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Hadrien Jean hadrienj

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hadrienj / preprocessing-data-for-machine-learning-and-deep-learning_code5.py
Last active August 26, 2018 17:13
def plotDataAndCov(data):
ACov = np.cov(data, rowvar=False, bias=True)
print 'Covariance matrix:\n', ACov
fig, ax = plt.subplots(nrows=1, ncols=2)
fig.set_size_inches(10, 10)
ax0 = plt.subplot(2, 2, 1)
# Choosing the colors
@hadrienj
hadrienj / preprocessing-data-for-machine-learning-and-deep-learning_code1.py
Last active August 27, 2018 14:35
# First things first: let's import some libraries
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
A = np.array([[1, 3, 5], [5, 4, 1], [3, 8, 6]])
A
@hadrienj
hadrienj / preprocessing-data-for-machine-learning-and-deep-learning_code2.py
Created August 26, 2018 12:48
np.cov(A, rowvar=False, bias=True)
@hadrienj
hadrienj / preprocessing-data-for-machine-learning-and-deep-learning_code3.py
Created August 26, 2018 12:50
def calculateCovariance(X):
meanX = np.mean(X, axis = 0)
lenX = X.shape[0]
X = X - meanX
covariance = X.T.dot(X)/lenX
return covariance
@hadrienj
hadrienj / preprocessing-data-for-machine-learning-and-deep-learning_code4.py
Created August 26, 2018 12:53
calculateCovariance(A)
@hadrienj
hadrienj / preprocessing-data-for-machine-learning-and-deep-learning_code6.py
Created August 26, 2018 17:17
np.random.seed(1234)
a1 = np.random.normal(2, 1, 300)
a2 = np.random.normal(1, 1, 300)
A = np.array([a1, a2]).T
A.shape
@hadrienj
hadrienj / preprocessing-data-for-machine-learning-and-deep-learning_code7.py
Created August 26, 2018 17:18
A[:10,:]
@hadrienj
hadrienj / preprocessing-data-for-machine-learning-and-deep-learning_code8.py
Created August 26, 2018 17:20
sns.distplot(A[:,0], color="#53BB04")
sns.distplot(A[:,1], color="#0A98BE")
plt.show()
plt.close()
@hadrienj
hadrienj / preprocessing-data-for-machine-learning-and-deep-learning_code9.py
Created August 26, 2018 17:21
plotDataAndCov(A)
plt.show()
plt.close()
@hadrienj
hadrienj / preprocessing-data-for-machine-learning-and-deep-learning_code10.py
Created August 26, 2018 17:24
np.random.seed(1234)
b1 = np.random.normal(3, 1, 300)
b2 = b1 + np.random.normal(7, 1, 300)/2.
B = np.array([b1, b2]).T
plotDataAndCov(B)
plt.show()
plt.close()