precise
Online covariance, correlation and precision matrix computations
Install
pip install precise
Example
from precise.covariance.util import create_correlated_dataset
from precise.covariance.onlineempirical import online_empirical_cov
import numpy as np
from pprint import pprint
data = create_correlated_dataset(10000, (2.2, 4.4, 1.5),
np.array([[0.2, 0.5, 0.7], [0.3, 0.2, 0.2], [0.5, 0.3, 0.1]]), (1, 5, 3))
s = online_empirical_cov(n_dim=data.shape[1])
for observation in data:
s = online_empirical_cov(s=s, y=observation)
pprint(s)
This will return the running state, which includes the mean and cov
{'count': 10000,
'cov': array([[0.38549265, 1.55198303, 0.73809891],
[1.55198303, 9.41813338, 6.20756741],
[0.73809891, 6.20756741, 4.79042088]]),
'identity': array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
'mean': array([2.19919374, 4.42189785, 1.52406171]),
'n_dim': 3,
'ones': array([1., 1., 1.]),
'shape': (3, 3)}
To covert to corrcoef,
from precision.covariance.util import cov_to_corrcoef
pprint( cov_to_corrcoef(s['cov']) )
returns:
array([[1. , 0.81749783, 0.55345955],
[0.81749783, 1. , 0.92689045],
[0.55345955, 0.92689045, 1. ]])
See also
If you only need univariate, there is a really minimalise package momentum which avoids use of numpy.
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