[Repo Assist] feat: add Imputation.kNearestWeightedImpute for distance-weighted KNN

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Summary

Implements the distance-weighted KNN imputation variant requested in #318, adding Imputation.kNearestWeightedImpute to FSharp.Stats.ML.

Motivation

The existing kNearestImpute treats all k neighbours equally (simple mean). Issue #318 asks for:

• A weighted version where the contribution of each neighbour is scaled by a user-supplied function of its distance
• Support for pluggable distance metrics (the existing function hard-codes euclideanNaNSquared)
• Support for similarity measures like Pearson correlation (where the user passes a reciprocal converter)

Changes

src/FSharp.Stats/ML/Imputation.fs

New function:

val kNearestWeightedImpute :
    distanceMetric   : DistanceMetrics.Distance<float[]>
    -> distanceToWeight : (float -> float)
    -> k             : int
    -> MatrixBaseImputation<float[],float>

Parameters

Parameter	Purpose
distanceMetric	Any float[] → float[] → float distance; use DistanceMetrics.Array.euclideanNaNSquared to skip NaN positions
distanceToWeight	Converts a raw distance to a non-negative weight. For Euclidean use fun d → 1.0 / (d + epsilon); for a correlation similarity measure pass id or its reciprocal
k	Number of nearest neighbours

Behaviour

• Selects the k nearest complete rows by distanceMetric.
• Computes a weighted average of their values at the missing index, proportional to distanceToWeight(distance).
• If totalWeight = 0 (all weights zero), falls back to an unweighted mean (graceful degradation).
• Returns nan if the complete-rows pool is empty.

Typical usage

open FSharp.Stats.ML

let isMissing = System.Double.IsNaN
let invDist d = 1.0 / (d + System.Double.Epsilon)   // inverse-distance weighting
let imputer = Imputation.kNearestWeightedImpute
                  FSharp.Stats.DistanceMetrics.Array.euclideanNaNSquared
                  invDist 3
let imputed = Imputation.imputeBy imputer isMissing rawData
```

### `tests/FSharp.Stats.Tests/Imputation.fs` (new file)

6 tests covering both `kNearestImpute` and `kNearestWeightedImpute`:

| Test | What it checks |
|---|---|
| `kNearestImpute` – unweighted mean | Simple mean of 2 nearest neighbours |
| `kNearestImpute` – k = dataset size | Mean over all rows |
| `kNearestWeightedImpute` – k=1 | Single neighbour → value unchanged |
| `kNearestWeightedImpute` – inverse-distance | Analytical result: `11.0` |
| `kNearestWeightedImpute` – equal distances | Equal weights → simple mean |
| `kNearestWeightedImpute` – empty dataset | Returns `nan` gracefully |
| `imputeBy` integration test | End-to-end: NaN is replaced, result in plausible range |

## Test Status

✅ **1200 / 1200 tests pass** (0 failures, 0 ignored)

```
EXPECTO! 1,200 tests run in 00:00:02.8s – 1,200 passed, 0 ignored, 0 failed, 0 errored. Success!

Notes & Trade-offs

• No breaking changes – the existing kNearestImpute is unchanged.
• Remaining items from [Feature Request] weighted KNN imputation #318 not addressed here: module rename (Impute → already deprecated), missing-value encoding parameterisation, and documentation examples. These could be tackled in follow-up PRs or directly by the maintainer.
• Overflow guard: when two complete rows have identical non-NaN values as the query, euclideanNaNSquared returns 0. Callers using 1/(d+epsilon) still get numerically stable results because epsilon prevents true division-by-zero and the equal-weight case degrades to the arithmetic mean.

Closes #318

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