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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
0 S; g. D# {8 M
import utilities

# Load input data
1 J8 S' B6 y7 s6 ~& b0 i' Xinput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
5 j2 N. E+ V% e% {7 kX, y = utilities.load_data(input_file)

  t7 e0 K% }) X. M###############################################
8 S4 [( v8 P& e2 W" s# Separate the data into classes based on 'y'
class_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
; m# A- n, e. z( Q# |: W3 @class_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
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# Plot the input data
plt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
" T: |% V0 w( v9 P8 d1 K* wplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
# J' \' d" g+ W, \1 `plt.title('Input data')

###############################################
# Train test split and SVM training
0 I, S! x- N2 O; l# {from sklearn import cross_validation
5 {) \' m3 Y5 j9 G7 C; ifrom sklearn.svm import SVC
9 y, S8 `3 R! o
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
# H0 g9 ?5 [* m8 f% S$ m, h
8 x: P* n7 U0 E: f+ I#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
. V+ Y8 U( _% x. G6 B9 k, Tparams = {'kernel': 'rbf'}
- B# e- J) L3 y7 V; a5 c7 u/ Zclassifier = SVC(**params)
; h" S; n3 S' l) k5 Iclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')

, ^7 L; x& V1 k7 Q: }1 ^y_test_pred = classifier.predict(X_test)
- t9 G1 z; y7 w6 F) K% E) jutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
2 N6 F5 k4 t) s# d* [3 f
###############################################
9 `) ]1 h, L9 [4 P) {# Evaluate classifier performance

, d0 L+ {6 |% p' T0 P! Pfrom sklearn.metrics import classification_report
6 S4 _0 ~, x- }; ~' j2 T
target_names = ['Class-' + str(int(i)) for i in set(y)]
print "\n" + "#"*30
# P' z9 |& A1 |( G- [3 r. kprint "\nClassifier performance on training dataset\n"
0 x/ x( }/ z) a" @( Fprint classification_report(y_train, classifier.predict(X_train), target_names=target_names)
- f" g- j$ K0 A) Y$ Rprint "#"*30 + "\n"
9 j' B: o6 d; |/ S  g+ q% X8 s3 x
print "#"*30
6 Y, ?1 j  w; d( |  kprint "\nClassification report on test dataset\n"
print classification_report(y_test, y_test_pred, target_names=target_names)
print "#"*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