/******************************************************************************* * Copyright 2012 Analog Devices, Inc. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. ********************************************************************************/ package com.analog.lyric.dimple.factorfunctions; import com.analog.lyric.dimple.exceptions.DimpleException; import com.analog.lyric.dimple.factorfunctions.core.FactorFunction; import com.analog.lyric.dimple.model.values.Value; /** * Deterministic subtraction for real-joint variables. This is a deterministic directed factor (if smoothing is * not enabled). * * Optional smoothing may be applied, by providing a smoothing value in the * constructor. If smoothing is enabled, the distribution is smoothed by * exp(-difference^2/smoothing), where difference is the distance between the * output value and the deterministic output value for the corresponding inputs. * * The variables are ordered as follows in the argument list: * * 1) Output (difference = positive input - sum of subtracted inputs) * 2) Positive input (RealJoint) * 3...) An arbitrary number of subtracted inputs (RealJoint) * * @since 0.05 */ public class RealJointSubtract extends FactorFunction { protected double _beta = 0; protected boolean _smoothingSpecified = false; public RealJointSubtract() {this(0);} public RealJointSubtract(double smoothing) { super(); if (smoothing > 0) { _beta = 1 / smoothing; _smoothingSpecified = true; } } @Override public final double evalEnergy(Value[] arguments) { // Output variable final int length = arguments.length; final double[] out = arguments[0].getDoubleArray(); final int dimension = out.length; // Positive input variable final double[] sum = new double[dimension]; final double[] posIn = arguments[1].getDoubleArray(); for (int d = 0; d < dimension; d++) sum[d] = posIn[d]; // Remaining subtracted input variables for (int i = 2; i < length; i++) { final double[] arg = arguments[i].getDoubleArray(); if (dimension != arg.length) throw new DimpleException("Argument variables must all have the same dimension"); for (int d = 0; d < dimension; d++) sum[d] -= arg[d]; } if (_smoothingSpecified) { double potential = 0; for (int d = 0; d < dimension; d++) { final double diff = sum[d] - out[d]; potential += diff*diff; } return potential*_beta; } else { boolean equal = true; for (int d = 0; d < dimension; d++) if (sum[d] != out[d]) equal = false; return (equal) ? 0 : Double.POSITIVE_INFINITY; } } @Override public final boolean isDirected() {return true;} @Override public final int[] getDirectedToIndices() {return new int[]{0};} @Override public final boolean isDeterministicDirected() {return !_smoothingSpecified;} @Override public final void evalDeterministic(Value[] arguments) { // Output variable final int length = arguments.length; final double[] out = arguments[0].getDoubleArray(); final int dimension = out.length; // Positive input variable final double[] posIn = arguments[1].getDoubleArray(); for (int d = 0; d < dimension; d++) out[d] = posIn[d]; // Remaining subtracted input variables for (int i = 2; i < length; i++) { final double[] arg = arguments[i].getDoubleArray(); if (dimension != arg.length) throw new DimpleException("Argument variables must all have the same dimension"); for (int d = 0; d < dimension; d++) out[d] -= arg[d]; } } }