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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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// @formatter:off
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 *
 * This file is part of tensorics.
 * 
 * Copyright (c) 2008-2016, 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
 * limitations under the License.
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// @formatter:on
package org.tensorics.core.function;

import java.util.Collection;
import java.util.Comparator;
import java.util.stream.Collectors;

import org.tensorics.core.commons.operations.Conversion;
import org.tensorics.core.function.interpolation.InterpolationStrategy;
import org.tensorics.core.lang.Tensorics;
import org.tensorics.core.reduction.ToFunctions;
import org.tensorics.core.tensor.Tensor;

import com.google.common.base.Preconditions;

/**
 * Provides utility method for transforming {@link MathFunction}s
 * 
 * @author caguiler, kfuchsbe
 */
public class MathFunctions {

    public static  Tensor> functionsFrom(Tensor tensor, Class dimensionClass) {
        Preconditions.checkArgument(tensor.shape().dimensionality() >= 1, "tensor must contain at least one dimension");
        return Tensorics.from(tensor).reduce(dimensionClass).by(new ToFunctions<>());
    }

    public static  DiscreteFunction functionFrom1DTensor(Tensor tensor, Class dimensionClass) {
        Preconditions.checkArgument(tensor.shape().dimensionality() == 1, "tensor must be one-dimensional");
        return functionsFrom(tensor, dimensionClass).get();
    }

    public static  InterpolatedFunction interpolated(DiscreteFunction function,
            InterpolationStrategy strategy, Conversion conversion, Comparator comparator) {
        return new DefaultInterpolatedFunction<>(function, strategy, conversion, comparator);
    }

    public static  Collection yValuesOf(DiscreteFunction function) {
        Preconditions.checkNotNull(function, "function cannot be null!");
        return function.definedXValues().stream().map(function::apply).collect(Collectors.toList());
    }
}