Python Machine Learning Cookbook - Second Edition

How to do it...

Let's see how we can perform a blind source separation:

1. Create a new Python file and import the following packages (the full code is given in the blind_source_separation.py file that is provided for you):

import numpy as np 
import matplotlib.pyplot as plt 
from sklearn.decomposition import PCA, FastICA  

2. We will use data from the mixture_of_signals.txt file that's already provided for you. Let's load the data:

# Load data 
input_file = 'mixture_of_signals.txt' 
X = np.loadtxt(input_file) 

3. Create the ICA object:

# Compute ICA 
ica = FastICA(n_components=4) 

4. Reconstruct the signals based on ICA:

# Reconstruct the signals 
signals_ica = ica.fit_transform(X) 

5. Extract the mixing matrix:

# Get estimated mixing matrix mixing_mat ...