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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
, g2 q& F0 N8 Vimport matplotlib.pyplot as plt

import utilities

2 y8 J! p. x9 y, s0 @# Load input data
  x  e: a8 M5 d6 d" ainput_file = 'D:\\1.Modeling material\\Py_Study\\2.code_model\\Python-Machine-Learning-Cookbook\\Python-Machine-Learning-Cookbook-master\\Chapter03\\data_multivar.txt'
5 ~6 Q& m. u) \- mX, y = utilities.load_data(input_file)

###############################################
' B. A4 a2 ?/ n7 m6 s9 Q& w# Separate the data into classes based on 'y'
+ q7 z% V$ \# l9 t) n4 l0 ^% g" N5 i3 \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])
, l2 T$ i) M3 {4 Z4 j/ j' A. G
- b/ d7 @* P7 J$ F5 s! ~# Plot the input data
& y7 C4 H0 U# }* v, H) _plt.figure()
plt.scatter(class_0[:,0], class_0[:,1], facecolors='black', edgecolors='black', marker='s')
. i! x! T7 a, c$ \9 o3 J% Qplt.scatter(class_1[:,0], class_1[:,1], facecolors='None', edgecolors='black', marker='s')
5 v1 {( M$ f: }; o. Y; pplt.title('Input data')

+ s; T- h  o3 s- N###############################################
) \% c9 V( |# C. r: I& ]4 k2 Q# Train test split and SVM training
from sklearn import cross_validation
4 y# P( d2 q+ P7 D! jfrom sklearn.svm import SVC
. h0 e5 S# T  J/ K( O
# n5 C9 m8 J8 q# F6 uX_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)

#params = {'kernel': 'linear'}
#params = {'kernel': 'poly', 'degree': 3}
params = {'kernel': 'rbf'}
. x8 i4 A, q0 |1 I% N6 ^* xclassifier = SVC(**params)
classifier.fit(X_train, y_train)
utilities.plot_classifier(classifier, X_train, y_train, 'Training dataset')

* d; N9 c( A/ g8 x. a1 x3 U' Ey_test_pred = classifier.predict(X_test)
! `3 a; j9 H3 @) S) w3 t0 k* {0 Nutilities.plot_classifier(classifier, X_test, y_test, 'Test dataset')
8 L$ ?: d! Y# y$ C. F$ E  a/ r* _
- P! J: r& w6 }- t###############################################
1 B& p" j" X7 q# Evaluate classifier performance

from sklearn.metrics import classification_report
' ]( U4 z: o8 d2 S6 E' h% v
' S' ^9 `; U! V) \, u+ U) U$ Ytarget_names = ['Class-' + str(int(i)) for i in set(y)]
print "\n" + "#"*30
7 ~% s# y4 S2 A, B/ B  m" B1 Cprint "\nClassifier performance on training dataset\n"
print classification_report(y_train, classifier.predict(X_train), target_names=target_names)
8 y9 V# |$ b8 d) |# ]; U( j0 Aprint "#"*30 + "\n"
2 X7 _. Z6 l! g* z! h7 y% q
print "#"*30
print "\nClassification report on test dataset\n"
# A1 A% T* ?6 Tprint classification_report(y_test, y_test_pred, target_names=target_names)
) W# D0 ?0 \, q1 o1 X6 M3 }print "#"*30 + "\n"

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jteo21

好片强烈支持

wxw0214

你就我们摸我