from sklearn import svm
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
from matplotlib.colors import ListedColormap
from mlxtend.plotting import plot_decision_regions
X_xor = np.random.randn(200,2)
y_xor=np.logical_xor(X_xor [:, 0]>0,X_xor[:, 1]>0)
y_xor = np.where (y_xor, 1, -1)
plt.scatter (X_xor [y_xor == 1, 0], X_xor [y_xor == 1, 1], c = 'b', marker = 'x', label = '1')
plt.scatter (X_xor [y_xor ==-1, 0], X_xor [y_xor ==-1, 1], c = 'r', marker = 's', label = '-1')
plt.ylim (-3.0)
plt.legend (loc=2)
plt.show ()
X_train, X_test, y_train, y_test=train_test_split(X_xor,y_xor,test_size=0.3, random_state=0)
sc=StandardScaler() #기본 스케일, 평균과 표준편차 사용
#평균을 제거하고 데이터를 단위 분산으로 조정
sc.fit(X_train)
X_train_std=sc.transform(X_train)
X_test_std=sc.transform(X_test)
ml=svm.SVC(kernel='rbf', C=10000.0, gamma=0.10, random_state=0)
ml.fit(X_train_std, y_train)
y_pred = ml.predict(X_test_std)
print('테스트개수: %d, 오류개수:%d' %(len(y_test), (y_test != y_pred).sum()))
print ('정확도: %.2f' %accuracy_score(y_test, y_pred))
X_combined_std = np.vstack((X_train_std, X_test_std)) #행을 추가
y_combined = np.hstack((y_train, y_test)) #열을 추가
plot_decision_regions(X_combined_std,y_combined,ml)
plt.legend()
plt.tight_layout() #여백조절
plt.show() #화면 표시 jupyter나 ipython에서 호출안해도댐
