def Error(X_train,Y_train,alpha,bias,X_test,Y_test,sigma): ''' Error for the test data''' Y_predict =np.zeros(shape=(Y_test.shape[0],1)) #predicted by svm Y_t_predict = np.zeros(shape=(Y_train.shape[0],1)) for i in range(X_test.shape[0]): if (smo.predict(X_train,Y_train,alpha,bias,X_test[i,:],sigma) >= 0 ): Y_predict[i]=1.0 else: Y_predict[i]=-1.0 for i in range(X_train.shape[0]): if (smo.predict(X_train,Y_train,alpha,bias,X_train[i,:],sigma) >= 0 ): Y_t_predict[i]=1.0 else: Y_t_predict[i]=-1.0 test_error=0.0 train_error=0.0 for i in range(Y_predict.shape[0]): if (Y_predict[i]!=Y_test[i]): test_error+=1.0 for i in range(Y_t_predict.shape[0]): if (Y_t_predict[i]!=Y_train[i]): train_error+=1.0 return (1-(train_error/Y_train.shape[0]))*100.0,(1-(test_error/Y_test.shape[0]))*100.0
def decision_function(alpha, y, X, x_test, bias, sigma):
'''returns which class it belongs to'''
fun_value = smo.predict(X, y, alpha, bias, x_test, sigma)
if (fun_value >= 0.0):
return 1.0
else:
return -1.0