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Tensorics is a java framework which uses a tensor as a central object. A tensor represents a set of values placed in an N-dimensional space. Wherever you are tempted to use maps of maps, a tensor might be a good choice ;-) Tensorics provides methods to create, transform and performing calculations with those tensors.

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 * This file is part of tensorics.
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 * Copyright (c) 2008-2011, CERN. All rights reserved.
 *
 * 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
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// @formatter:on
package org.tensorics.core.tensor.operations;

import java.util.Collections;

import org.tensorics.core.commons.options.ManipulationOption;
import org.tensorics.core.commons.options.OptionRegistry;
import org.tensorics.core.math.operations.BinaryFunction;
import org.tensorics.core.tensor.Context;
import org.tensorics.core.tensor.ImmutableTensor;
import org.tensorics.core.tensor.ImmutableTensor.Builder;
import org.tensorics.core.tensor.Position;
import org.tensorics.core.tensor.Shape;
import org.tensorics.core.tensor.Tensor;
import org.tensorics.core.tensor.TensorPair;
import org.tensorics.core.tensor.options.BroadcastingStrategy;
import org.tensorics.core.tensor.options.ContextPropagationStrategy;
import org.tensorics.core.tensor.options.ShapingStrategy;

public class ElementBinaryFunction implements BinaryFunction, Tensor> {

    protected final BinaryFunction operation;
    protected final OptionRegistry optionRegistry;

    public ElementBinaryFunction(BinaryFunction operation, OptionRegistry optionRegistry) {
        super();
        this.operation = operation;
        this.optionRegistry = optionRegistry;
    }

    @Override
    public Tensor perform(Tensor left, Tensor right) {
        TensorPair broadcastedPair = broadcast(left, right);
        Shape resultingShape = shape(broadcastedPair);
        Context resultingContext = contextLeftRight(left, right);
        return performOperation(broadcastedPair.left(), broadcastedPair.right(), resultingShape, resultingContext);
    }

    private Context contextLeftRight(Tensor left, Tensor right) {
        ContextPropagationStrategy strategy = optionRegistry.get(ContextPropagationStrategy.class);
        return strategy.contextForLeftRight(left.context(), right.context());
    }

    private Tensor performOperation(Tensor left, Tensor right, Shape resultingShape, Context resultingContext) {
        Builder tensorBuilder = ImmutableTensor.builder(resultingShape.dimensionSet());
        for (Position position : resultingShape.positionSet()) {
            tensorBuilder.at(position).put(operation.perform(left.get(position), right.get(position)));
        }
        tensorBuilder.setTensorContext(resultingContext);
        return tensorBuilder.build();
    }

    private Shape shape(TensorPair broadcasted) {
        ShapingStrategy strategy = optionRegistry.get(ShapingStrategy.class);
        return strategy.shapeLeftRight(broadcasted.left(), broadcasted.right());
    }

    private TensorPair broadcast(Tensor left, Tensor right) {
        BroadcastingStrategy broadcastingStrategy = optionRegistry.get(BroadcastingStrategy.class);
        return broadcastingStrategy.broadcast(left, right, Collections.> emptySet());
    }

}