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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
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import utilities
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, a6 a+ ?# y: k2 X: R* H7 b; w. x# Load input data
: }8 X6 i/ e8 Q- Qinput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
' @! [2 [9 Z9 D& s" \0 E2 PX, y = utilities.load_data(input_file)
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3 n9 ?8 g7 P8 n###############################################
2 Z! g1 V# w8 J) 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])
. ?4 A# c' Q+ f8 M1 |* A* rclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
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, a4 Q7 V: `5 ?; G: E# Plot the input data
plt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
( B( v) p! H: f. l9 a8 X  v' kplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
+ Y1 J, Q5 e; s+ Splt.title('Input data')
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###############################################
6 @) F" X" T2 e# h+ S7 w% I# Train test split and SVM training
from sklearn import cross_validation
- t, ^) w. `9 \& t2 W* h2 Cfrom sklearn.svm import SVC

8 h4 n; X9 |5 t; @3 j/ N0 C$ cX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

* r" ~* [1 o0 z( E#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
1 Q; a: t4 E9 E7 ]+ Pclassifier = SVC(**params)
' z" b- C5 @& U' s  w$ mclassifier.fit(X_train, y_train)
0 n# {, q4 H/ ^* h% r" zutilities.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')

* H( J1 J0 H0 p+ t2 ~- }& X# [' I* g###############################################
* b+ }7 z. S" f, g) G4 [# Evaluate classifier performance
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5 t4 L3 j* n! i2 Ufrom sklearn.metrics import classification_report

target_names = ['Class-' + str(int(i)) for i in set(y)]
" d- @$ U: l+ C0 _) \+ h* ?+ I- oprint "\n" + "#"*30
5 `% f5 m2 @2 Q7 d) S9 vprint "\nClassifier performance on training dataset\n"
& ^) m* {" g3 o% V! @0 y1 t& bprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"
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9 ~. [  ~& P) i% ?print "#"*30
print "\nClassification report on test dataset\n"
5 v6 I: i- c- }6 i" rprint classification_report(y_test, y_test_pred, target_names=target_names)
# I& j/ l9 c) l4 nprint "#"*30 + "\n"

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jteo21

好片强烈支持

wxw0214

你就我们摸我

正大符

精华帖，力鼎，巨乳

hzfdy

很具有野性

阑珊灯焱

看上去还不错

wongyuro

感觉还是不错

violetrain2

看着图片很有感觉的