forked from tsai/mathnet-numerics
Christoph Ruegg
9 years ago
3 changed files with 162 additions and 262 deletions
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// <copyright file="KernelDensityEstimator.cs" company="Math.NET">
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// Math.NET Numerics, part of the Math.NET Project
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// http://numerics.mathdotnet.com
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// http://github.com/mathnet/mathnet-numerics
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//
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// Copyright (c) 2009-2018 Math.NET
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//
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// Permission is hereby granted, free of charge, to any person
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// obtaining a copy of this software and associated documentation
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// files (the "Software"), to deal in the Software without
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// restriction, including without limitation the rights to use,
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// copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the
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// Software is furnished to do so, subject to the following
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// conditions:
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//
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// The above copyright notice and this permission notice shall be
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// included in all copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
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// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
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// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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// OTHER DEALINGS IN THE SOFTWARE.
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// </copyright>
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using System; |
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using System.Collections.Generic; |
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using MathNet.Numerics.Distributions; |
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using MathNet.Numerics.Threading; |
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namespace MathNet.Numerics.Statistics |
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{ |
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/// <summary>
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/// Kernel density estimation (KDE).
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/// </summary>
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public static class KernelDensity |
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{ |
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/// <summary>
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/// Estimate the probability density function of a random variable.
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/// </summary>
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/// <remarks>
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/// The routine assumes that the provided kernel is well defined, i.e. a real non-negative function that integrates to 1.
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/// </remarks>
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public static double Estimate(double x, double bandwidth, IList<double> samples, Func<double, double> kernel) |
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{ |
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if (bandwidth <= 0) |
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{ |
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throw new ArgumentException("The bandwidth must be a positive number!"); |
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} |
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var n = samples.Count; |
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var estimate = CommonParallel.Aggregate(0, n, |
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i => kernel((x - samples[i]) / bandwidth), |
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(a, b) => a + b, |
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0d) / (n * bandwidth); |
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return estimate; |
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} |
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/// <summary>
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/// Estimate the probability density function of a random variable with a Gaussian kernel.
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/// </summary>
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public static double EstimateGaussian(double x, double bandwidth, IList<double> samples) |
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{ |
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return Estimate(x, bandwidth, samples, GaussianKernel); |
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} |
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/// <summary>
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/// Estimate the probability density function of a random variable with an Epanechnikov kernel.
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/// The Epanechnikov kernel is optimal in a mean square error sense.
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/// </summary>
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public static double EstimateEpanechnikov(double x, double bandwidth, IList<double> samples) |
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{ |
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return Estimate(x, bandwidth, samples, EpanechnikovKernel); |
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} |
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/// <summary>
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/// Estimate the probability density function of a random variable with a uniform kernel.
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/// </summary>
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public static double EstimateUniform(double x, double bandwidth, IList<double> samples) |
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{ |
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return Estimate(x, bandwidth, samples, UniformKernel); |
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} |
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/// <summary>
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/// Estimate the probability density function of a random variable with a triangular kernel.
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/// </summary>
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public static double EstimateTriangular(double x, double bandwidth, IList<double> samples) |
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{ |
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return Estimate(x, bandwidth, samples, TriangularKernel); |
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} |
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/// <summary>
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/// A Gaussian kernel (PDF of Normal distribution with mean 0 and variance 1).
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/// This kernel is the default.
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/// </summary>
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public static double GaussianKernel(double x) |
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{ |
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return Normal.PDF(0.0, 1.0, x); |
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} |
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/// <summary>
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/// Epanechnikov Kernel:
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/// x => Math.Abs(x) <= 1.0 ? 3.0/4.0(1.0-x^2) : 0.0
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/// </summary>
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public static double EpanechnikovKernel(double x) |
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{ |
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return Math.Abs(x) <= 1.0 ? 0.75 * (1 - x * x) : 0.0; |
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} |
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/// <summary>
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/// Uniform Kernel:
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/// x => Math.Abs(x) <= 1.0 ? 1.0/2.0 : 0.0
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/// </summary>
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public static double UniformKernel(double x) |
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{ |
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return ContinuousUniform.PDF(-1.0, 1.0, x); |
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} |
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/// <summary>
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/// Triangular Kernel:
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/// x => Math.Abs(x) <= 1.0 ? (1.0-Math.Abs(x)) : 0.0
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/// </summary>
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public static double TriangularKernel(double x) |
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{ |
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return Triangular.PDF(-1.0, 1.0, 0.0, x); |
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} |
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} |
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} |
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@ -1,204 +0,0 @@ |
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// <copyright file="KernelDensityEstimator.cs" company="Math.NET">
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// Math.NET Numerics, part of the Math.NET Project
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// http://numerics.mathdotnet.com
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// http://github.com/mathnet/mathnet-numerics
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//
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// Copyright (c) 2009-2018 Math.NET
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//
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// Permission is hereby granted, free of charge, to any person
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|
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// obtaining a copy of this software and associated documentation
|
|
||||
// files (the "Software"), to deal in the Software without
|
|
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// restriction, including without limitation the rights to use,
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|
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// copy, modify, merge, publish, distribute, sublicense, and/or sell
|
|
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// copies of the Software, and to permit persons to whom the
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// Software is furnished to do so, subject to the following
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// conditions:
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//
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// The above copyright notice and this permission notice shall be
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// included in all copies or substantial portions of the Software.
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//
|
|
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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|
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// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
|
|
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// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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|
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// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
|
|
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// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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// OTHER DEALINGS IN THE SOFTWARE.
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// </copyright>
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using System; |
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using System.Collections.Generic; |
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using MathNet.Numerics.Distributions; |
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using MathNet.Numerics.Threading; |
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namespace MathNet.Numerics.Statistics |
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{ |
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/// <summary>
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/// An enum of the methods the <see cref="KernelDensityEstimator"/> supports
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/// for automatic bandwidth selection.
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/// </summary>
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public enum KDEBandwidthSelectionMethod |
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{ |
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/// <summary>
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/// TBD
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/// </summary>
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SilvermansRuleOfThumb, |
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/// <summary>
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/// TBD
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/// </summary>
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SolveTheEquation |
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} |
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/// <summary>
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/// The <see cref="KernelDensityEstimator"/> supports several predefined Kernels.
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/// Note that you can set your own custom kernel by setting <see cref="KernelDensityEstimator.Kernel"/>
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/// </summary>
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public enum KDEKernelType |
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{ |
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/// <summary>
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/// A Gaussian kernel (PDF of Normal distribution with mean 0 and variance 1).
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/// This kernel is the default.
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/// </summary>
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Gaussian, |
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/// <summary>
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/// Epanechnikov Kernel
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/// x => Math.Abs(x) <= 1.0 ? 3.0/4.0(1.0-x^2) : 0.0
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/// </summary>
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Epanechnikov, |
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/// <summary>
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/// Uniform Kernel
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/// x => Math.Abs(x) <= 1.0 ? 1.0/2.0 : 0.0
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/// </summary>
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Uniform, |
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/// <summary>
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/// Triangular Kernel
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/// x => Math.Abs(x) <= 1.0 ? (1.0-Math.Abs(x)) : 0.0
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/// </summary>
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Triangular, |
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/// <summary>
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/// A custom kernel can be set by property <see cref="KernelDensityEstimator.Kernel"/>
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/// </summary>
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Custom |
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} |
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/// <summary>
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/// Kernel density estimation
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/// </summary>
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public class KernelDensityEstimator |
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{ |
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public KernelDensityEstimator(IList<double> samples) |
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{ |
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_samples = samples; |
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KernelType = KDEKernelType.Gaussian; |
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} |
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public double EstimateDensity(double x) |
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{ |
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var n = Samples.Count; |
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var estimate = CommonParallel.Aggregate(0, n, |
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i => |
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{ |
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var s = Samples[i]; |
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return Kernel((x - s) / Bandwidth); |
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}, |
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(a, b) => a + b, |
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0d) / (n * Bandwidth); |
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return estimate; |
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} |
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private readonly IList<double> _samples; |
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public IList<double> Samples |
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{ |
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get { return _samples; } |
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} |
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private double _bandwidth = 1; |
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public double Bandwidth |
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{ |
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get { return _bandwidth; } |
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set |
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{ |
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if (value <= 0) |
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{ |
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throw new ArgumentException("The bandwidth must be a positive number!"); |
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} |
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_bandwidth = value; |
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} |
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} |
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private KDEKernelType _kernelType; |
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public KDEKernelType KernelType |
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{ |
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get { return _kernelType; } |
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set |
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{ |
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switch (value) |
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{ |
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case KDEKernelType.Gaussian: |
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{ |
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Kernel = x => Normal.PDF(0.0, 1.0, x); |
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_kernelType = KDEKernelType.Gaussian; |
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} |
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break; |
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case KDEKernelType.Epanechnikov: |
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{ |
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Kernel = x => Math.Abs(x) <= 1.0 ? 0.75 * (1 - x * x) : 0.0; |
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_kernelType = KDEKernelType.Epanechnikov; |
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} |
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break; |
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case KDEKernelType.Uniform: |
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{ |
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Kernel = x => ContinuousUniform.PDF(-1.0, 1.0, x); |
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_kernelType = KDEKernelType.Uniform; |
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} |
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break; |
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case KDEKernelType.Triangular: |
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{ |
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Kernel = x => Triangular.PDF(-1.0, 1.0, 0.0, x); |
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_kernelType = KDEKernelType.Triangular; |
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} |
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break; |
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case KDEKernelType.Custom: |
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throw new ArgumentException("In order to set a custom Kernel, property Kernel must be set directly."); |
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} |
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} |
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} |
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private Func<double, double> _kernel; |
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/// <summary>
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/// Sets or Gets the Kernel used for the density estimate.
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/// Setting the Kernel changes the <see cref="KernelDensityEstimator.KernelType"/> to <see cref="KDEKernelType.Custom"/>
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/// A Kernel is a real function with Integral 1. Typically, it is also positive and symmetric about 0.
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/// Note that none of these properties are checked.
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/// </summary>
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public Func<double, double> Kernel |
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{ |
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get { return _kernel; } |
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set |
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{ |
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_kernel = value; |
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_kernelType = KDEKernelType.Custom; |
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} |
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} |
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public double SelectBandwidth(KDEBandwidthSelectionMethod bandwidthSelectionMethod) |
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{ |
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throw new NotImplementedException(); |
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} |
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} |
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} |
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