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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
( g& {* C0 ?) |$ T" A' s; j6 Aimport matplotlib.pyplot as plt
; @% [; m, X3 c! j* p
import utilities

) E  q" S* i" p/ e- p. Z8 J2 G8 s$ o# Load input data
" e7 x1 p% u7 h5 Y  ~; ^input_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
: O; Z. Y) i' \X, y = utilities.load_data(input_file)

1 O- Q0 T3 M% ~( r7 ^- n###############################################
: X% G( f$ }+ C+ z) p0 w$ t4 i# 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])
8 n0 V' y# \: O$ P
# Plot the input data
% Z4 i: e" u$ O/ E/ P# splt.figure()
3 N& L3 K; W& E4 z1 V- s. hplt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
4 d; C# p, k8 i) T5 c* g  dplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
8 }; Q  F7 c  z* S  A2 P. Pplt.title('Input data')
  G- t* G8 w& ?) W6 p7 \+ [& z
###############################################
# Train test split and SVM training
- i4 Y4 U; C! W! L4 ~' T- b, |5 u- afrom sklearn import cross_validation
- Y/ O$ T4 V% D1 Yfrom sklearn.svm import SVC
5 j1 i% Q3 S! Z! v+ h  Z. j
" ^5 @, S& ~- AX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
7 y4 C7 Q/ g: d% y
#params = {'kernel': 'linear'}
& r, O; X4 y, ^1 m#params = {'kernel': 'poly', 'degree': 3}
* l  j' t  o3 T9 b3 R' m5 G7 Iparams = {'kernel': 'rbf'}
classifier = SVC(**params)
; D  k  _  W) Z# r9 ~. ]classifier.fit(X_train, y_train)
" n- h$ Z$ P* l, L8 }. o( i8 Cutilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
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8 S0 z! r( Y) ]1 uy_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
5 R. E, G- h( L
3 L6 U" ~5 A$ f6 n###############################################
# Evaluate classifier performance

8 ^7 b" ^! k4 Wfrom sklearn.metrics import classification_report
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target_names = ['Class-' + str(int(i)) for i in set(y)]
7 s# w. O  G7 \5 x4 M: B2 ~: x7 N* eprint "\n" + "#"*30
- ^. b) [" k9 G( I2 `; O3 pprint "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
% n" p& p9 f* v  W3 H: ~print "#"*30 + "\n"

print "#"*30
: m1 r- [6 t- k( h; _0 gprint "\nClassification report on test dataset\n"
5 j5 W1 Z- c  T0 k7 k. `' Z7 `9 Nprint 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