In [1]:

    

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
%load_ext autoreload
%autoreload 2


    

In [2]:

    

num_samples, num_features = 10, 5


    

In [3]:

    

np.random.seed(10)
data = np.random.rand(num_samples, num_features)


    

In [37]:

    

print(data)


    

    




[[0.77132064 0.02075195 0.63364823 0.74880388 0.49850701]
 [0.22479665 0.19806286 0.76053071 0.16911084 0.08833981]
 [0.68535982 0.95339335 0.00394827 0.51219226 0.81262096]
 [0.61252607 0.72175532 0.29187607 0.91777412 0.71457578]
 [0.54254437 0.14217005 0.37334076 0.67413362 0.44183317]
 [0.43401399 0.61776698 0.51313824 0.65039718 0.60103895]
 [0.8052232  0.52164715 0.90864888 0.31923609 0.09045935]
 [0.30070006 0.11398436 0.82868133 0.04689632 0.62628715]
 [0.54758616 0.819287   0.19894754 0.8568503  0.35165264]
 [0.75464769 0.29596171 0.88393648 0.32551164 0.1650159 ]]

In [38]:

    

def standardize(x):
    x = (x - np.mean(x, axis=0)) / np.std(x, axis=0)
    return x

std_data = standardize(data)


    

In [39]:

    

print(std_data, "\n\n", np.mean(std_data, axis=0), "\n\n", np.std(std_data, axis=0))


    

    




[[ 1.0775774  -1.34411605  0.31525355  0.80665878  0.24128662]
 [-1.81711634 -0.77630186  0.74088404 -1.25592235 -1.42276759]
 [ 0.62228127  1.64254169 -1.797091   -0.03521894  1.51565143]
 [ 0.23651339  0.90075228 -0.83122987  1.40786459  1.11788073]
 [-0.13414844 -0.95529104 -0.55795449  0.54097769  0.01136005]
 [-0.70898541  0.56774371 -0.08900028  0.45652209  0.65726018]
 [ 1.2571441   0.25993298  1.23775021 -0.72176808 -1.4141686 ]
 [-1.41508984 -1.04555188  0.96949701 -1.69076861  0.75969247]
 [-0.10744434  1.21308427 -1.14296098  1.19109415 -0.35450368]
 [ 0.98926822 -0.46279408  1.15485183 -0.69943932 -1.11169162]] 

 [-1.66533454e-16  4.99600361e-17 -2.22044605e-17  1.11022302e-17
  3.33066907e-16] 

 [1. 1. 1. 1. 1.]

In [ ]: