def _calculateEfficiency(self):
if self.Xtrain is None:
pass
else:
predTrain = self.computePredictions(self.Xtrain)
predValid = self.computePredictions(self.Xvalid)
predTest = self.computePredictions(self.Xtest)
self.trainError.append(
100 -
utils.calculatePredictionsEfficiency(predTrain, self.ytrain))
self.epochData.append("{:.3f}".format(
self.trainError[len(self.trainError) - 1]))
self.validError.append(
100 -
utils.calculatePredictionsEfficiency(predValid, self.yvalid))
self.epochData[len(self.epochData) - 1] += ";" + "{:.3f}".format(
self.validError[len(self.validError) - 1])
self.testError.append(
100 -
utils.calculatePredictionsEfficiency(predTest, self.ytest))
self.epochData[len(self.epochData) - 1] += ";" + "{:.3f}".format(
self.testError[len(self.testError) - 1])
def test():
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMoonFile()
h = 200
wd = 0.0001
K = 200
maxIter = 500
# neuralNetwork = NeuralNetwork(len(Xtrain[0]), h, utils.getClassCount(ytrain), K=10, wd=wd)
neuralNetworkEfficient = NeuralNetworkEfficient(
len(Xtrain[0]), h, utils.getClassCount(ytrain), K, wd=wd)
# neuralNetworkEfficient._w1 = neuralNetwork._w1
# neuralNetworkEfficient._w2 = neuralNetwork._w2
# neuralNetwork.train(Xtrain, ytrain, maxIter)
# predTrain = neuralNetwork.computePredictions(Xtrain)
# predValid = neuralNetwork.computePredictions(Xvalid)
# predTest = neuralNetwork.computePredictions(Xtest)
# trainEfficiency = utils.calculatePredictionsEfficiency(predTrain, ytrain)
# validEfficiency = utils.calculatePredictionsEfficiency(predValid, yvalid)
# testEfficiency = utils.calculatePredictionsEfficiency(predTest, ytest)
# print( "Train Err: " + "{:.2f}".format(100 - trainEfficiency) + "%" \
# + " / Valid Err: " + "{:.2f}".format(100 - validEfficiency) + "%" \
# + " / Test Err: " + "{:.2f}".format(100 - testEfficiency) + "%")
neuralNetworkEfficient.train(Xtrain, ytrain, maxIter)
predTrain = neuralNetworkEfficient.computePredictions(Xtrain)
predValid = neuralNetworkEfficient.computePredictions(Xvalid)
predTest = neuralNetworkEfficient.computePredictions(Xtest)
trainEfficiency = utils.calculatePredictionsEfficiency(predTrain, ytrain)
validEfficiency = utils.calculatePredictionsEfficiency(predValid, yvalid)
testEfficiency = utils.calculatePredictionsEfficiency(predTest, ytest)
print( "Train Err: " + "{:.2f}".format(100 - trainEfficiency) + "%" \
+ " / Valid Err: " + "{:.2f}".format(100 - validEfficiency) + "%" \
+ " / Test Err: " + "{:.2f}".format(100 - testEfficiency) + "%")
def test():
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMoonFile()
h = 200
wd = 0.0001
K = 200
maxIter = 500
# neuralNetwork = NeuralNetwork(len(Xtrain[0]), h, utils.getClassCount(ytrain), K=10, wd=wd)
neuralNetworkEfficient = NeuralNetworkEfficient(len(Xtrain[0]), h, utils.getClassCount(ytrain), K, wd=wd)
# neuralNetworkEfficient._w1 = neuralNetwork._w1
# neuralNetworkEfficient._w2 = neuralNetwork._w2
# neuralNetwork.train(Xtrain, ytrain, maxIter)
# predTrain = neuralNetwork.computePredictions(Xtrain)
# predValid = neuralNetwork.computePredictions(Xvalid)
# predTest = neuralNetwork.computePredictions(Xtest)
# trainEfficiency = utils.calculatePredictionsEfficiency(predTrain, ytrain)
# validEfficiency = utils.calculatePredictionsEfficiency(predValid, yvalid)
# testEfficiency = utils.calculatePredictionsEfficiency(predTest, ytest)
# print( "Train Err: " + "{:.2f}".format(100 - trainEfficiency) + "%" \
# + " / Valid Err: " + "{:.2f}".format(100 - validEfficiency) + "%" \
# + " / Test Err: " + "{:.2f}".format(100 - testEfficiency) + "%")
neuralNetworkEfficient.train(Xtrain, ytrain, maxIter)
predTrain = neuralNetworkEfficient.computePredictions(Xtrain)
predValid = neuralNetworkEfficient.computePredictions(Xvalid)
predTest = neuralNetworkEfficient.computePredictions(Xtest)
trainEfficiency = utils.calculatePredictionsEfficiency(predTrain, ytrain)
validEfficiency = utils.calculatePredictionsEfficiency(predValid, yvalid)
testEfficiency = utils.calculatePredictionsEfficiency(predTest, ytest)
print( "Train Err: " + "{:.2f}".format(100 - trainEfficiency) + "%" \
+ " / Valid Err: " + "{:.2f}".format(100 - validEfficiency) + "%" \
+ " / Test Err: " + "{:.2f}".format(100 - testEfficiency) + "%")
def trainAndPrint(Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest, h, wd, maxIter):
neuralNetwork = NeuralNetworkEfficient(len(Xtrain[0]), h, utils.getClassCount(ytrain), K=50, wd=wd)
neuralNetwork.train(Xtrain, ytrain, maxIter)
predTrain = neuralNetwork.computePredictions(Xtrain)
predValid = neuralNetwork.computePredictions(Xvalid)
predTest = neuralNetwork.computePredictions(Xtest)
trainEfficiency = utils.calculatePredictionsEfficiency(predTrain, ytrain)
validEfficiency = utils.calculatePredictionsEfficiency(predValid, yvalid)
testEfficiency = utils.calculatePredictionsEfficiency(predTest, ytest)
hparams = "[h: " + str(h) + " / wd: " + str(wd) + " / " + str(maxIter) + " epoques]"
title = "Train Err: " + "{:.2f}".format(100 - trainEfficiency) + "%" \
+ " / Valid Err: " + "{:.2f}".format(100 - validEfficiency) + "%" \
+ " / Test Err: " + "{:.2f}".format(100 - testEfficiency) + "%"
name = "regions_decision" + str(h) + "_" + str(wd) + "_" + str(maxIter)
utils.plotRegionsDescision(Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest, neuralNetwork, title, name,hparams)
def _calculateEfficiency(self):
if self.Xtrain is None:
pass
else:
predTrain = self.computePredictions(self.Xtrain)
predValid = self.computePredictions(self.Xvalid)
predTest = self.computePredictions(self.Xtest)
self.trainError.append(100-utils.calculatePredictionsEfficiency(predTrain, self.ytrain))
self.epochData.append("{:.3f}".format(self.trainError[len(self.trainError)-1]))
self.validError.append(100-utils.calculatePredictionsEfficiency(predValid, self.yvalid))
self.epochData[len(self.epochData)-1] += ";"+"{:.3f}".format(self.validError[len(self.validError)-1])
self.testError.append(100-utils.calculatePredictionsEfficiency(predTest, self.ytest))
self.epochData[len(self.epochData)-1] += ";"+"{:.3f}".format(self.testError[len(self.testError)-1])
def trainAndPrint(Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest, h, wd,
maxIter):
neuralNetwork = NeuralNetworkEfficient(len(Xtrain[0]),
h,
utils.getClassCount(ytrain),
K=50,
wd=wd)
neuralNetwork.train(Xtrain, ytrain, maxIter)
predTrain = neuralNetwork.computePredictions(Xtrain)
predValid = neuralNetwork.computePredictions(Xvalid)
predTest = neuralNetwork.computePredictions(Xtest)
trainEfficiency = utils.calculatePredictionsEfficiency(predTrain, ytrain)
validEfficiency = utils.calculatePredictionsEfficiency(predValid, yvalid)
testEfficiency = utils.calculatePredictionsEfficiency(predTest, ytest)
hparams = "[h: " + str(h) + " / wd: " + str(wd) + " / " + str(
maxIter) + " epoques]"
title = "Train Err: " + "{:.2f}".format(100 - trainEfficiency) + "%" \
+ " / Valid Err: " + "{:.2f}".format(100 - validEfficiency) + "%" \
+ " / Test Err: " + "{:.2f}".format(100 - testEfficiency) + "%"
name = "regions_decision" + str(h) + "_" + str(wd) + "_" + str(maxIter)
utils.plotRegionsDescision(Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest,
neuralNetwork, title, name, hparams)
