KernelRdDecisionFunction.java
package org.drip.learning.svm;
/*
* -*- 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>KernelRdDecisionFunction</i> implements the Kernel-based R<sup>d</sup> Decision Function-Based SVM
* Functionality for Classification and Regression.
*
* <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</a></li>
* <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/learning">Agnostic Learning Bounds under Empirical Loss Minimization Schemes</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/learning/svm">Kernel SVM Decision Function Operator</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public abstract class KernelRdDecisionFunction extends org.drip.learning.svm.RdDecisionFunction {
private double[][] _aadblKernelPredictorPivot = null;
private org.drip.learning.kernel.SymmetricRdToNormedRdKernel _kernel = null;
/**
* KernelRdDecisionFunction Constructor
*
* @param rdInverseMargin The Inverse Margin Weights R^d Space
* @param adblInverseMarginWeight Array of Inverse Margin Weights
* @param dblB The Kernel Offset
* @param kernel The Kernel
* @param aadblKernelPredictorPivot Array of the Kernel R^d Predictor Pivot Nodes
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public KernelRdDecisionFunction (
final org.drip.spaces.metric.RdNormed rdInverseMargin,
final double[] adblInverseMarginWeight,
final double dblB,
final org.drip.learning.kernel.SymmetricRdToNormedRdKernel kernel,
final double[][] aadblKernelPredictorPivot)
throws java.lang.Exception
{
super (kernel.inputMetricVectorSpace(), rdInverseMargin, adblInverseMarginWeight, dblB);
if (null == (_kernel = kernel) || null == (_aadblKernelPredictorPivot = aadblKernelPredictorPivot))
throw new java.lang.Exception ("KernelRdDecisionFunction ctr: Invalid Inputs");
int iKernelInputDimension = _kernel.inputMetricVectorSpace().dimension();
int iNumPredictorPivot = adblInverseMarginWeight.length;
if (0 == iNumPredictorPivot || iNumPredictorPivot != _aadblKernelPredictorPivot.length)
throw new java.lang.Exception ("KernelRdDecisionFunction ctr: Invalid Inputs");
for (int i = 0; i < iNumPredictorPivot; ++i) {
if (null == _aadblKernelPredictorPivot[i] || _aadblKernelPredictorPivot[i].length !=
iKernelInputDimension)
throw new java.lang.Exception ("KernelRdDecisionFunction ctr: Invalid Inputs");
}
}
@Override public double evaluate (
final double[] adblX)
throws java.lang.Exception
{
if (null == adblX || adblX.length != _kernel.inputMetricVectorSpace().dimension())
throw new java.lang.Exception ("KernelRdDecisionFunction::evaluate => Invalid Inputs");
double[] adblInverseMarginWeight = inverseMarginWeights();
double dblDotProduct = 0.;
int iNumPredictorPivot = adblInverseMarginWeight.length;
for (int i = 0; i < iNumPredictorPivot; ++i)
dblDotProduct += adblInverseMarginWeight[i] * _kernel.evaluate (_aadblKernelPredictorPivot[i],
adblX);
return dblDotProduct + offset();
}
/**
* Retrieve the Decision Kernel
*
* @return The Decision Kernel
*/
public org.drip.learning.kernel.SymmetricRdToNormedRdKernel kernel()
{
return _kernel;
}
/**
* Retrieve the Decision Kernel Predictor Pivot Nodes
*
* @return The Decision Kernel Predictor Pivot Nodes
*/
public double[][] kernelPredictorPivot()
{
return _aadblKernelPredictorPivot;
}
}