def trainModel(unitsInHiddenLayer = 8, debug = True): if debug: print("Reading Training Data RNN Jordan Model (Currency Exchange problem)") # Reading Training data trainData = utl.readDataSetAsMatrix(\ utl.CURRENCY_EXCHANGE_TRAINING_FILE(\ utl.SAMPLING_TYPE.HOURLY ) ) inputs = trainData[:, 2:] if debug: print("Training Model RNN hourly Model (Currency Exchange problem)") configuration = utl.RecurrentTrainProcessConfiguration() configuration.unitsInHiddenLayer = unitsInHiddenLayer configuration.maxEpochs = 100 configuration.learningrate = 0.001 configuration.momentum = 0.95 return utl.trainJordanRecurrentNetwork(\ inputs, 1, configuration )
def trainModel(unitsInHiddenLayer=8, debug=True): if debug: print("Reading Training Data MLP Model (Currency Exchange problem)") # Reading Training data trainData = utl.readDataSetAsMatrix(\ utl.CURRENCY_EXCHANGE_TRAINING_FILE(\ utl.SAMPLING_TYPE.HOURLY ) ) width = utl.HOURLY_WIDTH inputs = utl.createPattern(trainData[:-1, 2:], width) outputs = trainData[(width + 1):, 2:] if debug: print("Training Model MLP Model (Currency Exchange problem)") configuration = utl.MLPTrainProcessConfiguration() configuration.unitsInHiddenLayer = unitsInHiddenLayer configuration.outputLayer = LinearLayer configuration.maxEpochs = 1000 configuration.learningrate = 0.001 return utl.trainMLPNetwork(\ inputs, outputs, configuration )
def trainModel(unitsInHiddenLayer = -1, debug = True): if debug: print("Reading Training Data RBF Model (Currency Exchange problem)") # Reading Training data trainData = utl.readDataSetAsMatrix(\ utl.CURRENCY_EXCHANGE_TRAINING_FILE(\ utl.SAMPLING_TYPE.HOURLY ) ) width = utl.HOURLY_WIDTH inputs = utl.createPattern(trainData[:-1, 2:], width) outputs = trainData[(width+1):, 2:].astype(np.float64) if debug: print("Training Model RBF Model (Currency Exchange problem)") configuration = utl.RBFTrainProcessConfiguration() configuration.outputLayer = LinearLayer if unitsInHiddenLayer <= 0: configuration.performClustering = False else: configuration.unitsInHiddenLayer = unitsInHiddenLayer configuration.performClustering = True configuration.maxEpochs = 1000 configuration.learningrate = 0.001 return utl.trainRBFNetwork(\ inputs, outputs, configuration )
def getTestData(): testingData = utl.readDataSetAsMatrix(\ utl.CURRENCY_EXCHANGE_TESTING_FILE(\ utl.SAMPLING_TYPE.HOURLY ) ) width = utl.HOURLY_WIDTH inputs = utl.createPattern(testingData[:-1, 2:], width) outputs = testingData[(width + 1):, 2:] return inputs, outputs
def evaluate(model, debug = True, inputs = None, outputs = None): if debug: print ("Reading Testing Data for cancer problem") if inputs is None or outputs is None: # Reading testing data testingData = utl.readDataSetAsMatrix(utl.BREAST_CANCER_TESTING_FILE, 1, ',') inputs = testingData[:, :9] outputs = testingData[:, 9:] return utl.evaluateClassificationModel\ ( model, inputs, outputs, 'RBF: cancer problem', debug )
def evaluate(model, debug = True, inputs = None, outputs = None): if inputs is None or outputs is None: # Reading testing data testingData = utl.readDataSetAsMatrix(\ utl.CURRENCY_EXCHANGE_TESTING_FILE(\ utl.SAMPLING_TYPE.HOURLY ) ) width = utl.HOURLY_WIDTH inputs = utl.createPattern(testingData[:-1, 2:], width) outputs = testingData[(width+1):, 2:] return utl.evaluateRegressionModel(\ model, inputs, outputs, "MLP : Currency exchange problem 'hourly'", debug )
def trainModel(unitsInHiddenLayer=9, debug=True): if debug: print("Reading Training Data (Cancer Problem)") # Reading Training data trainData = utl.readDataSetAsMatrix(utl.BREAST_CANCER_TRAINING_FILE, 1, ',') # Separating outputs from inputs inputs = trainData[:, :9] outputs = trainData[:, 9:] if debug: print("Training RBF Model (Cancer problem)") configuration = utl.RBFTrainProcessConfiguration() configuration.unitsInHiddenLayer = unitsInHiddenLayer return utl.trainRBFNetwork(\ inputs, outputs, configuration )
def evaluate(model, debug = True, inputs = None, outputs = None): if inputs is None or outputs is None: # Reading testing data testingData = utl.readDataSetAsMatrix(\ utl.CURRENCY_EXCHANGE_TESTING_FILE(\ utl.SAMPLING_TYPE.AT_CLOSING_DAY ) ) inputs = testingData[:-1, 2:] outputs = testingData[1:, 2:] return utl.evaluateRegressionModel(\ model, inputs, outputs, 'RNN Jordan : Currency exchange problem', debug )
def getTestData(): testingData = utl.readDataSetAsMatrix(utl.BREAST_CANCER_TESTING_FILE, 1, ',') inputs = testingData[:, :9] outputs = testingData[:, 9:] return inputs, outputs