BigR2Array.java
package org.drip.spaces.big;
/*
* -*- 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>BigR2Array</i> contains an Implementation Navigation and Processing Algorithms for Big Double
* R<sup>2</sup> Arrays.
*
* <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/big/README.md">Big-data In-place Manipulator</a></li>
* </ul>
* <br><br>
*
* @author Lakshmi Krishnamurthy
*/
public abstract class BigR2Array {
private int _iXLength = -1;
private int _iYLength = -1;
private double[][] _aadblR2 = null;
/**
* BigR2Array Constructor
*
* @param aadblR2 2D Big Array Inputs
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public BigR2Array (
final double[][] aadblR2)
throws java.lang.Exception
{
if (null == (_aadblR2 = aadblR2))
throw new java.lang.Exception ("BigR2Array Constructor => Invalid Inputs");
if (0 == (_iXLength = _aadblR2.length) || 0 == (_iYLength = _aadblR2[0].length))
throw new java.lang.Exception ("BigR2Array Constructor => Invalid Inputs");
}
/**
* Compute the Path Response Associated with all the Nodes in the Path up to the Current One.
*
* @param iX The Current X Node
* @param iY The Current Y Node
* @param dblPriorPathResponse The Path Product Associated with the Given Prior Navigation Sequence
*
* @return The Path Response
*
* @throws java.lang.Exception Thrown if Inputs are Invalid
*/
abstract public double pathResponse (
final int iX,
final int iY,
final double dblPriorPathResponse)
throws java.lang.Exception;
/**
* Compute the Maximum Response Associated with all the Left/Right Adjacent Paths starting from the Top
* Left Node.
*
* @return The Maximum Response Associated with all the Left/Right Adjacent Paths starting from the
* Current Node
*
* @throws java.lang.Exception Thrown if Inputs are Invalid
*/
abstract public double maxPathResponse()
throws java.lang.Exception;
/**
* Retrieve the Length of the X R^1 Array
*
* @return The Length of the X R^1 Array
*/
public int xLength()
{
return _iXLength;
}
/**
* Retrieve the Length of the Y R^1 Array
*
* @return The Length of the Y R^1 Array
*/
public int yLength()
{
return _iYLength;
}
/**
* Retrieve the R^2 Instance Array
*
* @return The R^2 Instance Array
*/
public double[][] instance()
{
return _aadblR2;
}
/**
* Validate the Specified Index Pair.
*
* @param iX The Current X Node
* @param iY The Current Y Node
*
* @return TRUE - The Index Pair is Valid
*/
public boolean validateIndex (
final int iX,
final int iY)
{
return iX < 0 || iX >= _iXLength || iY < 0 || iY >= _iYLength ? false : true;
}
/**
* Compute the Maximum Response Associated with all the Left/Right Adjacent Paths starting from the
* Current Node.
*
* @param iX The Current X Node
* @param iY The Current Y Node
* @param dblPriorPathResponse The Path Response Associated with the Given Prior Navigation Sequence
*
* @return The Maximum Response Associated with all the Left/Right Adjacent Paths starting from the
* Current Node
*
* @throws java.lang.Exception Thrown if Inputs are Invalid
*/
public double maxPathResponse (
final int iX,
final int iY,
final double dblPriorPathResponse)
throws java.lang.Exception
{
double dblCurrentPathResponse = pathResponse (iX, iY, dblPriorPathResponse);
if (iY == _iYLength - 1 && iX == _iXLength - 1) return dblCurrentPathResponse;
double dblXShiftMaxPathResponse = java.lang.Double.NaN;
double dblYShiftMaxPathResponse = java.lang.Double.NaN;
if (iX < _iXLength - 1)
dblXShiftMaxPathResponse = maxPathResponse (iX + 1, iY, dblCurrentPathResponse);
if (iY < _iYLength - 1)
dblYShiftMaxPathResponse = maxPathResponse (iX, iY + 1, dblCurrentPathResponse);
if (iY == _iYLength - 1) return dblXShiftMaxPathResponse;
if (iX == _iXLength - 1) return dblYShiftMaxPathResponse;
return dblXShiftMaxPathResponse > dblYShiftMaxPathResponse ? dblXShiftMaxPathResponse :
dblYShiftMaxPathResponse;
}
}