關(guān)于sklearn.decomposition.FastICA的介紹http://lijiancheng0614.github.io/scikit-learn/modules/generated/sklearn.decomposition.FastICA.html

import numpy as np import matplotlib.pyplot as plt from sklearn.decomposition import FastICAC = 200 # 樣本數(shù) x = np.arange(C) s1 = 2 * np.sin(0.02 * np.pi * x) # 正弦信號(hào)a = np.linspace(-2, 2, 25) s2 = np.array([a, a, a, a, a, a, a, a]).reshape(200, ) # 鋸齒信號(hào) s3 = np.array(20 * (5 * [2] + 5 * [-2])) # 方波信號(hào) s4 = 4 * (np.random.random([1, C]) - 0.5).reshape(200, ) #隨機(jī)信號(hào) """畫出4種信號(hào)""" ax1 = plt.subplot(411) ax2 = plt.subplot(412) ax3 = plt.subplot(413) ax4 = plt.subplot(414) ax1.plot(x,s1) ax2.plot(x,s2) ax3.plot(x,s3) ax4.plot(x,s4) plt.show() """將4種信號(hào)混合 其中mix矩陣是混合后的信號(hào)矩陣，shape=[4,200]""" s=np.array([s1,s2,s3,s4]) ran=2*np.random.random([4,4]) mix=ran.dot(s) ax1 = plt.subplot(411) ax2 = plt.subplot(412) ax3 = plt.subplot(413) ax4 = plt.subplot(414) ax1.plot(x,mix[0,:]) ax2.plot(x,mix[1,:]) ax3.plot(x,mix[2,:]) ax4.plot(x,mix[3,:]) plt.show()ica = FastICA(n_components=4) #獨(dú)立成分為4個(gè) mix = mix.T #將信號(hào)矩陣轉(zhuǎn)為[n_samples,n_features],即[200,4] u = ica.fit_transform(mix) # u為解混后的4個(gè)獨(dú)立成分，shape=[200,4] u = u.T # shape=[4,200] print(ica.n_iter_) # 算法迭代次數(shù) """畫出解混后的4種獨(dú)立成分""" ax1 = plt.subplot(411) ax2 = plt.subplot(412) ax3 = plt.subplot(413) ax4 = plt.subplot(414) ax1.plot(x,u[0,:]) ax2.plot(x,u[1,:]) ax3.plot(x,u[2,:]) ax4.plot(x,u[3,:]) plt.show()

還有另外一種得到獨(dú)立成分的寫法，思路是將求得的解混矩陣和混合信號(hào)矩陣相乘得到獨(dú)立成分，代碼如下：

"""前面一樣""" ica = FastICA(n_components=4) mix = mix.T ica.fit(mix) w = ica.components_ #解混矩陣,shape=[n_components, n_features]，即[4,4] u = np.dot(w,mix.T) #解混矩陣和混合信號(hào)相乘得到獨(dú)立成分 print(ica.n_iter_) ax1 = plt.subplot(411) ax2 = plt.subplot(412) ax3 = plt.subplot(413) ax4 = plt.subplot(414) ax1.plot(x,u[0,:]) ax2.plot(x,u[1,:]) ax3.plot(x,u[2,:]) ax4.plot(x,u[3,:]) plt.show()

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