Added distributions, tensor factories for random numbers, and RNG operators to Bonsai.ML.Torch

src/Bonsai.ML.Torch/Distributions/Bernoulli.cs

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+using System;
+using System.ComponentModel;
+using System.Reactive.Linq;
+using System.Xml.Serialization;
+using static TorchSharp.torch;
+
+namespace Bonsai.ML.Torch.Distributions;
+
+/// <summary>
+/// Represents an operator that creates a Bernoulli probability distribution parameterized by event probabilities.
+/// </summary>
+[Combinator]
+[Description("Creates a Bernoulli distribution with event probabilities and emits a TorchSharp distribution module.")]
+[WorkflowElementCategory(ElementCategory.Source)]
+[TypeConverter(typeof(TensorOperatorConverter))]
+public class Bernoulli : IScalarTypeProvider
+{
+    /// <summary>
+    /// The event probabilities in [0, 1]. Can be a scalar or a tensor; the shape determines the batch/event shape.
+    /// </summary>
+    [XmlIgnore]
+    [TypeConverter(typeof(TensorConverter))]
+    [Description("The event probabilities in [0, 1]. Can be a scalar or a tensor; shape sets the batch/event shape of the distribution.")]
+    public Tensor Probabilities { get; set; } = null;
+
+    /// <summary>
+    /// The values of the probabilities in XML string format.
+    /// </summary>
+    [Browsable(false)]
+    [XmlElement(nameof(Probabilities))]
+    [EditorBrowsable(EditorBrowsableState.Never)]
+    public string ProbabilitiesXml
+    {
+        get => TensorConverter.ConvertToString(Probabilities, Type);
+        set => Probabilities = TensorConverter.ConvertFromString(value, Type);
+    }
+
+    /// <summary>
+    /// Gets or sets the data type of the tensor elements.
+    /// </summary>
+    [Description("The data type of the tensor elements.")]
+    [TypeConverter(typeof(ScalarTypeConverter))]
+    public ScalarType Type { get; set; } = ScalarType.Float32;
+
+    /// <summary>
+    /// Creates a Bernoulli distribution.
+    /// </summary>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Bernoulli> Process()
+    {
+        return Observable.Return(distributions.Bernoulli(Probabilities));
+    }
+
+    /// <summary>
+    /// Creates a Bernoulli distribution using the incoming RNG Generator.
+    /// </summary>
+    /// <param name="source"></param>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Bernoulli> Process(IObservable<Generator> source)
+    {
+        return source.Select(generator => distributions.Bernoulli(Probabilities, generator: generator));
+    }
+
+    /// <summary>
+    /// For each element of the source stream, emits a Bernoulli distribution.
+    /// </summary>
+    /// <typeparam name="T"></typeparam>
+    /// <param name="source"></param>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Bernoulli> Process<T>(IObservable<T> source)
+    {
+        return source.Select(_ => distributions.Bernoulli(Probabilities));
+    }
+}

src/Bonsai.ML.Torch/Distributions/Beta.cs

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+using System;
+using System.ComponentModel;
+using System.Reactive.Linq;
+using System.Xml.Serialization;
+using TorchSharp;
+using static TorchSharp.torch;
+
+namespace Bonsai.ML.Torch.Distributions;
+
+/// <summary>
+/// Represents an operator that creates a beta probability distribution parameterized by two concentration parameters (alpha, beta).
+/// </summary>
+[Combinator]
+[Description("Creates a Beta distribution with concentration parameters (alpha, beta).")]
+[WorkflowElementCategory(ElementCategory.Source)]
+[TypeConverter(typeof(TensorOperatorConverter))]
+public class Beta : IScalarTypeProvider
+{
+    /// <summary>
+    /// The first concentration parameter alpha (> 0). Can be a scalar or tensor; the shape determines the batch/event shape.
+    /// </summary>
+    [XmlIgnore]
+    [TypeConverter(typeof(TensorConverter))]
+    [Description("Concentration alpha (> 0). Can be a scalar or tensor; shape sets the batch/event shape of the distribution.")]
+    public Tensor Concentration1 { get; set; } = null;
+
+    /// <summary>
+    /// The values of concentration 1 in XML string format.
+    /// </summary>
+    [Browsable(false)]
+    [XmlElement(nameof(Concentration1))]
+    [EditorBrowsable(EditorBrowsableState.Never)]
+    public string Concentration1Xml
+    {
+        get => TensorConverter.ConvertToString(Concentration1, Type);
+        set => Concentration1 = TensorConverter.ConvertFromString(value, Type);
+    }
+
+    /// <summary>
+    /// Concentration parameter beta (> 0). Can be a scalar or tensor; the shape determines the batch/event shape.
+    /// </summary>
+    [XmlIgnore]
+    [TypeConverter(typeof(TensorConverter))]
+    [Description("Concentration beta (> 0). Can be a scalar or tensor; shape sets the batch/event shape of the distribution.")]
+    public Tensor Concentration0 { get; set; } = null;
+
+    /// <summary>
+    /// The values of concentration 0 in XML string format.
+    /// </summary>
+    [Browsable(false)]
+    [XmlElement(nameof(Concentration0))]
+    [EditorBrowsable(EditorBrowsableState.Never)]
+    public string Concentration0Xml
+    {
+        get => TensorConverter.ConvertToString(Concentration0, Type);
+        set => Concentration0 = TensorConverter.ConvertFromString(value, Type);
+    }
+
+    /// <summary>
+    /// Gets or sets the data type of the tensor elements.
+    /// </summary>
+    [Description("The data type of the tensor elements.")]
+    [TypeConverter(typeof(ScalarTypeConverter))]
+    public ScalarType Type { get; set; } = ScalarType.Float32;
+
+    /// <summary>
+    /// Creates a Beta distribution.
+    /// </summary>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Beta> Process()
+    {
+        return Observable.Return(distributions.Beta(Concentration1, Concentration0));
+    }
+
+    /// <summary>
+    /// Creates a Beta distribution using the incoming RNG generator.
+    /// </summary>
+    /// <param name="source"></param>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Beta> Process(IObservable<Generator> source)
+    {
+        return source.Select(generator => distributions.Beta(Concentration1, Concentration0, generator: generator));
+    }
+
+    /// <summary>
+    /// For each element of the source stream, emits a Beta distribution.
+    /// </summary>
+    /// <typeparam name="T"></typeparam>
+    /// <param name="source"></param>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Beta> Process<T>(IObservable<T> source)
+    {
+        return source.Select(_ => distributions.Beta(Concentration1, Concentration0));
+    }
+}

src/Bonsai.ML.Torch/Distributions/Binomial.cs

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+using System;
+using System.ComponentModel;
+using System.Reactive.Linq;
+using System.Xml.Serialization;
+using static TorchSharp.torch;
+
+namespace Bonsai.ML.Torch.Distributions;
+
+/// <summary>
+/// Creates a Binomial probability distribution with a given number of trials and success probability.
+/// </summary>
+[Combinator]
+[Description("Creates a Binomial distribution with count (number of trials) and probability of success.")]
+[WorkflowElementCategory(ElementCategory.Source)]
+[TypeConverter(typeof(TensorOperatorConverter))]
+public class Binomial : IScalarTypeProvider
+{
+    /// <summary>
+    /// The number of trials (non-negative). Can be a scalar or tensor. If it is a tensor, values should be non-negative integers.
+    /// </summary>
+    [XmlIgnore]
+    [TypeConverter(typeof(TensorConverter))]
+    [Description("The number of trials (non-negative). Can be a scalar or tensor. If it is a tensor, values should be non-negative integers.")]
+    public Tensor Count { get; set; } = null;
+
+    /// <summary>
+    /// The values of count in XML string format.
+    /// </summary>
+    [Browsable(false)]
+    [XmlElement(nameof(Count))]
+    [EditorBrowsable(EditorBrowsableState.Never)]
+    public string CountXml
+    {
+        get => TensorConverter.ConvertToString(Count, Type);
+        set => Count = TensorConverter.ConvertFromString(value, Type);
+    }
+
+    /// <summary>
+    /// Probability of success p in [0, 1]. Can be a scalar or tensor; the shape should be broadcastable to <see cref="Count"/>.
+    /// </summary>
+    [XmlIgnore]
+    [TypeConverter(typeof(TensorConverter))]
+    [Description("Probability of success in [0, 1]. Can be a scalar or tensor; the shape should be broadcastable to Count.")]
+    public Tensor Probabilities { get; set; } = null;
+
+    /// <summary>
+    /// The values of probabilities in XML string format.
+    /// </summary>
+    [Browsable(false)]
+    [XmlElement(nameof(Probabilities))]
+    [EditorBrowsable(EditorBrowsableState.Never)]
+    public string ProbabilitiesXml
+    {
+        get => TensorConverter.ConvertToString(Probabilities, Type);
+        set => Probabilities = TensorConverter.ConvertFromString(value, Type);
+    }
+
+    /// <summary>
+    /// Gets or sets the data type of the tensor elements.
+    /// </summary>
+    [Description("The data type of the tensor elements.")]
+    [TypeConverter(typeof(ScalarTypeConverter))]
+    public ScalarType Type { get; set; } = ScalarType.Float32;
+
+    /// <summary>
+    /// Creates a Binomial distribution.
+    /// </summary>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Binomial> Process()
+    {
+        return Observable.Return(distributions.Binomial(Count, Probabilities));
+    }
+
+    /// <summary>
+    /// Creates a Binomial distribution for each incoming RNG generator.
+    /// </summary>
+    /// <param name="source"></param>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Binomial> Process(IObservable<Generator> source)
+    {
+        return source.Select(generator => distributions.Binomial(Count, Probabilities, generator: generator));
+    }
+
+    /// <summary>
+    /// For each element of the source stream, emits a Binomial distribution.
+    /// </summary>
+    /// <typeparam name="T"></typeparam>
+    /// <param name="source"></param>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Binomial> Process<T>(IObservable<T> source)
+    {
+        return source.Select(_ => distributions.Binomial(Count, Probabilities));
+    }
+}

src/Bonsai.ML.Torch/Distributions/Categorical.cs

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+using System;
+using System.ComponentModel;
+using System.Reactive.Linq;
+using System.Xml.Serialization;
+using TorchSharp;
+using static TorchSharp.torch;
+
+namespace Bonsai.ML.Torch.Distributions;
+
+/// <summary>
+/// Creates a categorical (discrete) distribution over classes given event probabilities.
+/// </summary>
+[Combinator]
+[Description("Creates a categorical (discrete) distribution over classes given event probabilities.")]
+[WorkflowElementCategory(ElementCategory.Source)]
+[TypeConverter(typeof(TensorOperatorConverter))]
+public class Categorical : IScalarTypeProvider
+{
+    /// <summary>
+    /// The class probabilities. Values must be non-negative and typically sum to 1 per row. Can be a 1D vector or higher-rank tensor for batched distributions.
+    /// </summary>
+    [XmlIgnore]
+    [TypeConverter(typeof(TensorConverter))]
+    [Description("The class probabilities. Values must be non-negative and typically sum to 1 per row. Can be a 1D vector or higher-rank tensor for batched distributions.")]
+    public Tensor Probabilities { get; set; } = null;
+
+    /// <summary>
+    /// The values of probabilities in XML string format.
+    /// </summary>
+    [Browsable(false)]
+    [XmlElement(nameof(Probabilities))]
+    [EditorBrowsable(EditorBrowsableState.Never)]
+    public string ProbabilitiesXml
+    {
+        get => TensorConverter.ConvertToString(Probabilities, Type);
+        set => Probabilities = TensorConverter.ConvertFromString(value, Type);
+    }
+
+    /// <summary>
+    /// Gets or sets the data type of the tensor elements.
+    /// </summary>
+    [Description("The data type of the tensor elements.")]
+    [TypeConverter(typeof(ScalarTypeConverter))]
+    public ScalarType Type { get; set; } = ScalarType.Float32;
+
+    /// <summary>
+    /// Creates a categorical distribution.
+    /// </summary>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Categorical> Process()
+    {
+        return Observable.Return(distributions.Categorical(Probabilities));
+    }
+
+    /// <summary>
+    /// Creates a categorical distribution for each incoming RNG generator.
+    /// </summary>
+    /// <param name="source"></param>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Categorical> Process(IObservable<Generator> source)
+    {
+        return source.Select(generator => distributions.Categorical(Probabilities, generator: generator));
+    }
+
+    /// <summary>
+    /// For each element of the source stream, emits a categorical distribution.
+    /// </summary>
+    /// <typeparam name="T"></typeparam>
+    /// <param name="source"></param>
+    /// <returns></returns>
+    public IObservable<TorchSharp.Modules.Categorical> Process<T>(IObservable<T> source)
+    {
+        return source.Select(_ => distributions.Categorical(Probabilities));
+    }
+}