print() is a function in Python 3

MJGao · Jun 6, 2018 · 4f12cc3 · 4f12cc3
1 parent 73b04e5
commit 4f12cc3
Showing 1 changed file with 13 additions and 13 deletions.
diff --git a/src/py3.x/ML/13.PCA/pca.py b/src/py3.x/ML/13.PCA/pca.py
@@ -33,11 +33,11 @@ def pca(dataMat, topNfeat=9999999):
 
     # 计算每一列的均值
     meanVals = mean(dataMat, axis=0)
-    # print ('meanVals', meanVals)
+    # print('meanVals', meanVals)
 
     # 每个向量同时都减去 均值
     meanRemoved = dataMat - meanVals
-    # print ('meanRemoved=', meanRemoved)
+    # print('meanRemoved=', meanRemoved)
 
     # cov协方差=[(x1-x均值)*(y1-y均值)+(x2-x均值)*(y2-y均值)+...+(xn-x均值)*(yn-y均值)+]/(n-1)
     '''
@@ -53,8 +53,8 @@ def pca(dataMat, topNfeat=9999999):
 
     # eigVals为特征值， eigVects为特征向量
     eigVals, eigVects = linalg.eig(mat(covMat))
-    # print ('eigVals=', eigVals)
-    # print( 'eigVects=', eigVects)
+    # print('eigVals=', eigVals)
+    # print('eigVects=', eigVects)
     # 对特征值，进行从小到大的排序，返回从小到大的index序号
     # 特征值的逆序就可以得到topNfeat个最大的特征向量
     '''
@@ -70,20 +70,20 @@ def pca(dataMat, topNfeat=9999999):
     array([0, 2, 1])
     '''
     eigValInd = argsort(eigVals)
-    # print ('eigValInd1=', eigValInd)
+    # print('eigValInd1=', eigValInd)
 
     # -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)
+    # print('eigValInd2=', eigValInd)
     # 重组 eigVects 最大到最小
     redEigVects = eigVects[:, eigValInd]
-    # print ('redEigVects=', redEigVects.T)
+    # 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)
-    # print ('reconMat=', reconMat)
+    # print('lowDDataMat=', lowDDataMat)
+    # print('reconMat=', reconMat)
     return lowDDataMat, reconMat
 
 
@@ -130,7 +130,7 @@ 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, '4.2f'), 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__":
@@ -140,14 +140,14 @@ def analyse_data(dataMat):
     # lowDmat, reconMat = pca(dataMat, 1)
     # # 只需要2个特征向量，和原始数据一致，没任何变化
     # # lowDmat, reconMat = pca(dataMat, 2)
-    # # print (shape(lowDmat))
+    # # print(shape(lowDmat))
     # show_picture(dataMat, reconMat)
 
     # 利用PCA对半导体制造数据降维
     dataMat = replaceNanWithMean()
-    print (shape(dataMat))
+    print(shape(dataMat))
     # 分析数据
     analyse_data(dataMat)
     # lowDmat, reconMat = pca(dataMat, 20)
-    # print (shape(lowDmat))
+    # print(shape(lowDmat))
     # show_picture(dataMat, reconMat)