Parallel Processing Logic #27
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Made changes to the Multiple Regression class for performance improvement. Introduced parallel processing for better performance when handling large datasets. Methods were also modified to include additional options for customization, such as matrix decomposition and data normalization. Code structure was enhanced for better readability and comprehensibility.
| var inputs0Length = inputs[0].Length; | ||
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| // Use parallel processing for large data sets | ||
| Parallel.For(0, inputsLength, i => |
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I understand the need for Parallel processing for large datasets but this will actually create a performance bottleneck for small to average sized datasets so we should dynamically use Parallel.For when the inputs Length is a certain minimum size
| // Initialize predictions array and store lengths for optimization | ||
| var predictions = new double[inputs[0].Length]; | ||
| var inputsLength = inputs.Length; | ||
| var inputs0Length = inputs[0].Length; |
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It is a minor issue but you are referencing the same inputs[0].Length on line 121 and 123 so you should move up your variable and use that variable for line 121
| namespace AiDotNet.Regression; | ||
| using System; | ||
| using System.Threading.Tasks; | ||
| using MathNet.Numerics.LinearAlgebra; |
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It is another minor issue but we moved all using statements to Helpers/UsingsHelper.cs so if you can please move the necessary using statements there.
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Main point is the example of parallel processing logic, which I mentioned on our call. Feel free to scavenge whatever your need from this PR and modify the formatting to whatever your preference is.