【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
( R+ d6 y- H0 ?5 ~8 h  b2 V. _2 Gimport matplotlib.pyplot as plt

/ f# E0 j/ o: _6 V- s- @import utilities
3 _2 n' [. R) S' F1 p) R: z- s" @' O
# 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'
) v/ Y# r& u  @" H6 K/ [X, y = utilities.load_data(input_file)
: J- S" Q5 z4 f' I6 _
###############################################
5 P. [9 z# a) Y7 c# Separate the data into classes based on 'y'
) T* J9 p* V: e2 tclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
& Z) j5 @: H7 J3 U& x' ^class_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
. @7 v9 v; w/ t
  i- I/ }0 I1 ]( F, e/ q! a  _# O9 @# Plot the input data
plt.figure()
! `( U  ?% s6 C- Z+ G  @plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
7 `* h0 c5 Z+ v  Y. W4 a, ^: l- U1 Qplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
+ N0 o& }  i; U; Z2 T# c1 c. rplt.title('Input data')

9 S% e6 V  C( z: I* n: d4 w###############################################
$ X- @% S" b) f# [5 R& |# Train test split and SVM training
from sklearn import cross_validation
  B! G( E$ v, E  _. j" Cfrom sklearn.svm import SVC
( x5 Z; T3 z" d1 E* [4 |( Q
3 i/ q9 @. d" `8 r$ S" v7 s) K  BX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
" s: K: T8 s* h7 r
$ L  f7 g  N! ^! _. K- l#params = {'kernel': 'linear'}
' T  [1 d  U6 l  @7 g4 {0 i#params = {'kernel': 'poly', 'degree': 3}
8 Y4 G+ n) v# F  _; g6 r+ Vparams = {'kernel': 'rbf'}
classifier = SVC(**params)
' V# U. R  ?/ z) u1 mclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
3 b4 L' }# m3 L* ?. {
0 V. U2 B8 C2 I/ @y_test_pred = classifier.predict(X_test)
0 K( ?4 z2 r9 F! U% sutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
6 B: w$ v9 r2 \& i9 V2 y" O3 p! h8 }
###############################################
# Evaluate classifier performance

* r# c1 j* u' ~& w, i: lfrom sklearn.metrics import classification_report

target_names = ['Class-' + str(int(i)) for i in set(y)]
print "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
' v1 f7 g9 g& Yprint "#"*30 + "\n"
% K3 b7 Z& j5 Z
print "#"*30
$ r* ~& z- w: A: o( y9 bprint "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"
6 J- T4 X& V: r4 Y) X8 X

jteo21

好片强烈支持