/*
* Redberry: symbolic tensor computations.
*
* Copyright (c) 2010-2012:
* Stanislav Poslavsky <stvlpos@mail.ru>
* Bolotin Dmitriy <bolotin.dmitriy@gmail.com>
*
* 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 <http://www.gnu.org/licenses/>.
*/
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<PatternSplit> {
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();
}
}