【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
, ]6 {) w/ i# S; n8 kimport matplotlib.pyplot as plt

import utilities
2 Q4 R9 r/ X& r+ U# A/ m( g  H
2 j0 o' m7 c  w9 u, [. P# 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, y = utilities.load_data(input_file)
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- G& X; d5 M# N/ S/ s, _- S###############################################
# 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])
7 P6 t& U' M' W* m
" |, B% t( K# h' y# Plot the input data
9 {! m! w1 X8 p' t, P+ D$ ]0 hplt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
3 F; [2 _6 K4 j7 c8 C( [# `plt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
plt.title('Input data')
' U3 Y2 S2 z1 F- i' b' @
###############################################
& r  G# C+ n% a3 ~2 _' q$ H2 q1 _3 D# Train test split and SVM training
( }1 D, `/ x6 hfrom sklearn import cross_validation
. l, N( @* z& y4 X: R8 q3 K* O/ |from 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)
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' y3 e9 u8 h5 T* T1 w4 P#params = {'kernel': 'linear'}
; b- o" C" v* L# I' {#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
, k4 T- D, P' [: Lclassifier = SVC(**params)
. N- V0 o6 _  z! _5 j1 pclassifier.fit(X_train, y_train)
" m1 M4 j7 O* l) s0 x/ i+ Lutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
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5 ?2 b% b' `- O+ f2 }% g! n' }y_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')

###############################################
# Evaluate classifier performance
( O) R$ `# a; M1 E
from sklearn.metrics import classification_report
) Z1 D7 r( Z5 n0 Q, W/ P+ L
# N" L% k1 r" X9 `% k6 wtarget_names = ['Class-' + str(int(i)) for i in set(y)]
5 f5 j$ A8 g& r1 Lprint "\n" + "#"*30
/ t5 X/ r& |" aprint "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"

print "#"*30
9 D) |; o- u4 r+ w* Xprint "\nClassification report on test dataset\n"
  f! W/ F0 l( X5 |# l& Sprint classification_report(y_test, y_test_pred, target_names=target_names)
! R4 N; u7 j1 k0 Iprint "#"*30 + "\n"
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jteo21

好片强烈支持