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

rogercheng

尝试下西片吧

gu80542

偶尔换个口味

功夫茶

import numpy as np
, j/ i7 s& b/ E6 l2 v* zimport matplotlib.pyplot as plt
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; E1 O6 W* Q. B5 h! r9 z) ?import utilities
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, a9 ]+ S" a3 N# 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'
. x7 X  Q% u$ o* g; x! a8 C1 C+ ~X, y = utilities.load_data(input_file)

9 X2 i% Z* ^) G& r+ l###############################################
" x+ X6 e% L& b' e6 Q  l8 u" G# Separate the data into classes based on 'y'
  Y6 \# A! U+ ^2 v- Uclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
. H$ q# G# p: r) ~* ~class_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])

# Plot the input data
& a5 T9 R& I7 i- f# F4 J' }1 Zplt.figure()
! l6 q% y! ?% L8 Z8 b! Rplt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
; b4 ^/ Z# B8 _1 i! J0 @; y3 ^plt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
& d) `; h; Y) q1 o9 |4 Y: G. F- iplt.title('Input data')

###############################################
  v) ^1 E5 j  z- q7 i# Train test split and SVM training
from sklearn import cross_validation
" _. a0 A  g) w- K& xfrom sklearn.svm import SVC
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: U" V3 i8 u4 C3 h7 TX_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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3 Q$ u. t1 C- j1 v. c3 X- ~#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
" G0 o/ {+ Y* Q6 F+ Y1 {' a, Kparams = {'kernel': 'rbf'}
classifier = SVC(**params)
3 `. R: g6 Z1 D- p2 pclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
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y_test_pred = classifier.predict(X_test)
, R, G7 k$ E) m% Tutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')

$ e) g7 v) X5 M# u###############################################
# Evaluate classifier performance
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# a5 |' X* W. k  r2 y2 J, tfrom sklearn.metrics import classification_report
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target_names = ['Class-' + str(int(i)) for i in set(y)]
4 T) R/ N6 }0 G- Q& E: t, ^& `print "\n" + "#"*30
- ^( x0 Q. \* w: \: N/ E6 P5 f: G; {print "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"
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) d& ]$ z0 d. ?- {. \5 }( ?print "#"*30
print "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"
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jteo21

好片强烈支持

wxw0214

你就我们摸我

正大符

精华帖，力鼎，巨乳

hzfdy

很具有野性

阑珊灯焱

看上去还不错

wongyuro

感觉还是不错

violetrain2

看着图片很有感觉的