fix: add range clamp for fitted Constant (0-18, reconfigurable)

README.md

@@ -110,6 +110,8 @@ else if (!result.IsCancelled)
 - `RawValues` 和 `Scores` 始终保持公开形状：六个雷达轴加
   `fitted_constant`；不可用项为 `null`。
 - 只有七维 raw 全部成功时才生成 `FittedConstant` 和映射后的 score。
+- 拟合输出在模块内部限位；上下限由 `RegressionBetaModel` 的
+  `MinimumFittedConstant` / `MaximumFittedConstant` 常量配置，当前为 0～18。
 - `partial` 会保留已完成的特征，但不会生成拟合定数。
 
 
@@ -266,6 +268,8 @@ else if (!result.IsCancelled)
   axes plus `fitted_constant`; unavailable entries are `null`.
 - `FittedConstant` and mapped scores are produced only when all seven raw
   features succeed.
+- Predictions are clamped inside the module using `MinimumFittedConstant` and
+  `MaximumFittedConstant` in `RegressionBetaModel` (currently 0 and 18).
 - `partial` preserves completed feature results but does not produce a fitted
   constant.
 - `fitted_constant` is identity-mapped and is not on the radar axes' 0-250

Runtime/Regression/RegressionBetaModel.cs

@@ -6,6 +6,10 @@ namespace MajRadar.Regression;
 
 public sealed class RegressionBetaModel
 {
+    // Output limits can be tuned independently of the fitted coefficients.
+    internal const double MinimumFittedConstant = 0;
+    internal const double MaximumFittedConstant = 18;
+
     public IReadOnlyList<string> InputFeatures => RadarFeatureNames.ModelInputOrder;
 
     public double Predict(IReadOnlyList<double> rawValues)
@@ -32,7 +36,7 @@ public double Predict(IReadOnlyList<double> rawValues)
                 result += coefficients[coefficientIndex++] * normalized[left] * normalized[right];
         if (coefficientIndex != coefficients.Length || double.IsNaN(result) || double.IsInfinity(result))
             throw new InvalidOperationException("Regression prediction is invalid.");
-        return result;
+        return Math.Clamp(result, MinimumFittedConstant, MaximumFittedConstant);
     }
 
     public double Predict(IReadOnlyDictionary<string, double> rawFeatures)

Tests~/MajRadar.Tests/AnalysisAndRegressionTests.cs

@@ -360,14 +360,19 @@ public void FeatureFailuresAreIsolatedInsideRadarAnalyzer()
 
     [Theory]
     [MemberData(nameof(RegressionVectors))]
-    public void EmbeddedRegressionMatchesFrozenPythonVectors(double[] raw, double expected)
+    public void EmbeddedRegressionMatchesPythonVectorsWithinOutputLimits(double[] raw, double expected)
     {
         var actual = new RegressionBetaModel().Predict(raw);
-        Assert.Equal(expected, actual, 9);
+        Assert.Equal(Math.Clamp(expected,
+            RegressionBetaModel.MinimumFittedConstant,
+            RegressionBetaModel.MaximumFittedConstant), actual, 9);
     }
 
     public static IEnumerable<object[]> RegressionVectors()
     {
+        // Unbounded Python oracle outputs exercise both ends of the configured range.
+        yield return Vector(new[] { 8.0, 15.0, 20000.0, 10.0, 3.0, 10.0, 1.0 }, -136224.7610705242);
+        yield return Vector(new[] { 8.0, 15.0, 20.0, 10.0, 3.0, 10.0, 1000.0 }, 435992.9429645393);
         yield return Vector(new[] { 6.727708533441772, 10.213333333333333, 0.0, 3.500000000000045, 2.3435703486210016, 5.223136339994041, 0.32687803060913356 }, 13.18826234659077);
         yield return Vector(new[] { 5.3448652850017435, 9.373333333333333, 5.151860505357921, 3.8688372913206157, 1.527537949608422, 3.4463156768134153, 0.5098107142089721 }, 13.04867082557079);
         yield return Vector(new[] { 6.556341447489688, 9.728309501643864, 2.7754445459480404, 4.146342997793008, 1.977721867042476, 10.365354504785971, 0.276398788581657 }, 13.335359785629928);