NeuralNetworkVisualization/neuralnetworkui/abstractdense.h

/*
 * MIT License
 *
 * Copyright (c) 2019 Alexey Edelev <semlanik@gmail.com>
 *
 * This file is part of NeuralNetwork project https://git.semlanik.org/semlanik/NeuralNetwork
 *
 * 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.
 */

#pragma once

template <typename T>
class AbstractDense {
public:
    AbstractDense() = default;
    AbstractDense(int rows, int columns, const T &data) :
        m_rows(rows)
      , m_columns(columns)
      , m_data(data) {}

    void setDimentions(int rows, int columns) {
        m_rows = rows;
        m_columns = columns;
    }

    void setData(const T &data) {
        m_data = data;
    }

    int rows() const {
        return m_rows;
    }

    int columns() const {
        return m_columns;
    }

    template<typename R>
    R value(int row, int column) const {
        return m_data[(m_columns - 1) * row + column + row];
    }

protected:
    int m_rows;
    int m_columns;
    T m_data;
};