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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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% Q  v. u9 n7 j- mimport utilities

# 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'
" N) N# h, i$ h* B, s% MX, y = utilities.load_data(input_file)

###############################################
2 d" Q/ V2 A, N3 N) F# Separate the data into classes based on 'y'
class_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
5 L) n) V$ j; i4 c' Aclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
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3 s! n; ~( m$ O3 B: d; ~# Plot the input data
& }6 B7 n0 M: b3 M) Vplt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
! Z- O# x' P+ q5 A1 m2 V) V9 d+ O. ^. iplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
% M3 |" I* b% a: U6 t3 i  ?plt.title('Input data')
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###############################################
# Train test split and SVM training
# S  Y$ Q) |; k3 zfrom sklearn import cross_validation
( k; z9 L( n0 Q7 z* q9 Ufrom sklearn.svm import SVC
' {1 r, [0 ^8 `4 l# g6 A
7 B+ C+ S$ m( w! ~2 G- ?X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
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" _/ ~, ^& G$ n3 O9 B#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
! f$ c2 ]0 w) i: u: vparams = {'kernel': 'rbf'}
. _' f- k, x5 Z8 |' E# gclassifier = SVC(**params)
" @/ l6 I  F. tclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')

* e1 X4 _; \0 U& My_test_pred = classifier.predict(X_test)
5 X6 o! C( e- X- dutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
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###############################################
# Evaluate classifier performance
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from sklearn.metrics import classification_report
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) i' b4 z7 [$ L% n+ ztarget_names = ['Class-' + str(int(i)) for i in set(y)]
/ P7 r6 M6 G- {. L* iprint "\n" + "#"*30
, t1 F2 o0 r9 Y7 C& X6 M' Qprint "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
- o$ W  G) ]+ S4 Xprint "#"*30 + "\n"

print "#"*30
0 T& \+ |8 j/ p  j. U/ r" m* l! ]print "\nClassification report on test dataset\n"
: ]+ g( ^# ?& |- o. e! j, X: tprint classification_report(y_test, y_test_pred, target_names=target_names)
8 x/ Y4 |. n" W# zprint "#"*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