【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
1 i" n8 Y6 v# r6 a
. y6 q7 i1 ]) A/ `import utilities
% Q$ x: m6 U- N9 d, g3 L: B
( G0 m' E. D  e4 o( A1 F/ x# 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: ?& N0 K* _  B9 uX, y = utilities.load_data(input_file)
% A/ r  M! O" {5 v( h5 A: K/ F
###############################################
# Separate the data into classes based on 'y'
$ G6 r/ r% _. Y# xclass_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])
+ C+ h  y% ~4 [6 n$ v
# Plot the input data
7 Q  H, G) ?( b1 ]0 N0 ]) D! gplt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
plt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
8 U# G$ r, A; t) \plt.title('Input data')

0 D+ l: i5 O7 n4 F9 X& P###############################################
6 a3 y$ @: a* H& J# Train test split and SVM training
! N! b! X4 l" y# o' F+ gfrom sklearn import cross_validation
3 d) P, D% c+ P+ M3 Y% c* Hfrom 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)
2 T' Z# s0 {: q) i, h
/ B2 Q, i% Z+ U  Z" @) q#params = {'kernel': 'linear'}
3 H2 z6 X5 T. |3 i/ v#params = {'kernel': 'poly', 'degree': 3}
7 j( \2 \6 ^+ F# h3 J5 Nparams = {'kernel': 'rbf'}
classifier = SVC(**params)
( V  h  G" H9 H) q5 o/ t9 Cclassifier.fit(X_train, y_train)
4 O0 j  @4 Q( A& S0 Tutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
3 t) j, q8 k+ M0 H: Y! u( @8 @
( A  t2 \7 l! ]2 q, my_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
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###############################################
# 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)]
print "\n" + "#"*30
1 T4 b1 ^5 T7 U3 H( {print "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"

& M* G1 N5 Q# U" L- W$ g3 Jprint "#"*30
  V, C# u- ~8 O8 M* [1 S  Aprint "\nClassification report on test dataset\n"
" x, X# f2 U, V, @. ^4 Lprint classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"
* C3 S1 K7 h& }" R

jteo21

好片强烈支持