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更新 PCA.md文档

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jiangzhonglian
2017-08-28 14:32:03 +08:00
parent 21fbd14c76
commit c1fe6d683c
3 changed files with 113 additions and 5 deletions
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9
src/python/13.PCA/pca.py
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@@ -74,10 +74,11 @@ def pca(dataMat, topNfeat=9999999):
# -1表示倒序，返回topN的特征值[-1 到 -(topNfeat+1) 但是不包括-(topNfeat+1)本身的倒叙]
eigValInd = eigValInd[:-(topNfeat+1):-1]
# print 'eigValInd2=', eigValInd
# 重组eig vects 最大到最小
# 重组 eigVects 最大到最小
redEigVects = eigVects[:, eigValInd]
# print 'redEigVects=', redEigVects.T
# 将数据转换到新空间
# print "---", shape(meanRemoved), shape(redEigVects)
lowDDataMat = meanRemoved * redEigVects
reconMat = (lowDDataMat * redEigVects.T) + meanVals
# print 'lowDDataMat=', lowDDataMat
@@ -114,10 +115,10 @@ def analyse_data(dataMat):
topNfeat = 20
eigValInd = eigValInd[:-(topNfeat+1):-1]
cov_all_score = sum(eigvals)
cov_all_score = float(sum(eigvals))
sum_cov_score = 0
for i in range(0, len(eigValInd)):
line_cov_score = eigvals[eigValInd[i]]
line_cov_score = float(eigvals[eigValInd[i]])
sum_cov_score += line_cov_score
'''
我们发现其中有超过20%的特征值都是0。
@@ -128,7 +129,7 @@ def analyse_data(dataMat):
最后，我们可能会注意到有一些小的负值，他们主要源自数值误差应该四舍五入成0.
'''
print '主成分：%s, 方差占比：%s%%, 累积方差占比：%s%%' % (format(i+1, '2.0f'), format(line_cov_score/cov_all_score*100, '4.1f'), format(sum_cov_score/cov_all_score*100, '4.1f'))
print '主成分：%s, 方差占比：%s%%, 累积方差占比：%s%%' % (format(i+1, '2.0f'), format(line_cov_score/cov_all_score*100, '4.2f'), format(sum_cov_score/cov_all_score*100, '4.1f'))
if __name__ == "__main__":