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analysis.py
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analysis.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as stats
import scipy.optimize as opt
def get_data():
data = pd.read_excel('TaskDataVar1.xls')
data = data.dropna()
return data
def std_dev(data):
# Calculate the standard deviation of the data
# data: pandas dataframe
# return: standard deviation
return np.std(data)
# def f(x)
# def min_bfgs(data):
# # Calculate the minimum of the data using BFGS
# # data: pandas dataframe
# # return: minimum
# return opt.minimize(std_dev, data, method='BFGS')
data = get_data()
# read first column to X and second column to Y
X = data.iloc[:, 0]
Y = data.iloc[:, 1]
X = np.array(X)
Y = np.array(Y)
s = 0
arr = [0.99132407, 0.76975029]
for i in range(len(X)):
s += (Y[i] - arr[0]*np.sin(2*X[i]) - arr[1])**2
print(s)
print(np.sum((Y - (1*np.sin(2*X) + 0.73))**2))
print(sum[(Y - (1*np.sin(2*X) + 0.73))**2])