RdCombinatorialVector.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>RdCombinatorialVector</i> exposes the Normed/Non-normed Discrete Spaces with R<sup>d</sup>
* Combinatorial Vector 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>
*
* @author Lakshmi Krishnamurthy
*/
public class RdCombinatorialVector extends org.drip.spaces.tensor.RdAggregate {
/**
* RdCombinatorialVector Constructor
*
* @param aR1CV Array of the Underlying R^1 Combinatorial Vector Spaces
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public RdCombinatorialVector (
final org.drip.spaces.tensor.R1CombinatorialVector[] aR1CV)
throws java.lang.Exception
{
super (aR1CV);
}
@Override public org.drip.spaces.tensor.Cardinality cardinality()
{
org.drip.spaces.tensor.R1GeneralizedVector[] aR1GV = vectorSpaces();
int iDimension = aR1GV.length;
double dblCardinalNumber = 1.;
for (int i = 0; i < iDimension; ++i)
dblCardinalNumber *= ((org.drip.spaces.tensor.R1CombinatorialVector)
aR1GV[i]).cardinality().number();
return org.drip.spaces.tensor.Cardinality.CountablyFinite (dblCardinalNumber);
}
/**
* Retrieve the Multidimensional Iterator associated with the Underlying Vector Space
*
* @return The Multidimensional Iterator associated with the Underlying Vector Space
*/
public org.drip.spaces.iterator.RdSpanningCombinatorialIterator iterator()
{
org.drip.spaces.tensor.R1GeneralizedVector[] aR1GV = vectorSpaces();
int iDimension = aR1GV.length;
org.drip.spaces.tensor.R1CombinatorialVector[] aR1CV = new
org.drip.spaces.tensor.R1CombinatorialVector[iDimension];
for (int i = 0; i < iDimension; ++i)
aR1CV[i] = (org.drip.spaces.tensor.R1CombinatorialVector) aR1GV[i];
return org.drip.spaces.iterator.RdSpanningCombinatorialIterator.Standard (aR1CV);
}
@Override public double[] leftDimensionEdge()
{
org.drip.spaces.tensor.R1GeneralizedVector[] aR1GV = vectorSpaces();
int iDimension = aR1GV.length;
double[] adblLeftEdge = new double[iDimension];
for (int i = 0; i < iDimension; ++i)
adblLeftEdge[i] = ((org.drip.spaces.tensor.R1ContinuousVector) aR1GV[i]).leftEdge();
return adblLeftEdge;
}
@Override public double[] rightDimensionEdge()
{
org.drip.spaces.tensor.R1GeneralizedVector[] aR1GV = vectorSpaces();
int iDimension = aR1GV.length;
double[] adblRightEdge = new double[iDimension];
for (int i = 0; i < iDimension; ++i)
adblRightEdge[i] = ((org.drip.spaces.tensor.R1ContinuousVector) aR1GV[i]).rightEdge();
return adblRightEdge;
}
@Override public double leftEdge()
{
double[] adblLeftEdge = leftDimensionEdge();
int iDimension = adblLeftEdge.length;
double dblLeftEdge = adblLeftEdge[0];
for (int i = 1; i < iDimension; ++i) {
if (dblLeftEdge > adblLeftEdge[i]) dblLeftEdge = adblLeftEdge[i];
}
return dblLeftEdge;
}
@Override public double rightEdge()
{
double[] adblRightEdge = rightDimensionEdge();
int iDimension = adblRightEdge.length;
double dblRightEdge = adblRightEdge[0];
for (int i = 1; i < iDimension; ++i) {
if (dblRightEdge < adblRightEdge[i]) dblRightEdge = adblRightEdge[i];
}
return dblRightEdge;
}
@Override public double hyperVolume()
throws java.lang.Exception
{
if (!isPredictorBounded())
throw new java.lang.Exception ("RdCombinatorialVector::hyperVolume => Space not Bounded");
double[] adblLeftEdge = leftDimensionEdge();
double dblHyperVolume = 1.;
int iDimension = adblLeftEdge.length;
double[] adblRightEdge = rightDimensionEdge();
for (int i = 0; i < iDimension; ++i)
dblHyperVolume *= (adblRightEdge[i] - adblLeftEdge[i]);
return dblHyperVolume;
}
}