import numpy as np
import pandas as pd
from sklearn.svm import SVC
#==============================
# 載入資料
#==============================
df=pd.read_csv('testSet-NOT-linearSeparable.txt',
sep='\t',
names=['feature01', 'feature02', 'label'])
# 訓練/測試資料個數(總資料100個)
numOfTraining=80
numOfTesting=20
# 資料重新亂數排序
df=df.sample(frac=1)
# 所有資料及標籤
dfData=df[['feature01', 'feature02']]
dfLabel=df['label']
# 訓練資料及標籤
dfTrainingData=dfData.head(numOfTraining)
dfTrainingLabel=dfLabel.head(numOfTraining)
# 測試資料及標籤
dfTestingData=dfData.tail(numOfTesting)
dfTestingLabel=dfLabel.tail(numOfTesting)
#================
# 建立模型
#================
clf = SVC()
clf.fit(dfTrainingData, dfTrainingLabel)
print('分類模型')
print(clf)
#==================
# 進行預測
#==================
prediction = clf.predict(dfTestingData)
print('正確標籤')
print(dfTestingLabel.as_matrix())
print('預測標籤')
print(prediction)
#==================
# 計算正確率
#==================
correctCount=np.sum(dfTestingLabel.as_matrix()==prediction)
print('正確率')
print(correctCount/len(prediction))
3.542485 1.977398 -1
3.018896 2.556416 -1
7.551510 -1.580030 -1
2.114999 -0.004466 -1
8.127113 1.274372 1
7.108772 -0.986906 -1
8.610639 2.046708 1
2.326297 0.265213 -1
3.634009 1.730537 -1
0.341367 -0.894998 -1
3.125951 0.293251 -1
2.123252 -0.783563 -1
0.887835 -2.797792 -1
7.139979 -2.329896 1
1.696414 -1.212496 -1
8.117032 0.623493 1
8.497162 -0.266649 1
4.658191 3.507396 -1
8.197181 1.545132 1
1.208047 0.213100 -1
1.928486 -0.321870 -1
2.175808 -0.014527 -1
7.886608 0.461755 1
3.223038 -0.552392 -1
3.628502 2.190585 -1
7.407860 -0.121961 1
7.286357 0.251077 1
2.301095 -0.533988 -1
-0.232542 -0.547690 -1
3.457096 -0.082216 1
3.023938 -0.057392 -1
8.015003 0.885325 1
8.991748 0.923154 1
7.916831 -1.781735 -1
7.616862 -0.217958 1
2.450939 0.744967 -1
7.270337 -2.507834 1
1.749721 -0.961902 -1
1.803111 -0.176349 -1
8.804461 3.044301 1
1.231257 -0.568573 -1
2.074915 1.410550 -1
-0.743036 -1.736103 -1
3.536555 3.964960 -1
8.410143 0.025606 1
7.382988 -0.478764 1
6.960661 -0.245353 1
8.234460 0.701868 1
8.168618 -0.903835 1
1.534187 -0.622492 -1
9.229518 2.066088 1
7.886242 0.191813 1
2.893743 -1.643468 -1
1.870457 -1.040420 -1
5.286862 -2.358286 1
6.080573 0.418886 1
2.544314 1.714165 -1
6.016004 -3.753712 1
0.926310 -0.564359 -1
0.870296 -0.109952 -1
2.369345 1.375695 -1
1.363782 -0.254082 -1
7.279460 -0.189572 1
1.896005 0.515080 -1
8.102154 -0.603875 1
2.529893 0.662657 1
1.963874 -0.365233 -1
8.132048 0.785914 1
8.245938 0.372366 1
6.543888 0.433164 1
-0.236713 -5.766721 -1
8.112593 0.295839 1
9.803425 1.495167 1
1.497407 -0.552916 -1
1.336267 -1.632889 -1
9.205805 -0.586480 1
1.966279 -1.840439 -1
8.398012 1.584918 1
7.239953 -1.764292 1
7.556201 0.241185 1
9.015509 0.345019 1
8.266085 -0.230977 1
8.545620 2.788799 1
9.295969 1.346332 1
2.404234 0.570278 -1
2.037772 0.021919 -1
1.727631 -0.453143 -1
1.979395 -0.050773 -1
8.092288 -1.372433 1
1.667645 0.239204 -1
9.854303 1.365116 1
7.921057 -1.327587 -1
8.500757 1.492372 1
1.339746 -0.291183 -1
3.107511 0.758367 -1
2.609525 0.902979 1
3.263585 1.367898 -1
2.912122 -0.202359 1
1.731786 0.589096 -1
2.387003 1.573131 -1
