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

朝着太阳

具有野性啊！

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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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'
2 N. S# }  j( \7 gX, y = utilities.load_data(input_file)
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+ c' S# S# f2 i2 ?( t###############################################
1 ^0 y" Y$ j7 N+ f2 l8 ~8 B$ Q# Separate the data into classes based on 'y'
! `- B! _2 H! Hclass_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])
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# Plot the input data
3 w  H2 T2 a* ], `5 [4 u: P9 bplt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
5 T! J: r4 @/ C1 h: p* M" {plt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
plt.title('Input data')

# N6 q; H6 W' G( d- g2 ~' a$ J, I5 a( I###############################################
5 C0 A- ^! v9 H! t7 ~6 f  X2 U7 y# Train test split and SVM training
from sklearn import cross_validation
8 z  e4 n9 S9 h9 r3 `0 O% l0 Wfrom sklearn.svm import SVC

  z" e2 y% K+ c" _: O3 A; {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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#params = {'kernel': 'linear'}
2 E! X! c2 |: V1 M#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
: r) y$ t, [: g. @; O* B. y( lclassifier = SVC(**params)
! ?0 P  l+ E$ ?& N, A9 G/ S9 j, Xclassifier.fit(X_train, y_train)
8 h7 z9 i- n( M' M0 n4 Tutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
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  z% J# I& ]/ m- dy_test_pred = classifier.predict(X_test)
8 `) T5 N- J% ~% ]utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')

###############################################
# Evaluate classifier performance

from sklearn.metrics import classification_report
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/ t- [, j8 t$ Y. W6 V  y+ u. c2 Etarget_names = ['Class-' + str(int(i)) for i in set(y)]
( V: q% K2 `. y4 v! i! I5 }  Wprint "\n" + "#"*30
% z1 N# @/ P# C- \print "\nClassifier performance on training dataset\n"
& r" ~1 E+ I" q/ u# B% \2 F0 Xprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
9 i+ p. b. j' B4 Jprint "#"*30 + "\n"
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: J) T8 K. G1 Z. ^/ r; iprint "#"*30
print "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
/ a4 |; [& f3 c& u+ Oprint "#"*30 + "\n"
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jteo21

好片强烈支持