Python中的獨立分量分析（ICA）

Question

python中是否有可執行獨立分量分析（ICA）的可用軟件包？

請提供一些指針和鏈接，以便我可以從python開始。

Answer 1

Scikit learn提供了執行獨立組件分析的方法。

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()