import numpy as np
from sklearn import svm
from sklearn.linear_model import LogisticRegressionmy_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],len(data_x[0]),len(data_x))(array([ 6. , 148. , 72. , 35. , 0. , 33.6 ,
0.627, 50. ]), 8, 768)
n_train=int(len(data_y)*0.7)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()
clf1.fit(X_train,y_train)
clf2=LogisticRegression()
clf2.fit(X_train,y_train)LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
y_predictions1=clf1.predict(X_test)
y_predictions2=clf2.predict(X_test)print(y_predictions1)[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
print(y_predictions2)[ 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. 1. 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. 0. 0. 0. 0. 0.
1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0.
1. 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. 1. 0. 1. 1. 1. 0. 0.
1. 1. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0.]
print(y_test)[ 0. 0. 1. 1. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.
0. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1.
0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0.
0. 0. 1. 1. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0.
0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 1.
1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0.
1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0.
0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1.
0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0.
0. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 0.
1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 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)(152, 183)
由于SVM用了经典模型，未对参数进行调整，导致预测结果出现了全为0的情况，后面将对SVM模型进行深入的学习，调整参数，以改善模型的效果