【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
import matplotlib.pyplot as plt

import utilities
3 N, r; X6 v6 D* a: S; T
# Load input data
& K) d' }( B1 o) y. {7 m9 Pinput_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)
# L% L. C5 x9 b& C
9 o- _% d+ {6 s, s6 K& I. P3 d& u###############################################
0 ?& n5 i) i) q4 [1 }& k# Separate the data into classes based on 'y'
class_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
class_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
- O5 X1 M2 o6 b
  l2 m3 \3 @6 A, \* ^# 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')
plt.title('Input data')

* ~9 S" z; K" R6 E###############################################
9 z! z+ H- E2 ?9 `# Train test split and SVM training
( d6 V, N% }6 K4 `$ Nfrom sklearn import cross_validation
from sklearn.svm import SVC
! c" \, j+ h: W5 C, A9 \
! t2 b) m! Q4 eX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

4 m  q0 E  `3 J( b% R#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
( \# i* B3 d: z% j. tparams = {'kernel': 'rbf'}
classifier = SVC(**params)
1 H5 c( `1 y+ m& f4 O" z$ wclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
8 r5 n! v+ y! w& }" G& y4 D( |
y_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
+ I. [2 p* B% |1 {1 X
###############################################
( P8 |7 e  m. {9 M7 V  F7 U, I5 C# Evaluate classifier performance
  J0 M. `3 i; ^# ~& v
from sklearn.metrics import classification_report

7 z& j+ b. ~1 t& {% C$ B9 Btarget_names = ['Class-' + str(int(i)) for i in set(y)]
. s5 j. p+ C7 u/ s& p5 \print "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
7 D8 K0 e. B) dprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
( J0 o- Q" C+ b9 U! u' wprint "#"*30 + "\n"
* C6 t/ U& q1 f0 ^+ h* Q/ {
print "#"*30
print "\nClassification report on test dataset\n"
* X- a# t8 T8 ?# o( r8 L: mprint classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"

+ S( Y, ^% f4 L+ e( ?0 `3 ?

jteo21

好片强烈支持