【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
$ l! X* _. M; E! _9 _import matplotlib.pyplot as plt

% y4 w% p( B! |* `% }% _import utilities

7 g% X. f# ^5 i7 _# Load input data
+ H- f- l* t9 C# j* z- Q9 binput_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)

###############################################
9 e3 {: I: j& G3 q; l4 }" {# Separate the data into classes based on 'y'
7 {, Z% n3 d2 n3 Iclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
+ b$ S+ z- t: ]class_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
/ m5 m3 E" ]" E7 f2 L- b: ^7 z
1 O7 p) Z; u9 N, B+ H2 f# Plot the input data
plt.figure()
7 E  e5 _( J7 p& Y8 t0 ~plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
% d& q, u% d, bplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
0 f, w! f7 o6 y: q+ oplt.title('Input data')
0 O* \6 T' |: H% {- h) h
###############################################
" ?7 p1 K) `" }/ E1 r# Train test split and SVM training
( r# r# U: e. E. Q7 kfrom sklearn import cross_validation
from sklearn.svm import SVC
1 @! V" J, X! [0 r5 Q7 \, P7 C) s
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
. s+ _9 Y8 @+ O! i1 r. m
#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
7 f# z8 n. E3 pparams = {'kernel': 'rbf'}
8 w8 r- I& W: M1 }3 a" ~1 \classifier = SVC(**params)
classifier.fit(X_train, y_train)
4 a8 l& [+ J. m1 g3 |utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
) R% L4 g8 L+ j7 Q% S4 _" x4 B
y_test_pred = classifier.predict(X_test)
! @* m( x% S6 f; B' h! z+ v) yutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')

###############################################
% z3 w* \; I1 r  k- o6 f# Evaluate classifier performance
$ l  [; N6 [8 {# @. `+ g
3 {4 E' c/ u9 U! ^/ Y, lfrom sklearn.metrics import classification_report
6 o8 ~* z5 Q0 _
, L6 I% }, f9 e; [4 Starget_names = ['Class-' + str(int(i)) for i in set(y)]
print "\n" + "#"*30
/ Q* h" a3 R! ]1 e( fprint "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"

, A+ y* z& E6 l0 S; L+ J8 sprint "#"*30
print "\nClassification report on test dataset\n"
/ x5 H4 [. I( O) ?+ ~/ C$ D/ R9 xprint classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"
  g) x1 I& F# u4 O9 R% i
5 u! n; a0 q( V4 y

jteo21

好片强烈支持