【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
1 X9 }. k2 e$ w% x/ Uimport matplotlib.pyplot as plt
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import utilities
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3 V& @& }1 I" @0 @+ a" H/ c) `) G4 X# Load input data
9 c1 t/ L4 q# @! finput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
+ H. }- i  g5 M/ P( x/ S4 \X, y = utilities.load_data(input_file)

9 f' e$ T2 A7 M, r. P# T###############################################
# Separate the data into classes based on 'y'
class_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
! p: A2 `9 u  Iplt.figure()
! p1 t0 E8 k8 {+ Uplt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
0 F! ~/ g7 I2 E- Z; bplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
% u% V0 h. _- C$ n$ F# Jplt.title('Input data')
% G8 N( c( e( C$ f! [
% X& N( s* D1 x4 L2 H7 ~3 {' Y& ^4 A###############################################
# Train test split and SVM training
from sklearn import cross_validation
from 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)
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; L/ a( K3 d. Z+ @1 D7 d! R#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
7 |9 `, h5 s4 T" ]1 w7 bclassifier = SVC(**params)
4 g9 X* }6 c. I$ s& `" Aclassifier.fit(X_train, y_train)
' v7 j. E! E2 l  M7 ~utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
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y_test_pred = classifier.predict(X_test)
, m) ]! C  R# e1 w, mutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
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; m- a& s% U) l$ G7 d###############################################
# Evaluate classifier performance

( d9 {# |, x) s; S/ h. rfrom sklearn.metrics import classification_report

target_names = ['Class-' + str(int(i)) for i in set(y)]
print "\n" + "#"*30
4 ~# d; `* d5 zprint "\nClassifier performance on training dataset\n"
- O4 w+ \8 y! `0 w6 v" A& }print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"
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  r+ \6 `# `9 U8 |, }* X5 _print "#"*30
; w, U3 T+ w$ G  |print "\nClassification report on test dataset\n"
6 X4 i2 |( ^6 M4 |2 r, F! C( }print classification_report(y_test, y_test_pred, target_names=target_names)
$ o! L( |4 I) |3 `! A; `; Cprint "#"*30 + "\n"
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jteo21

好片强烈支持