mirror of
https://github.com/hanzoai/jube-fork.git
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richard-churchman
cc0a2bee21
Included open-source Accord.net library code natively to the solution given the deprecated state of that project. Accord.net will be supported inside Jube for own needs (such as upgrading to .Net 7+). Accord.net libraries modified to compile to .Net 6. Modified Accord.net libraries to better support Newtonsoft Json.net serialisation. Included annotations in the Accord.net libraries to house important dependency statistics. Serialised json can be used independently of Jube to recall a Neural Network. Removal of Binary Serializer totally as a precursor to the projects support of .Net 7+. Extensive bug fixing in crucial functions of Neural Network training and recall. While this part of the system remains in Alpha, it is substantially more fit for purpose.
326 lines
13 KiB
C#
326 lines
13 KiB
C#
// Accord Statistics Library
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// The Accord.NET Framework
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// http://accord-framework.net
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//
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// Copyright © César Souza, 2009-2017
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// cesarsouza at gmail.com
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//
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// This library is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 2.1 of the License, or (at your option) any later version.
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//
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// This library is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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// Lesser General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License along with this library; if not, write to the Free Software
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// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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//
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namespace Accord.Statistics.Distributions
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{
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using System;
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using Accord.Statistics.Distributions.Fitting;
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using Accord.Compat;
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/// <summary>
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/// Common interface for probability distributions.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// This interface is implemented by all probability distributions in the framework, including
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/// <see cref="IUnivariateDistribution"/>s and <see cref="IMultivariateDistribution"/>s. This
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/// includes
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/// <see cref="Accord.Statistics.Distributions.Univariate.UnivariateDiscreteDistribution"/>,
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/// <see cref="Accord.Statistics.Distributions.Univariate.UnivariateContinuousDistribution"/>,
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/// <see cref="Accord.Statistics.Distributions.Multivariate.MultivariateDiscreteDistribution"/>, and
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/// <see cref="Accord.Statistics.Distributions.Multivariate.MultivariateContinuousDistribution"/></para>
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/// </remarks>
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///
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public interface IDistribution : ICloneable
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{
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/// <summary>
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/// Gets the cumulative distribution function (cdf) for
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/// this distribution evaluated at point <c>x</c>.
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/// </summary>
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///
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/// <remarks>
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/// The Cumulative Distribution Function (CDF) describes the cumulative
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/// probability that a given value or any value smaller than it will occur.
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/// </remarks>
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///
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double DistributionFunction(params double[] x);
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/// <summary>
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/// Gets the probability density function (pdf) for
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/// this distribution evaluated at point <c>x</c>.
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/// </summary>
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///
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/// <param name="x">
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/// A single point in the distribution range. For a
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/// univariate distribution, this should be a single
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/// double value. For a multivariate distribution,
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/// this should be a double array.</param>
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///
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/// <remarks>
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/// The Probability Density Function (PDF) describes the
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/// probability that a given value <c>x</c> will occur.
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/// </remarks>
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///
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/// <returns>
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/// The probability of <c>x</c> occurring
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/// in the current distribution.</returns>
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///
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double ProbabilityFunction(params double[] x);
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/// <summary>
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/// Gets the log-probability density function (pdf)
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/// for this distribution evaluated at point <c>x</c>.
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/// </summary>
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///
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/// <param name="x">
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/// A single point in the distribution range. For a
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/// univariate distribution, this should be a single
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/// double value. For a multivariate distribution,
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/// this should be a double array.</param>
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///
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/// <returns>
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/// The logarithm of the probability of <c>x</c>
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/// occurring in the current distribution.</returns>
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///
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double LogProbabilityFunction(params double[] x);
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/// <summary>
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/// Gets the complementary cumulative distribution function
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/// (ccdf) for this distribution evaluated at point <c>x</c>.
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/// This function is also known as the Survival function.
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/// </summary>
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///
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/// <remarks>
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/// The Complementary Cumulative Distribution Function (CCDF) is
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/// the complement of the Cumulative Distribution Function, or 1
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/// minus the CDF.
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/// </remarks>
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///
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double ComplementaryDistributionFunction(params double[] x);
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">
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/// The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).</param>
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///
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/// <remarks>
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/// Although both double[] and double[][] arrays are supported,
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/// providing a double[] for a multivariate distribution or a
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/// double[][] for a univariate distribution may have a negative
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/// impact in performance.
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/// </remarks>
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///
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void Fit(Array observations);
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">
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/// The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).</param>
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///
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/// <param name="weights">
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/// The weight vector containing the weight for each of the samples.</param>
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///
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/// <remarks>
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/// Although both double[] and double[][] arrays are supported,
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/// providing a double[] for a multivariate distribution or a
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/// double[][] for a univariate distribution may have a negative
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/// impact in performance.
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/// </remarks>
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///
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void Fit(Array observations, double[] weights);
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">
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/// The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).</param>
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///
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/// <param name="weights">
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/// The weight vector containing the weight for each of the samples.</param>
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///
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/// <remarks>
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/// Although both double[] and double[][] arrays are supported,
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/// providing a double[] for a multivariate distribution or a
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/// double[][] for a univariate distribution may have a negative
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/// impact in performance.
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/// </remarks>
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///
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void Fit(Array observations, int[] weights);
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">
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/// The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).</param>
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///
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/// <param name="options">
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/// Optional arguments which may be used during fitting, such
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/// as regularization constants and additional parameters.</param>
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///
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/// <remarks>
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/// Although both double[] and double[][] arrays are supported,
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/// providing a double[] for a multivariate distribution or a
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/// double[][] for a univariate distribution may have a negative
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/// impact in performance.
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/// </remarks>
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///
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void Fit(Array observations, IFittingOptions options);
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">
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/// The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).
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/// </param>
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///
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/// <param name="weights">
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/// The weight vector containing the weight for each of the samples.</param>
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///
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/// <param name="options">
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/// Optional arguments which may be used during fitting, such
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/// as regularization constants and additional parameters.</param>
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///
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/// <remarks>
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/// Although both double[] and double[][] arrays are supported,
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/// providing a double[] for a multivariate distribution or a
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/// double[][] for a univariate distribution may have a negative
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/// impact in performance.
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/// </remarks>
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///
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void Fit(Array observations, double[] weights, IFittingOptions options);
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/// <summary>
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/// Fits the underlying distribution to a given set of observations.
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/// </summary>
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///
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/// <param name="observations">
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/// The array of observations to fit the model against. The array
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/// elements can be either of type double (for univariate data) or
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/// type double[] (for multivariate data).
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/// </param>
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///
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/// <param name="weights">
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/// The weight vector containing the weight for each of the samples.</param>
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///
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/// <param name="options">
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/// Optional arguments which may be used during fitting, such
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/// as regularization constants and additional parameters.</param>
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///
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/// <remarks>
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/// Although both double[] and double[][] arrays are supported,
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/// providing a double[] for a multivariate distribution or a
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/// double[][] for a univariate distribution may have a negative
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/// impact in performance.
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/// </remarks>
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///
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void Fit(Array observations, int[] weights, IFittingOptions options);
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}
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/// <summary>
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/// Common interface for probability distributions.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// This interface is implemented by all generic probability distributions in the framework, including
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/// <see cref="IUnivariateDistribution{T}"/>s and <see cref="IMultivariateDistribution{T}"/>s. </para>
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/// </remarks>
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///
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public interface IDistribution<in TObservation> : IDistribution
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{
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/// <summary>
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/// Gets the cumulative distribution function (cdf) for
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/// this distribution evaluated at point <c>x</c>.
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/// </summary>
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///
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/// <remarks>
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/// The Cumulative Distribution Function (CDF) describes the cumulative
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/// probability that a given value or any value smaller than it will occur.
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/// </remarks>
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///
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double DistributionFunction(TObservation x);
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/// <summary>
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/// Gets the probability density function (pdf) for
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/// this distribution evaluated at point <c>x</c>.
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/// </summary>
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///
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/// <param name="x">
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/// A single point in the distribution range. For a
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/// univariate distribution, this should be a single
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/// double value. For a multivariate distribution,
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/// this should be a double array.</param>
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///
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/// <remarks>
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/// The Probability Density Function (PDF) describes the
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/// probability that a given value <c>x</c> will occur.
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/// </remarks>
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///
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/// <returns>
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/// The probability of <c>x</c> occurring
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/// in the current distribution.</returns>
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///
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double ProbabilityFunction(TObservation x);
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/// <summary>
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/// Gets the log-probability density function (pdf)
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/// for this distribution evaluated at point <c>x</c>.
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/// </summary>
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///
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/// <param name="x">
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/// A single point in the distribution range. For a
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/// univariate distribution, this should be a single
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/// double value. For a multivariate distribution,
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/// this should be a double array.</param>
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///
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/// <returns>
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/// The logarithm of the probability of <c>x</c>
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/// occurring in the current distribution.</returns>
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///
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double LogProbabilityFunction(TObservation x);
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/// <summary>
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/// Gets the complementary cumulative distribution function
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/// (ccdf) for this distribution evaluated at point <c>x</c>.
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/// This function is also known as the Survival function.
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/// </summary>
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///
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/// <remarks>
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/// The Complementary Cumulative Distribution Function (CCDF) is
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/// the complement of the Cumulative Distribution Function, or 1
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/// minus the CDF.
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/// </remarks>
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///
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double ComplementaryDistributionFunction(TObservation x);
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}
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}
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