Analyse indépendante des composants (ICA) en Python

Question

Existe-t-il un package disponible en python pour effectuer l'analyse ICA (Independent Component Analysis)?

veuillez fournir quelques pointeurs et liens afin que je puisse commencer avec python pour le même. Scikit learn fournit une méthode pour effectuer une analyse indépendante des composants.

Answer 1

scikit learn - ICA

print(__doc__) 

import numpy as np 
import matplotlib.pyplot as plt 
from scipy import signal 

from sklearn.decomposition import FastICA, PCA 

############################################################################### 
# Generate sample data 
np.random.seed(0) 
n_samples = 2000 
time = np.linspace(0, 8, n_samples) 

s1 = np.sin(2 * time) # Signal 1 : sinusoidal signal 
s2 = np.sign(np.sin(3 * time)) # Signal 2 : square signal 
s3 = signal.sawtooth(2 * np.pi * time) # Signal 3: saw tooth signal 

S = np.c_[s1, s2, s3] 
S += 0.2 * np.random.normal(size=S.shape) # Add noise 

S /= S.std(axis=0) # Standardize data 
# Mix data 
A = np.array([[1, 1, 1], [0.5, 2, 1.0], [1.5, 1.0, 2.0]]) # Mixing matrix 
X = np.dot(S, A.T) # Generate observations 

# Compute ICA 
ica = FastICA(n_components=3) 
S_ = ica.fit_transform(X) # Reconstruct signals 
A_ = ica.mixing_ # Get estimated mixing matrix 

# We can `prove` that the ICA model applies by reverting the unmixing. 
assert np.allclose(X, np.dot(S_, A_.T) + ica.mean_) 

# For comparison, compute PCA 
pca = PCA(n_components=3) 
H = pca.fit_transform(X) # Reconstruct signals based on orthogonal components 

############################################################################### 
# Plot results 

plt.figure() 

models = [X, S, S_, H] 
names = ['Observations (mixed signal)', 
     'True Sources', 
     'ICA recovered signals', 
     'PCA recovered signals'] 
colors = ['red', 'steelblue', 'orange'] 

for ii, (model, name) in enumerate(zip(models, names), 1): 
    plt.subplot(4, 1, ii) 
    plt.title(name) 
    for sig, color in zip(model.T, colors): 
     plt.plot(sig, color=color) 

plt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.46) 
plt.show()