.9927103456638918, + 0.9926958554611852, + 0.9926813366675453, + 0.992666789227404, + 0.992652213085088, + 0.9926376081848196, + 0.992622974470716, + 0.992608311886789, + 0.9925936203769454, + 0.992578899884986, + 0.9925641503546064, + 0.9925493717293957, + 0.9925345639528373, + 0.992519726968308, + 0.9925048607190785, + 0.9924899651483123, + 0.9924750401990665, + 0.992460085814291, + 0.9924451019368284, + 0.992430088509414, + 0.9924150454746753, + 0.9923999727751323, + 0.9923848703531967, + 0.9923697381511721, + 0.992354576111254, + 0.992339384175529, + 0.9923241622859754, + 0.992308910384462, + 0.9922936284127488, + 0.9922783163124866, + 0.9922629740252165, + 0.9922476014923699, + 0.9922321986552686, + 0.9922167654551239, + 0.9922013018330371, + 0.9921858077299992, + 0.9921702830868901, + 0.9921547278444793, + 0.992139141943425, + 0.9921235253242743, + 0.9921078779274628, + 0.9920921996933145, + 0.9920764905620418, + 0.9920607504737448, + 0.9920449793684115, + 0.9920291771859177, + 0.9920133438660262, + 0.9919974793483877, + 0.9919815835725392, + 0.991965656477905, + 0.9919496980037958, + 0.991933708089409, + 0.9919176866738277, + 0.9919016336960219, + 0.9918855490948467, + 0.9918694328090433, + 0.9918532847772379, + 0.9918371049379425, + 0.9918208932295539, + 0.9918046495903536, + 0.991788373958508, + 0.9917720662720682, + 0.9917557264689687, + 0.991739354487029, + 0.9917229502639521, + 0.9917065137373245, + 0.9916900448446165, + 0.9916735435231814, + 0.9916570097102557, + 0.9916404433429588, + 0.9916238443582924, + 0.9916072126931413, + 0.9915905482842722, + 0.9915738510683336, + 0.9915571209818563, + 0.9915403579612526, + 0.9915235619428163, + 0.9915067328627222, + 0.9914898706570264, + 0.9914729752616659, + 0.9914560466124579, + 0.9914390846451007, + 0.9914220892951722, + 0.9914050604981305, + 0.9913879981893139, + 0.9913709023039398, + 0.9913537727771052, + 0.9913366095437866, + 0.9913194125388388, + 0.991302181696996, + 0.9912849169528708, + 0.9912676182409541, + 0.991250285495615, + 0.9912329186511006, + 0.9912155176415357, + 0.9911980824009227, + 0.9911806128631413, + 0.9911631089619483, + 0.9911455706309773, + 0.991127997803739, + 0.9911103904136201, + 0.9910927483938841, + 0.9910750716776701, + 0.9910573601979933, + 0.9910396138877446, + 0.9910218326796902, + 0.9910040165064716, + 0.9909861653006055, + 0.990968278994483, + 0.9909503575203703, + 0.9909324008104075, + 0.9909144087966091, + 0.9908963814108637, + 0.9908783185849335, + 0.9908602202504541, + 0.9908420863389347, + 0.9908239167817574, + 0.9908057115101773, + 0.990787470455322, + 0.990769193548192, + 0.9907508807196594, + 0.9907325319004691, + 0.990714147021237, + 0.990695726012451, + 0.9906772688044707, + 0.9906587753275263, + 0.9906402455117194, + 0.9906216792870219, + 0.9906030765832767, + 0.9905844373301965, + 0.9905657614573645, + 0.9905470488942336, + 0.9905282995701263, + 0.9905095134142345, + 0.9904906903556195, + 0.9904718303232114, + 0.9904529332458091, + 0.9904339990520802, + 0.9904150276705603, + 0.9903960190296536, + 0.9903769730576316, + 0.990357889682634, + 0.9903387688326677, + 0.9903196104356067, + 0.9903004144191923, + 0.9902811807110322, + 0.9902619092386011, + 0.9902425999292398, + 0.9902232527101551, + 0.9902038675084199, + 0.9901844442509727, + 0.9901649828646174, + 0.9901454832760233, + 0.9901259454117242, + 0.9901063691981192, + 0.9900867545614719, + 0.9900671014279098, + 0.990047409723425, + 0.990027679373873, + 0.9900079103049735, + 0.989988102442309, + 0.9899682557113256, + 0.9899483700373323, + 0.9899284453455007, + 0.9899084815608651, + 0.9898884786083219, + 0.9898684364126297, + 0.9898483548984087, + 0.9898282339901411, + 0.9898080736121702, + 0.9897878736887002, + 0.9897676341437968, + 0.9897473549013859, + 0.989727035885254, + 0.9897066770190479, + 0.9896862782262743, + 0.9896658394302997, + 0.9896453605543503, + 0.9896248415215111, + 0.9896042822547266, + 0.9895836826768001, + 0.9895630427103934, + 0.9895423622780267, + 0.9895216413020784, + 0.9895008797047846, + 0.9894800774082394, + 0.9894592343343941, + 0.9894383504050572, + 0.9894174255418944, + 0.9893964596664278, + 0.9893754527000364, + 0.9893544045639551, + 0.989333315179275, + 0.9893121844669432, + 0.989291012347762, + 0.9892697987423893, + 0.9892485435713378, + 0.9892272467549755, + 0.9892059082135246, + 0.9891845278670619, + 0.9891631056355183, + 0.9891416414386788, + 0.9891201351961816, + 0.9890985868275187, + 0.9890769962520354, + 0.9890553633889296, + 0.9890336881572521, + 0.9890119704759063, + 0.9889902102636476, + 0.9889684074390835, + 0.9889465619206735, + 0.9889246736267282, + 0.9889027424754099, + 0.9888807683847314, + 0.988858751272557, + 0.9888366910566009, + 0.9888145876544281, + 0.9887924409834535, + 0.9887702509609417, + 0.9887480175040072, + 0.9887257405296135, + 0.9887034199545734, + 0.9886810556955488, + 0.9886586476690497, + 0.9886361957914348, + 0.9886136999789109, + 0.9885911601475328, + 0.9885685762132026, + 0.9885459480916703, + 0.9885232756985326, + 0.9885005589492334, + 0.9884777977590632, + 0.9884549920431587, + 0.9884321417165032, + 0.9884092466939256, + 0.9883863068901007, + 0.9883633222195484, + 0.9883402925966341, + 0.9883172179355683, + 0.9882940981504057, + 0.9882709331550459, + 0.9882477228632324, + 0.9882244671885527, + 0.9882011660444382, + 0.9881778193441637, + 0.988154427000847, + 0.988130988927449, + 0.9881075050367734, + 0.9880839752414663, + 0.9880603994540158, + 0.9880367775867525, + 0.988013109551848, + 0.9879893952613158, + 0.9879656346270106, + 0.987941827560628, + 0.9879179739737042, + 0.987894073777616, + 0.9878701268835803, + 0.9878461332026541, + 0.987822092645734, + 0.9877980051235559, + 0.9877738705466951, + 0.9877496888255658, + 0.9877254598704209, + 0.9877011835913517, + 0.9876768598982877, + 0.9876524887009961, + 0.9876280699090823, + 0.9876036034319884, + 0.9875790891789944, + 0.9875545270592168, + 0.9875299169816086, + 0.9875052588549595, + 0.9874805525878955, + 0.987455798088878, + 0.9874309952662041, + 0.9874061440280067, + 0.9873812442822536, + 0.9873562959367471, + 0.9873312988991246, + 0.9873062530768577, + 0.9872811583772518, + 0.9872560147074466, + 0.9872308219744151, + 0.9872055800849635, + 0.9871802889457313, + 0.9871549484631905, + 0.987129558543646, + 0.9871041190932348, + 0.987078630017926, + 0.9870530912235199, + 0.9870275026156492, + 0.9870018640997773, + 0.9869761755811987, + 0.9869504369650383, + 0.986924648156252, + 0.9868988090596256, + 0.9868729195797746, + 0.9868469796211448, + 0.9868209890880107, + 0.9867949478844765, + 0.9867688559144749, + 0.9867427130817674, + 0.986716519289944, + 0.9866902744424222, + 0.9866639784424481, + 0.9866376311930949, + 0.9866112325972631, + 0.9865847825576807, + 0.9865582809769017, + 0.9865317277573072, + 0.9865051228011045, + 0.9864784660103266, + 0.9864517572868324, + 0.9864249965323063, + 0.9863981836482578, + 0.9863713185360212, + 0.9863444010967557, + 0.9863174312314448, + 0.9862904088408959, + 0.9862633338257406, + 0.9862362060864338, + 0.9862090255232541, + 0.9861817920363025, + 0.9861545055255035, + 0.9861271658906037, + 0.986099773031172, + 0.9860723268465994, + 0.9860448272360984, + 0.9860172740987033, + 0.9859896673332692, + 0.9859620068384723, + 0.9859342925128095, + 0.9859065242545978, + 0.9858787019619746, + 0.9858508255328969, + 0.9858228948651415, + 0.9857949098563042, + 0.9857668704038001, + 0.9857387764048627, + 0.9857106277565444, + 0.9856824243557155, + 0.9856541660990644, + 0.985625852883097, + 0.9855974846041368, + 0.9855690611583241, + 0.9855405824416165, + 0.9855120483497877, + 0.9854834587784281, + 0.985454813622944, + 0.9854261127785572, + 0.9853973561403053, + 0.9853685436030412, + 0.9853396750614323, + 0.9853107504099611, + 0.9852817695429241, + 0.9852527323544324, + 0.9852236387384106, + 0.9851944885885968, + 0.9851652817985428, + 0.9851360182616129, + 0.9851066978709845, + 0.9850773205196475, + 0.9850478861004036, + 0.9850183945058669, + 0.984988845628463, + 0.9849592393604287, + 0.984929575593812, + 0.9848998542204717, + 0.9848700751320771, + 0.9848402382201081, + 0.984810343375854, + 0.9847803904904143, + 0.9847503794546977, + 0.9847203101594221, + 0.9846901824951143, + 0.9846599963521098, + 0.9846297516205521, + 0.9845994481903931, + 0.9845690859513925, + 0.984538664793117, + 0.9845081846049412, + 0.984477645276046, + 0.9844470466954193, + 0.9844163887518552, + 0.9843856713339544, + 0.9843548943301226, + 0.9843240576285717, + 0.9842931611173186, + 0.9842622046841851, + 0.9842311882167979, + 0.984200111602588, + 0.9841689747287907, + 0.9841377774824449, + 0.9841065197503933, + 0.9840752014192817, + 0.9840438223755591, + 0.9840123825054773, + 0.9839808816950902, + 0.9839493198302544, + 0.9839176967966279, + 0.9838860124796706, + 0.9838542667646436, + 0.9838224595366093, + 0.9837905906804305, + 0.9837586600807707, + 0.9837266676220936, + 0.9836946131886629, + 0.9836624966645418, + 0.9836303179335928, + 0.9835980768794778, + 0.9835657733856571, + 0.9835334073353897, + 0.983500978611733, + 0.983468487097542, + 0.9834359326754697, + 0.9834033152279661, + 0.9833706346372787, + 0.9833378907854515, + 0.9833050835543253, + 0.9832722128255369, + 0.9832392784805192, + 0.9832062804005007, + 0.9831732184665055, + 0.9831400925593524, + 0.9831069025596555, + 0.9830736483478231, + 0.983040329804058, + 0.9830069468083565, + 0.9829734992405095, + 0.9829399869801002, + 0.9829064099065058, + 0.9828727678988959, + 0.9828390608362327, + 0.9828052885972707, + 0.9827714510605564, + 0.982737548104428, + 0.982703579607015, + 0.9826695454462383, + 0.9826354454998092, + 0.9826012796452299, + 0.9825670477597928, + 0.98253274972058, + 0.9824983854044638, + 0.9824639546881054, + 0.9824294574479555, + 0.9823948935602534, + 0.9823602629010271, + 0.9823255653460927, + 0.9822908007710545, + 0.9822559690513042, + 0.9822210700620211, + 0.9821861036781716, + 0.9821510697745088, + 0.9821159682255726, + 0.9820807989056888, + 0.9820455616889694, + 0.9820102564493122, + 0.9819748830604, + 0.9819394413957011, + 0.9819039313284683, + 0.9818683527317393, + 0.9818327054783357, + 0.9817969894408631, + 0.981761204491711, + 0.9817253505030521, + 0.9816894273468422, + 0.9816534348948199, + 0.9816173730185065, + 0.9815812415892051, + 0.9815450404780014, + 0.9815087695557622, + 0.9814724286931358, + 0.9814360177605518, + 0.9813995366282202, + 0.981362985166132, + 0.981326363244058, + 0.981289670731549, + 0.9812529074979357, + 0.9812160734123279, + 0.9811791683436145, + 0.9811421921604634, + 0.9811051447313206, + 0.9810680259244106, + 0.9810308356077356, + 0.9809935736490758, + 0.9809562399159882, + 0.9809188342758074, + 0.9808813565956443, + 0.9808438067423866, + 0.9808061845826979, + 0.980768489983018, + 0.9807307228095621, + 0.9806928829283208, + 0.9806549702050595, + 0.980616984505319, + 0.9805789256944137, + 0.9805407936374327, + 0.9805025881992391, + 0.9804643092444689, + 0.9804259566375323, + 0.9803875302426117, + 0.9803490299236628, + 0.9803104555444137, + 0.980271806968364, + 0.9802330840587862, + 0.9801942866787234, + 0.9801554146909908, + 0.9801164679581739, + 0.9800774463426294, + 0.9800383497064843, + 0.9799991779116355, + 0.9799599308197501, + 0.9799206082922646, + 0.9798812101903848, + 0.9798417363750853, + 0.9798021867071097, + 0.97976256104697, + 0.9797228592549461, + 0.9796830811910857, + 0.9796432267152044, + 0.9796032956868848, + 0.9795632879654764, + 0.9795232034100957, + 0.9794830418796253, + 0.9794428032327142, + 0.9794024873277768, + 0.9793620940229935, + 0.9793216231763098, + 0.9792810746454359, + 0.9792404482878472, + 0.9791997439607829, + 0.9791589615212468, + 0.9791181008260064, + 0.9790771617315925, + 0.9790361440942994, + 0.9789950477701843, + 0.9789538726150671, + 0.9789126184845298, + 0.9788712852339171, + 0.9788298727183349, + 0.9787883807926511, + 0.9787468093114946, + 0.9787051581292553, + 0.9786634271000839, + 0.9786216160778913, + 0.9785797249163487, + 0.9785377534688869, + 0.9784957015886965, + 0.9784535691287272, + 0.9784113559416877, + 0.9783690618800454, + 0.9783266867960261, + 0.9782842305416136, + 0.9782416929685498, + 0.9781990739283339, + 0.9781563732722225, + 0.9781135908512291, + 0.978070726516124, + 0.9780277801174339, + 0.9779847515054415, + 0.9779416405301854, + 0.97789844704146, + 0.9778551708888148, + 0.9778118119215542, + 0.9777683699887374, + 0.9777248449391781, + 0.9776812366214442, + 0.9776375448838575, + 0.9775937695744931, + 0.9775499105411797, + 0.9775059676314989, + 0.9774619406927851, + 0.9774178295721254, + 0.9773736341163586, + 0.9773293541720758, + 0.9772849895856195, + 0.9772405402030837, + 0.9771960058703135, + 0.9771513864329047, + 0.9771066817362035, + 0.9770618916253067, + 0.9770170159450606, + 0.9769720545400615, + 0.976927007254655, + 0.9768818739329357, + 0.9768366544187473, + 0.9767913485556818, + 0.9767459561870798, + 0.9767004771560295, + 0.9766549113053671, + 0.9766092584776761, + 0.9765635185152876, + 0.9765176912602789, + 0.9764717765544746, + 0.9764257742394451, + 0.9763796841565074, + 0.9763335061467239, + 0.9762872400509026, + 0.9762408857095969, + 0.9761944429631051, + 0.9761479116514702, + 0.9761012916144794, + 0.9760545826916647, + 0.9760077847223011, + 0.975960897545408, + 0.9759139209997476, + 0.9758668549238255, + 0.9758196991558898, + 0.9757724535339315, + 0.9757251178956835, + 0.9756776920786208, + 0.97563017591996, + 0.9755825692566594, + 0.9755348719254183, + 0.9754870837626769, + 0.975439204604616, + 0.9753912342871568, + 0.9753431726459606, + 0.9752950195164284, + 0.975246774733701, + 0.9751984381326584, + 0.9751500095479193, + 0.9751014888138415, + 0.9750528757645213, + 0.9750041702337929, + 0.9749553720552289, + 0.9749064810621392, + 0.9748574970875712, + 0.9748084199643096, + 0.9747592495248758, + 0.974709985601528, + 0.9746606280262606, + 0.9746111766308044, + 0.9745616312466256, + 0.9745119917049262, + 0.9744622578366436, + 0.9744124294724501, + 0.9743625064427528, + 0.9743124885776934, + 0.9742623757071477, + 0.9742121676607258, + 0.9741618642677713, + 0.9741114653573614, + 0.9740609707583063, + 0.9740103802991497, + 0.9739596938081673, + 0.9739089111133677, + 0.9738580320424919, + 0.9738070564230121, + 0.9737559840821329, + 0.9737048148467901, + 0.9736535485436504, + 0.9736021849991119, + 0.9735507240393029, + 0.9734991654900823, + 0.9734475091770393, + 0.9733957549254927, + 0.9733439025604913, + 0.9732919519068131, + 0.973239902788965, + 0.9731877550311833, + 0.9731355084574325, + 0.9730831628914058, + 0.9730307181565245, + 0.9729781740759376, + 0.9729255304725218, + 0.9728727871688815, + 0.9728199439873477, + 0.9727670007499789, + 0.9727139572785598, + 0.9726608133946018, + 0.9726075689193421, + 0.9725542236737443, + 0.9725007774784974, + 0.9724472301540158, + 0.9723935815204393, + 0.9723398313976325, + 0.9722859796051845, + 0.9722320259624093, + 0.9721779702883447, + 0.972123812401753, + 0.9720695521211197, + 0.972015189264654, + 0.9719607236502885, + 0.9719061550956787, + 0.9718514834182029, + 0.971796708434962, + 0.971741829962779, + 0.9716868478181995, + 0.9716317618174903, + 0.9715765717766403, + 0.9715212775113593, + 0.9714658788370789, + 0.9714103755689512, + 0.9713547675218489, + 0.9712990545103652, + 0.9712432363488139, + 0.9711873128512284, + 0.9711312838313618, + 0.971075149102687, + 0.971018908478396, + 0.9709625617714001, + 0.9709061087943293, + 0.9708495493595322, + 0.9707928832790758, + 0.9707361103647454, + 0.9706792304280442, + 0.970622243280193, + 0.9705651487321303, + 0.9705079465945118, + 0.9704506366777101, + 0.9703932187918151, + 0.9703356927466327, + 0.9702780583516858, + 0.9702203154162131, + 0.9701624637491695, + 0.9701045031592254, + 0.9700464334547669, + 0.9699882544438956, + 0.9699299659344279, + 0.9698715677338953, + 0.9698130596495438, + 0.969754441488334, + 0.9696957130569408, + 0.9696368741617531, + 0.9695779246088737, + 0.9695188642041189, + 0.9694596927530185, + 0.9694004100608155, + 0.9693410159324659, + 0.9692815101726386, + 0.9692218925857151, + 0.9691621629757892, + 0.969102321146667, + 0.9690423669018663, + 0.9689823000446172, + 0.968922120377861, + 0.9688618277042504, + 0.9688014218261496, + 0.9687409025456336, + 0.9686802696644881, + 0.9686195229842095, + 0.9685586623060047, + 0.9684976874307908, + 0.9684365981591947, + 0.9683753942915534, + 0.9683140756279133, + 0.9682526419680305, + 0.9681910931113701, + 0.9681294288571065, + 0.9680676490041229, + 0.9680057533510111, + 0.9679437416960716, + 0.9678816138373129, + 0.967819369572452, + 0.9677570086989136, + 0.9676945310138304, + 0.9676319363140425, + 0.9675692243960975, + 0.9675063950562501, + 0.9674434480904623, + 0.9673803832944029, + 0.9673172004634473, + 0.9672538993926773, + 0.9671904798768814, + 0.9671269417105541, + 0.9670632846878959, + 0.9669995086028131, + 0.9669356132489179, + 0.9668715984195276, + 0.9668074639076651, + 0.9667432095060585, + 0.9666788350071408, + 0.9666143402030497, + 0.9665497248856278, + 0.9664849888464221, + 0.966420131876684, + 0.9663551537673689, + 0.9662900543091365, + 0.9662248332923501, + 0.966159490507077, + 0.9660940257430877, + 0.9660284387898564, + 0.9659627294365604, + 0.9658968974720801, + 0.9658309426849989, + 0.9657648648636029, + 0.9656986637958809, + 0.9656323392695241, + 0.9655658910719263, + 0.9654993189901832, + 0.9654326228110925, + 0.9653658023211544, + 0.9652988573065702, + 0.965231787553243, + 0.9651645928467775, + 0.9650972729724799, + 0.9650298277153573, + 0.9649622568601179, + 0.9648945601911713, + 0.964826737492627, + 0.9647587885482962, + 0.96469071314169, + 0.96462251105602, + 0.9645541820741983, + 0.964485725978837, + 0.9644171425522482, + 0.964348431576444, + 0.9642795928331361, + 0.9642106261037363, + 0.9641415311693554, + 0.964072307810804, + 0.9640029558085917, + 0.9639334749429277, + 0.9638638649937198, + 0.9637941257405751, + 0.9637242569627993, + 0.963654258439397, + 0.9635841299490714, + 0.9635138712702241, + 0.9634434821809553, + 0.9633729624590632, + 0.9633023118820446, + 0.963231530227094, + 0.9631606172711039, + 0.9630895727906652, + 0.9630183965620659, + 0.9629470883612922, + 0.9628756479640275, + 0.962804075145653, + 0.9627323696812473, + 0.9626605313455862, + 0.9625885599131426, + 0.9625164551580867, + 0.9624442168542858, + 0.962371844775304, + 0.9622993386944024, + 0.9622266983845389, + 0.9621539236183679, + 0.9620810141682408, + 0.9620079698062051, + 0.9619347903040051, + 0.9618614754330814, + 0.9617880249645709, + 0.9617144386693071, + 0.961640716317819, + 0.9615668576803321, + 0.9614928625267682, + 0.9614187306267445, + 0.9613444617495748, + 0.9612700556642679, + 0.9611955121395293, + 0.9611208309437593, + 0.9610460118450549, + 0.9609710546112079, + 0.9608959590097059, + 0.9608207248077323, + 0.9607453517721655, + 0.9606698396695794, + 0.9605941882662437, + 0.9605183973281227, + 0.9604424666208767, + 0.9603663959098606, + 0.960290184960125, + 0.9602138335364152, + 0.9601373414031719, + 0.9600607083245306, + 0.9599839340643221, + 0.9599070183860722, + 0.9598299610530014, + 0.9597527618280255, + 0.9596754204737548, + 0.9595979367524947, + 0.9595203104262456, + 0.9594425412567026, + 0.9593646290052554, + 0.959286573432989, + 0.9592083743006827, + 0.9591300313688109, + 0.9590515443975425, + 0.9589729131467413, + 0.9588941373759657, + 0.9588152168444691, + 0.9587361513111995, + 0.9586569405347992, + 0.958577584273606, + 0.9584980822856516, + 0.958418434328663, + 0.9583386401600616, + 0.9582586995369639, + 0.9581786122161805, + 0.9580983779542173, + 0.9580179965072747, + 0.957937467631248, + 0.957856791081727, + 0.9577759666139969, + 0.9576949939830369, + 0.9576138729435216, + 0.9575326032498204, + 0.9574511846559973, + 0.9573696169158116, + 0.9572878997827171, + 0.9572060330098631, + 0.9571240163500934, + 0.9570418495559472, + 0.9569595323796585, + 0.9568770645731566, + 0.956794445888066, + 0.9567116760757062, + 0.9566287548870921, + 0.9565456820729339, + 0.9564624573836369, + 0.9563790805693021, + 0.9562955513797259, + 0.9562118695644001, + 0.9561280348725121, + 0.9560440470529449, + 0.9559599058542771, + 0.9558756110247834, + 0.9557911623124338, + 0.9557065594648947, + 0.9556218022295282, + 0.9555368903533922, + 0.9554518235832412, + 0.9553666016655256, + 0.9552812243463921, + 0.9551956913716836, + 0.9551100024869398, + 0.9550241574373964, + 0.9549381559679864, + 0.9548519978233388, + 0.9547656827477798, + 0.9546792104853323, + 0.9545925807797164, + 0.9545057933743489, + 0.9544188480123443, + 0.9543317444365141, + 0.9542444823893671, + 0.9541570616131098, + 0.9540694818496461, + 0.9539817428405782, + 0.9538938443272055, + 0.9538057860505256, + 0.9537175677512343, + 0.9536291891697254, + 0.9535406500460915, + 0.9534519501201232, + 0.9533630891313097, + 0.9532740668188395, + 0.9531848829215994, + 0.9530955371781754, + 0.9530060293268526, + 0.9529163591056156, + 0.9528265262521484, + 0.9527365305038342, + 0.9526463715977567, + 0.9525560492706987, + 0.9524655632591437, + 0.9523749132992749, + 0.9522840991269763, + 0.9521931204778322, + 0.9521019770871277, + 0.9520106686898487, + 0.9519191950206825, + 0.9518275558140169, + 0.9517357508039418, + 0.9516437797242483, + 0.9515516423084295, + 0.9514593382896803, + 0.9513668674008977, + 0.9512742293746812, + 0.9511814239433326, + 0.9510884508388567, + 0.950995309792961, + 0.950902000537056, + 0.9508085228022559, + 0.9507148763193781, + 0.9506210608189439, + 0.9505270760311786, + 0.9504329216860115, + 0.9503385975130761, + 0.950244103241711, + 0.9501494386009594, + 0.9500546033195694, + 0.9499595971259944, + 0.9498644197483936, + 0.9497690709146318, + 0.9496735503522795, + 0.9495778577886138, + 0.9494819929506182, + 0.9493859555649826, + 0.9492897453581044, + 0.9491933620560875, + 0.9490968053847442, + 0.9490000750695935, + 0.9489031708358632, + 0.9488060924084889, + 0.9487088395121149, + 0.9486114118710942, + 0.9485138092094889, + 0.9484160312510704, + 0.9483180777193199, + 0.948219948337428, + 0.948121642828296, + 0.9480231609145354, + 0.9479245023184686, + 0.9478256667621288, + 0.9477266539672607, + 0.9476274636553209, + 0.9475280955474775, + 0.9474285493646111, + 0.9473288248273147, + 0.9472289216558943, + 0.9471288395703693, + 0.947028578290472, + 0.9469281375356491, + 0.9468275170250613, + 0.9467267164775836, + 0.946625735611806, + 0.9465245741460334, + 0.9464232317982867, + 0.946321708286302, + 0.946220003327532, + 0.9461181166391458, + 0.9460160479380291, + 0.9459137969407853, + 0.945811363363735, + 0.9457087469229168, + 0.9456059473340879, + 0.9455029643127235, + 0.9453997975740186, + 0.945296446832887, + 0.9451929118039628, + 0.9450891922015999, + 0.9449852877398728, + 0.944881198132577, + 0.9447769230932292, + 0.9446724623350682, + 0.9445678155710544, + 0.9444629825138711, + 0.9443579628759243, + 0.9442527563693435, + 0.9441473627059817, + 0.9440417815974161, + 0.9439360127549489, + 0.9438300558896068, + 0.9437239107121419, + 0.9436175769330325, + 0.9435110542624829, + 0.9434043424104243, + 0.9432974410865148, + 0.9431903500001403, + 0.9430830688604146, + 0.9429755973761802, + 0.9428679352560083, + 0.9427600822081995, + 0.9426520379407847, + 0.9425438021615243, + 0.9424353745779103, + 0.9423267548971654, + 0.9422179428262445, + 0.9421089380718342, + 0.9419997403403543, + 0.9418903493379573, + 0.9417807647705297, + 0.9416709863436923, + 0.9415610137628, + 0.9414508467329434, + 0.9413404849589486, + 0.9412299281453776, + 0.9411191759965296, + 0.9410082282164407, + 0.9408970845088845, + 0.9407857445773735, + 0.9406742081251583, + 0.9405624748552291, + 0.9404505444703161, + 0.9403384166728895, + 0.9402260911651609, + 0.9401135676490828, + 0.9400008458263502, + 0.9398879253984005, + 0.9397748060664142, + 0.9396614875313154, + 0.9395479694937728, + 0.9394342516541997, + 0.9393203337127547, + 0.9392062153693427, + 0.9390918963236148, + 0.9389773762749695, + 0.938862654922553, + 0.93874773196526, + 0.9386326071017337, + 0.9385172800303672, + 0.9384017504493037, + 0.9382860180564371, + 0.9381700825494127, + 0.9380539436256278, + 0.9379376009822321, + 0.9378210543161289, + 0.9377043033239754, + 0.9375873477021828, + 0.9374701871469178, + 0.937352821354103, + 0.9372352500194172, + 0.9371174728382966, + 0.9369994895059347, + 0.9368812997172837, + 0.9367629031670549, + 0.9366442995497193, + 0.9365254885595081, + 0.936406469890414, + 0.9362872432361912, + 0.9361678082903564, + 0.9360481647461895, + 0.9359283122967341, + 0.9358082506347988, + 0.9356879794529568, + 0.9355674984435478, + 0.9354468072986779, + 0.9353259057102206, + 0.9352047933698177, + 0.9350834699688796, + 0.9349619351985863, + 0.9348401887498884, + 0.9347182303135071, + 0.9345960595799357, + 0.9344736762394401, + 0.9343510799820589, + 0.9342282704976055, + 0.9341052474756675, + 0.9339820106056086, + 0.9338585595765683, + 0.9337348940774637, + 0.9336110137969893, + 0.9334869184236189, + 0.9333626076456051, + 0.9332380811509813, + 0.9331133386275615, + 0.932988379762942, + 0.9328632042445015, + 0.9327378117594022, + 0.9326122019945906, + 0.9324863746367985, + 0.9323603293725433, + 0.9322340658881295, + 0.9321075838696491, + 0.9319808830029824, + 0.9318539629737993, + 0.9317268234675594, + 0.9315994641695138, + 0.9314718847647051, + 0.9313440849379688, + 0.9312160643739339, + 0.9310878227570238, + 0.9309593597714576, + 0.9308306751012502, + 0.9307017684302136, + 0.9305726394419582, + 0.9304432878198929, + 0.9303137132472269, + 0.9301839154069694, + 0.9300538939819318, + 0.929923648654728, + 0.9297931791077749, + 0.9296624850232946, + 0.9295315660833134, + 0.9294004219696652, + 0.9292690523639899, + 0.9291374569477362, + 0.929005635402162, + 0.9288735874083345, + 0.928741312647133, + 0.928608810799248, + 0.928476081545183, + 0.928343124565256, + 0.9282099395395993, + 0.9280765261481615, + 0.9279428840707079, + 0.9278090129868218, + 0.9276749125759054, + 0.9275405825171806, + 0.9274060224896906, + 0.9272712321723003, + 0.9271362112436975, + 0.9270009593823944, + 0.9268654762667274, + 0.9267297615748601, + 0.9265938149847822, + 0.9264576361743121, + 0.9263212248210972, + 0.9261845806026154, + 0.9260477031961759, + 0.9259105922789199, + 0.9257732475278226, + 0.9256356686196936, + 0.9254978552311779, + 0.9253598070387579, + 0.925221523718753, + 0.9250830049473221, + 0.9249442504004642, + 0.9248052597540191, + 0.9246660326836691, + 0.9245265688649399, + 0.9243868679732018, + 0.9242469296836708, + 0.9241067536714094, + 0.9239663396113285, + 0.9238256871781877, + 0.9236847960465973, + 0.9235436658910188, + 0.9234022963857661, + 0.9232606872050073, + 0.9231188380227651, + 0.9229767485129187, + 0.9228344183492042, + 0.9226918472052166, + 0.9225490347544103, + 0.9224059806701009, + 0.9222626846254661, + 0.922119146293547, + 0.9219753653472488, + 0.9218313414593432, + 0.9216870743024687, + 0.9215425635491319, + 0.9213978088717092, + 0.9212528099424475, + 0.9211075664334661, + 0.9209620780167574, + 0.9208163443641885, + 0.9206703651475021, + 0.9205241400383182, + 0.9203776687081353, + 0.9202309508283315, + 0.920083986070166, + 0.9199367741047801, + 0.919789314603199, + 0.9196416072363327, + 0.9194936516749775, + 0.9193454475898172, + 0.9191969946514246, + 0.9190482925302625, + 0.9188993408966859, + 0.9187501394209422, + 0.9186006877731733, + 0.9184509856234166, + 0.918301032641607, + 0.9181508284975772, + 0.9180003728610601, + 0.9178496654016897, + 0.9176987057890024, + 0.9175474936924387, + 0.9173960287813445, + 0.9172443107249725, + 0.9170923391924833, + 0.9169401138529476, + 0.9167876343753466, + 0.9166349004285744, + 0.9164819116814389, + 0.9163286678026631, + 0.9161751684608872, + 0.9160214133246694, + 0.9158674020624876, + 0.9157131343427413, + 0.9155586098337521, + 0.9154038282037663, + 0.9152487891209554, + 0.9150934922534183, + 0.9149379372691825, + 0.9147821238362056, + 0.9146260516223768, + 0.9144697202955185, + 0.9143131295233882, + 0.9141562789736789, + 0.913999168314022, + 0.9138417972119878, + 0.9136841653350876, + 0.9135262723507754, + 0.9133681179264488, + 0.913209701729451, + 0.9130510234270726, + 0.9128920826865525, + 0.9127328791750804, + 0.9125734125597973, + 0.9124136825077981, + 0.9122536886861327, + 0.9120934307618075, + 0.9119329084017876, + 0.9117721212729978, + 0.9116110690423245, + 0.9114497513766173, + 0.9112881679426907, + 0.9111263184073258, + 0.9109642024372718, + 0.9108018196992476, + 0.9106391698599436, + 0.9104762525860237, + 0.9103130675441264, + 0.9101496144008668, + 0.9099858928228381, + 0.9098219024766138, + 0.9096576430287485, + 0.9094931141457808, + 0.9093283154942339, + 0.9091632467406181, + 0.9089979075514321, + 0.9088322975931649, + 0.9086664165322975, + 0.908500264035305, + 0.9083338397686576, + 0.9081671433988231, + 0.9080001745922683, + 0.9078329330154609, + 0.9076654183348711, + 0.9074976302169738, + 0.9073295683282501, + 0.9071612323351887, + 0.9069926219042891, + 0.9068237367020615, + 0.9066545763950302, + 0.9064851406497346, + 0.9063154291327313, + 0.9061454415105962, + 0.9059751774499256, + 0.9058046366173388, + 0.9056338186794798, + 0.9054627233030189, + 0.9052913501546548, + 0.9051196989011163, + 0.9049477692091646, + 0.904775560745595, + 0.904603073177238, + 0.9044303061709629, + 0.904257259393678, + 0.9040839325123338, + 0.9039103251939242, + 0.9037364371054887, + 0.9035622679141141, + 0.903387817286937, + 0.9032130848911452, + 0.9030380703939798, + 0.9028627734627375, + 0.9026871937647722, + 0.902511330967497, + 0.9023351847383866, + 0.9021587547449786, + 0.9019820406548763, + 0.9018050421357501, + 0.9016277588553397, + 0.9014501904814565, + 0.9012723366819849, + 0.9010941971248849, + 0.9009157714781942, + 0.9007370594100298, + 0.9005580605885902, + 0.9003787746821579, + 0.9001992013591009, + 0.9000193402878751, + 0.8998391911370264, + 0.8996587535751925, + 0.8994780272711057, + 0.8992970118935937, + 0.8991157071115832, + 0.8989341125941012, + 0.8987522280102771, + 0.8985700530293453, + 0.8983875873206468, + 0.898204830553632, + 0.8980217823978618, + 0.8978384425230113, + 0.8976548105988701, + 0.8974708862953464, + 0.8972866692824679, + 0.8971021592303842, + 0.8969173558093693, + 0.8967322586898236, + 0.8965468675422763, + 0.8963611820373875, + 0.89617520184595, + 0.8959889266388927, + 0.8958023560872813, + 0.8956154898623216, + 0.895428327635362, + 0.8952408690778945, + 0.895053113861558, + 0.8948650616581402, + 0.8946767121395802, + 0.8944880649779705, + 0.8942991198455591, + 0.8941098764147523, + 0.8939203343581166, + 0.8937304933483812, + 0.8935403530584403, + 0.8933499131613557, + 0.8931591733303583, + 0.8929681332388515, + 0.8927767925604128, + 0.8925851509687964, + 0.892393208137936, + 0.892200963741946, + 0.8920084174551253, + 0.8918155689519587, + 0.8916224179071195, + 0.8914289639954722, + 0.8912352068920748, + 0.8910411462721809, + 0.8908467818112423, + 0.8906521131849117, + 0.8904571400690449, + 0.8902618621397028, + 0.8900662790731548, + 0.8898703905458807, + 0.8896741962345729, + 0.8894776958161393, + 0.8892808889677059, + 0.8890837753666185, + 0.8888863546904464, + 0.8886886266169837, + 0.8884905908242525, + 0.8882922469905052, + 0.8880935947942271, + 0.8878946339141389, + 0.8876953640291991, + 0.8874957848186068, + 0.8872958959618038, + 0.8870956971384779, + 0.8868951880285645, + 0.8866943683122501, + 0.886493237669974, + 0.8862917957824319, + 0.8860900423305773, + 0.885887976995625, + 0.8856855994590537, + 0.8854829094026075, + 0.8852799065083001, + 0.8850765904584165, + 0.8848729609355153, + 0.8846690176224326, + 0.8844647602022827, + 0.8842601883584633, + 0.8840553017746557, + 0.8838501001348289, + 0.8836445831232421, + 0.8834387504244468, + 0.8832326017232899, + 0.8830261367049165, + 0.8828193550547724, + 0.8826122564586071, + 0.8824048406024754, + 0.882197107172742, + 0.8819890558560827, + 0.8817806863394876, + 0.881571998310264, + 0.8813629914560389, + 0.8811536654647621, + 0.8809440200247085, + 0.8807340548244812, + 0.880523769553014, + 0.8803131638995746, + 0.880102237553767, + 0.8798909902055345, + 0.8796794215451624, + 0.8794675312632807, + 0.8792553190508671, + 0.87904278459925, + 0.8788299276001108, + 0.8786167477454874, + 0.8784032447277761, + 0.8781894182397354, + 0.8779752679744887, + 0.8777607936255263, + 0.8775459948867096, + 0.8773308714522726, + 0.8771154230168261, + 0.8768996492753595, + 0.8766835499232444, + 0.876467124656237, + 0.8762503731704814, + 0.8760332951625124, + 0.8758158903292584, + 0.8755981583680443, + 0.8753800989765943, + 0.875161711853035, + 0.8749429966958987, + 0.8747239532041257, + 0.8745045810770674, + 0.8742848800144898, + 0.8740648497165759, + 0.873844489883929, + 0.8736238002175754, + 0.8734027804189675, + 0.8731814301899872, + 0.8729597492329484, + 0.8727377372505999, + 0.8725153939461292, + 0.8722927190231649, + 0.8720697121857796, + 0.8718463731384932, + 0.8716227015862764, + 0.871398697234553, + 0.8711743597892032, + 0.8709496889565669, + 0.8707246844434463, + 0.8704993459571098, + 0.8702736732052941, + 0.8700476658962081, + 0.8698213237385352, + 0.8695946464414372, + 0.8693676337145572, + 0.8691402852680222, + 0.8689126008124468, + 0.8686845800589362, + 0.8684562227190892, + 0.8682275285050012, + 0.8679984971292681, + 0.8677691283049884, + 0.867539421745767, + 0.8673093771657184, + 0.8670789942794697, + 0.8668482728021635, + 0.8666172124494618, + 0.8663858129375483, + 0.8661540739831326, + 0.8659219953034525, + 0.8656895766162777, + 0.8654568176399128, + 0.8652237180932008, + 0.8649902776955258, + 0.8647564961668173, + 0.8645223732275518, + 0.8642879085987576, + 0.8640531020020171, + 0.8638179531594707, + 0.8635824617938194, + 0.8633466276283286, + 0.8631104503868311, + 0.8628739297937306, + 0.8626370655740045, + 0.8623998574532081, + 0.8621623051574769, + 0.8619244084135305, + 0.8616861669486757, + 0.86144758049081, + 0.8612086487684248, + 0.8609693715106086, + 0.8607297484470504, + 0.8604897793080434, + 0.8602494638244876, + 0.8600088017278943, + 0.8597677927503878, + 0.8595264366247106, + 0.8592847330842253, + 0.8590426818629189, + 0.8588002826954055, + 0.8585575353169304, + 0.8583144394633727, + 0.8580709948712495, + 0.8578272012777185, + 0.8575830584205824, + 0.8573385660382908, + 0.8570937238699454, + 0.8568485316553022, + 0.8566029891347752, + 0.8563570960494402, + 0.8561108521410375, + 0.8558642571519763, + 0.8556173108253374, + 0.8553700129048769, + 0.8551223631350295, + 0.8548743612609125, + 0.8546260070283287, + 0.8543773001837699, + 0.8541282404744209, + 0.8538788276481623, + 0.8536290614535744, + 0.8533789416399408, + 0.8531284679572515, + 0.8528776401562068, + 0.8526264579882203, + 0.8523749212054232, + 0.852123029560667, + 0.8518707828075276, + 0.8516181807003085, + 0.8513652229940444, + 0.8511119094445049, + 0.8508582398081979, + 0.8506042138423731, + 0.8503498313050258, + 0.8500950919549001, + 0.8498399955514927, + 0.8495845418550566, + 0.8493287306266042, + 0.8490725616279113, + 0.8488160346215206, + 0.8485591493707451, + 0.8483019056396719, + 0.8480443031931657, + 0.8477863417968723, + 0.8475280212172225, + 0.8472693412214354, + 0.8470103015775222, + 0.8467509020542897, + 0.8464911424213438, + 0.8462310224490935, + 0.8459705419087543, + 0.8457097005723517, + 0.8454484982127254, + 0.8451869346035317, + 0.8449250095192488, + 0.8446627227351791, + 0.8444000740274535, + 0.8441370631730354, + 0.8438736899497228, + 0.8436099541361541, + 0.8433458555118102, + 0.8430813938570187, + 0.842816568952958, + 0.8425513805816598, + 0.8422858285260139, + 0.8420199125697716, + 0.8417536324975494, + 0.8414869880948322, + 0.8412199791479777, + 0.8409526054442196, + 0.8406848667716715, + 0.840416762919331, + 0.8401482936770823, + 0.8398794588357014, + 0.8396102581868582, + 0.839340691523122, + 0.8390707586379635, + 0.8388004593257599, + 0.8385297933817975, + 0.8382587606022764, + 0.8379873607843138, + 0.8377155937259475, + 0.8374434592261402, + 0.8371709570847827, + 0.836898087102698, + 0.836624849081645, + 0.8363512428243223, + 0.8360772681343714, + 0.8358029248163815, + 0.8355282126758924, + 0.8352531315193985, + 0.8349776811543529, + 0.8347018613891706, + 0.8344256720332326, + 0.8341491128968899, + 0.8338721837914667, + 0.8335948845292647, + 0.8333172149235667, + 0.8330391747886403, + 0.8327607639397416, + 0.8324819821931198, + 0.8322028293660194, + 0.8319233052766858, + 0.8316434097443677, + 0.8313631425893215, + 0.8310825036328156, + 0.8308014926971327, + 0.8305201096055754, + 0.8302383541824687, + 0.8299562262531645, + 0.8296737256440451, + 0.829390852182527, + 0.8291076056970648, + 0.8288239860171553, + 0.828539992973341, + 0.8282556263972136, + 0.8279708861214186, + 0.8276857719796583, + 0.8274002838066965, + 0.8271144214383618, + 0.8268281847115511, + 0.8265415734642342, + 0.826254587535457, + 0.8259672267653458, + 0.8256794909951111, + 0.8253913800670505, + 0.8251028938245539, + 0.8248140321121069, + 0.8245247947752938, + 0.8242351816608029, + 0.8239451926164285, + 0.8236548274910769, + 0.8233640861347682, + 0.8230729683986419, + 0.8227814741349591, + 0.8224896031971076, + 0.8221973554396051, + 0.8219047307181038, + 0.8216117288893926, + 0.8213183498114032, + 0.8210245933432117, + 0.8207304593450444, + 0.8204359476782802, + 0.8201410582054554, + 0.8198457907902669, + 0.8195501452975762, + 0.8192541215934137, + 0.818957719544982, + 0.81866093902066, + 0.8183637798900065, + 0.8180662420237645, + 0.8177683252938648, + 0.8174700295734296, + 0.8171713547367768, + 0.8168723006594234, + 0.8165728672180896, + 0.8162730542907031, + 0.8159728617564017, + 0.8156722894955386, + 0.815371337389685, + 0.8150700053216348, + 0.814768293175408, + 0.814466200836255, + 0.8141637281906596, + 0.8138608751263438, + 0.8135576415322707, + 0.8132540272986494, + 0.8129500323169381, + 0.8126456564798479, + 0.8123408996813468, + 0.8120357618166639, + 0.8117302427822928, + 0.8114243424759955, + 0.811118060796806, + 0.810811397645035, + 0.8105043529222723, + 0.8101969265313923, + 0.8098891183765564, + 0.8095809283632177, + 0.8092723563981242, + 0.8089634023893231, + 0.8086540662461645, + 0.8083443478793053, + 0.8080342472007124, + 0.8077237641236672, + 0.8074128985627693, + 0.8071016504339403, + 0.8067900196544273, + 0.8064780061428066, + 0.8061656098189884, + 0.8058528306042196, + 0.8055396684210882, + 0.8052261231935267, + 0.8049121948468161, + 0.8045978833075897, + 0.804283188503837, + 0.8039681103649068, + 0.8036526488215122, + 0.8033368038057331, + 0.8030205752510208, + 0.8027039630922014, + 0.8023869672654798, + 0.8020695877084434, + 0.8017518243600654, + 0.8014336771607095, + 0.8011151460521325, + 0.8007962309774893, + 0.8004769318813356, + 0.8001572487096321, + 0.7998371814097481, + 0.7995167299304656, + 0.7991958942219826, + 0.7988746742359167, + 0.7985530699253096, + 0.7982310812446299, + 0.7979087081497775, + 0.797585950598087, + 0.7972628085483312, + 0.7969392819607254, + 0.7966153707969305, + 0.7962910750200571, + 0.7959663945946691, + 0.7956413294867869, + 0.7953158796638921, + 0.7949900450949301, + 0.7946638257503146, + 0.7943372216019307, + 0.7940102326231386, + 0.7936828587887781, + 0.7933551000751708, + 0.793026956460125, + 0.7926984279229389, + 0.7923695144444038, + 0.7920402160068087, + 0.791710532593943, + 0.7913804641911004, + 0.7910500107850833, + 0.7907191723642046, + 0.7903879489182934, + 0.7900563404386973, + 0.7897243469182862, + 0.7893919683514559, + 0.7890592047341319, + 0.7887260560637732, + 0.7883925223393752, + 0.7880586035614735, + 0.7877242997321475, + 0.7873896108550245, + 0.7870545369352822, + 0.786719077979653, + 0.7863832339964274, + 0.7860470049954571, + 0.7857103909881591, + 0.7853733919875192, + 0.7850360080080949, + 0.7846982390660193, + 0.7843600851790047, + 0.7840215463663458, + 0.7836826226489234, + 0.783343314049208, + 0.7830036205912627, + 0.7826635423007473, + 0.7823230792049211, + 0.7819822313326471, + 0.7816409987143949, + 0.7812993813822442, + 0.7809573793698882, + 0.7806149927126373, + 0.7802722214474223, + 0.7799290656127975, + 0.7795855252489448, + 0.7792416003976761, + 0.7788972911024378, + 0.7785525974083133, + 0.7782075193620268, + 0.7778620570119461, + 0.7775162104080868, + 0.7771699796021151, + 0.776823364647351, + 0.7764763655987719, + 0.7761289825130155, + 0.7757812154483841, + 0.7754330644648464, + 0.7750845296240423, + 0.7747356109892848, + 0.7743863086255641, + 0.7740366225995507, + 0.7736865529795989, + 0.773336099835749, + 0.772985263239732, + 0.7726340432649714, + 0.7722824399865874, + 0.7719304534813998, + 0.7715780838279309, + 0.7712253311064095, + 0.7708721953987725, + 0.7705186767886697, + 0.7701647753614664, + 0.769810491204246, + 0.7694558244058138, + 0.7691007750566998, + 0.7687453432491617, + 0.7683895290771883, + 0.7680333326365028, + 0.7676767540245647, + 0.7673197933405744, + 0.7669624506854753, + 0.766604726161957, + 0.7662466198744585, + 0.765888131929171, + 0.7655292624340414, + 0.7651700114987743, + 0.7648103792348363, + 0.764450365755458, + 0.764089971175637, + 0.7637291956121417, + 0.7633680391835131, + 0.7630065020100687, + 0.7626445842139049, + 0.7622822859188998, + 0.7619196072507166, + 0.7615565483368061, + 0.7611931093064097, + 0.7608292902905621, + 0.7604650914220945, + 0.7601005128356371, + 0.7597355546676219, + 0.759370217056286, + 0.7590045001416736, + 0.7586384040656398, + 0.7582719289718524, + 0.757905075005795, + 0.7575378423147703, + 0.7571702310479017, + 0.7568022413561375, + 0.7564338733922521, + 0.7560651273108497, + 0.7556960032683668, + 0.7553265014230744, + 0.7549566219350813, + 0.7545863649663364, + 0.7542157306806311, + 0.7538447192436029, + 0.7534733308227364, + 0.7531015655873676, + 0.7527294237086852, + 0.752356905359734, + 0.7519840107154168, + 0.7516107399524974, + 0.7512370932496026, + 0.7508630707872257, + 0.750488672747728, + 0.7501138993153416, + 0.7497387506761717, + 0.7493632270181999, + 0.7489873285312855, + 0.7486110554071685, + 0.7482344078394719, + 0.7478573860237043, + 0.747479990157262, + 0.7471022204394311, + 0.746724077071391, + 0.7463455602562151, + 0.7459666701988745, + 0.7455874071062397, + 0.7452077711870828, + 0.7448277626520801, + 0.7444473817138135, + 0.7440666285867747, + 0.7436855034873653, + 0.7433040066338997, + 0.7429221382466082, + 0.7425398985476377, + 0.742157287761055, + 0.7417743061128488, + 0.7413909538309307, + 0.7410072311451393, + 0.7406231382872404, + 0.7402386754909303, + 0.7398538429918371, + 0.7394686410275235, + 0.7390830698374884, + 0.7386971296631684, + 0.7383108207479412, + 0.7379241433371261, + 0.7375370976779869, + 0.7371496840197336, + 0.7367619026135241, + 0.7363737537124664, + 0.7359852375716206, + 0.7355963544480004, + 0.7352071046005751, + 0.7348174882902718, + 0.7344275057799767, + 0.7340371573345372, + 0.7336464432207641, + 0.7332553637074322, + 0.7328639190652833, + 0.732472109567027, + 0.7320799354873437, + 0.731687397102885, + 0.7312944946922755, + 0.7309012285361154, + 0.7305075989169814, + 0.7301136061194284, + 0.7297192504299919, + 0.7293245321371877, + 0.7289294515315163, + 0.7285340089054617, + 0.7281382045534948, + 0.7277420387720737, + 0.7273455118596465, + 0.7269486241166515, + 0.7265513758455195, + 0.7261537673506753, + 0.7257557989385379, + 0.7253574709175237, + 0.7249587835980469, + 0.7245597372925205, + 0.7241603323153591, + 0.7237605689829784, + 0.7233604476137976, + 0.7229599685282411, + 0.7225591320487386, + 0.7221579384997272, + 0.7217563882076524, + 0.7213544815009691, + 0.7209522187101436, + 0.7205496001676535, + 0.72014662620799, + 0.7197432971676583, + 0.7193396133851795, + 0.7189355752010906, + 0.7185311829579469, + 0.718126437000322, + 0.7177213376748088, + 0.7173158853300217, + 0.7169100803165964, + 0.7165039229871915, + 0.7160974136964888, + 0.7156905528011954, + 0.7152833406600434, + 0.7148757776337915, + 0.7144678640852258, + 0.7140596003791602, + 0.7136509868824379, + 0.7132420239639319, + 0.7128327119945455, + 0.7124230513472136, + 0.7120130423969033, + 0.7116026855206141, + 0.7111919810973796, + 0.7107809295082671, + 0.7103695311363794, + 0.7099577863668539, + 0.7095456955868653, + 0.7091332591856241, + 0.7087204775543786, + 0.7083073510864152, + 0.707893880177058, + 0.7074800652236707, + 0.7070659066256562, + 0.706651404784457, + 0.7062365601035568, + 0.705821372988479, + 0.705405843846789, + 0.7049899730880931, + 0.70457376112404, + 0.7041572083683205, + 0.7037403152366679, + 0.7033230821468585, + 0.7029055095187113, + 0.7024875977740894, + 0.7020693473368987, + 0.7016507586330893, + 0.7012318320906554, + 0.7008125681396349, + 0.7003929672121104, + 0.6999730297422084, + 0.6995527561661002, + 0.6991321469220017, + 0.6987112024501728, + 0.6982899231929188, + 0.6978683095945888, + 0.6974463621015767, + 0.6970240811623211, + 0.6966014672273048, + 0.6961785207490552, + 0.6957552421821431, + 0.6953316319831844, + 0.694907690610838, + 0.6944834185258073, + 0.6940588161908385, + 0.6936338840707212, + 0.6932086226322882, + 0.6927830323444146, + 0.6923571136780184, + 0.6919308671060591, + 0.6915042931035382, + 0.6910773921474982, + 0.690650164717023, + 0.6902226112932366, + 0.6897947323593028, + 0.6893665284004253, + 0.6889379999038467, + 0.6885091473588483, + 0.688079971256749, + 0.6876504720909052, + 0.6872206503567102, + 0.6867905065515931, + 0.686360041175019, + 0.6859292547284876, + 0.6854981477155326, + 0.6850667206417214, + 0.6846349740146536, + 0.6842029083439611, + 0.683770524141307, + 0.6833378219203842, + 0.6829048021969153, + 0.6824714654886517, + 0.6820378123153721, + 0.6816038431988821, + 0.6811695586630131, + 0.6807349592336213, + 0.6803000454385868, + 0.6798648178078128, + 0.6794292768732235, + 0.6789934231687647, + 0.6785572572304013, + 0.6781207795961169, + 0.6776839908059126, + 0.677246891401805, + 0.6768094819278268, + 0.6763717629300234, + 0.6759337349564534, + 0.6754953985571867, + 0.6750567542843026, + 0.6746178026918894, + 0.6741785443360431, + 0.673738979774865, + 0.6732991095684611, + 0.6728589342789406, + 0.6724184544704144, + 0.6719776707089935, + 0.6715365835627878, + 0.6710951936019041, + 0.6706535013984447, + 0.6702115075265065, + 0.6697692125621783, + 0.66932661708354, + 0.6688837216706608, + 0.6684405269055969, + 0.6679970333723906, + 0.6675532416570683, + 0.6671091523476387, + 0.6666647660340907, + 0.6662200833083922, + 0.6657751047644881, + 0.665329830998298, + 0.6648842626077149, + 0.6644384001926027, + 0.6639922443547949, + 0.6635457956980921, + 0.6630990548282605, + 0.6626520223530296, + 0.6622046988820897, + 0.6617570850270909, + 0.6613091814016401, + 0.6608609886212993, + 0.6604125073035837, + 0.6599637380679584, + 0.6595146815358379, + 0.6590653383305822, + 0.6586157090774959, + 0.658165794403825, + 0.6577155949387552, + 0.6572651113134093, + 0.6568143441608444, + 0.6563632941160508, + 0.6559119618159484, + 0.6554603478993842, + 0.6550084530071312, + 0.6545562777818843, + 0.6541038228682592, + 0.6536510889127882, + 0.6531980765639195, + 0.6527447864720133, + 0.65229121928934, + 0.6518373756700766, + 0.651383256270305, + 0.6509288617480087, + 0.6504741927630708, + 0.65001924997727, + 0.6495640340542792, + 0.6491085456596618, + 0.6486527854608691, + 0.6481967541272378, + 0.6477404523299864, + 0.6472838807422135, + 0.6468270400388931, + 0.6463699308968736, + 0.6459125539948729, + 0.6454549100134774, + 0.6449969996351376, + 0.6445388235441646, + 0.6440803824267289, + 0.6436216769708556, + 0.6431627078664219, + 0.642703475805154, + 0.6422439814806238, + 0.6417842255882455, + 0.6413242088252729, + 0.6408639318907954, + 0.6404033954857351, + 0.6399426003128436, + 0.6394815470766986, + 0.6390202364837003, + 0.6385586692420684, + 0.6380968460618379, + 0.6376347676548566, + 0.6371724347347811, + 0.6367098480170734, + 0.6362470082189975, + 0.6357839160596153, + 0.6353205722597839, + 0.6348569775421511, + 0.634393132631153, + 0.6339290382530087, + 0.6334646951357177, + 0.6330001040090563, + 0.6325352656045731, + 0.6320701806555857, + 0.6316048498971776, + 0.6311392740661925, + 0.6306734539012326, + 0.6302073901426534, + 0.6297410835325601, + 0.6292745348148039, + 0.6288077447349782, + 0.6283407140404136, + 0.6278734434801757, + 0.6274059338050595, + 0.6269381857675855, + 0.6264702001219972, + 0.6260019776242547, + 0.6255335190320326, + 0.625064825104715, + 0.6245958966033903, + 0.6241267342908494, + 0.6236573389315792, + 0.6231877112917599, + 0.6227178521392599, + 0.6222477622436314, + 0.621777442376107, + 0.6213068933095944, + 0.6208361158186726, + 0.6203651106795871, + 0.6198938786702459, + 0.6194224205702147, + 0.6189507371607127, + 0.618478829224608, + 0.6180066975464131, + 0.6175343429122799, + 0.6170617661099964, + 0.6165889679289804, + 0.6161159491602766, + 0.6156427105965504, + 0.6151692530320843, + 0.6146955772627727, + 0.6142216840861177, + 0.6137475743012238, + 0.6132732487087931, + 0.6127987081111212, + 0.6123239533120917, + 0.6118489851171717, + 0.611373804333407, + 0.6108984117694166, + 0.6104228082353889, + 0.6099469945430755, + 0.6094709715057878, + 0.6089947399383902, + 0.6085183006572961, + 0.6080416544804634, + 0.6075648022273883, + 0.607087744719101, + 0.6066104827781602, + 0.6061330172286483, + 0.6056553488961658, + 0.6051774786078266, + 0.6046994071922529, + 0.6042211354795696, + 0.6037426643013986, + 0.6032639944908547, + 0.6027851268825397, + 0.6023060623125371, + 0.6018268016184067, + 0.6013473456391795, + 0.6008676952153518, + 0.6003878511888805, + 0.5999078144031775, + 0.5994275857031037, + 0.5989471659349641, + 0.5984665559465023, + 0.5979857565868947, + 0.5975047687067453, + 0.5970235931580794, + 0.5965422307943391, + 0.5960606824703771, + 0.5955789490424511, + 0.5950970313682182, + 0.594614930306729, + 0.5941326467184223, + 0.5936501814651195, + 0.5931675354100183, + 0.5926847094176872, + 0.5922017043540602, + 0.59171852108643, + 0.591235160483443, + 0.5907516234150934, + 0.590267910752717, + 0.5897840233689853, + 0.5892999621378998, + 0.5888157279347865, + 0.5883313216362892, + 0.5878467441203633, + 0.5873619962662711, + 0.5868770789545746, + 0.5863919930671302, + 0.5859067394870823, + 0.5854213190988566, + 0.5849357327881557, + 0.5844499814419514, + 0.5839640659484792, + 0.5834779871972322, + 0.5829917460789547, + 0.582505343485636, + 0.5820187803105047, + 0.5815320574480218, + 0.5810451757938748, + 0.5805581362449711, + 0.5800709396994326, + 0.5795835870565879, + 0.5790960792169673, + 0.5786084170822965, + 0.5781206015554883, + 0.5776326335406389, + 0.5771445139430197, + 0.5766562436690712, + 0.5761678236263974, + 0.5756792547237576, + 0.5751905378710619, + 0.5747016739793636, + 0.5742126639608525, + 0.573723508728849, + 0.5732342091977974, + 0.5727447662832589, + 0.5722551809019052, + 0.5717654539715127, + 0.571275586410954, + 0.5707855791401936, + 0.5702954330802792, + 0.5698051491533365, + 0.5693147282825612, + 0.5688241713922135, + 0.5683334794076105, + 0.5678426532551197, + 0.5673516938621526, + 0.5668606021571574, + 0.5663693790696125, + 0.5658780255300193, + 0.5653865424698961, + 0.5648949308217702, + 0.5644031915191723, + 0.5639113254966284, + 0.5634193336896535, + 0.5629272170347448, + 0.5624349764693743, + 0.5619426129319827, + 0.561450127361971, + 0.5609575206996948, + 0.5604647938864571, + 0.5599719478645009, + 0.559478983577002, + 0.5589859019680624, + 0.5584927039827035, + 0.5579993905668581, + 0.5575059626673644, + 0.557012421231958, + 0.556518767209265, + 0.5560250015487952, + 0.5555311252009347, + 0.555037139116939, + 0.554543044248925, + 0.5540488415498648, + 0.5535545319735782, + 0.5530601164747251, + 0.5525655960087986, + 0.5520709715321178, + 0.5515762440018201, + 0.5510814143758547, + 0.5505864836129746, + 0.5500914526727294, + 0.5495963225154584, + 0.5491010941022828, + 0.5486057683950987, + 0.5481103463565695, + 0.5476148289501188, + 0.5471192171399225, + 0.546623511890902, + 0.5461277141687166, + 0.545631824939756, + 0.5451358451711327, + 0.5446397758306748, + 0.5441436178869187, + 0.5436473723091013, + 0.5431510400671529, + 0.5426546221316888, + 0.542158119474003, + 0.5416615330660604, + 0.541164863880488, + 0.5406681128905699, + 0.5401712810702366, + 0.53967436939406, + 0.5391773788372451, + 0.5386803103756217, + 0.5381831649856375, + 0.5376859436443504, + 0.537188647329421, + 0.5366912770191044, + 0.5361938336922434, + 0.5356963183282604, + 0.5351987319071492, + 0.5347010754094688, + 0.5342033498163343, + 0.5337055561094101, + 0.5332076952709016, + 0.532709768283548, + 0.5322117761306145, + 0.5317137197958841, + 0.5312156002636509, + 0.5307174185187109, + 0.5302191755463557, + 0.529720872332364, + 0.5292225098629938, + 0.5287240891249754, + 0.528225611105502, + 0.527727076792224, + 0.5272284871732393, + 0.5267298432370875, + 0.52623114597274, + 0.5257323963695932, + 0.5252335954174612, + 0.5247347441065671, + 0.5242358434275354, + 0.5237368943713849, + 0.5232378979295191, + 0.52273885509372, + 0.5222397668561404, + 0.521740634209294, + 0.5212414581460498, + 0.5207422396596233, + 0.5202429797435679, + 0.5197436793917682, + 0.5192443395984317, + 0.5187449613580806, + 0.5182455456655441, + 0.5177460935159508, + 0.51724660590472, + 0.5167470838275552, + 0.5162475282804337, + 0.5157479402596017, + 0.5152483207615639, + 0.5147486707830773, + 0.5142489913211421, + 0.513749283372994, + 0.5132495479360966, + 0.5127497860081335, + 0.5122499985869995, + 0.5117501866707939, + 0.5112503512578112, + 0.5107504933465341, + 0.5102506139356257, + 0.5097507140239199, + 0.509250794610416, + 0.508750856694268, + 0.5082509012747785, + 0.50775092935139, + 0.5072509419236775, + 0.5067509399913395, + 0.5062509245541902, + 0.5057508966121527, + 0.5052508571652499, + 0.5047508072135964, + 0.5042507477573912, + 0.5037506797969091, + 0.5032506043324934, + 0.502750522364547, + 0.502250434893525, + 0.5017503429199266, + 0.5012502474442868, + 0.5007501494671689, + 0.500250049989156, + 0.4997499500108439, + 0.499249850532831, + 0.4987497525557133, + 0.4982496570800735, + 0.49774956510647494, + 0.497249477635453, + 0.4967493956675064, + 0.49624932020309076, + 0.4957492522426089, + 0.49524919278640356, + 0.49474914283475024, + 0.4942491033878471, + 0.4937490754458098, + 0.49324906000866087, + 0.4927490580763224, + 0.4922490706486099, + 0.49174909872522143, + 0.4912491433057321, + 0.4907492053895841, + 0.49024928597607986, + 0.48974938606437446, + 0.48924950665346567, + 0.48874964874218885, + 0.48824981332920625, + 0.4877500014130003, + 0.4872502139918665, + 0.4867504520639032, + 0.48625071662700603, + 0.485751008678858, + 0.4852513292169226, + 0.4847516792384361, + 0.4842520597403983, + 0.48375247171956626, + 0.48325291617244504, + 0.48275339409527973, + 0.4822539064840492, + 0.48175445433445596, + 0.48125503864191943, + 0.48075566040156836, + 0.48025632060823176, + 0.4797570202564322, + 0.4792577603403768, + 0.47875854185395006, + 0.47825936579070605, + 0.4777602331438595, + 0.4772611449062799, + 0.4767621020704811, + 0.47626310562861507, + 0.4757641565724645, + 0.4752652558934328, + 0.4747664045825388, + 0.474267603630407, + 0.4737688540272601, + 0.4732701567629125, + 0.47277151282676044, + 0.47227292320777614, + 0.4717743888944982, + 0.47127591087502463, + 0.4707774901370061, + 0.4702791276676359, + 0.46978082445364433, + 0.4692825814812892, + 0.4687843997363491, + 0.4682862802041159, + 0.46778822386938546, + 0.4672902317164519, + 0.4667923047290986, + 0.46629444389058994, + 0.4657966501836658, + 0.4652989245905311, + 0.46480126809285083, + 0.46430368167173985, + 0.4638061663077565, + 0.46330872298089565, + 0.46281135267057893, + 0.4623140563556496, + 0.46181683501436266, + 0.4613196896243783, + 0.46082262116275496, + 0.4603256306059398, + 0.4598287189297635, + 0.4593318871094303, + 0.45883513611951177, + 0.45833846693393965, + 0.45784188052599684, + 0.4573453778683112, + 0.45684895993284724, + 0.45635262769089857, + 0.4558563821130813, + 0.4553602241693254, + 0.45486415482886733, + 0.4543681750602441, + 0.45387228583128325, + 0.4533764881090979, + 0.4528807828600776, + 0.4523851710498812, + 0.4518896536434305, + 0.4513942316049012, + 0.4508989058977172, + 0.4504036774845418, + 0.4499085473272706, + 0.44941351638702554, + 0.4489185856241451, + 0.44842375599817985, + 0.4479290284678824, + 0.4474344039912014, + 0.4469398835252749, + 0.4464454680264217, + 0.4459511584501352, + 0.44545695575107513, + 0.44496286088306103, + 0.44446887479906527, + 0.4439749984512047, + 0.44348123279073515, + 0.4429875787680422, + 0.4424940373326355, + 0.4420006094331419, + 0.44150729601729655, + 0.4410140980319377, + 0.44052101642299824, + 0.4400280521354991, + 0.4395352061135429, + 0.43904247930030504, + 0.438549872638029, + 0.43805738706801756, + 0.43756502353062554, + 0.4370727829652553, + 0.4365806663103464, + 0.43608867450337163, + 0.4355968084808278, + 0.43510506917822966, + 0.43461345753010383, + 0.43412197446998047, + 0.4336306209303874, + 0.4331393978428426, + 0.4326483061378473, + 0.4321573467448804, + 0.4316665205923894, + 0.43117582860778647, + 0.43068527171743876, + 0.4301948508466634, + 0.4297045669197207, + 0.4292144208598062, + 0.4287244135890459, + 0.42823454602848743, + 0.4277448190980947, + 0.42725523371674123, + 0.42676579080220267, + 0.42627649127115086, + 0.42578733603914753, + 0.42529832602063633, + 0.4248094621289381, + 0.42432074527624253, + 0.4238321763736027, + 0.4233437563309287, + 0.4228554860569802, + 0.4223673664593611, + 0.42187939844451183, + 0.4213915829177036, + 0.4209039207830326, + 0.42041641294341203, + 0.41992906030056754, + 0.419441863755029, + 0.4189548242061252, + 0.4184679425519783, + 0.4179812196894952, + 0.417494656514364, + 0.41700825392104546, + 0.4165220128027678, + 0.41603593405152084, + 0.4155500185580485, + 0.4150642672118443, + 0.41457868090114347, + 0.4140932605129177, + 0.4136080069328697, + 0.41312292104542525, + 0.41263800373372894, + 0.4121532558796368, + 0.4116686783637109, + 0.41118427206521346, + 0.41070003786209996, + 0.4102159766310147, + 0.4097320892472831, + 0.4092483765849064, + 0.40876483951655707, + 0.4082814789135699, + 0.40779829564593983, + 0.40731529058231286, + 0.4068324645899817, + 0.4063498185348805, + 0.40586735328157764, + 0.40538506969327104, + 0.4049029686317819, + 0.40442105095754877, + 0.40393931752962287, + 0.4034577692056607, + 0.4029764068419206, + 0.4024952312932548, + 0.4020142434131051, + 0.40153344405349767, + 0.4010528340650358, + 0.4005724142968964, + 0.4000921855968226, + 0.3996121488111194, + 0.39913230478464823, + 0.39865265436082065, + 0.39817319838159315, + 0.39769393768746286, + 0.3972148731174602, + 0.39673600550914534, + 0.39625733569860155, + 0.39577886452043043, + 0.3953005928077471, + 0.3948225213921732, + 0.3943446511038342, + 0.3938669827713519, + 0.3933895172218398, + 0.392912255280899, + 0.3924351977726116, + 0.3919583455195367, + 0.39148169934270394, + 0.39100526006160985, + 0.3905290284942122, + 0.39005300545692434, + 0.38957719176461125, + 0.3891015882305835, + 0.38862619566659296, + 0.3881510148828283, + 0.3876760466879082, + 0.38720129188887875, + 0.38672675129120704, + 0.38625242569877616, + 0.38577831591388234, + 0.3853044227372272, + 0.38483074696791575, + 0.3843572894034497, + 0.38388405083972343, + 0.38341103207101956, + 0.3829382338900035, + 0.38246565708772007, + 0.3819933024535871, + 0.38152117077539194, + 0.3810492628392873, + 0.3805775794297852, + 0.3801061213297542, + 0.379634889320413, + 0.3791638841813273, + 0.37869310669040557, + 0.3782225576238928, + 0.37775223775636857, + 0.3772821478607402, + 0.37681228870824, + 0.37634266106842074, + 0.3758732657091506, + 0.37540410339660957, + 0.37493517489528516, + 0.3744664809679672, + 0.3739980223757453, + 0.3735297998780027, + 0.37306181423241447, + 0.37259406619494073, + 0.37212655651982407, + 0.3716592859595863, + 0.3711922552650221, + 0.3707254651851959, + 0.3702589164674399, + 0.3697926098573464, + 0.36932654609876736, + 0.3688607259338076, + 0.3683951501028224, + 0.36792981934441427, + 0.3674647343954268, + 0.36699989599094374, + 0.3665353048642823, + 0.3660709617469913, + 0.36560686736884696, + 0.3651430224578487, + 0.3646794277402162, + 0.36421608394038496, + 0.3637529917810025, + 0.3632901519829266, + 0.36282756526521875, + 0.3623652323451434, + 0.3619031539381622, + 0.3614413307579315, + 0.36097976351629957, + 0.3605184529233013, + 0.36005739968715644, + 0.3595966045142651, + 0.3591360681092046, + 0.35867579117472714, + 0.35821577441175434, + 0.3577560185193762, + 0.35729652419484603, + 0.35683729213357795, + 0.3563783230291445, + 0.35591961757327106, + 0.3554611764558354, + 0.35500300036486254, + 0.35454508998652234, + 0.3540874460051271, + 0.35363006910312633, + 0.35317295996110687, + 0.35271611925778656, + 0.35225954767001344, + 0.35180324587276224, + 0.35134721453913076, + 0.3508914543403382, + 0.3504359659457209, + 0.3499807500227299, + 0.3495258072369293, + 0.3490711382519911, + 0.3486167437296951, + 0.3481626243299236, + 0.3477087807106599, + 0.3472552135279867, + 0.34680192343608063, + 0.3463489110872118, + 0.34589617713174103, + 0.3454437222181155, + 0.3449915469928687, + 0.3445396521006159, + 0.34408803818405165, + 0.34363670588394923, + 0.3431856558391555, + 0.34273488868659086, + 0.3422844050612448, + 0.34183420559617483, + 0.3413842909225042, + 0.34093466166941766, + 0.3404853184641622, + 0.34003626193204173, + 0.3395874926964163, + 0.3391390113787006, + 0.3386908185983599, + 0.33824291497290926, + 0.33779530111791045, + 0.33734797764697044, + 0.3369009451717395, + 0.33645420430190787, + 0.33600775564520513, + 0.3355615998073974, + 0.33511573739228506, + 0.334670169001702, + 0.3342248952355117, + 0.33377991669160767, + 0.33333523396590936, + 0.3328908476523613, + 0.3324467583429317, + 0.3320029666276093, + 0.33155947309440315, + 0.3311162783293393, + 0.33067338291645987, + 0.33023078743782175, + 0.3297884924734934, + 0.3293464986015553, + 0.32890480639809616, + 0.3284634164372121, + 0.3280223292910065, + 0.3275815455295855, + 0.3271410657210594, + 0.32670089043153905, + 0.3262610202251349, + 0.3258214556639568, + 0.32538219730811024, + 0.32494324571569744, + 0.32450460144281346, + 0.3240662650435465, + 0.32362823706997657, + 0.32319051807217314, + 0.32275310859819495, + 0.32231600919408754, + 0.32187922040388295, + 0.3214427427695987, + 0.32100657683123546, + 0.3205707231267765, + 0.3201351821921874, + 0.319699954561413, + 0.31926504076637874, + 0.318830441336987, + 0.3183961568011179, + 0.3179621876846279, + 0.3175285345113481, + 0.3170951978030846, + 0.31666217807961594, + 0.316229475858693, + 0.31579709165603886, + 0.3153650259853463, + 0.31493327935827864, + 0.31450185228446736, + 0.31407074527151224, + 0.31363995882498086, + 0.31320949344840676, + 0.3127793496432899, + 0.31234952790909487, + 0.31192002874325087, + 0.3114908526411517, + 0.31106200009615315, + 0.31063347159957466, + 0.31020526764069734, + 0.3097773887067634, + 0.309349835282977, + 0.3089226078525016, + 0.3084957068964618, + 0.3080691328939409, + 0.30764288632198156, + 0.3072169676555855, + 0.3067913773677118, + 0.30636611592927876, + 0.3059411838091616, + 0.3055165814741927, + 0.3050923093891619, + 0.3046683680168155, + 0.30424475781785687, + 0.30382147925094505, + 0.30339853277269513, + 0.30297591883767894, + 0.30255363789842316, + 0.3021316904054112, + 0.3017100768070813, + 0.30128879754982707, + 0.30086785307799835, + 0.30044724383389965, + 0.30002697025779157, + 0.2996070327878897, + 0.299187431860365, + 0.2987681679093447, + 0.29834924136691043, + 0.29793065266310126, + 0.2975124022259107, + 0.29709449048128855, + 0.29667691785314154, + 0.2962596847633322, + 0.2958427916316795, + 0.2954262388759601, + 0.2950100269119069, + 0.29459415615321116, + 0.2941786270115211, + 0.29376343989644316, + 0.2933485952155429, + 0.29293409337434384, + 0.2925199347763293, + 0.2921061198229421, + 0.2916926489135848, + 0.2912795224456214, + 0.2908667408143758, + 0.2904543044131348, + 0.2900422136331462, + 0.28963046886362065, + 0.2892190704917329, + 0.2888080189026203, + 0.2883973144793859, + 0.2879869576030969, + 0.2875769486527864, + 0.28716728800545455, + 0.286757976036068, + 0.2863490131175621, + 0.28594039962083984, + 0.2855321359147742, + 0.2851242223662084, + 0.2847166593399565, + 0.2843094471988046, + 0.2839025863035112, + 0.28349607701280854, + 0.2830899196834036, + 0.2826841146699781, + 0.28227866232519105, + 0.28187356299967825, + 0.28146881704205295, + 0.28106442479890936, + 0.2806603866148205, + 0.2802567028323417, + 0.27985337379201014, + 0.27945039983234643, + 0.2790477812898565, + 0.27864551849903074, + 0.2782436117923476, + 0.2778420615002729, + 0.2774408679512613, + 0.27704003147175893, + 0.2766395523862023, + 0.27623943101702164, + 0.27583966768464097, + 0.27544026270747934, + 0.2750412164019531, + 0.2746425290824761, + 0.2742442010614621, + 0.27384623264932484, + 0.27344862415448024, + 0.2730513758833484, + 0.27265448814035365, + 0.27225796122792634, + 0.27186179544650535, + 0.2714659910945383, + 0.2710705484684838, + 0.27067546786281227, + 0.27028074957000825, + 0.26988639388057156, + 0.26949240108301864, + 0.2690987714638847, + 0.26870550530772463, + 0.268312602897115, + 0.26792006451265615, + 0.26752789043297287, + 0.26713608093471686, + 0.26674463629256795, + 0.2663535567792359, + 0.2659628426654628, + 0.26557249422002316, + 0.2651825117097282, + 0.26479289539942497, + 0.2644036455519996, + 0.2640147624283794, + 0.2636262462875335, + 0.26323809738647597, + 0.2628503159802664, + 0.2624629023220131, + 0.2620758566628739, + 0.2616891792520587, + 0.26130287033683164, + 0.26091693016251183, + 0.26053135897247637, + 0.2601461570081629, + 0.25976132450906975, + 0.25937686171275953, + 0.2589927688548608, + 0.2586090461690692, + 0.25822569388715133, + 0.25784271223894484, + 0.2574601014523623, + 0.257077861753392, + 0.25669599336610016, + 0.2563144965126348, + 0.25593337141322514, + 0.2555526182861865, + 0.25517223734792005, + 0.2547922288129172, + 0.2544125928937603, + 0.25403332980112536, + 0.2536544397437849, + 0.2532759229286091, + 0.2528977795605688, + 0.25252000984273815, + 0.25214261397629556, + 0.2517655921605281, + 0.2513889445928317, + 0.2510126714687144, + 0.2506367729818001, + 0.2502612493238284, + 0.24988610068465844, + 0.24951132725227199, + 0.2491369292127742, + 0.24876290675039736, + 0.24838926004750272, + 0.24801598928458313, + 0.24764309464026601, + 0.24727057629131466, + 0.24689843441263237, + 0.24652666917726374, + 0.24615528075639714, + 0.24578426931936892, + 0.24541363503366354, + 0.24504337806491872, + 0.24467349857692566, + 0.24430399673163306, + 0.24393487268915026, + 0.2435661266077479, + 0.2431977586438625, + 0.2428297689520984, + 0.24246215768522972, + 0.24209492499420504, + 0.24172807102814764, + 0.24136159593436013, + 0.24099549985832647, + 0.24062978294371407, + 0.24026444533237812, + 0.2398994871643627, + 0.23953490857790538, + 0.23917070970943788, + 0.2388068906935903, + 0.23844345166319392, + 0.23808039274928328, + 0.2377177140811002, + 0.2373554157860952, + 0.2369934979899312, + 0.23663196081648685, + 0.2362708043878582, + 0.23591002882436296, + 0.23554963424454223, + 0.2351896207651636, + 0.23482998850122572, + 0.2344707375659586, + 0.23411186807082884, + 0.23375338012554148, + 0.23339527383804304, + 0.2330375493145247, + 0.23268020665942546, + 0.2323232459754353, + 0.23196666736349736, + 0.23161047092281162, + 0.23125465675083834, + 0.2308992249433004, + 0.23054417559418616, + 0.23018950879575395, + 0.22983522463853356, + 0.2294813232113302, + 0.22912780460122772, + 0.22877466889359055, + 0.22842191617206897, + 0.22806954651860012, + 0.2277175600134126, + 0.2273659567350288, + 0.22701473676026807, + 0.2266639001642511, + 0.22631344702040113, + 0.22596337740044925, + 0.2256136913744361, + 0.22526438901071522, + 0.22491547037595772, + 0.2245669355351534, + 0.22421878455161592, + 0.22387101748698457, + 0.22352363440122813, + 0.22317663535264898, + 0.2228300203978848, + 0.22248378959191306, + 0.222137942988054, + 0.2217924806379733, + 0.22144740259168683, + 0.22110270889756212, + 0.2207583996023239, + 0.22041447475105547, + 0.2200709343872025, + 0.21972777855257775, + 0.21938500728736243, + 0.21904262063011182, + 0.21870061861775592, + 0.21835900128560504, + 0.2180177686673529, + 0.21767692079507883, + 0.2173364576992528, + 0.21699637940873728, + 0.21665668595079202, + 0.2163173773510766, + 0.2159784536336542, + 0.21563991482099532, + 0.2153017609339808, + 0.21496399199190497, + 0.2146266080124808, + 0.2142896090118407, + 0.2139529950045429, + 0.2136167660035727, + 0.21328092202034687, + 0.21294546306471784, + 0.21261038914497543, + 0.21227570026785247, + 0.2119413964385266, + 0.2116074776606247, + 0.21127394393622678, + 0.21094079526586818, + 0.21060803164854425, + 0.21027565308171392, + 0.20994365956130256, + 0.20961205108170655, + 0.20928082763579536, + 0.2089499892149167, + 0.20861953580889947, + 0.20828946740605703, + 0.20795978399319126, + 0.20763048555559627, + 0.20730157207706112, + 0.206973043539875, + 0.20664489992482915, + 0.20631714121122202, + 0.20598976737686148, + 0.20566277839806935, + 0.20533617424968542, + 0.2050099549050698, + 0.20468412033610794, + 0.20435867051321321, + 0.204033605405331, + 0.20370892497994297, + 0.20338462920306943, + 0.2030607180392746, + 0.2027371914516689, + 0.202414049401913, + 0.20209129185022257, + 0.20176891875536995, + 0.2014469300746904, + 0.20112532576408326, + 0.20080410577801744, + 0.20048327006953437, + 0.20016281859025176, + 0.199842751290368, + 0.1995230681186645, + 0.19920376902251058, + 0.19888485394786737, + 0.19856632283929043, + 0.19824817563993458, + 0.1979304122915566, + 0.19761303273452013, + 0.19729603690779862, + 0.19697942474897912, + 0.19666319619426686, + 0.19634735117848778, + 0.19603188963509321, + 0.19571681149616305, + 0.19540211669241014, + 0.1950878051531839, + 0.1947738768064734, + 0.19446033157891174, + 0.19414716939578036, + 0.19383439018101156, + 0.19352199385719338, + 0.1932099803455728, + 0.19289834956605967, + 0.19258710143723057, + 0.1922762358763329, + 0.19196575279928774, + 0.1916556521206948, + 0.19134593375383524, + 0.19103659761067693, + 0.19072764360187588, + 0.1904190716367823, + 0.19011088162344347, + 0.18980307346860759, + 0.1894956470777277, + 0.18918860235496515, + 0.18888193920319396, + 0.18857565752400451, + 0.188269757217707, + 0.18796423818333607, + 0.1876591003186533, + 0.18735434352015223, + 0.18704996768306192, + 0.1867459727013504, + 0.18644235846772927, + 0.18613912487365636, + 0.18583627180934037, + 0.1855337991637449, + 0.18523170682459178, + 0.18492999467836524, + 0.1846286626103152, + 0.1843277105044614, + 0.18402713824359818, + 0.18372694570929682, + 0.18342713278191036, + 0.1831276993405767, + 0.1828286452632233, + 0.18252997042657038, + 0.1822316747061351, + 0.18193375797623546, + 0.18163622010999358, + 0.18133906097933994, + 0.18104228045501802, + 0.18074587840658618, + 0.1804498547024237, + 0.18015420920973324, + 0.17985894179454454, + 0.1795640523217198, + 0.17926954065495548, + 0.17897540665678835, + 0.17868165018859705, + 0.1783882711106073, + 0.17809526928189623, + 0.17780264456039463, + 0.1775103968028925, + 0.17721852586504105, + 0.17692703160135803, + 0.1766359138652317, + 0.17634517250892312, + 0.1760548073835715, + 0.17576481833919722, + 0.17547520522470617, + 0.1751859678878932, + 0.17489710617544618, + 0.17460861993294952, + 0.17432050900488905, + 0.17403277323465405, + 0.173745412464543, + 0.17345842653576593, + 0.17317181528844894, + 0.17288557856163833, + 0.17259971619330328, + 0.1723142280203417, + 0.17202911387858166, + 0.17174437360278638, + 0.1714600070266591, + 0.17117601398284454, + 0.17089239430293524, + 0.17060914781747316, + 0.17032627435595482, + 0.17004377374683544, + 0.16976164581753117, + 0.16947989039442468, + 0.16919850730286745, + 0.16891749636718445, + 0.16863685741067846, + 0.16835659025563232, + 0.1680766947233141, + 0.16779717063398047, + 0.16751801780688014, + 0.16723923606025826, + 0.1669608252113597, + 0.16668278507643342, + 0.16640511547073544, + 0.16612781620853323, + 0.16585088710311024, + 0.1655743279667673, + 0.16529813861082943, + 0.1650223188456471, + 0.16474686848060138, + 0.1644717873241076, + 0.16419707518361837, + 0.16392273186562856, + 0.16364875717567784, + 0.16337515091835486, + 0.16310191289730191, + 0.16282904291521727, + 0.16255654077385973, + 0.16228440627405238, + 0.1620126392156861, + 0.16174123939772356, + 0.16147020661820244, + 0.16119954067424025, + 0.16092924136203657, + 0.16065930847687793, + 0.1603897418131418, + 0.16012054116429864, + 0.15985170632291756, + 0.15958323708066902, + 0.15931513322832835, + 0.15904739455578043, + 0.15878002085202236, + 0.15851301190516776, + 0.15824636750245058, + 0.15798008743022818, + 0.15771417147398614, + 0.15744861941834032, + 0.15718343104704202, + 0.15691860614298125, + 0.1566541444881897, + 0.15639004586384586, + 0.15612631005027722, + 0.15586293682696462, + 0.15559992597254646, + 0.15533727726482094, + 0.15507499048075124, + 0.15481306539646844, + 0.15455150178727461, + 0.15429029942764816, + 0.15402945809124557, + 0.15376897755090657, + 0.1535088575786563, + 0.1532490979457103, + 0.1529896984224779, + 0.1527306587785645, + 0.1524719787827774, + 0.1522136582031277, + 0.1519556968068344, + 0.15169809436032822, + 0.15144085062925483, + 0.15118396537847945, + 0.15092743837208877, + 0.1506712693733958, + 0.1504154581449434, + 0.15016000444850708, + 0.14990490804509982, + 0.14965016869497438, + 0.14939578615762683, + 0.14914176019180214, + 0.148888090555495, + 0.14863477700595562, + 0.14838181929969163, + 0.1481292171924723, + 0.1478769704393329, + 0.14762507879457665, + 0.14737354201177966, + 0.1471223598437933, + 0.1468715320427484, + 0.14662105836005923, + 0.14637093854642547, + 0.14612117235183775, + 0.14587175952557918, + 0.1456226998162301, + 0.14537399297167142, + 0.14512563873908746, + 0.14487763686497046, + 0.1446299870951232, + 0.14438268917466257, + 0.14413574284802366, + 0.14388914785896256, + 0.14364290395055979, + 0.14339701086522483, + 0.14315146834469772, + 0.14290627613005458, + 0.14266143396170927, + 0.14241694157941764, + 0.1421727987222816, + 0.1419290051287505, + 0.1416855605366274, + 0.14144246468306976, + 0.14119971730459457, + 0.14095731813708112, + 0.14071526691577452, + 0.14047356337528938, + 0.14023220724961227, + 0.13999119827210582, + 0.13975053617551236, + 0.13951022069195662, + 0.1392702515529497, + 0.1390306284893914, + 0.13879135123157504, + 0.13855241950919006, + 0.13831383305132428, + 0.13807559158646943, + 0.13783769484252317, + 0.13760014254679187, + 0.13736293442599545, + 0.13712607020626932, + 0.1368895496131688, + 0.13665337237167152, + 0.13641753820618052, + 0.13618204684052926, + 0.13594689799798276, + 0.1357120914012424, + 0.13547762677244835, + 0.13524350383318273, + 0.13500972230447417, + 0.13477628190679913, + 0.13454318236008722, + 0.13431042338372234, + 0.1340780046965474, + 0.1338459260168674, + 0.13361418706245154, + 0.13338278755053823, + 0.13315172719783652, + 0.13292100572053034, + 0.13269062283428157, + 0.13246057825423296, + 0.13223087169501158, + 0.13200150287073198, + 0.1317724714949986, + 0.13154377728091093, + 0.13131541994106377, + 0.1310873991875532, + 0.13085971473197788, + 0.13063236628544284, + 0.13040535355856275, + 0.13017867626146495, + 0.12995233410379203, + 0.12972632679470586, + 0.12950065404289013, + 0.1292753155565537, + 0.12905031104343312, + 0.12882564021079668, + 0.1286013027654469, + 0.12837729841372347, + 0.12815362686150678, + 0.12793028781422067, + 0.12770728097683515, + 0.1274846060538708, + 0.1272622627494, + 0.1270402507670516, + 0.12681856981001283, + 0.12659721958103243, + 0.12637619978242476, + 0.12615551011607085, + 0.12593515028342406, + 0.12571511998551022, + 0.1254954189229326, + 0.12527604679587434, + 0.1250570033041013, + 0.12483828814696485, + 0.12461990102340581, + 0.12440184163195578, + 0.12418410967074156, + 0.1239667048374875, + 0.12374962682951862, + 0.12353287534376312, + 0.12331645007675562, + 0.12310035072464043, + 0.12288457698317379, + 0.12266912854772738, + 0.12245400511329041, + 0.12223920637447361, + 0.12202473202551145, + 0.12181058176026449, + 0.12159675527222391, + 0.12138325225451263, + 0.12117007239988908, + 0.12095721540075, + 0.12074468094913282, + 0.12053246873671941, + 0.12032057845483757, + 0.12010900979446548, + 0.11989776244623307, + 0.1196868361004253, + 0.11947623044698596, + 0.11926594517551892, + 0.11905597997529149, + 0.11884633453523796, + 0.11863700854396109, + 0.118428001689736, + 0.11821931366051253, + 0.11801094414391733, + 0.11780289282725787, + 0.11759515939752452, + 0.11738774354139292, + 0.11718064494522762, + 0.11697386329508352, + 0.11676739827671023, + 0.11656124957555336, + 0.11635541687675788, + 0.1161498998651711, + 0.11594469822534437, + 0.11573981164153668, + 0.11553523979771729, + 0.11533098237756756, + 0.11512703906448474, + 0.11492340954158342, + 0.11472009349169976, + 0.11451709059739257, + 0.11431440054094633, + 0.11411202300437506, + 0.1139099576694228, + 0.11370820421756811, + 0.1135067623300261, + 0.11330563168774987, + 0.11310481197143551, + 0.11290430286152209, + 0.11270410403819608, + 0.11250421518139331, + 0.11230463597080087, + 0.11210536608586119, + 0.11190640520577277, + 0.11170775300949487, + 0.11150940917574759, + 0.11131137338301633, + 0.11111364530955359, + 0.11091622463338147, + 0.11071911103229415, + 0.11052230418386078, + 0.11032580376542711, + 0.11012960945411931, + 0.10993372092684506, + 0.10973813786029718, + 0.10954285993095514, + 0.10934788681508822, + 0.10915321818875778, + 0.1089588537278191, + 0.10876479310792508, + 0.10857103600452778, + 0.1083775820928804, + 0.10818443104804132, + 0.10799158254487462, + 0.10779903625805398, + 0.10760679186206412, + 0.10741484903120346, + 0.10722320743958735, + 0.10703186676114851, + 0.10684082666964168, + 0.10665008683864441, + 0.10645964694155952, + 0.10626950665161883, + 0.10607966564188342, + 0.10589012358524763, + 0.10570088015444101, + 0.10551193502202938, + 0.10532328786041978, + 0.10513493834185994, + 0.10494688613844194, + 0.10475913092210554, + 0.10457167236463794, + 0.10438451013767824, + 0.1041976439127188, + 0.10401107336110738, + 0.10382479815404999, + 0.10363881796261243, + 0.1034531324577238, + 0.10326774131017635, + 0.10308264419063062, + 0.10289784076961583, + 0.10271333071753208, + 0.10252911370465356, + 0.10234518940112991, + 0.10216155747698874, + 0.1019782176021381, + 0.10179516944636802, + 0.10161241267935317, + 0.10142994697065477, + 0.1012477719897229, + 0.10106588740589895, + 0.10088429288841683, + 0.10070298810640643, + 0.10052197272889452, + 0.10034124642480735, + 0.10016080886297352, + 0.09998065971212489, + 0.09980079864089908, + 0.09962122531784201, + 0.09944193941140966, + 0.0992629405899702, + 0.09908422852180565, + 0.09890580287511497, + 0.09872766331801519, + 0.09854980951854353, + 0.09837224114466026, + 0.09819495786424992, + 0.09801795934512358, + 0.09784124525502147, + 0.0976648152616133, + 0.09748866903250297, + 0.09731280623522787, + 0.09713722653726253, + 0.09696192960602024, + 0.09678691510885495, + 0.09661218271306304, + 0.09643773208588591, + 0.09626356289451132, + 0.09608967480607578, + 0.09591606748766623, + 0.09574274060632204, + 0.09556969382903724, + 0.09539692682276202, + 0.09522443925440505, + 0.09505223079083536, + 0.09488030109888368, + 0.09470864984534522, + 0.09453727669698109, + 0.0943661813205201, + 0.0941953633826611, + 0.09402482255007438, + 0.09385455848940394, + 0.09368457086726856, + 0.09351485935026538, + 0.09334542360496967, + 0.09317626329793849, + 0.09300737809571091, + 0.09283876766481114, + 0.09267043167174993, + 0.09250236978302617, + 0.09233458166512898, + 0.09216706698453914, + 0.09199982540773166, + 0.0918328566011768, + 0.09166616023134233, + 0.09149973596469496, + 0.09133358346770248, + 0.09116770240683514, + 0.09100209244856794, + 0.0908367532593819, + 0.09067168450576601, + 0.09050688585421929, + 0.09034235697125148, + 0.09017809752338624, + 0.09001410717716185, + 0.08985038559913316, + 0.08968693245587356, + 0.08952374741397628, + 0.08936083014005625, + 0.08919818030075244, + 0.08903579756272817, + 0.08887368159267428, + 0.08871183205730915, + 0.08855024862338279, + 0.08838893095767553, + 0.08822787872700222, + 0.08806709159821247, + 0.0879065692381924, + 0.08774631131386745, + 0.08758631749220203, + 0.08742658744020271, + 0.08726712082491972, + 0.08710791731344736, + 0.08694897657292733, + 0.08679029827054896, + 0.0866318820735511, + 0.08647372764922456, + 0.08631583466491233, + 0.08615820278801234, + 0.08600083168597827, + 0.0858437210263211, + 0.08568687047661194, + 0.08553027970448135, + 0.08537394837762324, + 0.0852178761637945, + 0.08506206273081751, + 0.0849065077465817, + 0.08475121087904447, + 0.08459617179623369, + 0.08444139016624796, + 0.08428686565725874, + 0.08413259793751238, + 0.08397858667533065, + 0.0838248315391128, + 0.08367133219733691, + 0.08351808831856111, + 0.08336509957142557, + 0.08321236562465328, + 0.08305988614705251, + 0.08290766080751666, + 0.08275568927502741, + 0.08260397121865537, + 0.08245250630756129, + 0.08230129421099763, + 0.08215033459831045, + 0.08199962713893993, + 0.08184917150242288, + 0.08169896735839288, + 0.08154901437658324, + 0.08139931222682673, + 0.08124986057905781, + 0.0811006591033141, + 0.08095170746973734, + 0.08080300534857543, + 0.08065455241018282, + 0.08050634832502257, + 0.08035839276366719, + 0.08021068539680098, + 0.08006322589521986, + 0.07991601392983405, + 0.0797690491716685, + 0.0796223312918648, + 0.07947585996168183, + 0.079329634852498, + 0.07918365563581153, + 0.07903792198324255, + 0.07889243356653386, + 0.07874719005755249, + 0.07860219112829081, + 0.07845743645086822, + 0.07831292569753123, + 0.07816865854065669, + 0.07802463465275133, + 0.07788085370645315, + 0.07773731537453377, + 0.07759401932989896, + 0.07745096524558959, + 0.07730815279478342, + 0.07716558165079568, + 0.07702325148708133, + 0.07688116197723482, + 0.0767393127949928, + 0.0765977036142339, + 0.07645633410898134, + 0.07631520395340274, + 0.0761743128218122, + 0.07603366038867165, + 0.07589324632859062, + 0.07575307031632916, + 0.07561313202679809, + 0.07547343113506, + 0.07533396731633091, + 0.07519474024598105, + 0.07505574959953587, + 0.07491699505267801, + 0.07477847628124712, + 0.07464019296124214, + 0.074502144768822, + 0.07436433138030651, + 0.07422675247217736, + 0.07408940772107997, + 0.07395229680382409, + 0.07381541939738467, + 0.07367877517890264, + 0.0735423638256879, + 0.07340618501521778, + 0.07327023842513991, + 0.07313452373327245, + 0.07299904061760565, + 0.0728637887563025, + 0.07272876782769966, + 0.07259397751030938, + 0.07245941748281948, + 0.07232508742409449, + 0.07219098701317817, + 0.07205711592929209, + 0.0719234738518385, + 0.0717900604604006, + 0.07165687543474408, + 0.07152391845481687, + 0.07139118920075205, + 0.071258687352867, + 0.07112641259166541, + 0.07099436459783814, + 0.0708625430522637, + 0.07073094763601018, + 0.07059957803033479, + 0.07046843391668656, + 0.07033751497670548, + 0.07020682089222496, + 0.07007635134527201, + 0.06994610601806805, + 0.06981608459303046, + 0.06968628675277311, + 0.06955671218010706, + 0.06942736055804177, + 0.06929823156978632, + 0.06916932489874983, + 0.06904064022854228, + 0.06891217724297605, + 0.0687839356260661, + 0.06865591506203128, + 0.0685281152352949, + 0.06840053583048622, + 0.06827317653244047, + 0.06814603702620081, + 0.06801911699701757, + 0.06789241613035091, + 0.06776593411187048, + 0.06763967062745668, + 0.0675136253632016, + 0.06738779800540928, + 0.06726218824059793, + 0.06713679575549847, + 0.06701162023705787, + 0.06688666137243837, + 0.06676191884901872, + 0.06663739235439503, + 0.06651308157638103, + 0.0663889862030107, + 0.06626510592253632, + 0.06614144042343162, + 0.06601798939439141, + 0.06589475252433241, + 0.06577172950239463, + 0.0656489200179412, + 0.06552632376056, + 0.06540394042006425, + 0.06528176968649291, + 0.06515981125011161, + 0.06503806480141372, + 0.06491653003112052, + 0.06479520663018246, + 0.06467409428977933, + 0.0645531927013222, + 0.06443250155645219, + 0.06431202054704332, + 0.06419174936520133, + 0.06407168770326588, + 0.06395183525381054, + 0.06383219170964372, + 0.06371275676380872, + 0.06359353010958613, + 0.06347451144049199, + 0.06335570045028072, + 0.0632370968329452, + 0.06311870028271627, + 0.0630005104940653, + 0.0628825271617035, + 0.06276474998058279, + 0.06264717864589708, + 0.06252981285308223, + 0.06241265229781734, + 0.06229569667602475, + 0.062178945683870945, + 0.06206239901776789, + 0.06194605637437223, + 0.0618299174505873, + 0.06171398194356292, + 0.06159824955069626, + 0.06148271996963284, + 0.061367392898266204, + 0.06125226803474004, + 0.06113734507744706, + 0.06102262372503042, + 0.060908103676385106, + 0.06079378463065743, + 0.060679666287245415, + 0.06056574834580042, + 0.06045203050622716, + 0.0603385124686846, + 0.06022519393358583, + 0.06011207460159951, + 0.05999915417364987, + 0.059886432350917285, + 0.05977390883483913, + 0.05966158332711036, + 0.05954945552968394, + 0.059437525144770964, + 0.05932579187484177, + 0.05921425542262648, + 0.05910291549111535, + 0.0589917717835593, + 0.0588808240034705, + 0.05877007185462224, + 0.05865951504105138, + 0.058549153267056564, + 0.05843898623720001, + 0.058329013656307716, + 0.058219235229470256, + 0.058109650662042656, + 0.058000259659645836, + 0.05789106192816573, + 0.05778205717375551, + 0.05767324510283456, + 0.05756462542208973, + 0.057456197838475775, + 0.05734796205921544, + 0.05723991779180049, + 0.057132064743991795, + 0.05702440262381969, + 0.0569169311395854, + 0.056809649999859824, + 0.0567025589134853, + 0.05659565758957574, + 0.05648894573751706, + 0.05638242306696761, + 0.0562760892878581, + 0.05616994411039333, + 0.05606398724505102, + 0.05595821840258386, + 0.05585263729401835, + 0.05574724363065664, + 0.05564203712407567, + 0.055537017486128826, + 0.055432184428945486, + 0.055327537664931814, + 0.05522307690677064, + 0.05511880186742313, + 0.05501471226012711, + 0.054910807798400185, + 0.05480708819603719, + 0.05470355316711295, + 0.0546002024259814, + 0.054497035687276485, + 0.05439405266591224, + 0.05429125307708316, + 0.05418863663626505, + 0.054086203059214855, + 0.05398395206197082, + 0.0538818833608542, + 0.05377999667246791, + 0.05367829171369809, + 0.05357676820171331, + 0.053475425853966474, + 0.05337426438819404, + 0.05327328352241645, + 0.053172482974938706, + 0.053071862464350916, + 0.052971421709527955, + 0.052871160429630804, + 0.05277107834410555, + 0.05267117517268527, + 0.05257145063538904, + 0.05247190445252248, + 0.05237253634467898, + 0.052273346032739365, + 0.05217433323787124, + 0.052075497681531524, + 0.05197683908546458, + 0.05187835717170397, + 0.051780051662572046, + 0.05168192228068025, + 0.051583968748929565, + 0.05148619079051098, + 0.05138858812890568, + 0.05129116048788518, + 0.0511939075915111, + 0.051096829164136714, + 0.050999924930406504, + 0.05090319461525583, + 0.05080663794391249, + 0.05071025464189571, + 0.05061404443501738, + 0.05051800704938192, + 0.05042214221138619, + 0.050326449647720595, + 0.0502309290853683, + 0.05013558025160636, + 0.050040402874005574, + 0.04994539668043063, + 0.04985056139904065, + 0.04975589675828895, + 0.049661402486923856, + 0.049567078313988544, + 0.0494729239688213, + 0.04937893918105607, + 0.04928512368062188, + 0.04919147719774419, + 0.04909799946294402, + 0.049004690207039014, + 0.048911549161143375, + 0.048818576056667395, + 0.04872577062531891, + 0.04863313259910229, + 0.04854066171031979, + 0.04844835769157063, + 0.048356220275751705, + 0.048264249196058207, + 0.04817244418598321, + 0.04808080497931766, + 0.04798933131015126, + 0.04789802291287226, + 0.04780687952216767, + 0.04771590087302369, + 0.04762508670072518, + 0.047534436740856334, + 0.0474439507293013, + 0.04735362840224333, + 0.04726346949616578, + 0.047173473747851635, + 0.04708364089438444, + 0.04699397067314737, + 0.0469044628218247, + 0.04681511707840058, + 0.04672593318116036, + 0.04663691086869026, + 0.04654804987987682, + 0.046459349953908435, + 0.04637081083027461, + 0.046282432248765826, + 0.04619421394947443, + 0.046106155672794635, + 0.04601825715942187, + 0.04593051815035376, + 0.04584293838689035, + 0.04575551761063301, + 0.04566825556348597, + 0.045581151987655666, + 0.04549420662565107, + 0.045407419220283685, + 0.04532078951466756, + 0.04523431725222027, + 0.04514800217666115, + 0.04506184403201374, + 0.04497584256260345, + 0.04488999751306022, + 0.0448043086283163, + 0.04471877565360782, + 0.04463339833447433, + 0.04454817641675868, + 0.04446310964660771, + 0.04437819777047203, + 0.04429344053510531, + 0.04420883768756623, + 0.044124388975216644, + 0.04404009414572296, + 0.04395595294705523, + 0.04387196512748792, + 0.043788130435599926, + 0.043704448620274006, + 0.0436209194306979, + 0.04353754261636311, + 0.04345431792706611, + 0.04337124511290791, + 0.043288323924293715, + 0.04320555411193405, + 0.04312293542684331, + 0.04304046762034153, + 0.04295815044405282, + 0.042875983649906524, + 0.042793966990136934, + 0.042712100217283, + 0.04263038308418843, + 0.04254881534400268, + 0.04246739675017963, + 0.04238612705647837, + 0.042305006016963076, + 0.042224033386003, + 0.042143208918272834, + 0.042062532368752015, + 0.041982003492725406, + 0.04190162204578274, + 0.04182138778381961, + 0.041741300463036146, + 0.04166135983993824, + 0.041581565671337084, + 0.04150191771434841, + 0.04142241572639416, + 0.04134305946520078, + 0.04126384868880051, + 0.04118478315553087, + 0.04110586262403415, + 0.04102708685325862, + 0.04094845560245752, + 0.04086996863118919, + 0.04079162569931727, + 0.040713426567011046, + 0.04063537099474446, + 0.040557458743297525, + 0.04047968957375436, + 0.04040206324750517, + 0.04032457952624524, + 0.04024723817197451, + 0.040170038946998554, + 0.040092981613927936, + 0.04001606593567775, + 0.03993929167546939, + 0.03986265859682825, + 0.039786166463584904, + 0.03970981503987503, + 0.039633604090139385, + 0.039557533379123266, + 0.03948160267187728, + 0.03940581173375646, + 0.03933016033042058, + 0.03925464822783464, + 0.039179275192267715, + 0.039104040990294076, + 0.03902894538879209, + 0.03895398815494511, + 0.03887916905624067, + 0.03880448786047075, + 0.03872994433573207, + 0.038655538250425336, + 0.038581269373255345, + 0.038507137473231756, + 0.03843314231966788, + 0.038359283682181, + 0.03828556133069283, + 0.03821197503542895, + 0.038138524566918575, + 0.03806520969599503, + 0.037992030193795046, + 0.03791898583175923, + 0.03784607638163218, + 0.03777330161546122, + 0.03770066130559757, + 0.03762815522469598, + 0.03755578314571417, + 0.0374835448419133, + 0.037411440086857484, + 0.03733946865441373, + 0.0372676303187528, + 0.03719592485434697, + 0.03712435203597253, + 0.03705291163870783, + 0.03698160343793422, + 0.036910427209334795, + 0.036839382728896086, + 0.03676846977290604, + 0.036697688117955374, + 0.03662703754093677, + 0.03655651781904479, + 0.03648612872977586, + 0.03641587005092861, + 0.036345741560602995, + 0.036275743037200825, + 0.03620587425942501, + 0.03613613500628032, + 0.03606652505707231, + 0.035997044191408145, + 0.035927692189196114, + 0.035858468830644585, + 0.03578937389626369, + 0.035720407166863866, + 0.03565156842355599, + 0.0355828574477518, + 0.03551427402116303, + 0.03544581792580159, + 0.03537748894397996, + 0.03530928685831014, + 0.03524121145170378, + 0.03517326250737307, + 0.03510543980882885, + 0.03503774313988206, + 0.03497017228464272, + 0.034902727027520064, + 0.03483540715322242, + 0.034768212446757096, + 0.03470114269342983, + 0.03463419767884557, + 0.034567377188907455, + 0.03450068100981685, + 0.03443410892807375, + 0.03436766073047581, + 0.034301336204119126, + 0.03423513513639709, + 0.03416905731500108, + 0.034103102527919815, + 0.034037270563439526, + 0.03397156121014344, + 0.03390597425691233, + 0.033840509492923165, + 0.033775166707649906, + 0.033709945690863496, + 0.03364484623263109, + 0.03357986812331615, + 0.03351501115357791, + 0.03345027511437226, + 0.033385659796950296, + 0.03332116499285931, + 0.03325679049394148, + 0.03319253609233497, + 0.033128401580472366, + 0.03306438675108214, + 0.03300049139718675, + 0.03293671531210407, + 0.03287305828944587, + 0.03280952012311866, + 0.03274610060732275, + 0.03268279953655284, + 0.03261961670559721, + 0.032556551909537546, + 0.032493604943749865, + 0.032430775603902506, + 0.03236806368595746, + 0.032305468986169594, + 0.03224299130108632, + 0.03218063042754793, + 0.03211838616268703, + 0.03205625830392855, + 0.03199424664898898, + 0.03193235099587699, + 0.03187057114289338, + 0.03180890688862981, + 0.0317473580319696, + 0.03168592437208673, + 0.03162460570844672, + 0.031563401840805416, + 0.0315023125692091, + 0.031441337693995264, + 0.0313804770157905, + 0.03131973033551183, + 0.03125909745436628, + 0.03119857817385041, + 0.031138172295749555, + 0.03107787962213915, + 0.03101769995538295, + 0.030957633098133597, + 0.030897678853333166, + 0.030837837024210835, + 0.030778107414284772, + 0.030718489827361473, + 0.03065898406753409, + 0.030599589939184546, + 0.030540307246981535, + 0.030481135795880965, + 0.030422075391126402, + 0.03036312583824674, + 0.03030428694305909, + 0.030245558511665882, + 0.030186940350456215, + 0.030128432266104732, + 0.03007003406557207, + 0.03001174555610442, + 0.029953566545233068, + 0.029895496840774638, + 0.02983753625083052, + 0.02977968458378688, + 0.0297219416483141, + 0.029664307253367217, + 0.02960678120818483, + 0.029549363322289857, + 0.029492053405488328, + 0.02943485126786982, + 0.029377756719807135, + 0.02932076957195573, + 0.029263889635254614, + 0.02920711672092413, + 0.02915045064046784, + 0.02909389120567063, + 0.029037438228599943, + 0.028981091521603886, + 0.028924850897313004, + 0.028868716168638286, + 0.02881268714877161, + 0.028756763651185957, + 0.028700945489634755, + 0.028645232478151206, + 0.028589624431048732, + 0.028534121162920978, + 0.028478722488640695, + 0.028423428223359748, + 0.028368238182509664, + 0.028313152181800527, + 0.028258170037220864, + 0.02820329156503798, + 0.028148516581797067, + 0.02809384490432132, + 0.028039276349711484, + 0.027984810735345977, + 0.027930447878880216, + 0.02787618759824695, + 0.02782202971165515, + 0.02776797403759068, + 0.02771402039481563, + 0.02766016860236753, + 0.02760641847956058, + 0.027552769845984093, + 0.02749922252150261, + 0.027445776326255777, + 0.02739243108065792, + 0.027339186605398247, + 0.027286042721440196, + 0.027232999250021206, + 0.027180056012652165, + 0.027127212831118408, + 0.027074469527478162, + 0.027021825924062437, + 0.026969281843475357, + 0.02691683710859416, + 0.026864491542567537, + 0.026812244968816734, + 0.026760097211035117, + 0.026708048093187053, + 0.026656097439508697, + 0.026604245074507316, + 0.02655249082296085, + 0.026500834509917803, + 0.02644927596069724, + 0.026397815000888225, + 0.026346451456349507, + 0.026295185153210054, + 0.02624401591786707, + 0.02619294357698798, + 0.026141967957508117, + 0.026091088886632252, + 0.02604030619183262, + 0.02598961970085034, + 0.025939029241693667, + 0.02588853464263874, + 0.025838135732228706, + 0.025787832339274286, + 0.025737624292852312, + 0.02568751142230663, + 0.025637493557247204, + 0.025587570527550008, + 0.02553774216335647, + 0.025488008295073694, + 0.025438368753374463, + 0.02538882336919568, + 0.025339371973739366, + 0.025290014398472005, + 0.025240750475124196, + 0.025191580035690442, + 0.02514250291242881, + 0.025093518937860826, + 0.025044627944771025, + 0.024995829766207067, + 0.024947124235478624, + 0.02489851118615849, + 0.024849990452080806, + 0.024801561867341504, + 0.024753225266299084, + 0.024704980483571504, + 0.02465682735403929, + 0.024608765712843206, + 0.024560795395384027, + 0.0245129162373231, + 0.024465128074581788, + 0.024417430743340574, + 0.02436982408003996, + 0.024322307921379127, + 0.024274882104316386, + 0.024227546466068395, + 0.02418030084411016, + 0.02413314507617459, + 0.02408607900025239, + 0.024039102454591954, + 0.023992215277698903, + 0.023945417308335326, + 0.02389870838552055, + 0.02385208834852992, + 0.02380555703689491, + 0.023759114290403116, + 0.023712759949097384, + 0.023666493853276016, + 0.023620315843492556, + 0.02357422576055479, + 0.02352822344552541, + 0.023482308739721125, + 0.023436481484712446, + 0.0233907415223239, + 0.02334508869463281, + 0.02329952284397041, + 0.02325404381292029, + 0.023208651444318162, + 0.023163345581252548, + 0.023118126067064204, + 0.023072992745345022, + 0.02302794545993858, + 0.02298298405493948, + 0.022938108374693456, + 0.02289331826379648, + 0.022848613567095333, + 0.022803994129686478, + 0.022759459796916404, + 0.02271501041438051, + 0.02267064582792422, + 0.02262636588364153, + 0.022582170427874693, + 0.022538059307214864, + 0.022494032368501005, + 0.02245008945882021, + 0.022406230425506934, + 0.022362455116142543, + 0.02231876337855565, + 0.022275155060821894, + 0.022231630011262604, + 0.022188188078445914, + 0.022144829111185316, + 0.022101552958539883, + 0.022058359469814603, + 0.022015248494558493, + 0.021972219882566035, + 0.021929273483876077, + 0.021886409148770825, + 0.02184362672777751, + 0.02180092607166617, + 0.021758307031450208, + 0.02171576945838627, + 0.02167331320397392, + 0.021630938119954757, + 0.02158864405831229, + 0.021546430871272837, + 0.021504298411303524, + 0.02146224653111306, + 0.021420275083651408, + 0.021378383922108668, + 0.021336572899916195, + 0.021294841870744596, + 0.021253190688505508, + 0.02121161920734893, + 0.021170127281665008, + 0.021128714766082912, + 0.02108738151547007, + 0.021046127384932833, + 0.021004952229815577, + 0.02096385590570049, + 0.020922838268407462, + 0.020881899173993523, + 0.020841038478753182, + 0.02080025603921709, + 0.020759551712152824, + 0.02071892535456399, + 0.020678376823690336, + 0.020637905977006543, + 0.020597512672223317, + 0.020557196767285957, + 0.020516958120374573, + 0.020476796589904422, + 0.020436712034523685, + 0.020396704313115355, + 0.02035677328479557, + 0.020316918808914286, + 0.020277140745053934, + 0.020237438953029874, + 0.020197813292890276, + 0.020158263624914796, + 0.020118789809615345, + 0.020079391707735317, + 0.02004006918024992, + 0.02000082208836451, + 0.01996165029351582, + 0.019922553657370612, + 0.019883532041826135, + 0.019844585309009233, + 0.019805713321276452, + 0.019766915941213825, + 0.019728193031635977, + 0.01968954445558646, + 0.019650970076337204, + 0.019612469757388284, + 0.01957404336246782, + 0.019535690755531077, + 0.019497411800761033, + 0.01945920636256726, + 0.019421074305586372, + 0.019383015494681022, + 0.01934502979494057, + 0.019307117071679314, + 0.019269277190437806, + 0.01923151001698209, + 0.01919381541730203, + 0.01915619325761342, + 0.01911864340435565, + 0.019081165724192606, + 0.019043760084011763, + 0.0190064263509242, + 0.01896916439226437, + 0.01893197407558933, + 0.01889485526867929, + 0.01885780783953661, + 0.018820831656385373, + 0.01878392658767203, + 0.0187470925020643, + 0.018710329268451065, + 0.018673636755942025, + 0.018637014833867926, + 0.018600463371779785, + 0.018563982239448218, + 0.01852757130686422, + 0.01849123044423795, + 0.018454959521998604, + 0.018418758410794878, + 0.01838262698149351, + 0.018346565105180068, + 0.01831057265315772, + 0.018274649496947903, + 0.018238795508288996, + 0.01820301055913698, + 0.018167294521664212, + 0.018131647268260664, + 0.018096068671531684, + 0.018060558604298893, + 0.01802511693959996, + 0.01798974355068783, + 0.017954438311030607, + 0.017919201094311332, + 0.017884031774427545, + 0.017848930225491166, + 0.017813896321828504, + 0.017778929937978916, + 0.01774403094869581, + 0.017709199228945427, + 0.01767443465390739, + 0.01763973709897282, + 0.017605106439746554, + 0.017570542552044488, + 0.017536045311894566, + 0.01750161459553623, + 0.017467250279420088, + 0.017432952240207356, + 0.01739872035476997, + 0.0173645545001907, + 0.017330454553761587, + 0.017296420392984957, + 0.017262451895571962, + 0.01722854893944359, + 0.017194711402729324, + 0.017160939163767375, + 0.017127232101104117, + 0.017093590093494204, + 0.01706001301989979, + 0.017026500759490526, + 0.01699305319164346, + 0.016959670195942134, + 0.01692635165217682, + 0.016893097440344396, + 0.016859907440647692, + 0.016826781533494484, + 0.016793719599499157, + 0.016760721519480715, + 0.016727787174463216, + 0.016694916445674668, + 0.01666210921454847, + 0.016629365362721304, + 0.01659668477203402, + 0.016564067324530307, + 0.016531512902458023, + 0.016499021388266977, + 0.016466592664610258, + 0.016434226614342906, + 0.016401923120522244, + 0.0163696820664071, + 0.016337503335458137, + 0.016305386811337086, + 0.01627333237790629, + 0.01624133991922927, + 0.0162094093195696, + 0.016177540463390705, + 0.016145733235356508, + 0.016113987520329554, + 0.016082303203372228, + 0.016050680169745535, + 0.016019118304909763, + 0.01598761749452271, + 0.015956177624440793, + 0.01592479858071827, + 0.015893480249606795, + 0.01586222251755509, + 0.015831025271209276, + 0.015799888397411976, + 0.015768811783202108, + 0.015737795315814873, + 0.01570683888268154, + 0.015675942371428442, + 0.015645105669877424, + 0.015614328666045618, + 0.015583611248144669, + 0.015552953304580841, + 0.015522354723954135, + 0.015491815395058839, + 0.015461335206882976, + 0.015430914048607525, + 0.015400551809606866, + 0.015370248379447893, + 0.015340003647890343, + 0.015309817504885692, + 0.015279689840577926, + 0.015249620545302323, + 0.015219609509585563, + 0.015189656624145953, + 0.015159761779891867, + 0.01512992486792275, + 0.015100145779528229, + 0.015070424406187888, + 0.015040760639571382, + 0.015011154371536994, + 0.014981605494132966, + 0.014952113899596386, + 0.01492267948035253, + 0.014893302129015518, + 0.01486398173838721, + 0.014834718201457098, + 0.014805511411403072, + 0.014776361261589432, + 0.014747267645567441, + 0.014718230457075876, + 0.014689249590038922, + 0.014660324938567837, + 0.014631456396958842, + 0.014602643859694786, + 0.014573887221442816, + 0.014545186377056152, + 0.014516541221571866, + 0.014487951650212216, + 0.014459417558383425, + 0.014430938841675789, + 0.014402515395863236, + 0.014374147116903102, + 0.01434583390093569, + 0.014317575644284375, + 0.014289372243455611, + 0.014261223595137151, + 0.014233129596199934, + 0.014205090143695753, + 0.014177105134858592, + 0.014149174467103065, + 0.01412129803802531, + 0.014093475745402206, + 0.014065707487190604, + 0.014037993161527651, + 0.014010332666730796, + 0.013982725901296678, + 0.01395517276390168, + 0.0139276731534006, + 0.013900226968827978, + 0.013872834109396215, + 0.013845494474496456, + 0.013818207963697371, + 0.013790974476745932, + 0.013763793913566191, + 0.013736666174259393, + 0.013709591159104195, + 0.013682568768555337, + 0.013655598903244304, + 0.013628681463978776, + 0.01360181635174229, + 0.013575003467693691, + 0.013548242713167569, + 0.013521533989673484, + 0.013494877198895527, + 0.013468272242692758, + 0.01344171902309843, + 0.013415217442319327, + 0.013388767402736756, + 0.013362368806905112, + 0.01333602155755187, + 0.013309725557577812, + 0.01328348071005614, + 0.01325728691823258, + 0.01323114408552506, + 0.013205052115523475, + 0.013179010911989253, + 0.01315302037885524, + 0.013127080420225368, + 0.01310119094037443, + 0.013075351843747973, + 0.013049563034961631, + 0.013023824418801455, + 0.012998135900222585, + 0.01297249738435069, + 0.01294690877647997, + 0.012921369982074049, + 0.012895880906765078, + 0.012870441456354076, + 0.012845051536809593, + 0.012819711054268712, + 0.012794419915036603, + 0.01276917802558486, + 0.012743985292553384, + 0.012718841622748167, + 0.01269374692314218, + 0.012668701100875479, + 0.01264370406325277, + 0.0126187557177464, + 0.01259385597199314, + 0.012569004733795852, + 0.012544201911122044, + 0.012519447412104423, + 0.012494741145040456, + 0.012470083018391476, + 0.012445472940783353, + 0.012420910821005493, + 0.012396396568011614, + 0.01237193009091786, + 0.0123475112990038, + 0.012323140101712204, + 0.012298816408648272, + 0.012274540129578959, + 0.012250311174434092, + 0.012226129453304924, + 0.012201994876444244, + 0.012177907354265938, + 0.01215386679734587, + 0.012129873116419776, + 0.012105926222383934, + 0.012082026026295711, + 0.012058172439372017, + 0.01203436537298952, + 0.012010604738684205, + 0.011986890448152154, + 0.011963222413247432, + 0.011939600545984086, + 0.011916024758533705, + 0.01189249496322653, + 0.011869011072551117, + 0.011845572999153009, + 0.0118221806558364, + 0.011798833955561805, + 0.011775532811447276, + 0.01175227713676763, + 0.011729066844954117, + 0.011705901849594302, + 0.01168278206443163, + 0.01165970740336586, + 0.011636677780451632, + 0.011613693109899348, + 0.011590753306074397, + 0.011567858283496824, + 0.011545007956841213, + 0.011522202240936696, + 0.011499441050766612, + 0.01147672430146729, + 0.01145405190832971, + 0.01143142378679729, + 0.011408839852467323, + 0.011386300021089202, + 0.011363804208565198, + 0.01134135233095046, + 0.01131894430445135, + 0.011296580045426441, + 0.011274259470386516, + 0.011251982495992796, + 0.011229749039058157, + 0.011207559016546464, + 0.011185412345571799, + 0.011163308943399008, + 0.011141248727443154, + 0.01111923161526851, + 0.011097257524590232, + 0.011075326373271799, + 0.01105343807932646, + 0.011031592560916348, + 0.011009789736352471, + 0.010988029524093834, + 0.010966311842747878, + 0.010944636611070369, + 0.01092300374796451, + 0.010901413172481389, + 0.010879864803818418, + 0.010858358561321113, + 0.010836894364481542, + 0.010815472132938209, + 0.01079409178647539, + 0.01077275324502458, + 0.010751456428662265, + 0.010730201257610705, + 0.010708987652237933, + 0.010687815533056866, + 0.01066668482072497, + 0.01064559543604493, + 0.01062454729996365, + 0.010603540333572248, + 0.010582574458105731, + 0.010561649594942879, + 0.010540765665606022, + 0.0105199225917606, + 0.01049912029521538, + 0.010478358697921575, + 0.010457637721973279, + 0.010436957289606585, + 0.010416317323199809, + 0.01039571774527337, + 0.010375158478489022, + 0.01035463944564985, + 0.010334160569700157, + 0.010313721773725693, + 0.010293322980952202, + 0.010272964114746097, + 0.010252645098614122, + 0.01023236585620324, + 0.010212126311299752, + 0.010191926387829842, + 0.010171766009859029, + 0.010151645101591278, + 0.01013156358737033, + 0.010111521391678147, + 0.010091518439134917, + 0.01007155465449927, + 0.01005162996266773, + 0.010031744288674371, + 0.010011897557691052, + 0.009992089695026518, + 0.009972320626126963, + 0.009952590276575024, + 0.00993289857209012, + 0.009913245438528118, + 0.00989363080188077, + 0.009874054588275727, + 0.009854516723976747, + 0.009835017135382484, + 0.009815555749027371, + 0.009796132491580067, + 0.009776747289845011, + 0.009757400070760092, + 0.009738090761398754, + 0.009718819288967784, + 0.009699585580807857, + 0.009680389564393321, + 0.009661231167332418, + 0.009642110317365948, + 0.009623026942368273, + 0.009603980970346537, + 0.009584972329439778, + 0.009566000947919706, + 0.009547066754190814, + 0.0095281696767886, + 0.009509309644380348, + 0.009490486585765456, + 0.009471700429873664, + 0.009452951105766272, + 0.009434238542635365, + 0.009415562669803479, + 0.009396923416723268, + 0.009378320712978061, + 0.009359754488280636, + 0.009341224672473558, + 0.009322731195529177, + 0.009304273987549072, + 0.009285852978763054, + 0.00926746809953094, + 0.009249119280340445, + 0.00923080645180796, + 0.009212529544677883, + 0.009194288489822733, + 0.009176083218242481, + 0.009157913661065331, + 0.00913977974954594, + 0.00912168141506664, + 0.00910361858913622, + 0.009085591203390808, + 0.009067599189592657, + 0.009049642479629805, + 0.009031721005516968, + 0.00901383469939443, + 0.008995983493528259, + 0.00897816732030976, + 0.008960386112255359, + 0.008942639802006602, + 0.008924928322329828, + 0.008907251606115829, + 0.008889609586379854, + 0.008872002196261053, + 0.008854429369022698, + 0.008836891038051853, + 0.008819387136858703, + 0.008801917599077336, + 0.008784482358464407, + 0.008767081348899475, + 0.008749714504384887, + 0.008732381759045893, + 0.008715083047129202, + 0.008697818303003979, + 0.008680587461161293, + 0.008663390456213449, + 0.008646227222894654, + 0.00862909769606024, + 0.00861200181068611, + 0.008594939501869403, + 0.00857791070482794, + 0.008560915354899334, + 0.008543953387541992, + 0.008527024738334221, + 0.00851012934297346, + 0.008493267137277827, + 0.008476438057183788, + 0.008459642038747273, + 0.008442879018143556, + 0.008426148931666266, + 0.008409451715727712, + 0.008392787306858662, + 0.008376155641707683, + 0.008359556657041245, + 0.00834299028974439, + 0.008326456476818511, + 0.008309955155383353, + 0.008293486262675454, + 0.008277049736047926, + 0.00826064551297101, + 0.008244273531031299, + 0.008227933727931847, + 0.008211626041491948, + 0.008195350409646474, + 0.008179106770446198, + 0.008162895062057474, + 0.008146715222762113, + 0.008130567190956617, + 0.00811445090515317, + 0.0080983663039782, + 0.008082313326172264, + 0.008066291910591161, + 0.008050301996204157, + 0.008034343522095089, + 0.008018416427460817, + 0.008002520651612333, + 0.007986656133973757, + 0.007970822814082346, + 0.007955020631588483, + 0.007939249526255132, + 0.007923509437958276, + 0.007907800306685364, + 0.007892122072537311, + 0.00787647467572572, + 0.007860858056575104, + 0.007845272155520666, + 0.007829716913109852, + 0.007814192270000797, + 0.007798698166962992, + 0.007783234544876061, + 0.007767801344731318, + 0.007752398507629987, + 0.00773702597478354, + 0.007721683687513359, + 0.007706371587251071, + 0.007691089615537994, + 0.007675837714024691, + 0.007660615824470862, + 0.007645423888746117, + 0.007630261848827868, + 0.007615129646803442, + 0.007600027224867745, + 0.007584954525324705, + 0.0075699114905858345, + 0.007554898063171556, + 0.007539914185708985, + 0.007524959800933373, + 0.007510034851687664, + 0.007495139280921492, + 0.007480273031691964, + 0.007465436047162766, + 0.007450628270604276, + 0.007435849645393455, + 0.007421100115013957, + 0.007406379623054571, + 0.0073916881132110035, + 0.007377025529283987, + 0.00736239181518028, + 0.007347786914911891, + 0.007333210772596077, + 0.007318663332454678, + 0.007304144538814783, + 0.007289654336108176, + 0.007275192668871, + 0.007260759481743206, + 0.00724635471946955, + 0.0072319783268981475, + 0.007217630248981477, + 0.007203310430774934, + 0.007189018817437165, + 0.007174755354230844, + 0.007160519986520897, + 0.007146312659775167, + 0.0071321333195645265, + 0.007117981911561766, + 0.007103858381542372, + 0.007089762675383637, + 0.00707569473906533, + 0.007061654518667804, + 0.007047641960374329, + 0.0070336570104686524, + 0.007019699615336106, + 0.007005769721462718, + 0.00699186727543577, + 0.006977992223943019, + 0.0069641445137726965, + 0.006950324091813065, + 0.006936530905053306, + 0.006922764900581857, + 0.006909026025586851, + 0.006895314227356786, + 0.006881629453278637, + 0.006867971650839633, + 0.0068543407676252555, + 0.006840736751320242, + 0.006827159549707917, + 0.0068136091106704155, + 0.006800085382188237, + 0.006786588312339581, + 0.006773117849301125, + 0.006759673941347577, + 0.006746256536850903, + 0.006732865584280767, + 0.006719501032204089, + 0.006706162829284934, + 0.006692850924284732 + ], + "yaxis": "y" + } + ], + "layout": { + "legend": { + "tracegroupgap": 0 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "y3" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "x" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "y" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from bofire.data_models.objectives.api import MinimizeSigmoidObjective\n", "from bofire.plot.api import plot_objective_plotly\n", @@ -416,7 +11548,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -439,7 +11571,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -460,18 +11592,173 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TypeDescription
x1ContinuousInput[0.0,1.0]
x2ContinuousInput[0.0,1.0]
x3ContinuousInput[0.0,1.0]
x4DiscreteInputtype='DiscreteInput' key='x4' unit=None values...
x6CategoricalDescriptorInput3 categories
x5CategoricalInput3 categories
y1ContinuousOutputContinuousOutputFeature
y2ContinuousOutputContinuousOutputFeature
y3ContinuousOutputContinuousOutputFeature
\n", + "
" + ], + "text/plain": [ + " Type \n", + "x1 ContinuousInput \\\n", + "x2 ContinuousInput \n", + "x3 ContinuousInput \n", + "x4 DiscreteInput \n", + "x6 CategoricalDescriptorInput \n", + "x5 CategoricalInput \n", + "y1 ContinuousOutput \n", + "y2 ContinuousOutput \n", + "y3 ContinuousOutput \n", + "\n", + " Description \n", + "x1 [0.0,1.0] \n", + "x2 [0.0,1.0] \n", + "x3 [0.0,1.0] \n", + "x4 type='DiscreteInput' key='x4' unit=None values... \n", + "x6 3 categories \n", + "x5 3 categories \n", + "y1 ContinuousOutputFeature \n", + "y2 ContinuousOutputFeature \n", + "y3 ContinuousOutputFeature " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "domain.get_feature_reps_df()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TypeDescription
0LinearEqualityConstraint1.0 * x1 + 1.0 * x2 + 1.0 * x3 = 1.0
1LinearInequalityConstraint1.0 * x1 + 2.0 * x3 <= 0.8
\n", + "
" + ], + "text/plain": [ + " Type Description\n", + "0 LinearEqualityConstraint 1.0 * x1 + 1.0 * x2 + 1.0 * x3 = 1.0\n", + "1 LinearInequalityConstraint 1.0 * x1 + 2.0 * x3 <= 0.8" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "domain.get_constraint_reps_df()" ] @@ -498,9 +11785,82 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aaron/Desktop/bofire/bofire/surrogates/xgb.py:12: UserWarning:\n", + "\n", + "xgboost not installed, BoFire's `XGBoostSurrogate` cannot be used.\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
x1x2x3x4x6x5
00.4575740.4629420.0794841.0c2B
10.0425750.9069090.0505167.5c2A
\n", + "
" + ], + "text/plain": [ + " x1 x2 x3 x4 x6 x5\n", + "0 0.457574 0.462942 0.079484 1.0 c2 B\n", + "1 0.042575 0.906909 0.050516 7.5 c2 A" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from bofire.data_models.strategies.api import RandomStrategy\n", "\n", @@ -528,9 +11888,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TypeDescription
x_1ContinuousInput[-6.0,6.0]
x_2ContinuousInput[-6.0,6.0]
yContinuousOutputContinuousOutputFeature
\n", + "
" + ], + "text/plain": [ + " Type Description\n", + "x_1 ContinuousInput [-6.0,6.0]\n", + "x_2 ContinuousInput [-6.0,6.0]\n", + "y ContinuousOutput ContinuousOutputFeature" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from bofire.benchmarks.single import Himmelblau\n", "\n", @@ -549,9 +11966,130 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
x_1x_2yvalid_y
0-3.6687004.13823085.2113201
14.041522-2.69675625.5652521
20.444125-0.806100169.6472121
33.165932-2.16995310.6674771
4-1.8582094.432227126.0492851
51.058336-5.689454940.8562861
6-3.5186143.83274544.5793381
7-0.076872-3.937059293.8956921
8-5.209119-1.009629353.9854151
95.1860023.184612433.3684161
\n", + "
" + ], + "text/plain": [ + " x_1 x_2 y valid_y\n", + "0 -3.668700 4.138230 85.211320 1\n", + "1 4.041522 -2.696756 25.565252 1\n", + "2 0.444125 -0.806100 169.647212 1\n", + "3 3.165932 -2.169953 10.667477 1\n", + "4 -1.858209 4.432227 126.049285 1\n", + "5 1.058336 -5.689454 940.856286 1\n", + "6 -3.518614 3.832745 44.579338 1\n", + "7 -0.076872 -3.937059 293.895692 1\n", + "8 -5.209119 -1.009629 353.985415 1\n", + "9 5.186002 3.184612 433.368416 1" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "samples = benchmark.domain.inputs.sample(10)\n", "\n", @@ -570,9 +12108,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
x_1x_2y_predy_sdy_des
0-6.02.459544115.313408198.34636-115.313408
\n", + "
" + ], + "text/plain": [ + " x_1 x_2 y_pred y_sd y_des\n", + "0 -6.0 2.459544 115.313408 198.34636 -115.313408" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from bofire.data_models.strategies.api import SoboStrategy\n", "from bofire.data_models.acquisition_functions.api import qNEI\n", @@ -600,9 +12189,153 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit https://github.com/coin-or/Ipopt\n", + "******************************************************************************\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
x1x2x3
exp05.000000e-01-9.992500e-095.000000e-01
exp15.000000e-01-9.992500e-095.000000e-01
exp25.000000e-015.000000e-01-9.992500e-09
exp35.000000e-015.000000e-01-9.992500e-09
exp4-4.998329e-09-4.998335e-091.000000e+00
exp55.000000e-01-9.992500e-095.000000e-01
exp61.000000e+00-4.998956e-09-4.999377e-09
exp7-9.992500e-095.000000e-015.000000e-01
exp8-4.998333e-09-4.998332e-091.000000e+00
exp95.000000e-015.000000e-01-9.992500e-09
exp10-9.992500e-095.000000e-015.000000e-01
exp11-4.998976e-091.000000e+00-4.999357e-09
exp12-9.992500e-095.000000e-015.000000e-01
\n", + "
" + ], + "text/plain": [ + " x1 x2 x3\n", + "exp0 5.000000e-01 -9.992500e-09 5.000000e-01\n", + "exp1 5.000000e-01 -9.992500e-09 5.000000e-01\n", + "exp2 5.000000e-01 5.000000e-01 -9.992500e-09\n", + "exp3 5.000000e-01 5.000000e-01 -9.992500e-09\n", + "exp4 -4.998329e-09 -4.998335e-09 1.000000e+00\n", + "exp5 5.000000e-01 -9.992500e-09 5.000000e-01\n", + "exp6 1.000000e+00 -4.998956e-09 -4.999377e-09\n", + "exp7 -9.992500e-09 5.000000e-01 5.000000e-01\n", + "exp8 -4.998333e-09 -4.998332e-09 1.000000e+00\n", + "exp9 5.000000e-01 5.000000e-01 -9.992500e-09\n", + "exp10 -9.992500e-09 5.000000e-01 5.000000e-01\n", + "exp11 -4.998976e-09 1.000000e+00 -4.999357e-09\n", + "exp12 -9.992500e-09 5.000000e-01 5.000000e-01" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from bofire.strategies.doe.design import find_local_max_ipopt\n", "import numpy as np\n", @@ -627,9 +12360,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -679,7 +12433,8 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" - } + }, + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 diff --git a/tutorials/models_serial.ipynb b/tutorials/models_serial.ipynb index 405258baf..5d89e7827 100644 --- a/tutorials/models_serial.ipynb +++ b/tutorials/models_serial.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -47,9 +47,130 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
x_1x_2yvalid_y
05.010799-0.612165184.7468781
1-1.779981-0.137665140.2658921
2-5.063193-3.811183123.2361291
35.1148255.4192701178.8978321
4-2.921467-2.80800531.9525441
50.9060904.477183227.1483551
63.319714-2.2119236.2720531
73.629923-0.7481499.9377921
8-1.6122154.451890141.1929941
90.2425124.767127293.0965811
\n", + "
" + ], + "text/plain": [ + " x_1 x_2 y valid_y\n", + "0 5.010799 -0.612165 184.746878 1\n", + "1 -1.779981 -0.137665 140.265892 1\n", + "2 -5.063193 -3.811183 123.236129 1\n", + "3 5.114825 5.419270 1178.897832 1\n", + "4 -2.921467 -2.808005 31.952544 1\n", + "5 0.906090 4.477183 227.148355 1\n", + "6 3.319714 -2.211923 6.272053 1\n", + "7 3.629923 -0.748149 9.937792 1\n", + "8 -1.612215 4.451890 141.192994 1\n", + "9 0.242512 4.767127 293.096581 1" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "benchmark = Himmelblau()\n", "samples = benchmark.domain.inputs.sample(n=50)\n", @@ -68,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -78,18 +199,40 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}]}'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "input_features.json()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"y\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}]}'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "output_features.json()" ] @@ -106,9 +249,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"type\": \"SingleTaskGPSurrogate\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"y\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}]}, \"input_preprocessing_specs\": {}, \"dump\": null, \"kernel\": {\"type\": \"ScaleKernel\", \"base_kernel\": {\"type\": \"MaternKernel\", \"ard\": true, \"nu\": 2.5, \"lengthscale_prior\": {\"type\": \"GammaPrior\", \"concentration\": 3.0, \"rate\": 6.0}}, \"outputscale_prior\": {\"type\": \"GammaPrior\", \"concentration\": 2.0, \"rate\": 0.15}}, \"noise_prior\": {\"type\": \"GammaPrior\", \"concentration\": 1.1, \"rate\": 0.05}, \"scaler\": \"NORMALIZE\"}'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# we setup the data model, here a Single Task GP\n", "surrogate_data = SingleTaskGPSurrogate(\n", @@ -132,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -149,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -166,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -201,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -221,9 +375,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "surrogate_data = parse_obj_as(AnySurrogate, json.loads(jspec))\n", "surrogate = surrogates.map(surrogate_data)\n", @@ -250,9 +415,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"type\": \"RandomForestSurrogate\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"y\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}]}, \"input_preprocessing_specs\": {}, \"dump\": null, \"n_estimators\": 100, \"criterion\": \"squared_error\", \"max_depth\": null, \"min_samples_split\": 2, \"min_samples_leaf\": 1, \"min_weight_fraction_leaf\": 0.0, \"max_features\": 1.0, \"max_leaf_nodes\": null, \"min_impurity_decrease\": 0.0, \"bootstrap\": true, \"oob_score\": false, \"random_state\": 42, \"ccp_alpha\": 0.0, \"max_samples\": null}'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# we setup the data model, here a Single Task GP\n", "surrogate_data = RandomForestSurrogate(\n", @@ -269,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -289,9 +465,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "surrogate_data = parse_obj_as(AnySurrogate, json.loads(jspec))\n", "surrogate = surrogates.map(surrogate_data)\n", @@ -318,9 +505,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"type\": \"MLPEnsemble\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"y\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}]}, \"input_preprocessing_specs\": {}, \"dump\": null, \"n_estimators\": 2, \"hidden_layer_sizes\": [100], \"activation\": \"relu\", \"dropout\": 0.0, \"batch_size\": 10, \"n_epochs\": 200, \"lr\": 0.0001, \"weight_decay\": 0.0, \"subsample_fraction\": 1.0, \"shuffle\": true, \"scaler\": \"NORMALIZE\"}'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# we setup the data model, here a Single Task GP\n", "surrogate_data = MLPEnsemble(\n", @@ -357,9 +555,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "surrogate_data = parse_obj_as(AnySurrogate, json.loads(jspec))\n", "surrogate = surrogates.map(surrogate_data)\n", @@ -386,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -407,9 +616,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"type\": \"EmpiricalSurrogate\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"y\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}]}, \"input_preprocessing_specs\": {}, \"dump\": null}'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# we setup the data model, here a Single Task GP\n", "surrogate_data = EmpiricalSurrogate(\n", @@ -425,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -445,9 +665,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "surrogate_data = parse_obj_as(AnySurrogate, json.loads(jspec))\n", "surrogate = surrogates.map(surrogate_data)\n", @@ -474,9 +705,197 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
base_eqt_restemperaturebasecatalystyieldcostvalid_costvalid_yield
02.042351647.31644552.297576TMGAlPhos0.0932650.41908511
11.044297690.01172086.559150DBUAlPhos0.9529350.42015111
21.258711144.33256592.814988TEAtBuXPhos0.0412490.24869711
32.4959151116.11523885.396238BTMGAlPhos0.9302430.52803311
41.2195491764.31952872.869934TEAtBuXPhos0.1354030.24868311
51.0108811544.72325937.309690TEAtBuBrettPhos0.1186550.27863811
62.1973481678.50846168.742290BTMGtBuXPhos0.9542170.34421911
71.0810801330.51754930.354525DBUtBuXPhos0.2147380.24942011
82.326009994.82676989.459671TEAtBuXPhos0.0999640.24908611
91.8996611712.02746358.522522TEAAlPhos0.1369840.41970311
\n", + "
" + ], + "text/plain": [ + " base_eq t_res temperature base catalyst yield cost \\\n", + "0 2.042351 647.316445 52.297576 TMG AlPhos 0.093265 0.419085 \n", + "1 1.044297 690.011720 86.559150 DBU AlPhos 0.952935 0.420151 \n", + "2 1.258711 144.332565 92.814988 TEA tBuXPhos 0.041249 0.248697 \n", + "3 2.495915 1116.115238 85.396238 BTMG AlPhos 0.930243 0.528033 \n", + "4 1.219549 1764.319528 72.869934 TEA tBuXPhos 0.135403 0.248683 \n", + "5 1.010881 1544.723259 37.309690 TEA tBuBrettPhos 0.118655 0.278638 \n", + "6 2.197348 1678.508461 68.742290 BTMG tBuXPhos 0.954217 0.344219 \n", + "7 1.081080 1330.517549 30.354525 DBU tBuXPhos 0.214738 0.249420 \n", + "8 2.326009 994.826769 89.459671 TEA tBuXPhos 0.099964 0.249086 \n", + "9 1.899661 1712.027463 58.522522 TEA AlPhos 0.136984 0.419703 \n", + "\n", + " valid_cost valid_yield \n", + "0 1 1 \n", + "1 1 1 \n", + "2 1 1 \n", + "3 1 1 \n", + "4 1 1 \n", + "5 1 1 \n", + "6 1 1 \n", + "7 1 1 \n", + "8 1 1 \n", + "9 1 1 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "benchmark = CrossCoupling()\n", "samples = benchmark.domain.inputs.sample(n=50)\n", @@ -487,9 +906,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"type\": \"MixedSingleTaskGPSurrogate\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"CategoricalDescriptorInput\", \"key\": \"catalyst\", \"categories\": [\"tBuXPhos\", \"tBuBrettPhos\", \"AlPhos\"], \"allowed\": [true, true, true], \"descriptors\": [\"area_cat\", \"M2_cat\"], \"values\": [[460.7543, 67.2057], [518.8408, 89.8738], [819.933, 129.0808]]}, {\"type\": \"CategoricalDescriptorInput\", \"key\": \"base\", \"categories\": [\"TEA\", \"TMG\", \"BTMG\", \"DBU\"], \"allowed\": [true, true, true, true], \"descriptors\": [\"area\", \"M2\"], \"values\": [[162.2992, 25.8165], [165.5447, 81.4847], [227.3523, 30.554], [192.4693, 59.8367]]}, {\"type\": \"ContinuousInput\", \"key\": \"base_eq\", \"unit\": null, \"bounds\": [1.0, 2.5], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"temperature\", \"unit\": null, \"bounds\": [30.0, 100.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"t_res\", \"unit\": null, \"bounds\": [60.0, 1800.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"yield\", \"unit\": null, \"objective\": {\"type\": \"MaximizeObjective\", \"w\": 1.0, \"bounds\": [0.0, 1.0]}}]}, \"input_preprocessing_specs\": {\"catalyst\": \"ONE_HOT\", \"base\": \"DESCRIPTOR\"}, \"dump\": null, \"continuous_kernel\": {\"type\": \"MaternKernel\", \"ard\": true, \"nu\": 2.5, \"lengthscale_prior\": null}, \"categorical_kernel\": {\"type\": \"HammondDistanceKernel\", \"ard\": true}, \"scaler\": \"NORMALIZE\"}'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# we setup the data model, here a Single Task GP\n", "surrogate_data = MixedSingleTaskGPSurrogate(\n", @@ -506,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -526,9 +956,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "surrogate_data = parse_obj_as(AnySurrogate, json.loads(jspec))\n", "surrogate = surrogates.map(surrogate_data)\n", @@ -569,6 +1010,7 @@ "pygments_lexer": "ipython3", "version": "3.9.16" }, + "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "b9bdc9e617e457afdaedd2563eddde9c04c87768cb2f63795a1786b83528ca68" diff --git a/tutorials/strategies_serial.ipynb b/tutorials/strategies_serial.ipynb index e3f2249ae..8f17c0f46 100644 --- a/tutorials/strategies_serial.ipynb +++ b/tutorials/strategies_serial.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -80,9 +80,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"type\": \"RandomStrategy\", \"domain\": {\"type\": \"Domain\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": []}, \"constraints\": {\"type\": \"Constraints\", \"constraints\": []}}, \"seed\": 814}'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# setup the data model\n", "domain = Domain(inputs=benchmark.domain.inputs)\n", @@ -96,9 +107,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Candidate(inputValues={'x_1': InputValue(value=2.391275372060866), 'x_2': InputValue(value=3.840747424614616)}, outputValues=None),\n", + " Candidate(inputValues={'x_1': InputValue(value=1.8419027332213282), 'x_2': InputValue(value=4.827080285159006)}, outputValues=None),\n", + " Candidate(inputValues={'x_1': InputValue(value=-5.558958182612878), 'x_2': InputValue(value=2.4506064409669825)}, outputValues=None),\n", + " Candidate(inputValues={'x_1': InputValue(value=-1.7856176259776593), 'x_2': InputValue(value=-3.1395668087949895)}, outputValues=None),\n", + " Candidate(inputValues={'x_1': InputValue(value=-3.19041491386414), 'x_2': InputValue(value=-5.4106947354567225)}, outputValues=None)]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# load it\n", "strategy_data = parse_obj_as(AnyStrategy, json.loads(jspec))\n", @@ -133,9 +159,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"type\": \"SoboStrategy\", \"domain\": {\"type\": \"Domain\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"y\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}]}, \"constraints\": {\"type\": \"Constraints\", \"constraints\": []}}, \"seed\": 564, \"num_sobol_samples\": 512, \"num_restarts\": 8, \"num_raw_samples\": 1024, \"descriptor_method\": \"EXHAUSTIVE\", \"categorical_method\": \"EXHAUSTIVE\", \"discrete_method\": \"EXHAUSTIVE\", \"surrogate_specs\": {\"surrogates\": [{\"type\": \"SingleTaskGPSurrogate\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [-6.0, 6.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"y\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}]}, \"input_preprocessing_specs\": {}, \"dump\": null, \"kernel\": {\"type\": \"ScaleKernel\", \"base_kernel\": {\"type\": \"MaternKernel\", \"ard\": true, \"nu\": 2.5, \"lengthscale_prior\": {\"type\": \"GammaPrior\", \"concentration\": 3.0, \"rate\": 6.0}}, \"outputscale_prior\": {\"type\": \"GammaPrior\", \"concentration\": 2.0, \"rate\": 0.15}}, \"noise_prior\": {\"type\": \"GammaPrior\", \"concentration\": 1.1, \"rate\": 0.05}, \"scaler\": \"NORMALIZE\"}]}, \"acquisition_function\": {\"type\": \"qNEI\"}}'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# setup the data model\n", "strategy_data = SoboStrategyDataModel(domain=benchmark.domain, acquisition_function=qNEI())\n", @@ -156,9 +193,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Candidate(inputValues={'x_1': InputValue(value=1.996188880409116), 'x_2': InputValue(value=6.0)}, outputValues={'y': OutputValue(predictedValue=1.0471829609699341, standardDeviation=120.62865978352136, objective=-1.0471829609699341)}),\n", + " Candidate(inputValues={'x_1': InputValue(value=4.870103412875196), 'x_2': InputValue(value=6.0)}, outputValues={'y': OutputValue(predictedValue=111.42409857106762, standardDeviation=233.52292562204042, objective=-111.42409857106762)})]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# load it\n", "strategy_data = parse_obj_as(AnyStrategy, json.loads(jspec))\n", @@ -192,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -232,9 +281,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"type\": \"QnehviStrategy\", \"domain\": {\"type\": \"Domain\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_0\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_3\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_4\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_5\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"f_0\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}, {\"type\": \"ContinuousOutput\", \"key\": \"f_1\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}]}, \"constraints\": {\"type\": \"Constraints\", \"constraints\": []}}, \"seed\": 471, \"num_sobol_samples\": 512, \"num_restarts\": 8, \"num_raw_samples\": 1024, \"descriptor_method\": \"EXHAUSTIVE\", \"categorical_method\": \"EXHAUSTIVE\", \"discrete_method\": \"EXHAUSTIVE\", \"surrogate_specs\": {\"surrogates\": [{\"type\": \"SingleTaskGPSurrogate\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_0\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_3\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_4\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_5\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"f_0\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}]}, \"input_preprocessing_specs\": {}, \"dump\": null, \"kernel\": {\"type\": \"ScaleKernel\", \"base_kernel\": {\"type\": \"RBFKernel\", \"ard\": false, \"lengthscale_prior\": null}, \"outputscale_prior\": null}, \"noise_prior\": {\"type\": \"GammaPrior\", \"concentration\": 1.1, \"rate\": 0.05}, \"scaler\": \"NORMALIZE\"}, {\"type\": \"SingleTaskGPSurrogate\", \"inputs\": {\"type\": \"Inputs\", \"features\": [{\"type\": \"ContinuousInput\", \"key\": \"x_0\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_1\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_2\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_3\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_4\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}, {\"type\": \"ContinuousInput\", \"key\": \"x_5\", \"unit\": null, \"bounds\": [0.0, 1.0], \"stepsize\": null}]}, \"outputs\": {\"type\": \"Outputs\", \"features\": [{\"type\": \"ContinuousOutput\", \"key\": \"f_1\", \"unit\": null, \"objective\": {\"type\": \"MinimizeObjective\", \"w\": 1.0, \"bounds\": [0, 1]}}]}, \"input_preprocessing_specs\": {}, \"dump\": null, \"kernel\": {\"type\": \"ScaleKernel\", \"base_kernel\": {\"type\": \"MaternKernel\", \"ard\": true, \"nu\": 2.5, \"lengthscale_prior\": {\"type\": \"GammaPrior\", \"concentration\": 3.0, \"rate\": 6.0}}, \"outputscale_prior\": {\"type\": \"GammaPrior\", \"concentration\": 2.0, \"rate\": 0.15}}, \"noise_prior\": {\"type\": \"GammaPrior\", \"concentration\": 1.1, \"rate\": 0.05}, \"scaler\": \"NORMALIZE\"}]}, \"ref_point\": null, \"alpha\": 0.0}'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# setup the data model\n", "strategy_data = QnehviStrategyDataModel(\n", @@ -266,9 +326,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Candidate(inputValues={'x_0': InputValue(value=1.0), 'x_1': InputValue(value=0.0), 'x_2': InputValue(value=0.0), 'x_3': InputValue(value=1.0), 'x_4': InputValue(value=0.0), 'x_5': InputValue(value=1.0)}, outputValues={'f_0': OutputValue(predictedValue=0.05837048644034282, standardDeviation=0.18559375905314565, objective=-0.05837048644034282), 'f_1': OutputValue(predictedValue=1.097383607430488, standardDeviation=0.3443568727244193, objective=-1.097383607430488)})]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# load it\n", "strategy_data = parse_obj_as(AnyStrategy, json.loads(jspec))\n", @@ -302,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -366,6 +437,7 @@ "pygments_lexer": "ipython3", "version": "3.9.16" }, + "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "b9bdc9e617e457afdaedd2563eddde9c04c87768cb2f63795a1786b83528ca68"