/*******************************************************************************
*   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.factorfunctions.core.FactorFunction;
import com.analog.lyric.dimple.model.values.Value;


/**
 * Deterministic linear equation, multiplying an input vector by a constant weight vector.
 * The constant vector is specified in the constructor.
 * This is a deterministic directed factor (if smoothing is not enabled).
 * <p>
 * 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.
 * <p>
 * The variables are ordered as follows in the argument list:
 * <ol>
 * <li>Output (inner product of Input vector with Weight vector)
 * <li>Input vector (double or integer array; length must be identical to Weight vector length)
 * </ol>
 * 
 */
public class LinearEquation extends FactorFunction
{
	protected double[] _weightVector;
	protected double _beta = 0;
	protected boolean _smoothingSpecified = false;
	public LinearEquation(double[] weightVector) {this(weightVector, 0);}
	public LinearEquation(double[] weightVector, double smoothing)
	{
		super();
		_weightVector = weightVector;
		if (smoothing > 0)
		{
			_beta = 1 / smoothing;
			_smoothingSpecified = true;
		}
	}
	
    @Override
    public final double evalEnergy(Value[] arguments)
    {
    	final int length = arguments.length;
    	final double out = arguments[0].getDouble();
    	
    	double sum= 1;
    	for (int i = 1; i < length; i++)
    		sum += _weightVector[i-1] * arguments[i].getDouble();


    	if (_smoothingSpecified)
    	{
    		final double diff = sum - out;
    		final double potential = diff*diff;
    		return potential*_beta;
    	}
    	else
    	{
    		return (sum == out) ? 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)
    {
    	final int length = arguments.length;

    	double sum= 1;
    	for (int i = 1; i < length; i++)
    		sum += _weightVector[i-1] * arguments[i].getDouble();
    	
    	arguments[0].setDouble(sum);		// Replace the output value
    }
    
    
    // Factor-specific methods
    public double[] getWeightArray()
    {
    	return _weightVector;
    }
}