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/*
* Copyright 1997-2022 Optimatika
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
package org.ojalgo.tensor;
import org.ojalgo.algebra.NormedVectorSpace;
import org.ojalgo.array.ArrayAnyD;
/**
* An n:th-rank tensor in m-dimensional space is a mathematical object that has n indices and m^n components
* and obeys certain transformation rules. Tensors are generalizations of scalars (that have no indices),
* vectors (that have exactly one index), and matrices (that have exactly two indices) to an arbitrary number
* of indices.
*
* If all you want is multi-dimesional arrays this interface and its implementations is NOT what you're
* looking for. In that case just use {@link ArrayAnyD} instead.
*
* @see https://mathworld.wolfram.com/Tensor.html
* @author apete
*/
public interface Tensor, T extends Tensor> extends NormedVectorSpace {
/**
* The total number of scalar components
*/
default long components() {
return Math.round(Math.pow(this.dimensions(), this.rank()));
}
/**
* The range of the indices that identify the scalar components. Each index of a tensor ranges over the
* number of dimensions.
*/
int dimensions();
default boolean isSameShape(final Tensor other) {
return this.rank() == other.rank() && this.dimensions() == other.dimensions();
}
/**
* The total number of indices required to uniquely identify each scalar component is called the order,
* degree or rank of the tensor.
*/
int rank();
}