Uses of Class
dk.alexandra.fresco.stat.mlp.MLP

Packages that use MLP

Package	Description
dk.alexandra.fresco.stat
dk.alexandra.fresco.stat.mlp
dk.alexandra.fresco.stat.mlp.evaluation

Uses of MLP in dk.alexandra.fresco.stat

Methods in dk.alexandra.fresco.stat that return types with arguments of type MLP

Modifier and Type	Method	Description
dk.alexandra.fresco.framework.DRes<MLP>	DefaultMachineLearning.fit(MLP network, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> data, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> labels, int epochs, double learningRate)
dk.alexandra.fresco.framework.DRes<MLP>	MachineLearning.fit(MLP network, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> data, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> labels, int epochs, double learningRate)	Fit the given multilayer perceptron to a dataset using back propagation.

Methods in dk.alexandra.fresco.stat with parameters of type MLP

Modifier and Type	Method	Description
dk.alexandra.fresco.framework.DRes<MLP>	DefaultMachineLearning.fit(MLP network, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> data, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> labels, int epochs, double learningRate)
dk.alexandra.fresco.framework.DRes<MLP>	MachineLearning.fit(MLP network, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> data, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> labels, int epochs, double learningRate)	Fit the given multilayer perceptron to a dataset using back propagation.
dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>	DefaultMachineLearning.predict(MLP network, ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> input)
dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>	MachineLearning.predict(MLP network, ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> input)	Assuming that the given MLP has n output neurons, this function applies the network to the given input and finds the index of the output i with 0 ≤ i < n containing the largest number.

Uses of MLP in dk.alexandra.fresco.stat.mlp

Methods in dk.alexandra.fresco.stat.mlp that return types with arguments of type MLP

Modifier and Type	Method	Description
dk.alexandra.fresco.framework.builder.Computation<MLP,dk.alexandra.fresco.framework.builder.numeric.ProtocolBuilderNumeric>	MLP.fit(List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> data, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> labels, int epochs, double learningRate)	Train this neural network using single step training (batch size = 1) and return a new neural network with the updated weights.

Constructors in dk.alexandra.fresco.stat.mlp with parameters of type MLP

Constructor	Description
Predict(MLP neuralNetwork, ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> input)

Uses of MLP in dk.alexandra.fresco.stat.mlp.evaluation

Constructors in dk.alexandra.fresco.stat.mlp.evaluation with parameters of type MLP

Constructor	Description
Accuracy(MLP neuralNetwork, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> data, ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> labels)
AccuracyBinary(MLP neuralNetwork, List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> data, ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> labels)