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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
import matplotlib.pyplot as plt

8 f0 v* Q; S% l) X1 w* ^import utilities
8 {  l6 }5 G# N" L  t$ h) w
# Load input data
. {  K8 q9 T% Y/ m3 [$ H0 o/ E9 Vinput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
$ s, I$ w- N  V; DX, y = utilities.load_data(input_file)
* F% S* @- n+ v; S* U# s
: t+ [) w' k% ^2 V# S###############################################
# Separate the data into classes based on 'y'
) q9 C" C: q- e6 Q% X  o3 l% Yclass_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
) W9 V9 I6 y: uclass_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])
2 Z6 N: n' N" C0 d' n. g
6 Z, s8 a7 p4 f# e# V# p# Plot the input data
& q/ @1 D5 T7 m5 I6 lplt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
; `4 u+ h% i7 D  S; [plt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
  u! S! v7 G+ T, I( u) Aplt.title('Input data')

###############################################
! S: k5 o" L" _" u2 u# Train test split and SVM training
9 g. Q5 M  V  Q) ffrom sklearn import cross_validation
2 ^8 v* @; y; h# hfrom sklearn.svm import SVC
% u1 h5 p4 H' ~* e( B
, m7 S8 S2 {) U- XX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
$ M$ A: Z8 t* V# d" `9 S
7 z4 X' g8 H, T  [9 _! ~#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
/ |- S8 o: i+ m  n& T! {: Nparams = {'kernel': 'rbf'}
classifier = SVC(**params)
1 `& |& ?, a& `( R5 C+ Tclassifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')
6 F. H9 t, c, q8 E  r6 A$ C
: ]! o& Z+ i4 F. w" o2 sy_test_pred = classifier.predict(X_test)
utilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
0 x% K, q+ d+ e4 G; ~4 f
0 F+ k4 y# Z. s& y0 _###############################################
6 R3 p; `% _6 E: Q: M: L1 K, @# Evaluate classifier performance
* q. ?+ {' P+ H
from sklearn.metrics import classification_report

target_names = ['Class-' + str(int(i)) for i in set(y)]
# D9 ^) ~2 j& y: }* Sprint "\n" + "#"*30
print "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
+ @! B! g, O$ Y/ K0 [- Y* ]print "#"*30 + "\n"

% [8 M+ @9 F, G5 M+ }1 c2 Oprint "#"*30
. Z5 x" W( F9 i; ^8 Mprint "\nClassification report on test dataset\n"
+ H5 p  A/ A$ P' L3 Z: ]% eprint classification_report(y_test, y_test_pred, target_names=target_names)
  o; [, P" V6 aprint "#"*30 + "\n"

jteo21

好片强烈支持

wxw0214

你就我们摸我