Fix IPipelineStep Generic Type Definitions (US-BF-001)

src/Interfaces/IPipelineStep.cs

@@ -0,0 +1,71 @@
+using AiDotNet.LinearAlgebra;
+using System.Collections.Generic;
+using System.Threading.Tasks;
+
+namespace AiDotNet.Interfaces
+{
+    /// <summary>
+    /// Represents a step in a data processing pipeline
+    /// </summary>
+    /// <typeparam name="T">The numeric type for computations</typeparam>
+    /// <typeparam name="TInput">The input data type for pipeline operations</typeparam>
+    /// <typeparam name="TOutput">The output data type for pipeline operations</typeparam>
+    /// <remarks>
+    /// <para><b>For Beginners:</b> A pipeline step is a modular component that processes data in stages.
+    /// Each step can fit (learn from data), transform (process data), or both. This pattern allows you to
+    /// chain multiple processing steps together to create complex data processing workflows.</para>
+    /// <para>The generic parameters allow this interface to work with different types of data while maintaining
+    /// type safety. T is typically a numeric type (like double or float) used for calculations, while TInput
+    /// and TOutput define what types of data the step accepts and produces.</para>
+    /// </remarks>
+    public interface IPipelineStep<T, TInput, TOutput>
+    {
+        /// <summary>
+        /// Fits/trains this pipeline step on the provided data
+        /// </summary>
+        /// <param name="inputs">Input data for training</param>
+        /// <param name="targets">Target data for supervised learning (optional)</param>
+        /// <returns>Task representing the asynchronous operation</returns>
+        Task FitAsync(TInput inputs, TOutput? targets = default);
+
+        /// <summary>
+        /// Transforms the input data using the fitted model
+        /// </summary>
+        /// <param name="inputs">Input data to transform</param>
+        /// <returns>Transformed output data</returns>
+        Task<TOutput> TransformAsync(TInput inputs);
+
+        /// <summary>
+        /// Fits and transforms in a single operation (convenience method)
+        /// </summary>
+        /// <param name="inputs">Input data</param>
+        /// <param name="targets">Target data (optional)</param>
+        /// <returns>Transformed output data</returns>
+        Task<TOutput> FitTransformAsync(TInput inputs, TOutput? targets = default);
+
+        /// <summary>
+        /// Gets the parameters of this pipeline step
+        /// </summary>
+        /// <returns>Dictionary of parameter names and values</returns>
+        Dictionary<string, object> GetParameters();
+
+        /// <summary>
+        /// Sets the parameters of this pipeline step
+        /// </summary>
+        /// <param name="parameters">Dictionary of parameter names and values</param>
+        void SetParameters(Dictionary<string, object> parameters);
+
+        /// <summary>
+        /// Validates that this step can process the given input
+        /// </summary>
+        /// <param name="inputs">Input data to validate</param>
+        /// <returns>True if valid, false otherwise</returns>
+        bool ValidateInput(TInput inputs);
+
+        /// <summary>
+        /// Gets metadata about this pipeline step
+        /// </summary>
+        /// <returns>Metadata dictionary</returns>
+        Dictionary<string, string> GetMetadata();
+    }
+}