【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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7 Q( ?$ W  @+ ?, ^import utilities

2 S- l) ]) C+ l, E# Load input data
' u* W- `7 g2 cinput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
  D3 h- Z4 ]; Q7 O, @% E5 jX, y = utilities.load_data(input_file)

###############################################
# Separate the data into classes based on 'y'
; z3 x4 F0 N: n% Dclass_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])

# Plot the input data
plt.figure()
" R: F7 j9 i! x) eplt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
2 P6 ^( x( o9 P/ C( @7 Tplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
0 @# W  ]3 y; h, v! Z5 f6 \plt.title('Input data')
  ?$ |' u: G8 H0 C* ?: o- X0 J; Z
###############################################
5 G- Q, X' M4 i' C# Train test split and SVM training
8 J9 S/ ?" ], V* J3 t" ~9 W% ?from sklearn import cross_validation
- ^- v& @" S: ^7 n6 xfrom sklearn.svm import SVC

+ Y+ c# Z8 ?1 k& p4 m! R( o% u9 mX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
4 a! m1 [  l) |8 S
0 Q+ I2 \* W) X6 u#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
" ^9 n2 u& h+ A9 e; a  Eparams = {'kernel': 'rbf'}
classifier = SVC(**params)
classifier.fit(X_train, y_train)
/ e* g8 Z; ?2 V+ D- L1 Wutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
$ L0 _8 Q% F: V5 Y7 `2 ^
6 A/ A' T* o1 }8 A  L+ x3 F/ sy_test_pred = classifier.predict(X_test)
. M/ `- Q' w' L: M1 @! butilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
- N, ?) r, y: G. L1 \- {7 x
; E. u0 w$ h! N3 j+ B, g' B9 M###############################################
# Evaluate classifier performance

5 u. q' i' F0 O# f' Y* jfrom sklearn.metrics import classification_report
- h3 T) J$ p: A
target_names = ['Class-' + str(int(i)) for i in set(y)]
print "\n" + "#"*30
0 r4 S7 O$ y) r" iprint "\nClassifier performance on training dataset\n"
+ N5 |$ \2 r, f/ s7 R, a+ n! sprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
8 l9 T( t' F% T( y# sprint "#"*30 + "\n"
' {7 ^4 ^# h4 V
2 b9 [7 O; d" Y$ o6 |print "#"*30
print "\nClassification report on test dataset\n"
! D( i* e2 d) wprint classification_report(y_test, y_test_pred, target_names=target_names)
* O. k$ C6 E! j$ s2 ^* I, S' w8 i8 h/ Uprint "#"*30 + "\n"
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jteo21

好片强烈支持