【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

9 ]7 ~  B( ?: D' ~; ^; t( a& U0 b9 Timport utilities

# Load input data
4 y' G! }' ~4 w! U: Q. ]input_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
: g( Y1 H, w6 d& jX, y = utilities.load_data(input_file)

2 v! c! G9 S4 ]! t5 O###############################################
# Separate the data into classes based on 'y'
; u, B0 P; p3 x# X( K) p) lclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
( ]: d- L3 [8 Xclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
! l4 d- k# I: k7 j% B0 S6 x
1 f# w: e" E# C2 M2 g1 Z# Plot the input data
, e7 D  z9 s! Q5 ^plt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
$ w* k7 e% R/ h  p! [5 q/ H5 Dplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
5 n/ Z2 \. x; C) ?) ]  fplt.title('Input data')
3 e5 W7 m& u; w0 z( n
3 u& O9 F* V% Z1 J5 O* ~' Y5 n( N###############################################
, g/ G) R4 i" J6 t2 a# Train test split and SVM training
' A* y% }% U3 K2 X4 Bfrom sklearn import cross_validation
from sklearn.svm import SVC
: q6 I5 ?/ D( P
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
% a: l* [, C6 y0 z8 N; G2 t
6 ]. n# A* A- }! g8 p#params = {'kernel': 'linear'}
& I' C  M9 V  @' S4 K& f#params = {'kernel': 'poly', 'degree': 3}
% C. {% f1 a+ Uparams = {'kernel': 'rbf'}
4 N7 C/ J: o" p/ U: u1 I6 N+ oclassifier = SVC(**params)
classifier.fit(X_train, y_train)
  E" e1 Y! l2 j5 \  j( u6 Mutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')

% R: r  d7 L2 W" My_test_pred = classifier.predict(X_test)
: ]. ~2 S8 C, _( jutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
' g: N9 C  k* B
; U% A+ E/ Y. _' L) Z8 Z###############################################
* D0 B! X2 Y- b# Evaluate classifier performance
' k- t2 I  K* L) k( u
, w/ J/ c$ J. K1 W8 Rfrom sklearn.metrics import classification_report
; o1 a$ ?7 x) Y, z7 W* P
target_names = ['Class-' + str(int(i)) for i in set(y)]
print "\n" + "#"*30
- ^, L" f7 K6 I9 E5 w2 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
print "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"

jteo21

好片强烈支持