smile.regression

Class MLP

java.lang.Object

smile.base.mlp.MultilayerPerceptron

smile.regression.MLP

All Implemented Interfaces:

java.io.Serializable, java.util.function.ToDoubleFunction<double[]>, OnlineRegression<double[]>, Regression<double[]>

public class MLP
extends MultilayerPerceptron
implements OnlineRegression<double[]>

Fully connected multilayer perceptron neural network for regression. An MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. The nodes are interconnected through weighted acyclic arcs from each preceding layer to the following, without lateral or feedback connections. Each node calculates a transformed weighted linear combination of its inputs (output activations from the preceding layer), with one of the weights acting as a trainable bias connected to a constant input. The transformation, called activation function, is a bounded non-decreasing (non-linear) function.

See Also:

Serialized Form

Field Summary

Fields inherited from class smile.base.mlp.MultilayerPerceptron

alpha, eta, lambda, net, output, p, target, x1

Constructor Summary

Constructors

Constructor and Description
MLP(int p, LayerBuilder... builders) Constructor.

Method Summary

Modifier and Type	Method and Description
double	predict(double[] x) Predicts the dependent variable of an instance.
void	update(double[][] x, double[] y) Updates the model with a (micro-)batch of new samples.
void	update(double[] x, double y) Online update the regression model with a new training instance.

Methods inherited from class smile.base.mlp.MultilayerPerceptron

backpropagate, getLearningRate, getMomentum, getWeightDecay, propagate, setLearningRate, setMomentum, setWeightDecay, toString, update

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface smile.regression.Regression

applyAsDouble, predict

Constructor Detail

MLP

public MLP(int p,
           LayerBuilder... builders)

Constructor.

Parameters:

p - the number of variables in input layer.

builders - the builders of hidden layers from bottom to top.

Method Detail

predict

public double predict(double[] x)

Description copied from interface: Regression

Predicts the dependent variable of an instance.

Specified by:

predict in interface Regression<double[]>

Parameters:

x - an instance.

Returns:

the predicted value of dependent variable.

update

public void update(double[] x,
                   double y)

Description copied from interface: OnlineRegression

Online update the regression model with a new training instance. In general, this method may be NOT multi-thread safe.

Specified by:

update in interface OnlineRegression<double[]>

Parameters:

x - training instance.

y - response variable.

update

public void update(double[][] x,
                   double[] y)

Description copied from interface: OnlineRegression

Updates the model with a (micro-)batch of new samples.

Specified by:

update in interface OnlineRegression<double[]>

Parameters:

x - the training instances.

y - the target values.