def __init__(self, ns=0, MLP=None):
QObject.__init__(self)
self.MLP = MLP
self.interrupted = False
self.layers = None
if self.MLP:
self.layers = self.getMlpTopology()
self.ns = ns # Neighbourhood size of training rasters.
self.data = None # Training data
self.catlist = None # List of unique output values of the output raster
self.train_error = None # Error on training set
self.val_error = None # Error on validation set
self.minValError = None # The minimum error that is achieved on the validation set
self.valKappa = 0 # Kappa on on the validation set
self.sampler = None # Sampler
# Results of the MLP prediction
self.prediction = None # Raster of the MLP prediction results
self.confidence = None # Raster of the MLP results confidence (1 = the maximum confidence, 0 = the least confidence)
self.transitionPotentials = None # Dictionary of transition potencial maps: {category1: map1, category2: map2, ...}
# Outputs of the activation function for small and big numbers
self.sigmax, self.sigmin = sigmoid(100), sigmoid(
-100) # Max and Min of the sigmoid function
self.sigrange = self.sigmax - self.sigmin # Range of the sigmoid
def __init__(self, ns=0, MLP=None):
QObject.__init__(self)
self.MLP = MLP
self.interrupted = False
self.layers = None
if self.MLP:
self.layers = self.getMlpTopology()
self.ns = ns # Neighbourhood size of training rasters.
self.data = None # Training data
self.catlist = None # List of unique output values of the output raster
self.train_error = None # Error on training set
self.val_error = None # Error on validation set
self.minValError = None # The minimum error that is achieved on the validation set
self.valKappa = 0 # Kappa on on the validation set
self.sampler = None # Sampler
# Results of the MLP prediction
self.prediction = None # Raster of the MLP prediction results
self.confidence = None # Raster of the MLP results confidence (1 = the maximum confidence, 0 = the least confidence)
self.transitionPotentials = None # Dictionary of transition potencial maps: {category1: map1, category2: map2, ...}
# Outputs of the activation function for small and big numbers
self.sigmax, self.sigmin = sigmoid(100), sigmoid(-100) # Max and Min of the sigmoid function
self.sigrange = self.sigmax - self.sigmin # Range of the sigmoid
def __init__(self, ns=0, MLP=None):
QObject.__init__(self)
self.MLP = MLP
self.layers = None
if self.MLP:
self.layers = self.getMlpTopology()
self.ns = ns # Neighbourhood size of training rasters.
self.data = None # Training data
self.classlist = None # List of unique output values of the output raster
self.train_error = None # Error on training set
self.val_error = None # Error on validation set
self.minValError = None # The minimum error that is achieved on the validation set
# Results of the MLP prediction
self.prediction = None # Raster of the MLP prediction results
self.confidence = None # Raster of the MLP results confidence
# Outputs of the activation function for small and big numbers
self.sigmax, self.sigmin = sigmoid(100), sigmoid(-100) # Max and Min of the sigmoid function
self.sigrange = self.sigmax - self.sigmin # Range of the sigmoid
