【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
4 r# I- _' f+ a, C2 [9 k9 B1 himport matplotlib.pyplot as plt

: u: z% D! E4 h; a, }import utilities
2 Q, o0 q/ |) i1 \. B9 T
# Load input data
4 w/ U( F% z  L$ {- `' Pinput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
3 Y9 F7 @5 Z% p2 EX, y = utilities.load_data(input_file)
5 v& R1 v# E/ t! X# t3 ]8 z# r; C
2 P8 t3 p0 h" \" m$ M9 b0 q###############################################
' q7 I# i7 N  W% R7 x6 d2 m" U# Separate the data into classes based on 'y'
class_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
% f/ e( t. w. Q9 Q3 @) ^% g" N* R# Jclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])

* y/ J% ~) h; J" c+ D* n. H# Plot the input data
plt.figure()
' W( t2 n' x5 x, ?* vplt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
. s! T3 h! c1 ]) d7 ]! Q+ t3 Mplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
plt.title('Input data')
3 x# L% z# Q8 b2 S" p0 h8 |$ \
2 k! f3 R5 Z" _2 M2 d$ k###############################################
  t% p! R3 o8 a5 q# Train test split and SVM training
from sklearn import cross_validation
, j! @" s: m; j1 Vfrom sklearn.svm import SVC

: f4 r0 }, [- U, i: ^& iX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

3 ?+ H- J  K6 P$ s#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
classifier = SVC(**params)
: S7 J) ]: H5 j7 Q' f" e  Y7 bclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
' o8 Z& Y. U: a5 G2 g+ ?
4 [" h# j5 ^6 m7 M/ j3 L* ty_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')

2 Z" z  S$ i8 a) a' |  x###############################################
1 s5 N/ z( ~+ ?5 U  _2 y! _# Evaluate classifier performance

" a/ Y; `  r7 M) T/ pfrom sklearn.metrics import classification_report

1 k7 a# n0 v$ p  x; {* w; r; Mtarget_names = ['Class-' + str(int(i)) for i in set(y)]
. P. C0 B/ y$ n, Nprint "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
* r4 c& ^! E) M* j! q9 U, j% tprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
7 ~8 x5 a7 V2 B8 P  [print "#"*30 + "\n"

print "#"*30
2 K+ ]8 F5 L& [' Nprint "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"
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/ {4 k  V- [5 {. x  u; d

jteo21

好片强烈支持