【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
( z' n, p0 M! Z, G: timport matplotlib.pyplot as plt
( P' O3 R! v* H) j* O& ~* {
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'
6 R9 U. u4 b( t( W( \( r$ fX, y = utilities.load_data(input_file)
6 }- m$ E  s. R( E" `6 o
4 C$ N+ U( a/ i! R  [2 m6 G! \###############################################
# Separate the data into classes based on 'y'
( `0 T  s9 F' }2 z' m2 c# m/ cclass_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])

% g$ j* |7 t6 [7 s& F# Plot the input data
) J) d* \4 Y6 }% d2 U  b& Iplt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
- j# b! V! M1 v$ I4 }$ Z& k* b# vplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
7 `/ @* u4 S' ^, h  f$ X/ rplt.title('Input data')
; \" _  a8 _) G) G. E
+ e) w  B# }9 o+ K# @1 s& K5 }###############################################
( D" l9 C  _$ ~* q# F: o+ I# r' W# Train test split and SVM training
5 b1 Z: K; [8 g- Z$ yfrom sklearn import cross_validation
8 T' D4 g3 u5 ~) s: Gfrom sklearn.svm import SVC

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
6 y9 \/ K" {: [) e8 w8 \
#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
' d* @2 L2 V+ o. z2 \params = {'kernel': 'rbf'}
classifier = SVC(**params)
classifier.fit(X_train, y_train)
5 N3 z7 S$ {$ Y- U: m7 x! c7 S6 j% ^& Hutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
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0 H" K* D: P0 g/ o: ty_test_pred = classifier.predict(X_test)
' X) q: B1 O0 }utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
2 d3 P8 l- M6 k: p' L
1 d9 u* B- x+ D  m) N###############################################
6 t  H- Y* ]% J0 c# Evaluate classifier performance
$ J  q: _0 a" s2 e# A
( [" Y* s, N% A9 F3 x) nfrom sklearn.metrics import classification_report
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target_names = ['Class-' + str(int(i)) for i in set(y)]
4 r+ V: j, o8 _- ?0 ]1 J: `print "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
; F! |1 M# d  G0 C2 ~9 m' _print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"
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) e1 t# T! O2 a0 t6 n4 f3 E  Xprint "#"*30
5 _- V! a8 X8 f6 Xprint "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
8 E$ _! [- P7 E, Lprint "#"*30 + "\n"
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jteo21

好片强烈支持