SVM模型应用（三）通过数据归一化改善SVM模型的预测效果

import numpy as np
from sklearn import svm
from sklearn.linear_model import LogisticRegression

my_matrix=np.loadtxt("E:\\pima-indians-diabetes.txt",delimiter=",",skiprows=0) 

lenth_x=len(my_matrix[0])

data_y=my_matrix[:,lenth_x-1]

data_x=my_matrix[:,0:lenth_x-1]
print(data_x[0:2],len(data_x[0]),len(data_x))
data_shape=data_x.shape
data_rows=data_shape[0]
data_cols=data_shape[1]

data_col_max=data_x.max(axis=0)#获取二维数组列向最大值
data_col_min=data_x.min(axis=0)#获取二维数组列向最小值
for i in xrange(0, data_rows, 1):#将输入数组归一化
    for j in xrange(0, data_cols, 1):
        data_x[i][j] = \
            (data_x[i][j] - data_col_min[j]) / \
            (data_col_max[j] - data_col_min[j])
print(data_x[0:2])

(array([[   6.   ,  148.   ,   72.   ,   35.   ,    0.   ,   33.6  ,
           0.627,   50.   ],
       [   1.   ,   85.   ,   66.   ,   29.   ,    0.   ,   26.6  ,
           0.351,   31.   ]]), 8, 768)
[[ 0.35294118  0.74371859  0.59016393  0.35353535  0.          0.50074516
   0.23441503  0.48333333]
 [ 0.05882353  0.42713568  0.54098361  0.29292929  0.          0.39642325
   0.11656704  0.16666667]]

n_train=int(len(data_y)*0.7)#选择70%的数据作为训练集，30%的数据作为测试集

X_train=data_x[:n_train]
y_train=data_y[:n_train]
X_test=data_x[n_train:]
y_test=data_y[n_train:]

clf1=svm.SVC()#模型1选择SVM经典模型
clf1.fit(X_train,y_train)
clf2=LogisticRegression()#模型2选择逻辑回归
clf2.fit(X_train,y_train)


y_predictions1=clf1.predict(X_test)
y_predictions2=clf2.predict(X_test)

print(y_predictions1)
print(y_predictions2)

[ 0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.
  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  1.
  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  1.  1.  0.  1.  0.  0.
  0.  0.  1.  1.  0.  1.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.
  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.
  1.  0.  0.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.  0.  0.  0.  0.  0.
  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.
  0.  0.  0.  1.  0.  0.  0.  0.  0.  1.  0.  0.  0.  1.  0.  0.  1.  1.
  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.
  0.  0.  0.  0.  0.  1.  0.  0.  1.  1.  0.  0.  0.  1.  0.  0.  0.  0.
  1.  1.  0.  0.  0.  0.  1.  0.  1.  0.  0.  0.  0.  0.  0.]
[ 0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.
  0.  0.  0.  1.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  1.
  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  1.  1.  0.  1.  0.  0.
  0.  0.  1.  1.  0.  1.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.
  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  1.  1.  1.
  1.  0.  0.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  0.
  1.  0.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  1.  0.  0.  0.  0.  0.
  0.  0.  0.  1.  1.  0.  0.  0.  0.  1.  0.  0.  0.  1.  0.  0.  1.  1.
  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.
  0.  0.  0.  0.  0.  1.  0.  0.  1.  1.  0.  0.  0.  1.  0.  0.  0.  0.
  1.  1.  0.  0.  0.  0.  1.  0.  1.  0.  0.  0.  0.  0.  0.]

k,h=0,0
for i in range(len(y_test)):
    if y_predictions1[i]==y_test[i]:
        k+=1
for i in range(len(y_test)):
    if y_predictions2[i]==y_test[i]:
        h+=1 
print(k,h)

(177, 181)

accuracy_svm=float(k)/float(len(y_test))
accuracy_LogR=float(h)/float(len(y_test))
print"The accuracy of SVM is %f, and the accuracy of LogisticRegression is %f"%(accuracy_svm,accuracy_LogR)

The accuracy of SVM is 0.766234, and the accuracy of LogisticRegression is 0.783550

通过实际预测效果检验，数据的归一化使得SVM模型的预测效果得到了改善，基本接近了逻辑回归模型，后续将通过对SVM模型的超参数选择来改善预测效果

SVM模型应用（三）通过数据归一化改善SVM模型的预测效果

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