One Example Three Languages

I wanted to post this example of my Google Summer of Code work because I think it’s neat. One of the cool things about Shogun is our great SWIG wrapper and our static interface which lets us use Shogun natively in a bunch of different languages. So here is an example program of doing Blind Source Separation using the Jade algorithm from Python, Octave and R:

"""
Blind Source Separation using the Jade Algorithm with Shogun
Based on the example from scikit-learn
http://scikit-learn.org/

Kevin Hughes 2013
"""

import numpy as np
import pylab as pl

from shogun.Features  import RealFeatures
from shogun.Converter import Jade

# Generate sample data
np.random.seed(0)
n_samples = 2000
time = np.linspace(0, 10, n_samples)

# Source Signals
s1 = np.sin(2 * time)  # sin wave
s2 = np.sign(np.sin(3 * time))  # square wave
S = np.c_[s1, s2]
S += 0.2 * np.random.normal(size=S.shape)  # add noise

# Standardize data
S /= S.std(axis=0)  
S = S.T

# Mixing Matrix
A = np.array([[1, 0.5], [0.5, 1]])

# Mix Signals
X = np.dot(A,S)
mixed_signals = RealFeatures(X)

# Separating
jade = Jade()
signals = jade.apply(mixed_signals)
S_ = signals.get_feature_matrix()
A_ = jade.get_mixing_matrix();

# Plot results
pl.figure()
pl.subplot(3, 1, 1)
pl.plot(S.T)
pl.title('True Sources')
pl.subplot(3, 1, 2)
pl.plot(X.T)
pl.title('Mixed Sources')
pl.subplot(3, 1, 3)
pl.plot(S_.T)
pl.title('Estimated Sources')
pl.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.36)
pl.show()

% Blind Source Separation using the Jade Algorithm with Shogun
%
% Based on the example from scikit-learn
% http://scikit-learn.org/
%
% Kevin Hughes 2013

% Generate sample data
n_samples = 2000;
time = linspace(0,10,n_samples);

% Source Signals
S = zeros(2, length(time));
S(1,:) = sin(2*time);
S(2,:) = sign(sin(3*time));
S += 0.2*rand(size(S));

% Standardize data
S = S ./ std(S,0,2);

% Mixing Matrix
A = [1 0.5; 0.5 1]

% Mix Signals
X = A*S;
mixed_signals = X;

% Separating
sg('set_converter', 'jade');
sg('set_features', 'TRAIN', mixed_signals);
S_ = sg('apply_converter');

% Plot
figure();
subplot(311);
plot(time, S(1,:), 'b');
hold on;
plot(time, S(2,:), 'g');
set(gca, 'xtick', [])
title("True Sources");

subplot(312);
plot(time, X(1,:), 'b');
hold on;
plot(time, X(2,:), 'g');
set(gca, 'xtick', [])
title("Mixed Sources");

subplot(313);
plot(time, S_(1,:), 'b');
hold on;
plot(time, S_(2,:), 'g');
title("Estimated Sources");

# Blind Source Separation using the Jade Algorithm with Shogun
#
# Based on the example from scikit-learn
# http://scikit-learn.org/
#
# Kevin Hughes 2013

library('sg')

# Generate sample data
n_samples <- 2000
time <- seq(0,10,length=n_samples)

# Source Signals
S <- matrix(0,2,n_samples)
S[1,] <- sin(2*time)
S[2,] <- sign(sin(3*time))
S <- S + 0.2*matrix(runif(2*n_samples),2,n_samples)

# Standardize data
S <- S * (1/apply(S,1,sd))

# Mixing Matrix
A <- rbind(c(1,0.5),c(0.5,1))

# Mix Signals
X <- A %*% S
mixed_signals <- matrix(X,2,n_samples)

# Separating
sg('set_converter', 'jade')
sg('set_features', 'TRAIN', mixed_signals)
S_ <- sg('apply_converter')

# Plot
par(mfcol=c(3,1));

plot(time, S[1,], type="l", col='blue', main="True Sources", ylab="", xlab="")
lines(time, S[2,], type="l", col='green')

plot(time, X[1,], type="l", col='blue', main="Mixed Sources", ylab="", xlab="")
lines(time, X[2,], type="l", col='green')

plot(time, S_[1,], type="l", col='blue', main="Estimated Sources", ylab="", xlab="")
lines(time, S_[2,], type="l", col='green')