def test(self, test_data, numClass):
numSample = test_data.shape[0]
self.flow = [test_data[:,1:]]
for i in range(0, len(self.architecture)):
self.flow.append(1 / ( 1 + np.exp(-np.dot(self.flow[i], self.architecture[i][0]) - np.matlib.repmat(self.architecture[i][1], numSample, 1))))
predictMat = getPrediction(self.flow[-1])
labelMat = oneOfK(test_data[:,0],numClass)
#Compute presicion
predictionAnalysis(predictMat, labelMat)
def test(self, test_data, numClass): numSample = test_data.shape[0] testingData = test_data[:, 1:] testingLabel = test_data[:, 0:1] prediction = np.zeros((numSample,1)) for i in range(0, numSample): prediction[i,0] = self.predict(testingData[i,:], numClass) predictMat = oneOfK(prediction, numClass) labelMat = oneOfK(testingLabel, numClass) predictionAnalysis(predictMat, labelMat)
def test(self, test_data, numClass, showAnalysis): numSample = test_data.shape[0] testingData = test_data[:, 1:] testingLabel = test_data[:, 0:1] prediction = np.zeros((numSample,1)) for i in range(0, numSample): prediction[i,0] = self.predict(testingData[i,:]) predictMat = oneOfK(prediction, numClass) labelMat = oneOfK(testingLabel, numClass) self.error = np.sum(np.abs(predictMat - labelMat)) / 2 if showAnalysis == 1: predictionAnalysis(predictMat, labelMat)
