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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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# 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'
  x7 U! w$ G, U  V! Y: Q7 [3 bX, y = utilities.load_data(input_file)

###############################################
: P2 n: L8 h, K# Separate the data into classes based on 'y'
/ N8 n6 \9 i3 Z  Cclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
$ U. g  o9 [% G5 Q- H* Z" O7 yclass_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')
8 o' p+ b9 l. o/ yplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
plt.title('Input data')
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###############################################
# Train test split and SVM training
from sklearn import cross_validation
from sklearn.svm import SVC

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

7 k0 N5 a7 k/ P5 U1 Z, I! ~: ?* o2 E#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
. A, T" @9 p5 @+ G0 }* g# Nclassifier = SVC(**params)
$ ~8 Z1 M* ^# B* Q2 Yclassifier.fit(X_train, y_train)
utilities.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')
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: }: v: a. W$ k- m, |###############################################
# Evaluate classifier performance
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" ~- D) X. Z; d1 afrom sklearn.metrics import classification_report

0 A* s: ^7 g' c3 u3 J3 w, ltarget_names = ['Class-' + str(int(i)) for i in set(y)]
) _- R1 ?2 \% p+ Z1 M$ bprint "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"
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: T( ]( |" g& s3 k* j; c1 Aprint "#"*30
print "\nClassification report on test dataset\n"
; t% f6 U0 B) h8 O1 z8 Eprint 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