Type definition for the library 'regression' (#31728)

* Type definition for the library 'regression'

* Fix to match the commonJS style used by the real library

types/regression/index.d.ts

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+// Type definitions for regression 2.0
+// Project: https://github.com/Tom-Alexander/regression-js
+// Definitions by: Mattias B. Martens <https://github.com/MattiasMartens>
+// Definitions: https://github.com/MattiasMartens/DefinitelyTyped
+
+/**
+ * [x, y]
+ */
+export type DataPoint = [number, number];
+
+export interface Options {
+    /**
+     * The number of decimal places to round to.
+     * This is used to round the calculated fitting coefficients,
+     * the output predictions, and the value of r^2.
+     */
+    precision?: number;
+    /**
+     * The number of terms to solve for (and therefore
+     * the number of coefficients to calculate). Only
+     * relevant for polynomial fitting.
+     */
+    order?: number;
+}
+
+export interface Result {
+    /**
+     * A human-readable string representation of the derived
+     * formula in the form y = f(x) where f depends on the
+     * fitting method used and the coefficients that were
+     * calculated.
+     */
+    string: string;
+    /**
+     * For each point (x, y) in the input data, a point
+     * corresponding to the regression prediction for that
+     * value of x.
+     * One could use this to directly evaluate the quality
+     * of the fit.
+     */
+    points: ReadonlyArray<DataPoint>;
+    /**
+     * Function that takes an arbitrary value of x and
+     * produces a coordinate representing the y-value of
+     * the regression curve at that point.
+     * Both the resulting x- and y-values are rounded to
+     * a number of decimal places defined in the options
+     * (default is 2).
+     */
+    predict: (x: number) => DataPoint;
+    /**
+     * The generated coefficients describing the equation
+     * of best fit.
+     *
+     * For a linear fit, the coefficients are `[a, b]` in `y = a * x + b`.
+     * For an exponential fit, the coefficients are `[a, b]` in `y = a * e ^ (b * x)`.
+     * For a logarithmic fit, the coefficients are `[a, b]` in `y = a + b * ln(x)`.
+     * For a power fit, the coefficients are `[a, b]` in `y = a * x^b`.
+     * For a polynomial fit, the coefficients are `[a0, a1, a2, ...aN]` in:
+     * ```y = a0 * x ^ N + a1 * x ^ (N - 1) + ... + aN```
+     * where N is the order (default 2).
+     */
+    equation: number[];
+    /**
+     * The value of R squared, a statistical measure of the conformance of the
+     * fitted curve to the input data where 1 is an exact fit and 0 is no fit
+     * at all.
+     *
+     * This value is rounded to the number of decimal places defined by
+     * the precision option (default 2).
+     */
+    r2: number;
+}
+
+export function _round(number: number, precision: number): number;
+export function linear(
+    data: ReadonlyArray<DataPoint>,
+    options?: Options
+): Result;
+export function exponential(
+    data: ReadonlyArray<DataPoint>,
+    options?: Options
+): Result;
+export function logarithmic(
+    data: ReadonlyArray<DataPoint>,
+    options?: Options
+): Result;
+export function power(
+    data: ReadonlyArray<DataPoint>,
+    options?: Options
+): Result;
+export function polynomial(
+    data: ReadonlyArray<DataPoint>,
+    options?: Options
+): Result;

types/regression/regression-tests.ts

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+import * as Regression from "regression";
+
+const data: ReadonlyArray<[number, number]> = [[0, 0], [1, 1], [2, 2]];
+const result1 = Regression.linear(data);
+const result2 = Regression.exponential(data);
+const result3 = Regression.logarithmic(data);
+const result4 = Regression.power(data);
+const result5 = Regression.polynomial(data);
+const result6 = Regression._round(10.312, 3);
+const result7 = Regression.polynomial(data, { order: 4 });
+const result8 = Regression.polynomial(data, { precision: 4 });
+const result9 = Regression.polynomial(data, { order: 4, precision: 4 });

types/regression/tsconfig.json

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+{
+    "compilerOptions": {
+        "module": "commonjs",
+        "lib": ["es6"],
+        "noImplicitAny": true,
+        "noImplicitThis": true,
+        "strictNullChecks": false,
+        "strictFunctionTypes": true,
+        "baseUrl": "../",
+        "typeRoots": ["../"],
+        "types": [],
+        "noEmit": true,
+        "forceConsistentCasingInFileNames": true
+    },
+    "files": ["index.d.ts", "regression-tests.ts"]
+}

types/regression/tslint.json

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+{
+    "extends": "dtslint/dt.json"
+}