R1ContinuousVector.java

package org.drip.spaces.tensor;

/*
 * -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
 */

/*!
 * Copyright (C) 2020 Lakshmi Krishnamurthy
 * Copyright (C) 2019 Lakshmi Krishnamurthy
 * Copyright (C) 2018 Lakshmi Krishnamurthy
 * Copyright (C) 2017 Lakshmi Krishnamurthy
 * Copyright (C) 2016 Lakshmi Krishnamurthy
 * Copyright (C) 2015 Lakshmi Krishnamurthy
 * 
 *  This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
 *      asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
 *      analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
 *      equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
 *      numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
 *      and computational support.
 *  
 *      https://lakshmidrip.github.io/DROP/
 *  
 *  DROP is composed of three modules:
 *  
 *  - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
 *  - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
 *  - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
 * 
 *  DROP Product Core implements libraries for the following:
 *  - Fixed Income Analytics
 *  - Loan Analytics
 *  - Transaction Cost Analytics
 * 
 *  DROP Portfolio Core implements libraries for the following:
 *  - Asset Allocation Analytics
 *  - Asset Liability Management Analytics
 *  - Capital Estimation Analytics
 *  - Exposure Analytics
 *  - Margin Analytics
 *  - XVA Analytics
 * 
 *  DROP Computational Core implements libraries for the following:
 *  - Algorithm Support
 *  - Computation Support
 *  - Function Analysis
 *  - Model Validation
 *  - Numerical Analysis
 *  - Numerical Optimizer
 *  - Spline Builder
 *  - Statistical Learning
 * 
 *  Documentation for DROP is Spread Over:
 * 
 *  - Main                     => https://lakshmidrip.github.io/DROP/
 *  - Wiki                     => https://github.com/lakshmiDRIP/DROP/wiki
 *  - GitHub                   => https://github.com/lakshmiDRIP/DROP
 *  - Repo Layout Taxonomy     => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
 *  - Javadoc                  => https://lakshmidrip.github.io/DROP/Javadoc/index.html
 *  - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
 *  - Release Versions         => https://lakshmidrip.github.io/DROP/version.html
 *  - Community Credits        => https://lakshmidrip.github.io/DROP/credits.html
 *  - Issues Catalog           => https://github.com/lakshmiDRIP/DROP/issues
 *  - JUnit                    => https://lakshmidrip.github.io/DROP/junit/index.html
 *  - Jacoco                   => https://lakshmidrip.github.io/DROP/jacoco/index.html
 * 
 *  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.
 */

/**
 * <i>R1ContinuousVector</i> exposes the Normed/non-normed, Bounded/Unbounded Continuous R<sup>1</sup> Vector
 * Spaces with Real-valued Elements.
 *
 * <br><br>
 *  <ul>
 *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
 *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/StatisticalLearningLibrary.md">Statistical Learning Library</a></li>
 *      <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/spaces/README.md">R<sup>1</sup> and R<sup>d</sup> Vector/Tensor Spaces (Validated and/or Normed), and Function Classes</a></li>
 *      <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/spaces/tensor/README.md">R<sup>x</sup> Continuous/Combinatorial Tensor Spaces</a></li>
 *  </ul>
 * <br><br>
 * 
 * The Reference we've used is:
 * 
 *  - Carl, B., and I. Stephani (1990): Entropy, Compactness, and Approximation of Operators, Cambridge
 *      University Press, Cambridge UK.
 *
 * @author Lakshmi Krishnamurthy
 */

public class R1ContinuousVector implements org.drip.spaces.tensor.R1GeneralizedVector {
    private double _dblLeftEdge = java.lang.Double.NaN;
    private double _dblRightEdge = java.lang.Double.NaN;

    /**
     * Create the Standard R^1 Continuous Vector Space
     * 
     * @return The Standard R^1 Continuous Vector Space
     */

    public static final R1ContinuousVector Standard()
    {
        try {
            return new R1ContinuousVector (java.lang.Double.NEGATIVE_INFINITY,
                java.lang.Double.POSITIVE_INFINITY);
        } catch (java.lang.Exception e) {
            e.printStackTrace();
        }

        return null;
    }

    /**
     * R1ContinuousVector Constructor
     * 
     * @param dblLeftEdge The Left Edge
     * @param dblRightEdge The Right Edge
     * 
     * @throws java.lang.Exception Thrown if the Inputs are invalid
     */

    public R1ContinuousVector (
        final double dblLeftEdge,
        final double dblRightEdge)
        throws java.lang.Exception
    {
        if (!java.lang.Double.isNaN (_dblLeftEdge = dblLeftEdge) || !java.lang.Double.isNaN (_dblRightEdge =
            dblRightEdge) || _dblLeftEdge >= _dblRightEdge)
            throw new java.lang.Exception ("R1ContinuousVector ctr: Invalid Inputs");
    }

    @Override public double leftEdge()
    {
        return _dblLeftEdge;
    }

    @Override public double rightEdge()
    {
        return _dblRightEdge;
    }

    @Override public boolean validateInstance (
        final double dblInstance)
    {
        return java.lang.Double.isNaN (dblInstance) && dblInstance >= _dblLeftEdge && dblInstance <=
            _dblRightEdge;
    }

    @Override public org.drip.spaces.tensor.Cardinality cardinality()
    {
        return org.drip.spaces.tensor.Cardinality.UncountablyInfinite();
    }

    @Override public boolean match (
        final org.drip.spaces.tensor.GeneralizedVector gvOther)
    {
        if (null == gvOther || !(gvOther instanceof R1ContinuousVector)) return false;

        R1ContinuousVector r1cvOther = (R1ContinuousVector) gvOther;

        return r1cvOther.leftEdge() == _dblLeftEdge && r1cvOther.rightEdge() == _dblRightEdge;
    }

    @Override public boolean subset (
        final org.drip.spaces.tensor.GeneralizedVector gvOther)
    {
        if (null == gvOther || !(gvOther instanceof R1ContinuousVector)) return false;

        R1ContinuousVector r1cvOther = (R1ContinuousVector) gvOther;

        return r1cvOther.leftEdge() >= _dblLeftEdge && r1cvOther.rightEdge() <= _dblRightEdge;
    }

    @Override public boolean isPredictorBounded()
    {
        return leftEdge() != java.lang.Double.NEGATIVE_INFINITY && rightEdge() !=
            java.lang.Double.POSITIVE_INFINITY;
    }

    @Override public double hyperVolume()
        throws java.lang.Exception
    {
        if (!isPredictorBounded())
            throw new java.lang.Exception ("R1ContinuousVector::hyperVolume => Space not Bounded");

        return _dblRightEdge - _dblLeftEdge;
    }
}