【申精】花花公子成人频道Playboy TV-Spring Break (春假派对系列 全）

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

! m9 j' C& Z5 \% o9 \3 Limport utilities
8 i& v) F$ a, h) ^) @7 h- {
# Load input data
- U  P5 M- T3 Q* Kinput_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)

% U; p6 N5 Z! H& ]$ I" P  }- A###############################################
+ e2 e5 }) e  e' H' @# Separate the data into classes based on 'y'
class_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
1 |: k' M9 ^2 Y  G1 u) Dclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
( v7 r2 Z8 K7 v; X6 t6 r
+ K: L2 \$ ^8 x; m2 F0 D# Plot the input data
  ]! w& H  O2 Dplt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
) V0 l; D7 y, K+ X2 S; X% P4 Tplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
; x& V$ r/ t0 X/ Y+ Wplt.title('Input data')
/ D: u$ D& L/ d  c& y" g; {4 j' l
###############################################
# Train test split and SVM training
5 V" P' T1 r% Vfrom sklearn import cross_validation
3 A1 y( K& L1 G: hfrom 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)

. h7 B6 E# H) a, K" m; \#params = {'kernel': 'linear'}
* ^& N5 u2 U" m#params = {'kernel': 'poly', 'degree': 3}
. V6 c& W: |3 X, v' [params = {'kernel': 'rbf'}
6 }. G% N$ m( \9 u* g1 c$ R# B- gclassifier = SVC(**params)
classifier.fit(X_train, y_train)
  }6 {% Y2 b) |5 Mutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
6 w& @5 o9 }" F4 _' n7 A8 ^
y_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
9 Z2 P, t8 l9 i- g
$ g$ H5 ]' l6 S5 W. s###############################################
, k- e/ c- M5 p* x# Evaluate classifier performance
. P0 l& X( W: A1 ^( ~4 T6 j
. Z4 x6 L! X+ q$ Z6 K% jfrom sklearn.metrics import classification_report
& @) s/ ^( r+ ]! I7 S$ }3 |
& p9 R" x( W- N- c7 Xtarget_names = ['Class-' + str(int(i)) for i in set(y)]
* H4 f: E3 j! s$ x+ x% B7 Tprint "\n" + "#"*30
* F' i6 P. \% p' mprint "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"

print "#"*30
print "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"
2 r' S( R/ A( v; S0 i" A! f

jteo21

好片强烈支持

wxw0214

你就我们摸我