spkit.entropy_svd

spkit.entropy_svd(x, order=3, delay=1, base=2, normalize=False)

Singular Value Decomposition Entropy \(H_{\Sigma}(X)\)

Singular Value Decomposition Entropy

Parameters:
x1d-array, shape (n,)

  • input signal

orderint, default=3

  • Embedding dimension (order).

delayint, default=1

  • Delay.

base: scalar>0, deafult=2

  • base of log, 2, 10, ‘e’

normalize: bool, default=False

  • if True, Hx is normalised

Returns:
H_svd: scalar,

  • Singular Value Decomposition Entropy

See also

entropy

Entropy

entropy_sample

Sample Entropy

entropy_approx

Approximate Entropy

dispersion_entropy

Dispersion Entropy

entropy_spectral

Spectral Entropy

entropy_permutation

Permutation Entropy

entropy_differential

Differential Entropy

References

  • wikipedia

Examples

>>> import numpy as np
>>> import spkit as sp
>>> t = np.linspace(0,2,200)
>>> x1 = np.sin(2*np.pi*1*t) + 0.01*np.random.randn(len(t))  # less noisy
>>> x2 = np.sin(2*np.pi*1*t) + 0.5*np.random.randn(len(t))  # very noisy
>>> #Entropy SVD
>>> H_x1 = sp.entropy_svd(x1,order=3, delay=1)
>>> H_x2 = sp.entropy_svd(x2,order=3, delay=1)
>>> print('Entropy SVD')
>>> print(r'Entropy of x1: H_s(x1) = ',H_x1)
>>> print(r'Entropy of x2: H_s(x2) = ',H_x2)
Entropy SVD
Entropy of x1: H_s(x1) =  0.34210072132884417
Entropy of x2: H_s(x2) =  1.394331263550738