cognitive-ts - npm Package Compare versions

Comparing version 2.0.1 to 2.0.2

__test__/base.test.ts

 import * as _ from "lodash"
-import { compose, sFn, isSquare, swap, sFill } from "../lib/base"
+import { compose, sFn, isSquare, swap, sFill, transpose } from "../lib/base"
 import { add } from "../lib/data-frame"
@@ -12,3 +12,3 @@
 
-  expect(sFn([[1, 2, 3]], (x: number[]) => _.map(x, (n) => n * 2))).toEqual([
+  expect(sFn([[1, 2, 3]])((x: number[]) => _.map(x, (n) => n * 2))).toEqual([
     [2, 4, 6]
@@ -19,2 +19,8 @@ ])
 
+  expect(transpose([[1, 2, 3], [1, 2, 3]])).toEqual([
+    [1, 1],
+    [2, 2],
+    [3, 3]
+  ])
+
   expect(
@@ -21,0 +27,0 @@ isSquare([

index.ts

@@ -1,6 +0,6 @@
-import * as dfMath from "./lib/df/math"
-import * as sMath from "./lib/s/math"
-import * as base from "./lib/base/base"
-import * as rotate from "./lib/rotate"
-import * as shape from "./lib/shape"
+import * as dfMath from './lib/df/math'
+import * as sMath from './lib/s/math'
+import * as base from './lib/base/base'
+import * as rotate from './lib/rotate'
+import * as shape from './lib/shape'
 
@@ -7,0 +7,0 @@ export default {

lib/algorithms/multivariate-linear-regression.ts

@@ -1,5 +0,5 @@
-import * as _ from "lodash"
-import { shape } from "../base"
-import { dot, sumSquares } from "../data-frame"
-import { sZeros } from "../series"
+import * as _ from 'lodash'
+import { shape } from '../base'
+import { dot, sumSquares } from '../data-frame'
+import { sZeros } from '../series'
 
@@ -31,3 +31,3 @@ export const multiVariateLinearRegression = (x: { features: Matrix, labels: Series}, iterations: number) => {
   const [m, n] = shape(features)
-  let weights = sZeros(n)
+  const weights = sZeros(n)
   let bias = 0
@@ -34,0 +34,0 @@

lib/base/index.ts

@@ -0,8 +1,10 @@
+/* eslint-disable @typescript-eslint/ban-types */
 import * as _ from 'lodash'
 
-export const compose = (...fns: Function[]) =>
-    (args: Matrix) =>
-      _.reduce(fns, (arg, fn: Function) => fn(arg), args)
+export const compose =
+  (...fns: Function[]) =>
+  (args: Matrix) =>
+    _.reduce(fns, (arg, fn: Function) => fn(arg), args)
 
-export const sFn = (x: Matrix, fn: (s: Series) => SeriesResult) =>
+export const sFn = (x: Matrix) => (fn: (s: Series) => SeriesResult) =>
   _.map(
@@ -15,3 +17,3 @@ x,
 
-export const swap = (arr: Array<any>, i: any, j: any) => {
+export const swap = (arr: number[], i: number, j: number) => {
   const temp = arr[i]
@@ -23,11 +25,14 @@ arr[i] = arr[j]
 
-export const transpose = _.memoize((x : Matrix) =>
-  _.map(_.first(x), (_v: Series, i: number) => _.reverse(_.map(x, (r) => r[i])))
-)
+export const transpose = (x: Matrix) =>
+  _.map(_.first(x), (_v: Series, i: number): number[] =>
+    _.reverse(_.map(x, (r) => r[i]))
+  )
 
-export const inverseTranspose = _.memoize((x : Matrix) =>
-  _.map(_.first(x), (_v: Series, i: number) => _.map(x, (r: number[]) => r[_.size(r) - 1 - i]))
+export const inverseTranspose = _.memoize((x: Matrix) =>
+  _.map(_.first(x), (_v: Series, i: number) =>
+    _.map(x, (r: number[]) => r[_.size(r) - 1 - i])
+  )
 )
 
-export const shape = _.memoize((x : Matrix) => [_.size(x), _.size(_.first(x))])
+export const shape = _.memoize((x: Matrix) => [_.size(x), _.size(_.first(x))])
 
@@ -34,0 +39,0 @@ export const isSquare = _.memoize(

lib/data-frame/index.ts

@@ -1,50 +0,68 @@
-import * as _ from "lodash"
-import * as b from "../base"
-import * as s from "../series"
+import * as _ from 'lodash'
+import { transpose, inverseTranspose, compose, sFn } from '../base'
+import {
+  sAbs,
+  sAdd,
+  sCumSum,
+  sDivide,
+  sMean,
+  sMultiply,
+  sPow,
+  sRandom,
+  sSqrt,
+  sStd,
+  sSubtract,
+  sZeros
+} from '../series'
 
-export const mean = (x : Matrix) =>
-  b.compose(b.transpose, (x : Matrix) => b.sFn(x, s.sMean), b.inverseTranspose)(x)
+export const mean = (x: Matrix) =>
+  compose(transpose, (x: Matrix) => sFn(x)(sMean), inverseTranspose)(x)
 
-export const sqrt = (x : Matrix) =>
-  b.compose(b.transpose, (x : Matrix) => b.sFn(x, s.sSqrt), b.inverseTranspose)(x)
+export const sqrt = (x: Matrix) =>
+  compose(transpose, (x: Matrix) => sFn(x)(sSqrt), inverseTranspose)(x)
 
-export const stddev = (x : Matrix) => b.compose(b.transpose, (x : Matrix) => b.sFn(x, s.sStd))(x)
+export const stddev = (x: Matrix) =>
+  compose(transpose, (x: Matrix) => sFn(x)(sStd))(x)
 
-export const cumSum = (x : Matrix) =>
-  b.compose(b.inverseTranspose, (x : Matrix) => b.sFn(x, s.sCumSum), b.transpose)(x)
+export const cumSum = (x: Matrix) =>
+  compose(inverseTranspose, (x: Matrix) => sFn(x)(sCumSum), transpose)(x)
 
-export const add = (x : Matrix) => b.compose(b.transpose, (x : Matrix) => b.sFn(x, s.sAdd))(x)
+export const add = (x: Matrix) =>
+  compose(transpose, (x: Matrix) => sFn(x)(sAdd))(x)
 
-export const subtract = (x : Matrix) =>
-  b.compose(b.transpose, (x : Matrix) => b.sFn(x, s.sSubtract))(x)
+export const subtract = (x: Matrix) =>
+  compose(transpose, (x: Matrix) => sFn(x)(sSubtract))(x)
 
-export const divide = (x : Matrix) =>
-  b.compose(b.transpose, (x : Matrix) => b.sFn(x, s.sDivide))(x)
+export const divide = (x: Matrix) =>
+  compose(transpose, (x: Matrix) => sFn(x)(sDivide))(x)
 
-export const multiply = (x : Matrix) =>
-  b.compose(b.transpose, (x : Matrix) => b.sFn(x, s.sMultiply))(x)
+export const multiply = (x: Matrix) =>
+  compose(transpose, (x: Matrix) => sFn(x)(sMultiply))(x)
 
 export const matMul = (x: Matrix, y: Matrix) =>
-  _.map(b.transpose(x), (r: number[], ri: number) =>
-    _.map(r, (n: number, i: number) => n * _.get(b.transpose(y), [ri, i]))
+  _.map(transpose(x), (r: number[], ri: number) =>
+    _.map(r, (n: number, i: number) => n * _.get(transpose(y), [ri, i]))
   )
 
-export const abs = (x : Matrix) => b.sFn(x, s.sAbs)
+export const abs = (x: Matrix) => sFn(x)(sAbs)
 
 export const fill = (i: number, t: number) => _.fill(Array(t), i)
 
-export const zeros = (r: number, c: number) => _.map(_.times(r, () => s.sZeros(c)))
+export const zeros = (r: number, c: number) =>
+  _.map(_.times(r, () => sZeros(c)))
 
 export const random = (r: number, c: number, min?: number, max?: number) =>
-  _.map(_.times(r, () => s.sRandom(c, min, max)))
+  _.map(_.times(r, () => sRandom(c, min, max)))
 
-export const pow = (x: Matrix, p: number) => b.sFn(x, (x : number[]) => s.sPow(x, p))
+export const pow = (x: Matrix, p: number) => sFn(x)((x: number[]) => sPow(x, p))
 
 export const dot = (a: number[], b: number[]): number =>
-  _.reduce(a, (acc: number, n: number, i: number) => _.add(acc, _.multiply(n, _.get(b, i))), 0)
+  _.reduce(
+    a,
+    (acc: number, n: number, i: number) =>
+      _.add(acc, _.multiply(n, _.get(b, i))),
+    0
+  )
 
 export const sumSquares = (x: number[], y: number[]) =>
-  _.divide(
-    _.sum(_.map(x, (n, i) => (n - _.get(y, i)) ** 2)),
-    _.size(x)
-  )
+  _.divide(_.sum(_.map(x, (n, i) => (n - _.get(y, i)) ** 2)), _.size(x))

package.json

 {
   "name": "cognitive-ts",
-  "version": "2.0.1",
+  "version": "2.0.2",
   "description": "",
@@ -13,5 +13,4 @@ "main": "index.js",
     "test:debug": "jest --runInBand",
-    "lint": "standard ./lib",
-    "lint:fix": "standard --fix ./lib",
-    "lint:fix:test": "standard --fix ./__test__"
+    "lint": "eslint . --ext .ts",
+    "lint:fix": "eslint --fix . --ext .ts"
   },
@@ -26,3 +25,6 @@ "author": "Richard Macarthy (rjmacarthy)",
     "@types/lodash": "^4.14.187",
+    "@typescript-eslint/eslint-plugin": "^5.42.0",
+    "@typescript-eslint/parser": "^5.42.0",
     "babel-jest": "^29.1.2",
+    "eslint": "^8.26.0",
     "jest": "^29.2.2",
@@ -29,0 +31,0 @@ "standard": "^17.0.0",

No alert changes

Improved metrics

Total package byte prevSize

20361

increased by17.9%

Number of package files

26

increased by18.18%

Lines of code

749

increased by21.79%

Worsened metrics

Dev dependency count

12

increased by33.33%

No dependency changes