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PCA_scikit-learn.py
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103 lines (85 loc) · 3.1 KB
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#-*- coding: utf-8 -*-
# Author:bob
# Date:2016.12.22
import numpy as np
from matplotlib import pyplot as plt
from scipy import io as spio
from sklearn.decomposition import pca
from sklearn.preprocessing import StandardScaler
'''二维降为一维主运行函数'''
def PCA_2d_example():
'''加载数据并作图'''
data = spio.loadmat('data.mat')
X = data['X']
plt = plot_data_2d(X,'bo')
plt.axis('square')
plt.title('original data')
plt.show()
'''归一化数据并作图'''
scaler = StandardScaler()
scaler.fit(X)
x_train = scaler.transform(X)
plot_data_2d(x_train, 'bo')
plt.axis('square')
plt.title('scaler data')
plt.show()
'''拟合数据'''
K=1 # 要降的维度
model = pca.PCA(n_components=K).fit(x_train) # 拟合数据，n_components定义要降的维度
Z = model.transform(x_train) # transform就会执行降维操作
'''数据恢复并作图'''
Ureduce = model.components_ # 得到降维用的Ureduce
x_rec = np.dot(Z,Ureduce) # 数据恢复
plot_data_2d(x_rec,'bo')
plt.plot()
plt.axis('square')
plt.title('recover data')
plt.show()
def PCA_face_example():
'''加载数据并显示'''
image_data = spio.loadmat('data_faces.mat')
X = image_data['X']
display_imageData(X[0:100,:]) # 显示100个最初图像
'''归一化数据'''
scaler = StandardScaler()
scaler.fit(X)
x_train = scaler.transform(X)
'''拟合模型'''
K=100
model = pca.PCA(n_components=K).fit(x_train)
Z = model.transform(x_train)
Ureduce = model.components_
display_imageData(Ureduce[0:36,:]) # 可视化部分U数据
x_rec = np.dot(Z,Ureduce)
display_imageData(x_rec[0:100,:]) # 显示恢复的数据
# 可视化二维数据
def plot_data_2d(X,marker):
plt.plot(X[:,0],X[:,1],marker)
return plt
# 显示图片
def display_imageData(imgData):
sum = 0
'''
显示100个数（若是一个一个绘制将会非常慢，可以将要画的图片整理好，放到一个矩阵中，显示这个矩阵即可）
- 初始化一个二维数组
- 将每行的数据调整成图像的矩阵，放进二维数组
- 显示即可
'''
m,n = imgData.shape
width = np.int32(np.round(np.sqrt(n)))
height = np.int32(n/width);
rows_count = np.int32(np.floor(np.sqrt(m)))
cols_count = np.int32(np.ceil(m/rows_count))
pad = 1
display_array = -np.ones((pad+rows_count*(height+pad),pad+cols_count*(width+pad)))
for i in range(rows_count):
for j in range(cols_count):
max_val = np.max(np.abs(imgData[sum,:]))
display_array[pad+i*(height+pad):pad+i*(height+pad)+height,pad+j*(width+pad):pad+j*(width+pad)+width] = imgData[sum,:].reshape(height,width,order="F")/max_val # order=F指定以列优先，在matlab中是这样的，python中需要指定，默认以行
sum += 1
plt.imshow(display_array,cmap='gray') #显示灰度图像
plt.axis('off')
plt.show()
if __name__ == '__main__':
#PCA_2d_example()
PCA_face_example()