【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
8 e- E/ e+ A2 o5 e: Gimport matplotlib.pyplot as plt

import utilities

# Load input data
" s+ M2 N+ l7 a) J+ ?' h# Sinput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
5 i! d& p' `( `- I9 ~3 mX, y = utilities.load_data(input_file)

###############################################
: f8 K3 h! p7 M) S: Y$ A# Separate the data into classes based on 'y'
) e8 T* w" |1 [3 x8 M/ qclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
3 o- r0 b& I( e+ E/ Tclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])

# Plot the input data
plt.figure()
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')
& I% W4 F1 D" K. \1 F1 G$ jplt.title('Input data')
1 P( p7 A& D8 A" w9 [* A7 g. P$ l" K
: ~5 Q$ k& p; u3 J! e7 P6 \$ H###############################################
3 }& E" `) _. X4 n# Train test split and SVM training
) R6 a$ K  F* T2 I3 W7 Vfrom sklearn import cross_validation
from sklearn.svm import SVC
+ ~* X$ x$ p  y1 n: R7 l
. p: @) Y: A3 a( |: N+ G% cX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

9 n- O, C7 d7 `6 r  d#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
6 t( H' J( \% X  F1 gparams = {'kernel': 'rbf'}
classifier = SVC(**params)
1 F4 [. B/ `! U( p& K8 oclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
4 A6 s" S- J- H, B
y_test_pred = classifier.predict(X_test)
9 |1 ^: ^  F* a- k3 u1 h9 Butilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')

5 K/ |% q( X0 p" K" K; O* D###############################################
6 I3 Z& d. \8 U; f& g0 }# Evaluate classifier performance
4 _# H1 B% t2 R
from sklearn.metrics import classification_report
5 ?; C! I1 q0 {
target_names = ['Class-' + str(int(i)) for i in set(y)]
- e, E! h# a0 V" t4 c8 d! O, mprint "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
. G& m0 R6 W  }" mprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
3 h& G0 y  O' Xprint "#"*30 + "\n"
  ~( D; l1 y+ E# m/ r) H
& W) U* ~5 i8 R) Tprint "#"*30
) Q! a' ^6 R. kprint "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
% H! x+ e( G4 ]2 A' wprint "#"*30 + "\n"
6 m. ]% t- }, `! L+ e( F
  R& P6 s+ ~4 m9 x2 t- f4 e- s1 m% y

jteo21

好片强烈支持