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Update docs #385

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9a392f6
Added get_parameters to ParticleSwarmOptimization
zStupan Mar 10, 2022
44250fa
Updated docs examples
zStupan Mar 10, 2022
e1e3d69
Update README
zStupan Mar 10, 2022
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63 changes: 31 additions & 32 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -110,33 +110,33 @@ $ python
Let's go through a basic and advanced example.

## Basic Example
Let’s say, we want to try out Gray Wolf Optimizer algorithm against the Pintér problem function. Firstly, we have to create new file, with name, for example *basic_example.py*. Then we have to import chosen algorithm from NiaPy, so we can use it. Afterwards we initialize GreyWolfOptimizer class instance and run the algorithm. Given bellow is the complete source code of basic example.
Let’s say, we want to try out PSO against the Pintér problem function. Firstly, we have to create new file, with name, for example *basic_example.py*. Then we have to import chosen algorithm from NiaPy, so we can use it. Afterwards we initialize ParticleSwarmAlgorithm class instance and run the algorithm. Given bellow is the complete source code of basic example.

```python
from niapy.algorithms.basic import GreyWolfOptimizer
from niapy.algorithms.basic import ParticleSwarmAlgorithm
from niapy.task import Task

# we will run 10 repetitions of Grey Wolf Optimizer against the Pinter problem
# we will run 10 repetitions of Weighed, velocity clamped PSO on the Pinter problem
for i in range(10):
task = Task(problem='pinter', dimension=10, max_evals=1000)
algorithm = GreyWolfOptimizer(population_size=20)
best = algorithm.run(task)
print(best[-1])
task = Task(problem='pinter', dimension=10, max_evals=10000)
algorithm = ParticleSwarmAlgorithm(population_size=100, w=0.9, c1=0.5, c2=0.3, min_velocity=-1, max_velocity=1)
best_x, best_fit = algorithm.run(task)
print(best_fit)
```

Given example can be run with *python basic_example.py* command and should give you similar output as following:

```sh
0.27046073106003377
50.89301186976975
1.089147452727528
1.18418058254198
102.46876441081712
0.11237241605812048
1.8869331711450696
0.04861881403346098
2.5748611081742325
135.6754069530421
0.008773534890863646
0.036616190934621755
186.75116812592546
0.024186452828927896
263.5697469837348
45.420706924365916
0.6946753611091367
7.756100204780568
5.839673314425907
0.06732518679742806
```

## Advanced Example
Expand All @@ -154,7 +154,7 @@ Now we should have something similar as is shown in code snippet bellow.
import numpy as np
from niapy.task import Task
from niapy.problems import Problem
from niapy.algorithms.basic import GreyWolfOptimizer
from niapy.algorithms.basic import ParticleSwarmAlgorithm


# our custom problem class
Expand All @@ -172,28 +172,27 @@ Now, all we have to do is to initialize our algorithm as in previous examples an
my_problem = MyProblem(dimension=20)
for i in range(10):
task = Task(problem=my_problem, max_iters=100)
algo = GreyWolfOptimizer(population_size=20)
algo = ParticleSwarmAlgorithm(population_size=100, w=0.9, c1=0.5, c2=0.3, min_velocity=-1, max_velocity=1)

# running algorithm returns best found minimum
best = algo.run(task)

best_x, best_fit = algo.run(task)
# printing best minimum
print(best[-1])
print(best_fit)
```

Now we can run our advanced example with following command: *python advanced_example.py*. The results should be similar to those bellow.

```sh
7.606465129178389e-09
5.288697102580944e-08
6.875762169124336e-09
1.386574251424837e-08
2.174923591233085e-08
2.578545710051624e-09
1.1400628541972142e-08
2.99387377733644e-08
7.029492316948289e-09
7.426212520156997e-09
0.002455614050761476
0.000557652972392164
0.0029791325679865413
0.0009443595274525336
0.001012658824492069
0.0006837236892816072
0.0026789725774685495
0.005017746993004601
0.0011654473402322196
0.0019074442166293853
```

For more usage examples please look at [examples](/examples) folder.
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87 changes: 45 additions & 42 deletions README.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,7 @@ To install NiaPy on Arch Linux, please use an `AUR helper <https://wiki.archlinu

$ yay -Syyu python-niapy

To install NiaPy on Alpine Linux, please enable Testing repository and use::
To install NiaPy on Alpine Linux, please enable Testing repository and use

.. code:: sh

Expand Down Expand Up @@ -105,38 +105,38 @@ Let’s go through a basic and advanced example.
Basic Example
-------------

Let’s say, we want to try out Gray Wolf Optimizer algorithm against the
Pintér problem. Firstly, we have to create a new file, called *basic_example.py*.
Then we have to import chosen algorithm from NiaPy, so we can use it. Afterwards we initialize
GreyWolfOptimizer class instance and run the algorithm. Given bellow is the complete source code of basic example.
Let's say, we want to try out Gray Wolf Optimizer algorithm against the Pintér problem.
Firstly, we have to create new file, called *basic_example.py*. Then we have to import chosen
algorithm from NiaPy, so we can use it. Afterwards we initialize ParticleSwarmAlgorithm class instance and run the algorithm.
Given bellow is complete source code of basic example.

.. code:: python

from niapy.algorithms.basic import GreyWolfOptimizer
from niapy.algorithms.basic import ParticleSwarmAlgorithm
from niapy.task import Task

# we will run 10 repetitions of Grey Wolf Optimizer against the Pinter problem
# we will run 10 repetitions of Weighed, velocity clamped PSO on the Pinter problem
for i in range(10):
task = Task(problem='pinter', dimension=10, max_evals=1000)
algorithm = GreyWolfOptimizer(population_size=20)
best = algorithm.run(task)
print(best[-1])
task = Task(problem='pinter', dimension=10, max_evals=10000)
algorithm = ParticleSwarmAlgorithm(population_size=100, w=0.9, c1=0.5, c2=0.3, min_velocity=-1, max_velocity=1)
best_x, best_fit = algorithm.run(task)
print(best_fit)

Given example can be run with *python basic_example.py* command and
should give you similar output as following:

.. code:: sh

0.27046073106003377
50.89301186976975
1.089147452727528
1.18418058254198
102.46876441081712
0.11237241605812048
1.8869331711450696
0.04861881403346098
2.5748611081742325
135.6754069530421
0.008773534890863646
0.036616190934621755
186.75116812592546
0.024186452828927896
263.5697469837348
45.420706924365916
0.6946753611091367
7.756100204780568
5.839673314425907
0.06732518679742806

Advanced Example
----------------
Expand All @@ -154,7 +154,8 @@ Now we should have something similar as is shown in code snippet bellow.

from niapy.task import Task
from niapy.problems import Problem
from niapy.algorithms.basic import GreyWolfOptimizer
from niapy.algorithms.basic import ParticleSwarmAlgorithm
import numpy as np

# our custom Problem class
class MyProblem(Problem):
Expand All @@ -164,36 +165,38 @@ Now we should have something similar as is shown in code snippet bellow.
def _evaluate(self, x):
return np.sum(x ** 2)

Now, all we have to do is to initialize our algorithm as in previous examples and pass an instance of our MyProblem class as the problem argument.

Now, all we have to do is to initialize our algorithm as in previous examples and pass as problem parameter,
instance of our *MyProblem* class.

.. code:: python

my_problem = MyProblem(dimension=20)
for i in range(10):
task = Task(problem=my_problem, max_iters=100)
algo = GreyWolfOptimizer(population_size=20)
algorithm = ParticleSwarmAlgorithm(population_size=100, w=0.9, c1=0.5, c2=0.3, min_velocity=-1, max_velocity=1)

# running algorithm returns best found minimum
best = algo.run(task)
best_x, best_fit = algorithm.run(task)

# printing best minimum
print(best[-1])

Now we can run our advanced example with following command: *python
advanced_example.py*. The results should be similar to those bellow.

.. code:: sh

7.606465129178389e-09
5.288697102580944e-08
6.875762169124336e-09
1.386574251424837e-08
2.174923591233085e-08
2.578545710051624e-09
1.1400628541972142e-08
2.99387377733644e-08
7.029492316948289e-09
7.426212520156997e-09
print(best_fit)

Now we can run our advanced example with following command python advanced_example.py. The results should be
similar to those bellow.

.. code:: bash

0.0009232355257327939
0.0012993317932349976
0.0026231249714186128
0.001404157010165644
0.0012822904697534436
0.002202199078241452
0.00216496834770605
0.0010092926171364153
0.0007432303831633373
0.0006545778971016809

For more usage examples please look at `examples </examples>`__ folder.

Expand Down
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