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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

5 V0 f8 e, [5 y* g# Load input data
; \% w8 ~6 \2 G) |% k9 w$ }input_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
. W$ X: [1 ~5 d& ?; C) W: QX, y = utilities.load_data(input_file)
! z) ?; x; C6 t7 w7 k$ j' R
###############################################
- x# E$ O: p# ^' O+ L# Separate the data into classes based on 'y'
class_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
) D% J9 P3 p) ?) cclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
) m* |; |; i, V6 n
# Plot the input data
( @( p3 [" E6 r' Z* X$ Xplt.figure()
( P( V# O/ d* S! lplt.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')
( V0 `# l1 g/ d/ I! h* k& g1 oplt.title('Input data')
9 m. M  X1 R: ~7 T6 R0 f+ T
5 E8 F/ @, L$ T- J& y###############################################
# Train test split and SVM training
( N# d/ N6 K* {2 H" m7 _! p$ \from sklearn import cross_validation
1 Q% L. P6 t! U. Q' U( B& b- Afrom sklearn.svm import SVC
1 `  I3 k  N# n5 G  b. o# o( u7 i
  D9 `6 y2 F5 m* PX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
" r! |% \8 @( u$ u& t! z0 S& U& n3 J( T% Tparams = {'kernel': 'rbf'}
# {% H! }" {8 l+ d: rclassifier = SVC(**params)
0 l8 t- @5 {6 K6 Z8 Eclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
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9 k2 r0 y% Q+ `* T$ R; K. ]y_test_pred = classifier.predict(X_test)
& Z( R. O, Q2 ~5 jutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')

###############################################
0 y. x6 u2 X+ ?$ F# Evaluate classifier performance
7 P8 P6 Y. X7 |" v6 P
5 F+ z( K/ P6 m% i( E0 S2 Gfrom sklearn.metrics import classification_report
. d% z3 n7 [  U6 b& M
target_names = ['Class-' + str(int(i)) for i in set(y)]
print "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
/ B) \* @4 o8 G5 |print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
+ p+ q5 W# ~3 f4 k: Lprint "#"*30 + "\n"
# A2 k4 T# _1 x4 X4 s3 m/ \
print "#"*30
2 ?- k, N3 C$ U& f3 Qprint "\nClassification report on test dataset\n"
) e; X, X. j# c, ^& {6 k" Gprint classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*30 + "\n"

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