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SpKit: Signal Processing toolkit

Project description

Signal Processing toolkit

Links: Github | PyPi - project

Table of contents


Requirement: numpy, matplotlib, scipy.stats, scikit-learn

with pip

pip install spkit

Build from the source

Download the repository or clone it with git, after cd in directory build it from source with

python install

Functions list

Signal Processing Techniques

Information Theory functions for real valued signals

  • Entropy : Shannon entropy, Rényi entropy of order α, Collision entropy
  • Joint entropy
  • Conditional entropy
  • Mutual Information
  • Cross entropy
  • Kullback–Leibler divergence
  • Computation of optimal bin size for histogram using FD-rule
  • Plot histogram with optimal bin size

Matrix Decomposition

  • SVD
  • ICA using InfoMax, Extended-InfoMax, FastICA & Picard

Linear Feedback Shift Register

  • pylfsr

Continuase Wavelet Transform and other functions comming soon..

Machine Learning models - with visualizations

  • Logistic Regression
  • Naive Bayes
  • Decision Trees
  • DeepNet (to be updated)


Information Theory

View in notebook

import numpy as np
import matplotlib.pyplot as plt
import spkit as sp

x = np.random.rand(10000)
y = np.random.randn(10000)

#Shannan entropy
H_x= sp.entropy(x,alpha=1)
H_y= sp.entropy(y,alpha=1)

#Rényi entropy
Hr_x= sp.entropy(x,alpha=2)
Hr_y= sp.entropy(y,alpha=2)

H_xy= sp.entropy_joint(x,y)

H_x1y= sp.entropy_cond(x,y)
H_y1x= sp.entropy_cond(y,x)

I_xy = sp.mutual_Info(x,y)

H_xy_cross= sp.entropy_cross(x,y)

D_xy= sp.entropy_kld(x,y)

print('Shannan entropy')
print('Entropy of x: H(x) = ',H_x)
print('Entropy of y: H(y) = ',H_y)
print('Rényi entropy')
print('Entropy of x: H(x) = ',Hr_x)
print('Entropy of y: H(y) = ',Hr_y)
print('Mutual Information I(x,y) = ',I_xy)
print('Joint Entropy H(x,y) = ',H_xy)
print('Conditional Entropy of : H(x|y) = ',H_x1y)
print('Conditional Entropy of : H(y|x) = ',H_y1x)
print('Cross Entropy of : H(x,y) = :',H_xy_cross)
print('Kullback–Leibler divergence : Dkl(x,y) = :',D_xy)




View in notebook

from spkit import ICA
from import load_data
X,ch_names = load_data.eegSample()

x = X[128*10:128*12,:]
t = np.arange(x.shape[0])/128.0

ica = ICA(n_components=14,method='fastica')
s1 = ica.transform(x.T)

ica = ICA(n_components=14,method='infomax')
s2 = ica.transform(x.T)

ica = ICA(n_components=14,method='picard')
s3 = ica.transform(x.T)

ica = ICA(n_components=14,method='extended-infomax')
s4 = ica.transform(x.T)

Machine Learning

  1. Logistic Regression - View in notebook
  2. Naive Bayes - View in notebook
  3. Decision Trees - View in notebook

**view in repository **


import numpy as np
from spkit.pylfsr import LFSR
## Example 1  ## 5 bit LFSR with x^5 + x^2 + 1
L = LFSR()
tempseq = L.runKCycle(10000)    # generate 10000 bits from current state


PhD Student: Queen Mary University of London & University of Genoa

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