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rogercheng

尝试下西片吧

gu80542

偶尔换个口味

功夫茶

import numpy as np
import matplotlib.pyplot as plt

import utilities

# Load input data
input_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
X, y = utilities.load_data(input_file)

###############################################
# Separate the data into classes based on 'y'
3 j+ `: y  `3 L& Q6 Uclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
class_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])

# Plot the input data
plt.figure()
) C! e+ o4 R  F0 m- k4 H0 C5 B  yplt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
plt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
plt.title('Input data')
( |9 v6 e1 ~6 N5 s. u+ |' |; U
% f: W( Y) f1 R3 R0 P###############################################
# Train test split and SVM training
from sklearn import cross_validation
: A& H9 i& z  @3 P  J( Rfrom sklearn.svm import SVC
3 {: J' l  A) K. R3 h6 v8 r
* a1 F5 N6 t% @& @; P7 ]X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

9 Q6 F  `, W+ H- U  ?: W) t, m#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
classifier = SVC(**params)
' ]- E' @8 |" b; Fclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')

1 L) ]3 p3 O$ W; s6 v4 Ny_test_pred = classifier.predict(X_test)
% A0 q" e: p2 ~+ s* ^0 S5 ^- tutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
* ^8 W7 q! i7 ~6 y
4 Q6 X( B9 i1 i1 k  S5 n###############################################
# Evaluate classifier performance
$ H: s! f# v: K& E- L$ ]6 l5 ]; B$ A
from sklearn.metrics import classification_report

9 ]) L# r! @! gtarget_names = ['Class-' + str(int(i)) for i in set(y)]
7 l$ ]; O0 H  d9 j, h. V+ g: \print "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"
- _) H4 z5 v5 O8 }& @. S8 [, q
print "#"*30
print "\nClassification report on test dataset\n"
" O7 \& o# g$ E6 Iprint classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"

! G1 q6 ]( r; ]- U& ]

jteo21

好片强烈支持

wxw0214

你就我们摸我

正大符

精华帖，力鼎，巨乳

hzfdy

很具有野性

阑珊灯焱

看上去还不错

wongyuro

感觉还是不错

violetrain2

看着图片很有感觉的