【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
3 O/ i! X5 |5 w8 cimport matplotlib.pyplot as plt
7 ?7 F9 T( \8 a( V5 N" Y0 l$ N
import utilities
5 P  l7 A, {  C
& b# |- B* l( S) _7 `4 d0 D# 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'
/ l  z* A9 x7 B& EX, y = utilities.load_data(input_file)

8 m  J/ z' ^4 K3 C; \0 z5 Z( s###############################################
9 K0 {: A7 k* D2 T# Separate the data into classes based on 'y'
) J7 c4 E" y) u0 v$ {class_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
! _- W1 U: q) cclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])

. D1 O; z/ r. E& D9 B, l# Plot the input data
plt.figure()
) {4 ]# ^/ ?% o: R/ fplt.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')
plt.title('Input data')
& Y0 L, h: |8 T& n
###############################################
# Train test split and SVM training
9 _! h2 y  [; S6 c( G9 w3 Bfrom sklearn import cross_validation
+ o: l: v! }6 m4 I, y5 Z1 ffrom sklearn.svm import SVC
7 e3 C" _2 I: n% E8 y
- z4 e% e  W8 y( z9 E! GX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
6 ~, M9 f* `. a7 R; \  ~0 F
#params = {'kernel': 'linear'}
$ K3 R& M& W# y8 ]#params = {'kernel': 'poly', 'degree': 3}
1 U. l4 M- V1 c; S  X4 I0 ~params = {'kernel': 'rbf'}
classifier = SVC(**params)
classifier.fit(X_train, y_train)
# v# i, q2 }0 T% A- B0 m1 @% \utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')

y_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
9 V: C5 s  X0 J( m2 e0 L0 g
. J( g) ^; f+ `2 ~+ i###############################################
# Evaluate classifier performance
* X6 [+ x# P: b% m4 ?8 |* v: H0 c
3 y( a. i0 T; j1 s% c' Q" }5 pfrom sklearn.metrics import classification_report

$ L  L; U/ X; B3 Ztarget_names = ['Class-' + str(int(i)) for i in set(y)]
print "\n" + "#"*30
4 [/ D' G- N. h  u% ]+ _! Kprint "\nClassifier performance on training dataset\n"
5 ]0 K* s- z: s. Gprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
4 j' y. c% C0 ^  p: @: @. q, ?print "#"*30 + "\n"
8 E8 n5 l! P8 H# ]8 u$ a* t+ o
8 O" ?; V; ~, c. d7 J6 t) ?print "#"*30
print "\nClassification report on test dataset\n"
3 `% B3 r, v! k2 ]print classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"

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jteo21

好片强烈支持