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https://github.com/oliverbooth/X10D
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feat: add MathUtility.Sigmoid (#60)
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@ -15,13 +15,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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- X10D: Added `IntrinsicExtensions` and `IntrinsicUtility` which offer methods for vectors in `System.Runtime.Instrinsics`. (#70)
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- X10D: Added `MathUtility.Bias(float, float)` and `MathUtility.Bias(double, double)`.
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- X10D: Added `MathUtility.ExponentialDecay(float, float, float)` and `MathUtility.ExponentialDecay(double, double, double)`.
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- X10D: Added `MathUtility.InverseLerp(float, float, float)` and `MathUtility.InverseLerp(double, double, double)`
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- X10D: Added `MathUtility.InverseLerp(float, float, float)` and `MathUtility.InverseLerp(double, double, double)`.
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- X10D: Added `MathUtility.ScaleRange(float, float, float, float, float)`
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and `MathUtility.ScaleRange(double, double, double, double, double)`
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- X10D:
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- X10D: Added `MathUtility.Sigmoid(float)` and `MathUtility.Sigmoid(double)`.
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- X10D: Added `MathUtility.SmoothStep(float, float, float)` and `MathUtility.SmoothStep(double, double, double)`.
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Added `Circle`, `CircleF`, `Cuboid`, `Ellipse`, `EllipseF`, `Line3D`, `Line`, `LineF`, `Polygon`, `PolygonF`, `Polyhedron`,
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and `Sphere`, to complement System.Drawing structs such as `Point` and `Rectangle`
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- X10D: Added `Circle`, `CircleF`, `Cuboid`, `Ellipse`, `EllipseF`, `Line3D`, `Line`, `LineF`, `Polygon`, `PolygonF`, `Polyhedron`,
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and `Sphere`, to complement System.Drawing structs such as `Point` and `Rectangle`.
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- X10D: Added `Color.Deconstruct()` - with optional alpha parameter.
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- X10D: Added `Color.GetClosestConsoleColor()`.
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- X10D: Added `DateTime.GetIso8601WeekOfYear()` and `DateTimeOffset.GetIso8601WeekOfYear()`.
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@ -177,6 +177,95 @@ public class MathUtilityTests
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Assert.AreEqual(7.5f, result);
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}
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[TestMethod]
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public void Sigmoid_ReturnsExpectedValue_UsingDouble()
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{
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const double input = 0.5f;
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const double expected = 0.622459331f;
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double actual = MathUtility.Sigmoid(input);
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Assert.AreEqual(expected, actual, 1e-6);
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}
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[TestMethod]
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public void Sigmoid_ReturnsExpectedValue_UsingSingle()
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{
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const float input = 0.5f;
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const float expected = 0.622459331f;
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float actual = MathUtility.Sigmoid(input);
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Assert.AreEqual(expected, actual, 1e-6f);
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}
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[TestMethod]
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public void Sigmoid_ReturnsZeroWhenInputIsNegativeInfinity_UsingDouble()
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{
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const double input = double.NegativeInfinity;
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const double expected = 0f;
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double actual = MathUtility.Sigmoid(input);
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Assert.AreEqual(expected, actual, 1e-6);
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}
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[TestMethod]
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public void Sigmoid_ReturnsZeroWhenInputIsNegativeInfinity_UsingSingle()
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{
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const float input = float.NegativeInfinity;
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const float expected = 0f;
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float actual = MathUtility.Sigmoid(input);
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Assert.AreEqual(expected, actual, 1e-6f);
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}
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[TestMethod]
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public void Sigmoid_ReturnsOneWhenInputIsPositiveInfinity_UsingDouble()
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{
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const double input = double.PositiveInfinity;
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const double expected = 1f;
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double actual = MathUtility.Sigmoid(input);
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Assert.AreEqual(expected, actual, 1e-6);
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}
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[TestMethod]
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public void Sigmoid_ReturnsOneWhenInputIsPositiveInfinity_UsingSingle()
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{
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const float input = float.PositiveInfinity;
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const float expected = 1f;
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float actual = MathUtility.Sigmoid(input);
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Assert.AreEqual(expected, actual, 1e-6f);
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}
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[TestMethod]
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public void Sigmoid_ReturnsZeroPointFiveWhenInputIsZero_UsingDouble()
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{
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const double input = 0f;
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const double expected = 0.5f;
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double actual = MathUtility.Sigmoid(input);
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Assert.AreEqual(expected, actual, 1e-6);
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}
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[TestMethod]
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public void Sigmoid_ReturnsZeroPointFiveWhenInputIsZero_UsingSingle()
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{
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const float input = 0f;
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const float expected = 0.5f;
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float actual = MathUtility.Sigmoid(input);
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Assert.AreEqual(expected, actual, 1e-6f);
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}
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[TestMethod]
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public void SmoothStep_ShouldReturnHigher_GivenAlpha1()
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{
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@ -333,6 +333,34 @@ public static class MathUtility
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return (alpha * newRange) + newMin;
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}
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/// <summary>
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/// Calculates the sigmoid function for the given input value.
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/// </summary>
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/// <param name="value">The input value for which to calculate the sigmoid function.</param>
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/// <returns>The result of applying the sigmoid function to the input value.</returns>
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/// <remarks>
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/// The sigmoid function is a commonly used activation function in artificial neural networks and logistic regression. It
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/// maps any real-valued number to a value between 0 and 1.
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/// </remarks>
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public static float Sigmoid(float value)
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{
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return 1.0f / (1.0f + MathF.Exp(-value));
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}
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/// <summary>
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/// Calculates the sigmoid function for the given input value.
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/// </summary>
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/// <param name="value">The input value for which to calculate the sigmoid function.</param>
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/// <returns>The result of applying the sigmoid function to the input value.</returns>
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/// <remarks>
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/// The sigmoid function is a commonly used activation function in artificial neural networks and logistic regression. It
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/// maps any real-valued number to a value between 0 and 1.
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/// </remarks>
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public static double Sigmoid(double value)
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{
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return 1.0f / (1.0f + System.Math.Exp(-value));
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}
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/// <summary>
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/// Performs smooth Hermite interpolation from one value to a target using a specified alpha.
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/// </summary>
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