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panjinhua2

可以的喜欢这类型

bladekiss

喜欢这种群交题材的

a740815223

好多人人啊

GAOg0036

感觉还是不错

小猫不2

看上去不错

rogercheng

尝试下西片吧

gu80542

偶尔换个口味

功夫茶

import numpy as np
/ ^! |" b4 H; O& Kimport matplotlib.pyplot as plt
" F+ D% [; N, r$ D4 c6 c
4 }  g6 z3 E) A: z' z' N0 B2 z/ Gimport utilities
( R+ W! r5 g5 L& v$ Q
# Load input data
* Z* H. i" F/ pinput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
0 E  V: U9 _% }; v4 @X, y = utilities.load_data(input_file)

###############################################
3 Q( V+ l* [7 t% n. \# Separate the data into classes based on 'y'
% d4 T/ H( Z! [. T9 \# vclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
8 B# L9 L* b# o! [class_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])

0 u0 r% C8 a, w: ^2 N( V8 d2 S) V# Plot the input data
plt.figure()
0 s& G5 X+ w$ e0 vplt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
/ B  [. s5 B" D3 ^. F% w) e8 x5 iplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
) }- F2 N( b6 o8 R; jplt.title('Input data')

###############################################
# Train test split and SVM training
! Y# h$ j2 O* X" c& K1 o3 ^from sklearn import cross_validation
from sklearn.svm import SVC

; ?; s0 x% R* |& DX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

; j' }& p8 i5 q1 E' k/ K/ h; [#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
classifier = SVC(**params)
classifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
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9 A  U$ m. k: J* r  N( U' Yy_test_pred = classifier.predict(X_test)
! }" c; d2 {6 \utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
% P/ T/ I$ Z9 V4 s/ F- n$ K
* r; N, s; k7 T###############################################
# Evaluate classifier performance
* z% V( V: E2 q$ O" j! j; u5 M
1 ~! a& j' j# m9 Xfrom sklearn.metrics import classification_report

$ S5 R- n, I( a( S4 Xtarget_names = ['Class-' + str(int(i)) for i in set(y)]
3 Z6 J7 ^" p, wprint "\n" + "#"*30
4 g# v6 e" Z8 o) P, |3 c5 wprint "\nClassifier performance on training dataset\n"
0 v' L. C# u4 k( s: x; J; X  Dprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
+ Z) [1 X: d" a" x# ]print "#"*30 + "\n"

, `+ D' G; h  e- g8 U" `1 S( [print "#"*30
( ]7 b5 Z1 e! }  U3 ^2 eprint "\nClassification report on test dataset\n"
/ M7 ^9 T7 m+ x+ _' ?# t1 I  {print classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"

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jteo21

好片强烈支持

wxw0214

你就我们摸我