RdSpanningCombinatorialIterator.java
package org.drip.spaces.iterator;
/*
* -*- 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>RdSpanningCombinatorialIterator</i> contains the Functionality to conduct a Spanning Iteration through
* an R<sup>d</sup> Combinatorial Space.
*
* <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/iterator/README.md">Iterative/Exhaustive Vector Space Scanners</a></li>
* </ul>
* <br><br>
*
* @author Lakshmi Krishnamurthy
*/
public class RdSpanningCombinatorialIterator extends
org.drip.spaces.iterator.RdExhaustiveStateSpaceScan {
private org.drip.spaces.tensor.R1CombinatorialVector[] _aR1CV = null;
/**
* Retrieve the RdSpanningCombinatorialIterator Instance associated with the Underlying Vector Space
*
* @param aR1CV Array of R^1 Combinatorial Vectors
*
* @return The RdSpanningCombinatorialIterator Instance associated with the Underlying Vector Space
*/
public static final RdSpanningCombinatorialIterator Standard (
final org.drip.spaces.tensor.R1CombinatorialVector[] aR1CV)
{
if (null == aR1CV) return null;
int iDimension = aR1CV.length;
int[] aiMax = new int[iDimension];
if (0 == iDimension) return null;
for (int i = 0; i < iDimension; ++i)
aiMax[i] = (int) aR1CV[i].cardinality().number();
try {
return new RdSpanningCombinatorialIterator (aR1CV, aiMax);
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return null;
}
/**
* RdSpanningCombinatorialIterator Constructor
*
* @param aR1CV Array of the R^1 Combinatorial Vectors
* @param aiMax The Array of Dimension Maximum
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public RdSpanningCombinatorialIterator (
final org.drip.spaces.tensor.R1CombinatorialVector[] aR1CV,
final int[] aiMax)
throws java.lang.Exception
{
super (aiMax, false);
if (null == (_aR1CV = aR1CV) || _aR1CV.length != aiMax.length)
throw new java.lang.Exception ("RdCombinatorialIterator ctr: Invalid Inputs");
}
/**
* Retrieve the Array of the R^1 Combinatorial Vectors
*
* @return The Array of the R^1 Combinatorial Vectors
*/
public org.drip.spaces.tensor.R1CombinatorialVector[] r1()
{
return _aR1CV;
}
/**
* Convert the Vector Space Index Array to the Variate Array
*
* @param aiIndex Vector Space Index Array
*
* @return Variate Array
*/
public double[] vectorSpaceIndexToVariate (
final int[] aiIndex)
{
if (null == aiIndex) return null;
int iDimension = _aR1CV.length;
double[] adblVariate = new double[iDimension];
if (iDimension != aiIndex.length) return null;
for (int i = 0; i < iDimension; ++i)
adblVariate[i] = _aR1CV[i].elementSpace().get (aiIndex[i]);
return adblVariate;
}
/**
* Retrieve the Cursor Variate Array
*
* @return The Cursor Variate Array
*/
public double[] cursorVariates()
{
return vectorSpaceIndexToVariate (stateIndexCursor());
}
/**
* Retrieve the Subsequent Variate Array
*
* @return The Subsequent Variate Array
*/
public double[] nextVariates()
{
return vectorSpaceIndexToVariate (nextStateIndexCursor());
}
}