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

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
import matplotlib.pyplot as plt
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; l, n/ F7 A. [( ]5 D4 g' aimport 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)
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###############################################
# I0 A8 ^" h0 f2 n6 q, y# Separate the data into classes based on 'y'
: _2 q, W, o$ O$ {# a" m/ cclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
% J% j" Y/ l5 c2 ?$ oclass_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')
9 d7 U, c. ~( j2 f" |7 e7 Q  ~* Q- [plt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
6 `: t2 N7 Z2 a- P) p( j0 ^7 Fplt.title('Input data')

; A7 t; X+ |! Q+ `###############################################
9 m( s3 D- Y& Z9 {1 `4 f  ]# Train test split and SVM training
) N: }, x7 _. f4 \6 zfrom sklearn import cross_validation
6 t: y0 p$ T4 d. Y4 ]& N6 J+ rfrom 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)

& b2 Q$ \  C; E# m4 V8 P#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
" h3 e1 A* N6 ]6 R* ?$ vparams = {'kernel': 'rbf'}
% s3 Q1 B- ^  _3 I/ ?: ]4 dclassifier = SVC(**params)
# R6 W9 _' ]* r, Y) a! D, L" b1 vclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
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4 ?" k" f# V# E0 O- M& O1 H# M; Xy_test_pred = classifier.predict(X_test)
9 Z9 r0 W+ |. }$ ]1 V! S. cutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
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& q9 J% S: u" U2 c  D! {###############################################
6 t- H  W! g/ V7 }# Evaluate classifier performance
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from sklearn.metrics import classification_report
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target_names = ['Class-' + str(int(i)) for i in set(y)]
' q5 j0 G. |- p5 {7 Cprint "\n" + "#"*30
2 D- b& V2 m) S  J5 ]" Oprint "\nClassifier performance on training dataset\n"
# l* U$ n, G1 t8 e. |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)
- K' U% i2 U( M1 j# R# ]print "#"*30 + "\n"
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jteo21

好片强烈支持

wxw0214

你就我们摸我