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1.7 KiB
1.7 KiB
Sure! Here's a Python script that demonstrates Independent Component Analysis (ICA) using the scikit-learn library:
import numpy as np
from sklearn.decomposition import FastICA
import matplotlib.pyplot as plt
# Generate 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
S = np.c_[s1, s2]
S += 0.2 * np.random.normal(size=S.shape) # Add noise
# Mix data
A = np.array([[1, 1], [0.5, 2]]) # Mixing matrix
X = np.dot(S, A.T) # Generate observations
# ICA
ica = FastICA(n_components=2)
S_ = ica.fit_transform(X) # Reconstruct signals
A_ = ica.mixing_ # Estimated mixing matrix
# Plot results
plt.figure(figsize=(8, 6))
models = [X, S, S_]
names = ['Observations (mixed signals)',
'True Sources',
'ICA estimated sources']
colors = ['red', 'steelblue']
for i, (model, name) in enumerate(zip(models, names), 1):
plt.subplot(3, 1, i)
plt.title(name)
for sig, color in zip(model.T, colors):
plt.plot(sig, color=color)
plt.tight_layout()
plt.show()
This script demonstrates ICA by generating two mixed signals, s1
and s2
, adding noise, and then applying ICA to separate the original signals. The original signals are represented by the S
matrix, and the observed mixed signals are stored in the X
matrix. The FastICA
class from scikit-learn is used to perform ICA.
The script plots three subplots: the observed mixed signals, the true sources, and the estimated sources achieved through ICA.
Make sure to install required dependencies (scikit-learn, numpy, matplotlib) before running the script.