【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
; ?) ]; f+ x$ r/ |# X& n
* n  q4 ?/ h& G+ ~import utilities

# Load input data
. ~: i( @* k1 F) f- Linput_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)

###############################################
8 X1 H' r- q& L6 q# Separate the data into classes based on 'y'
& V  r; y" o! M( I7 Y$ rclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
( N" n1 q8 r) ^4 \class_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
2 D% q9 j+ [9 D
2 |" H, S: ?" q& i# Plot the input data
" D, J# @/ L4 x, Iplt.figure()
) N; s; U5 l3 a/ O2 F# M5 _. {+ bplt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
7 `1 x% c- d' Tplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
plt.title('Input data')

###############################################
1 ^4 E) `+ T$ @7 L0 `# Train test split and SVM training
from sklearn import cross_validation
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)
; q, C, {0 |6 h7 G+ f$ ?
3 g. j; U2 Z8 |' U#params = {'kernel': 'linear'}
+ q8 y2 d0 M2 X0 {) J  O#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
/ w( J- T* k. J* N) Vclassifier = SVC(**params)
0 K. _; X1 u6 f& uclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')

1 `( W+ h# v  Dy_test_pred = classifier.predict(X_test)
; l$ R1 n" b$ xutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')

###############################################
# Evaluate classifier performance
9 Y& G  n0 b! }. B& \5 p
' B$ ^) D) n3 g. l. l4 F* kfrom sklearn.metrics import classification_report

target_names = ['Class-' + str(int(i)) for i in set(y)]
4 n  J$ N5 V( x, ^* @* S0 _3 i% I, \& Qprint "\n" + "#"*30
- d! j; _5 q7 Q5 i. iprint "\nClassifier performance on training dataset\n"
2 D$ t$ i' H# }% P! \3 I: Tprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
7 @, c( C4 f$ G- @/ @print "#"*30 + "\n"

  t  S8 c+ f) Z6 P+ j* V) {1 \print "#"*30
0 Y8 Q8 u# i" N$ b# w( Hprint "\nClassification report on test dataset\n"
7 U, s) U# _; Q# X4 dprint classification_report(y_test, y_test_pred, target_names=target_names)
  m  O! n3 E# N$ E; Q7 Cprint "#"*30 + "\n"
3 c9 P0 _7 A( p
6 z$ s4 `( T- I2 Y& S

jteo21

好片强烈支持