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
