cc.redberry.transformation.collect.PatternSplitCriteria Maven / Gradle / Ivy

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/*
 * Redberry: symbolic tensor computations.
 *
 * Copyright (c) 2010-2012:
 *   Stanislav Poslavsky   
 *   Bolotin Dmitriy       
 *
 * This file is part of Redberry.
 *
 * Redberry is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Redberry is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Redberry. If not, see .
 */
package cc.redberry.transformation.collect;

import cc.redberry.core.tensor.Product;
import cc.redberry.core.tensor.SimpleTensor;
import cc.redberry.core.tensor.Tensor;
import cc.redberry.core.tensor.TensorIterator;
import cc.redberry.core.tensor.TensorNumber;

/**
 *
 * @author Dmitry Bolotin
 * @author Stanislav Poslavsky
 */
public class PatternSplitCriteria implements SplitCriteria {
    private final SplitPattern splitPattern;
    private final boolean allowDiffStates;

    public PatternSplitCriteria(SplitPattern splitPattern, boolean allowDiffStates) {
        this.splitPattern = splitPattern;
        this.allowDiffStates = allowDiffStates;
    }

    public PatternSplitCriteria(SplitPattern splitPattern) {
        this(splitPattern, false);
    }

    @Override
    public boolean factorOut(Tensor tensor) {
        return splitPattern.factorOut(tensor);
    }

    @Override
    public PatternSplit split(Tensor tensor) {
        if (tensor instanceof Product) {
            TensorIterator it = tensor.iterator();
            Tensor current;
            Product factored = new Product();
            while (it.hasNext()) {
                current = it.next();
                if (splitPattern.factorOut(current)) {
                    factored.add(current);
                    it.remove();
                }
            }
            if (factored.isEmpty())
                factored.add(TensorNumber.createONE());
            if (((Product) tensor).isEmpty())
                return new PatternSplit(factored, TensorNumber.createONE(), allowDiffStates);
            return new PatternSplit(factored, tensor.equivalent(), allowDiffStates);
        }
        if (tensor instanceof SimpleTensor)
            if (splitPattern.factorOut(tensor)) {
                Product factoredOut = new Product();
                factoredOut.add(tensor);
                return new PatternSplit(factoredOut, allowDiffStates);
            } else {
                Product factoredOut = new Product();
                factoredOut.add(TensorNumber.createONE());
                return new PatternSplit(factoredOut, tensor, allowDiffStates);
            }
        throw new UnsupportedOperationException();
    }
}