【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
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import utilities

# Load input data
% m' t2 f7 o( v% U9 v9 y" D' Linput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
* W3 |2 Y  ~5 O' ~7 @! F( `9 R; {8 jX, y = utilities.load_data(input_file)
% D. {9 Y, h1 G- N) J1 p' a
% H( p8 v2 S. N  y+ o###############################################
# Separate the data into classes based on 'y'
7 {7 a8 ?' N% H! H7 z" U) j' a, Kclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
1 E, p% H" _3 P* t; S% Mclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
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# Plot the input data
) [- s6 K' `) P: mplt.figure()
; Q5 k9 T+ L) u# }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')
0 F4 Y/ ~9 A- e/ g% A' splt.title('Input data')
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###############################################
# Train test split and SVM training
from sklearn import cross_validation
from sklearn.svm import SVC
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) f, B' [+ r- v) b- k) CX_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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#params = {'kernel': 'linear'}
# z# R# Q  W" W7 u5 t#params = {'kernel': 'poly', 'degree': 3}
4 I2 M$ K2 S2 S$ wparams = {'kernel': 'rbf'}
classifier = SVC(**params)
8 D. |/ f& [; s$ m# cclassifier.fit(X_train, y_train)
: B. W! `3 C5 k9 B. c  H) mutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')

y_test_pred = classifier.predict(X_test)
( |3 T- O( r% {2 c2 R% tutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
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' y9 E) r" [5 M! W7 \$ a, {+ ]###############################################
# Evaluate classifier performance

( ^) _, o, v0 l8 `+ Xfrom sklearn.metrics import classification_report

target_names = ['Class-' + str(int(i)) for i in set(y)]
5 l: d- e4 @9 z4 Nprint "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
- G9 O/ U" L. j$ lprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"
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8 u: R* l( O- ?( @+ [7 \print "#"*30
- ?" {3 S' F$ b, Wprint "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
% f/ c$ h* ?7 o' Sprint "#"*30 + "\n"
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jteo21

好片强烈支持