Class: ANN

ANN()

new ANN()

MaiaScript Artificial Neural Network (ANN) library.
Source:

Methods

createANN(topology, numVertices, numEdges, edgeProbability, averageDegree, ni, no, nl, nhu) → {object}

Creates an untrained artificial neural network.
Parameters:
Name Type Description
topology string Graph topology. It can be: complete, random, small world, scale-free, hybrid or mlp.
numVertices number Number of vertices.
numEdges number Number of edges.
edgeProbability number Edge probability.
averageDegree number Average degree.
ni number Number of input neurons.
no number Number of output neurons.
nl number Number of layers.
nhu number Number of hidden units.
Source:
Returns:
A neural network.
Type
object

getLabels(NN) → {object}

Returns the labels of an adjacency matrix.
Parameters:
Name Type Description
NN object Adjacency matrix.
Source:
Returns:
The labels of an adjacency matrix.
Type
object

learn(NN, inMatrix, outMatrix, ni, no, lRate, AF, OAF) → {object}

Trains an artificial neural network, represented as an adjacency matrix.
Parameters:
Name Type Description
NN object Adjacency matrix.
inMatrix object Input data for training.
outMatrix object Output data for training.
ni number Number of input neurons.
no number Number of output neurons.
lRate number Learning rate.
AF string Activation function. It can be: linear, logistic or tanh.
OAF string Activation function of the last layer. It can be: linear, logistic or tanh.
Source:
Returns:
Trained neural network.
Type
object

prepare(ANNMatrix, randomize, allowLoops, negativeWeights) → {object}

It prepares a neural network, represented as an adjacency matrix, replacing cells with value one (1), with random real numbers.
Parameters:
Name Type Description
ANNMatrix object Adjacency matrix.
randomize boolean Fill cells with random real numbers.
allowLoops boolean Allow loops.
negativeWeights boolean Allow negative weights.
Source:
Returns:
Matrix filled with random numbers.
Type
object

reduce(ANNMatrix) → {object}

Remove the last row and last column from the matrix.
Parameters:
Name Type Description
ANNMatrix object Adjacency matrix.
Source:
Returns:
The matrix without the last row and last column.
Type
object

setLabels(NN, labels) → {object}

Sets the labels of an adjacency matrix.
Parameters:
Name Type Description
NN object Adjacency matrix.
labels object Matrix labels.
Source:
Returns:
The adjacency matrix.
Type
object

think(NN, inMatrix, ni, no, AF, OAF, OF, OFC) → {object}

It processes incoming data using a trained neural network.
Parameters:
Name Type Description
NN object adjacency matrix.
inMatrix object Input data for training.
ni number Number of input neurons.
no number Number of output neurons.
AF string Activation function. It can be: linear, logistic or tanh.
OAF string Activation function of the last layer. It can be: linear, logistic or tanh.
OF string Output function. It can be: linear, step, or none.
OFC object Output function coefficients.
Source:
Returns:
Trained neural network.
Type
object

training(NN, inMatrix, outMatrix, lRate, AF, OAF, OF, OFC, maxEpochs, minimumCorrectness, callback, interval) → {object}

Train an artificial neural network, represented as an adjacency matrix.
Parameters:
Name Type Description
NN object Adjacency matrix.
inMatrix object Input data for training.
outMatrix object Output data for training.
lRate number Learning rate.
AF string Activation function. It can be: linear, logistic or tanh.
OAF string Activation function of the last layer. It can be: linear, logistic or tanh.
OF string Output function. It can be: linear, step or none.
OFC string Output function coefficients.
maxEpochs number Maximum number of epochs.
minimumCorrectness number Minimum correctness.
callback function Callback function.
interval number Interval between calls from the callback function.
Source:
Returns:
Trained neural network.
Type
object

(inner) init()

Creates the attributes of the class.
Source: