【申精】花花公子成人频道Playboy TV-Spring Break (春假派对系列 全）

gu80542

偶尔换个口味

功夫茶

import numpy as np
9 m) B# J  O- s& Kimport matplotlib.pyplot as plt
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import utilities
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# 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'
; ~& U% P+ S" [- T# WX, y = utilities.load_data(input_file)
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1 ^$ D( J  `% C- @7 Z( i9 M; ^###############################################
6 }6 b- {4 _  L  T4 F# Separate the data into classes based on 'y'
1 T$ T* n( t$ _  ?' lclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
; L4 `; x: [: d, g% Wclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
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# Plot the input data
plt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
. k2 F  g' B: y4 I- X% Rplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
plt.title('Input data')
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' W: M6 ]+ W; y& A###############################################
# Train test split and SVM training
1 `0 P9 G  |; w1 s6 u8 v% Z; afrom sklearn import cross_validation
from sklearn.svm import SVC
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X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
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" P/ K5 q* Q8 I! k' U#params = {'kernel': 'linear'}
$ J, i" `, v! C( W) B" Z#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
# Q/ j3 j! ^3 O* n- x3 t1 `5 O9 Sclassifier = SVC(**params)
classifier.fit(X_train, y_train)
9 o/ Q: ]: J/ Vutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
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y_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')

###############################################
, @! O9 ?" u7 l" J  `# Evaluate classifier performance

from sklearn.metrics import classification_report
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# k# H' d0 N/ P( B  ]9 htarget_names = ['Class-' + str(int(i)) for i in set(y)]
6 I0 G. U- C0 @; e% W4 `print "\n" + "#"*30
2 G! r$ s. p4 Z) K0 o" C4 Rprint "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"

print "#"*30
print "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
5 h. g: H' R0 a+ dprint "#"*30 + "\n"
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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

K小小

牛逼的电视。。。