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panjinhua2

可以的喜欢这类型

bladekiss

喜欢这种群交题材的

a740815223

好多人人啊

GAOg0036

感觉还是不错

小猫不2

看上去不错

rogercheng

尝试下西片吧

gu80542

偶尔换个口味

功夫茶

import numpy as np
) O1 s) M" O( Ximport matplotlib.pyplot as plt
; S3 Q, i* \+ b9 ]
import utilities

' M1 w1 Y, f% P1 a& }: g# 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'
3 [3 ]7 |% ~! n) z- d2 FX, y = utilities.load_data(input_file)

9 G( O6 L: G( w3 y###############################################
0 \' P# H$ Q% k# Separate the data into classes based on 'y'
class_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
. Y7 z) R4 `; g( A. y/ W; Mclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
+ @4 u  s7 b+ j3 P+ `2 t% Q
: Q9 f8 \8 l# I# Plot the input data
  |% [: S# m' A3 B& Splt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
0 r; L0 P4 k+ A3 `9 q  s* c% hplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
0 ~0 T0 x" n( J/ Splt.title('Input data')

; C# s/ q0 x$ E  z7 S, q###############################################
# Train test split and SVM training
from sklearn import cross_validation
9 _: k6 I. r) r" F! L9 Y" ~from sklearn.svm import SVC
! `4 B  T8 S, n
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

" Y. f  w* x' `# C1 w#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
. E9 H5 c1 e/ n" b  zclassifier = SVC(**params)
' q8 a! @2 Z) K. Qclassifier.fit(X_train, y_train)
4 Z* ?; C7 z7 k/ A/ h( F6 K' Autilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')

* ]$ G9 l! f6 W  j6 by_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
, h# ^" c7 Z% Q3 \& Z: l( m
6 ]  z1 F' M3 C3 P###############################################
# Evaluate classifier performance
2 O3 p4 N  r1 w5 c
from sklearn.metrics import classification_report
% R1 f; m' x% u4 u/ X
target_names = ['Class-' + str(int(i)) for i in set(y)]
! U* O# D) I/ }5 T# B- l* C1 H: pprint "\n" + "#"*30
4 K/ P8 N$ @; Zprint "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
print "#"*30 + "\n"
  }0 I4 L; K3 j+ S6 E8 i
2 m% ?7 x  ^/ M4 hprint "#"*30
print "\nClassification report on test dataset\n"
7 r5 G$ B4 a( E' Q! D/ @5 n! ~print classification_report(y_test, y_test_pred, target_names=target_names)
* |5 w% e" m0 Y6 H7 `. _3 Mprint "#"*30 + "\n"

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jteo21

好片强烈支持

wxw0214

你就我们摸我