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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
1 K$ @$ S6 x# ^9 f+ ?, q. Wimport matplotlib.pyplot as plt
- x8 \3 _4 k6 c- w) f) l3 O
% S9 p& S' ~+ Y  u# aimport utilities
7 a$ ^( {8 D% d$ a
& G" y# g4 G" s9 G; I) V  `, q# Load input data
/ ~% a, {6 O! vinput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
X, y = utilities.load_data(input_file)

###############################################
: n3 `) J) d2 I# s; }+ t- d$ m- 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])
& \) o, Y; c& C5 R3 a. `6 c
; _+ R/ ]( V9 `; c6 t* c# Plot the input data
plt.figure()
6 f7 j% d% h+ ]4 J% F3 I; Wplt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
$ q% I* U. L4 r$ b# C% ]plt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
' l5 r8 L6 F# I3 A9 f+ Y# e# Dplt.title('Input data')
) w) @( h! E6 b2 `, k) y# z
5 N% ~; S% k. w% [' {###############################################
1 U  m' Y3 ^  H2 `2 Z# N% k; P# Train test split and SVM training
from sklearn import cross_validation
from sklearn.svm import SVC

% G# g1 J$ T8 t5 ?! w; g: hX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

+ w3 ~+ T( X- N: l' _8 Q: `#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
7 t8 F. B2 w. E0 Jclassifier = SVC(**params)
; u1 D4 J7 W% M) i, aclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
! w, F5 H: F0 N# r6 |
' v7 Y' e9 R: H) @& ry_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
& [* ~: K- u) l7 A1 P
###############################################
# Evaluate classifier performance
9 n, Y* Q* Z- ?& N: c) Y4 D% `" k
1 Y1 q4 J0 G% W5 m3 ~5 Z2 lfrom sklearn.metrics import classification_report

+ E4 {, j3 y+ h- _target_names = ['Class-' + str(int(i)) for i in set(y)]
) Q' L) \$ F' b4 W6 I# qprint "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
/ H' f4 T% u0 h! Vprint "#"*30 + "\n"

0 {# L7 Z4 \; P3 Yprint "#"*30
, |( j  U0 o" M+ Q  ?print "\nClassification report on test dataset\n"
3 E, Z9 o5 t+ }print classification_report(y_test, y_test_pred, target_names=target_names)
% N9 n7 {7 L5 k: L- gprint "#"*30 + "\n"

jteo21

好片强烈支持

wxw0214

你就我们摸我