原代码如下：

引自：http://scikit-learn.org/0.11/auto_examples/decomposition/plot_ica_blind_source_separation.html

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import numpy as np

import pylab as pl

from sklearn.decomposition import FastICA

################################################

# Generate sample data

np.random.seed(0)

n_samples = 2000

time = np.linspace(0, 10, 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

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

# Mix data

A = np.array([[1, 1], [0.5, 2]])  # Mixing matrix

X = np.dot(S, A.T)  # Generate observations

# Compute ICA

ica = FastICA()

S_ = ica.fit(X).transform(X)  # Get the estimated sources

A_ = ica.get_mixing_matrix()  # Get estimated mixing matrix

assert np.allclose(X, np.dot(S_, A_.T))

请问为什么以上例子中把产生的混合信号S变成2维才能用ICA进行处理？

如果我的实验采集的信号是一维，S = S1 + S2 ，在只有S的情况下如何分离出S1和S2？

如果有两个信号源的话，得有两组不同混合方式的采集数据才能分离吧。