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

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
8 Z" @4 D3 A0 p0 M3 J/ n  w8 k, t
3 J! k, y; q2 A1 jimport utilities
7 ]% R/ H" u5 J. l! D
" \7 `, ]4 `6 n! ?' N  V& S  s# Load input data
" z5 c; i& l) u' xinput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
. @7 z( Z5 o. Z8 c! T" NX, y = utilities.load_data(input_file)
! _6 N  O" k. ]1 @+ u3 z
/ U: J  J- l& s; ~" a###############################################
# Separate the data into classes based on 'y'
7 J9 p5 o, W* X4 E4 U! [class_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
3 P/ L2 J% {8 }* j2 @4 Oclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])

# Plot the input data
8 O. K$ t- K' B1 T) dplt.figure()
& c: Z% L# Z3 C! Iplt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
: H& Q$ H: I" [' Rplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
) S+ K/ _2 ^5 D. R: @$ Hplt.title('Input data')

/ Z% `( u" f" T  h' T8 m1 }8 k###############################################
# Train test split and SVM training
' ~  q, ^0 E- `* o7 p7 ~1 dfrom sklearn import cross_validation
from sklearn.svm import SVC
" j1 l8 h' \& J0 e$ y/ _
  O) V8 J9 q" q/ E9 y9 VX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

8 f. d5 S( a# ~/ i7 e( T#params = {'kernel': 'linear'}
" @5 @8 m/ ]4 w0 d' C#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
, Q6 X" D+ j6 a5 K6 l0 Gclassifier = SVC(**params)
) ~5 i) w. B! k1 b+ Sclassifier.fit(X_train, y_train)
/ z7 J$ }9 {' G3 S/ u8 L0 C7 nutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')

# S6 s; x: T, k- Y5 O8 Y( J' fy_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')

4 \+ }* }0 J: I; ]###############################################
5 a$ t1 O' x5 ?# Evaluate classifier performance
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from sklearn.metrics import classification_report

) c0 C# S. q6 o/ ~; jtarget_names = ['Class-' + str(int(i)) for i in set(y)]
8 w: B3 s! H- {% P+ Fprint "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
$ T& C( H; e7 c+ a9 [" bprint "#"*30 + "\n"

print "#"*30
print "\nClassification report on test dataset\n"
# r! j! i& A/ kprint classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"
% P7 Q4 l% z' V8 q

jteo21

好片强烈支持

wxw0214

你就我们摸我