fix: add prettier write script (#2)

Author: jobo322

package.json

@@ -18,6 +18,8 @@
     "eslint": "eslint src --ext ts --cache",
     "eslint-fix": "npm run eslint -- --fix",
     "prepublishOnly": "npm run tsc",
+    "prettier": "prettier --check src",
+    "prettier-write": "prettier --write src",
     "test": "npm run test-coverage && npm run eslint",
     "test-coverage": "npm run test-only -- --coverage",
     "test-only": "jest",

src/__tests__/test.ts

@@ -11,7 +11,9 @@ describe('linear sum problem', () => {
     const diff = a.map((aElement) => {
       return b.map((bElement) => Math.abs(bElement - aElement));
     });
-    const { columnAssignments, rowAssignments } = linearSumAssignment(diff, { maximaze: false});
+    const { columnAssignments, rowAssignments } = linearSumAssignment(diff, {
+      maximaze: false,
+    });
     expect(columnAssignments).toMatchCloseTo([2, 0, 1, 3, 4]);
     expect(rowAssignments).toMatchCloseTo([1, 2, 0, 3, 4]);
   });
@@ -22,7 +24,9 @@ describe('linear sum problem', () => {
     const diff = a.map((aElement) => {
       return b.map((bElement) => Math.abs(bElement - aElement));
     });
-    const { columnAssignments, rowAssignments } = linearSumAssignment(diff, { maximaze: false});
+    const { columnAssignments, rowAssignments } = linearSumAssignment(diff, {
+      maximaze: false,
+    });
     expect(columnAssignments).toMatchCloseTo([2, 0, 1, 3, 4]);
     expect(rowAssignments).toMatchCloseTo([1, 2, 0, 3, 4, -1]);
   });
@@ -32,7 +36,9 @@ describe('linear sum problem', () => {
     const diff = a.map((aElement) => {
       return b.map((bElement) => Math.abs(bElement - aElement));
     });
-    const { columnAssignments, rowAssignments } = linearSumAssignment(diff, { maximaze: false});
+    const { columnAssignments, rowAssignments } = linearSumAssignment(diff, {
+      maximaze: false,
+    });
     expect(columnAssignments).toMatchCloseTo([1, 2, 0, 3, 4, -1]);
     expect(rowAssignments).toMatchCloseTo([2, 0, 1, 3, 4]);
   });

src/getShortestPath.ts

@@ -3,12 +3,12 @@ import sequentialFill from 'ml-array-sequential-fill';
 import type { Matrix } from 'ml-matrix';
 
 interface GetShortestPathOptions {
-  currUnAssCol: number,
-  dualVariableForColumns: DoubleArray,
-  dualVariableForRows: DoubleArray,
-  rowAssignments: DoubleArray,
-  columnAssignments: DoubleArray,
-  matrix: Matrix,
+  currUnAssCol: number;
+  dualVariableForColumns: DoubleArray;
+  dualVariableForRows: DoubleArray;
+  rowAssignments: DoubleArray;
+  columnAssignments: DoubleArray;
+  matrix: Matrix;
 }
 
 export function getShortestPath(options: GetShortestPathOptions) {
@@ -21,7 +21,7 @@ export function getShortestPath(options: GetShortestPathOptions) {
     matrix,
   } = options;
 
-  let nbRows = matrix.rows
+  let nbRows = matrix.rows;
   let nbColumns = matrix.columns;
 
   let pred = new Float64Array(nbColumns);
@@ -43,10 +43,14 @@ export function getShortestPath(options: GetShortestPathOptions) {
     for (let curRowScan = 0; curRowScan < numRows2Scan; curRowScan++) {
       let curRow = rows2Scan[curRowScan];
 
-      let reducedCost = delta + matrix.get(curRow, curColumn) - dualVariableForColumns[curColumn] - dualVariableForRows[curRow];
+      let reducedCost =
+        delta +
+        matrix.get(curRow, curColumn) -
+        dualVariableForColumns[curColumn] -
+        dualVariableForRows[curRow];
       if (reducedCost < shortestPathCost[curRow]) {
         pred[curRow] = curColumn;
-        shortestPathCost[curRow] = reducedCost
+        shortestPathCost[curRow] = reducedCost;
       }
 
       if (shortestPathCost[curRow] < minVal) {
@@ -74,16 +78,20 @@ export function getShortestPath(options: GetShortestPathOptions) {
   for (let sel = 0; sel < nbColumns; sel++) {
     if (scannedColumns[sel] === 0) continue;
     if (sel === currUnAssCol) continue;
-    dualVariableForColumns[sel] += delta - shortestPathCost[columnAssignments[sel]];
+    dualVariableForColumns[sel] +=
+      delta - shortestPathCost[columnAssignments[sel]];
   }
   for (let sel = 0; sel < nbRows; sel++) {
     if (scannedRows[sel] === 0) continue;
-    dualVariableForRows[sel] -= (delta - shortestPathCost[sel]);
+    dualVariableForRows[sel] -= delta - shortestPathCost[sel];
   }
 
   return {
-    sink, pred, dualVariableForColumns, dualVariableForRows
-  }
+    sink,
+    pred,
+    dualVariableForColumns,
+    dualVariableForRows,
+  };
 }
 
 function getArrayOfInfinity(nbElements = 1, value = Number.POSITIVE_INFINITY) {
@@ -92,4 +100,4 @@ function getArrayOfInfinity(nbElements = 1, value = Number.POSITIVE_INFINITY) {
     array[i] = value;
   }
   return array;
-}
+}

src/index.ts

@@ -4,10 +4,13 @@ import { Matrix } from 'ml-matrix';
 import { getShortestPath } from './getShortestPath';
 
 interface Options {
-  maximaze?: boolean
+  maximaze?: boolean;
 }
 
-export default function linearSumAssignment(input: DoubleArray[], options: Options = {}) {
+export default function linearSumAssignment(
+  input: DoubleArray[],
+  options: Options = {},
+) {
   const { maximaze = true } = options;
 
   let matrix = Matrix.checkMatrix(input);
@@ -25,7 +28,6 @@ export default function linearSumAssignment(input: DoubleArray[], options: Optio
   matrix = matrix.subtract(matrixDelta);
   if (maximaze) matrix = matrix.mul(-1);
 
-
   let rowAssignments: DoubleArray = new Float64Array(nbRows).fill(-1);
   let columnAssignments: DoubleArray = new Float64Array(nbColumns).fill(-1);
   let dualVariableForColumns: DoubleArray = new Float64Array(nbColumns);
@@ -48,7 +50,7 @@ export default function linearSumAssignment(input: DoubleArray[], options: Optio
         rowAssignments: [],
         columnAssignments: [],
         gain: -1,
-      }
+      };
     }
 
     dualVariableForColumns = currentAugmenting.dualVariableForColumns;
@@ -69,18 +71,21 @@ export default function linearSumAssignment(input: DoubleArray[], options: Optio
     gain += matrix.get(columnAssignments[curCol], curCol);
   }
 
-  gain = ((maximaze ? -1 : 1) * gain) + (matrixDelta * nbColumns);
+  gain = (maximaze ? -1 : 1) * gain + matrixDelta * nbColumns;
 
   if (didFlip) {
     [columnAssignments, rowAssignments] = [rowAssignments, columnAssignments];
-    [dualVariableForColumns, dualVariableForRows] = [dualVariableForRows, dualVariableForColumns];
+    [dualVariableForColumns, dualVariableForRows] = [
+      dualVariableForRows,
+      dualVariableForColumns,
+    ];
   }
 
   return {
     rowAssignments,
     columnAssignments,
     gain,
     dualVariableForColumns,
-    dualVariableForRows
-  }
-}
+    dualVariableForRows,
+  };
+}