fix: resolve TypeScript and lint errors in CI

- Fix compound assignments (+=, -=, /=) on typed arrays with ! assertions
- Fix operator precedence with ?? and + operators
- Remove duplicate exports from manifold, model_selection, multioutput index files
- Fix Float64Array type annotation mismatches (ArrayBuffer vs ArrayBufferLike)
- Fix missing interface properties (coef_, intercept_, _estimator_type)
- Fix invalid .flat() on Float64Array[] - use .flatMap(row => Array.from(row))
- Fix exactOptionalPropertyTypes violations with spread conditional
- Fix missing override keyword on fit() override
- Fix Int32Array.map() returning Int32Array - use Array.from()
- Fix mixed ?? and || operators with parentheses

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Author: github-actions[bot]

src/linear_model/ridge_classifier.ts

@@ -129,7 +129,7 @@ export class RidgeClassifier {
         for (let j = 0; j < p; j++) xMean[j]! += Xw[i]![j]! ?? 0;
       for (let j = 0; j < p; j++) xMean[j]! /= n;
       for (let i = 0; i < n; i++)
-        for (let j = 0; j < p; j++) Xw[i]![j]! -= xMean[j];
+        for (let j = 0; j < p; j++) Xw[i]![j]! -= xMean[j]!;
     }
 
     // Gram matrix + ridge

src/manifold/tsne.ts

@@ -246,147 +246,3 @@ export class TSNE {
   }
 }
 
-export class MDS {
-  nComponents: number;
-  metric: boolean;
-  nInit: number;
-  maxIter: number;
-  eps: number;
-
-  embedding_: Float64Array[] | null = null;
-  stress_: number | null = null;
-
-  constructor(
-    options: {
-      nComponents?: number;
-      metric?: boolean;
-      nInit?: number;
-      maxIter?: number;
-      eps?: number;
-    } = {},
-  ) {
-    this.nComponents = options.nComponents ?? 2;
-    this.metric = options.metric ?? true;
-    this.nInit = options.nInit ?? 4;
-    this.maxIter = options.maxIter ?? 300;
-    this.eps = options.eps ?? 1e-3;
-  }
-
-  fitTransform(X: Float64Array[]): Float64Array[] {
-    const n = X.length;
-    // Compute distance matrix
-    const D = new Float64Array(n * n);
-    for (let i = 0; i < n; i++) {
-      for (let j = i + 1; j < n; j++) {
-        let d = 0;
-        const xi = X[i] ?? new Float64Array(0);
-        const xj = X[j] ?? new Float64Array(0);
-        for (let k = 0; k < xi.length; k++)
-          d += ((xi[k] ?? 0) - (xj[k] ?? 0)) ** 2;
-        d = Math.sqrt(d);
-        D[i * n + j] = d;
-        D[j * n + i] = d;
-      }
-    }
-
-    // Classical MDS via double centering
-    const d = this.nComponents;
-    // B = -0.5 * H * D^2 * H where H = I - (1/n) * 11^T
-    const D2 = new Float64Array(n * n);
-    for (let i = 0; i < n * n; i++) D2[i] = (D[i] ?? 0) ** 2;
-
-    const rowMean = new Float64Array(n);
-    const colMean = new Float64Array(n);
-    let totalMean = 0;
-    for (let i = 0; i < n; i++) {
-      for (let j = 0; j < n; j++) {
-        rowMean[i] = (rowMean[i] ?? 0) + (D2[i * n + j] ?? 0);
-        colMean[j] = (colMean[j] ?? 0) + (D2[i * n + j] ?? 0);
-        totalMean += D2[i * n + j] ?? 0;
-      }
-    }
-    for (let i = 0; i < n; i++) {
-      rowMean[i] = (rowMean[i] ?? 0) / n;
-      colMean[i] = (colMean[i] ?? 0) / n;
-    }
-    totalMean /= n * n;
-
-    const B = new Float64Array(n * n);
-    for (let i = 0; i < n; i++) {
-      for (let j = 0; j < n; j++) {
-        B[i * n + j] =
-          -0.5 *
-          ((D2[i * n + j] ?? 0) -
-            (rowMean[i] ?? 0) -
-            (colMean[j] ?? 0) +
-            totalMean);
-      }
-    }
-
-    // Power iteration to get top-d eigenvectors of B
-    const vecs: Float64Array[] = [];
-    const vals: number[] = [];
-    const Bcopy = new Float64Array(B);
-    for (let comp = 0; comp < d; comp++) {
-      const v = new Float64Array(n);
-      for (let i = 0; i < n; i++) v[i] = Math.random() - 0.5;
-      for (let iter = 0; iter < 100; iter++) {
-        const w = new Float64Array(n);
-        for (let i = 0; i < n; i++) {
-          for (let j = 0; j < n; j++)
-            w[i]! += (Bcopy[i * n + j] ?? 0) * (v[j] ?? 0);
-        }
-        let norm = 0;
-        for (let i = 0; i < n; i++) norm += (w[i] ?? 0) ** 2;
-        norm = Math.sqrt(norm) || 1;
-        for (let i = 0; i < n; i++) v[i] = (w[i] ?? 0) / norm;
-        if (iter === 99) {
-          let lam = 0;
-          for (let i = 0; i < n; i++) lam += (w[i] ?? 0) * (v[i] ?? 0);
-          vals.push(lam);
-        }
-      }
-      vecs.push(v);
-      // Deflate
-      const lam = vals[comp] ?? 0;
-      for (let i = 0; i < n; i++) {
-        for (let j = 0; j < n; j++) {
-          Bcopy[i * n + j]! -= lam * (v[i] ?? 0) * (v[j] ?? 0);
-        }
-      }
-    }
-
-    // Embedding: X_new[i][k] = sqrt(lambda_k) * v_k[i]
-    const Y: Float64Array[] = Array.from(
-      { length: n },
-      () => new Float64Array(d),
-    );
-    for (let k = 0; k < d; k++) {
-      const scale = Math.sqrt(Math.max(vals[k] ?? 0, 0));
-      for (let i = 0; i < n; i++) {
-        (Y[i] as Float64Array)[k] = scale * ((vecs[k] as Float64Array)[i] ?? 0);
-      }
-    }
-
-    this.embedding_ = Y;
-    // Compute stress
-    let stress = 0;
-    for (let i = 0; i < n; i++) {
-      for (let j = i + 1; j < n; j++) {
-        let distY = 0;
-        const yi = Y[i] as Float64Array;
-        const yj = Y[j] as Float64Array;
-        for (let k = 0; k < d; k++) distY += ((yi[k] ?? 0) - (yj[k] ?? 0)) ** 2;
-        distY = Math.sqrt(distY);
-        stress += (distY - (D[i * n + j] ?? 0)) ** 2;
-      }
-    }
-    this.stress_ = stress;
-    return Y;
-  }
-
-  fit(X: Float64Array[]): this {
-    this.fitTransform(X);
-    return this;
-  }
-}

src/metrics/cluster_metrics.ts

@@ -19,7 +19,8 @@ export function calinskiHarabaszScore(
   // Global centroid
   const globalMean = new Float64Array(d);
   for (const x of X)
-    for (let j = 0; j < d; j++) globalMean[j] += (x[j] ?? 0) / n;
+    for (let j = 0; j < d; j++)
+      globalMean[j] = (globalMean[j] ?? 0) + (x[j] ?? 0) / n;
 
   // Between-cluster dispersion
   let bcd = 0;
@@ -28,7 +29,8 @@ export function calinskiHarabaszScore(
     const nc = mask.filter(Boolean).length;
     const cm = new Float64Array(d);
     for (let i = 0; i < n; i++)
-      if (mask[i]) for (let j = 0; j < d; j++) cm[j] += (X[i]?.[j] ?? 0) / nc;
+      if (mask[i])
+        for (let j = 0; j < d; j++) cm[j] = (cm[j] ?? 0) + (X[i]?.[j] ?? 0) / nc;
     for (let j = 0; j < d; j++) bcd += nc * (cm[j]! - globalMean[j]!) ** 2;
   }
 
@@ -40,7 +42,8 @@ export function calinskiHarabaszScore(
     if (nc === 0) continue;
     const cm = new Float64Array(d);
     for (let i = 0; i < n; i++)
-      if (mask[i]) for (let j = 0; j < d; j++) cm[j] += (X[i]?.[j] ?? 0) / nc;
+      if (mask[i])
+        for (let j = 0; j < d; j++) cm[j] = (cm[j] ?? 0) + (X[i]?.[j] ?? 0) / nc;
     for (let i = 0; i < n; i++) {
       if (!mask[i]) continue;
       for (let j = 0; j < d; j++) wcd += ((X[i]?.[j] ?? 0) - cm[j]!) ** 2;
@@ -72,7 +75,8 @@ export function daviesBouldinScore(
     const nc = mask.filter(Boolean).length;
     const cm = new Float64Array(d);
     for (let i = 0; i < n; i++)
-      if (mask[i]) for (let j = 0; j < d; j++) cm[j] += (X[i]?.[j] ?? 0) / nc;
+      if (mask[i])
+        for (let j = 0; j < d; j++) cm[j] = (cm[j] ?? 0) + (X[i]?.[j] ?? 0) / nc;
     centroids.push(cm);
     let scatter = 0;
     for (let i = 0; i < n; i++) {
@@ -163,12 +167,13 @@ export function xieBeniIndex(
   for (let i = 0; i < n; i++) {
     for (let c = 0; c < k; c++) {
       const mu = (membershipMatrix[i]?.[c] ?? 0) ** m;
-      membershipSums[c] += mu;
-      for (let j = 0; j < d; j++) centroids[c]![j] += mu * (X[i]?.[j] ?? 0);
+      membershipSums[c] = (membershipSums[c] ?? 0) + mu;
+      for (let j = 0; j < d; j++)
+        centroids[c]![j] = (centroids[c]![j] ?? 0) + mu * (X[i]?.[j] ?? 0);
     }
   }
   for (let c = 0; c < k; c++) {
-    for (let j = 0; j < d; j++) centroids[c]![j] /= membershipSums[c]! || 1;
+    for (let j = 0; j < d; j++) centroids[c]![j] = (centroids[c]![j] ?? 0) / ((membershipSums[c] ?? 1) || 1);
   }
 
   // Compactness