calculate_correlation_matrix

[rebornwwp]

I use the calculate_correlation_matrix, and the result is greater than 1. is there any wrong?

it is convariance_matrix = (1 / (n_sample - 1)) * (X - X.mean(0)).T.dot(Y - Y.mean(0))?
is there some meaningful thing when the relation is greater than 1?

every function called has been tested. I think all are right.
x = np.array(np.random.random((10, 4))) print(calculate_variance(x)) print(calculate_std_dev(x)) print(calculate_convariance_matrix(x)) print(calculate_correlation_matrix(x))

the result is

[ 0.06257289 0.0854199 0.08613444 0.04435951]

[ 0.25014574 0.29226683 0.2934867 0.21061698]

[[ 0.06952543 -0.00037733 0.00416579 0.01641681]
[-0.00037733 0.094911 -0.02781782 0.0075379 ]
[ 0.00416579 -0.02781782 0.09570494 0.02909376]
[ 0.01641681 0.0075379 0.02909376 0.04928835]]

[[ 1.11111111 -0.00516115 0.05674344 0.31160352]
[-0.00516115 1.11111111 -0.32430615 0.1224553 ]
[ 0.05674344 -0.32430615 1.11111111 0.47067173]
[ 0.31160352 0.1224553 0.47067173 1.11111111]]

[eriklindernoren]

Yes, the diagonal values in the correlation matrix should be 1. I have compared my methods to the corresponding numpy implementations, and the scaling factor in calculating the correlation matrix should have been 1/N instead of 1/(N-1) (used in calculating the covariance matrix).

The latest commit fixed this.

> import numpy as np
> from data_operation import *
> x = np.array(np.random.random((10, 4)))
> calculate_correlation_matrix(x)
array([[ 1. , 0.18390596, 0.34851226, 0.83499889],
[ 0.18390596, 1. , 0.27027579, 0.44432891],
[ 0.34851226, 0.27027579, 1. , 0.52136316],
[ 0.83499889, 0.44432891, 0.52136316, 1. ]])
> np.corrcoef(x, rowvar=0)
array([[ 1. , 0.18390596, 0.34851226, 0.83499889],
[ 0.18390596, 1. , 0.27027579, 0.44432891],
[ 0.34851226, 0.27027579, 1. , 0.52136316],
[ 0.83499889, 0.44432891, 0.52136316, 1. ]])
> calculate_covariance_matrix(x)
array([[ 0.11678785, 0.019298 , 0.0229638 , 0.07239497],
[ 0.019298 , 0.09428342, 0.01600117, 0.03461353],
[ 0.0229638 , 0.01600117, 0.03717523, 0.02550297],
[ 0.07239497, 0.03461353, 0.02550297, 0.06436463]])
> np.cov(x, rowvar=0)
array([[ 0.11678785, 0.019298 , 0.0229638 , 0.07239497],
[ 0.019298 , 0.09428342, 0.01600117, 0.03461353],
[ 0.0229638 , 0.01600117, 0.03717523, 0.02550297],
[ 0.07239497, 0.03461353, 0.02550297, 0.06436463]])