def getUnclassifiedDataToClassify(self, nonInteractive,
unclassifiedDataArray):
if (nonInteractive == True):
parsedUnclassifiedData = unclassifiedDataArray
elif (nonInteractive == False):
unclassifiedDataPath = input(
"Please enter the path to a file with data, which should be classified: "
)
unclassifedDataParser = Parser()
unclassifedDataParser.setDestination(unclassifiedDataPath)
unclassifedDataParser.processData()
# Retrieve data back
parsedUnclassifiedData = unclassifedDataParser.getDataArray()
# Set necessary input arrays
(inputValues, inputLabels
) = self.setInputForClassificationScaled(parsedUnclassifiedData)
return (inputValues, inputLabels)
# Initialize and run parser
parser = Parser()
#parser.askForAverageCalculation() # Asks whether new average values should be calculated
# Ask whether an NN should be trained
nnTraining = input(
"Do you want to train a neural network for step recognition? (y(es) / n(o)): "
)
if (nnTraining == "yes") or (nnTraining == "y"):
NN = NeuralNetwork()
#NN.listen = True
#inArray = NN.listenOnPort()
#parser.processDataArray(inArray)
parser.askForDestination()
parser.processData()
# Retrieve parsed data as an array
parsedData = []
parsedData = parser.getDataArray()
# Set X (input step data values) and y (input labels corresponding to the step data values)
X = []
y = []
(X, y) = NN.setInputForClassificationScaled(parsedData)
# Start new training
print("Training: Started ...\n", end=" ")
t = time.process_time()
loss = 1.0 # initialize loss
trainingCounter = 0
