From cc0dcd0bf417e8c66a082319d2c29ad8fa3c2af0 Mon Sep 17 00:00:00 2001 From: Anton Nesterov Date: Sat, 28 Sep 2024 22:18:36 +0200 Subject: [PATCH] port gonum stat --- Makefile | 7 +- deno_tinygo_wasm.ipynb | 46 +++- split.ts | 14 +- stat/main.go | 81 ++++--- stat/mod.ts | 11 +- stat/mod.wasm | Bin 594559 -> 240836 bytes stat/src/GoNumStat.go | 514 +++++++++++++++++++++++++++++++++++++++++ stat/src/utils.go | 24 ++ stat/types.ts | 114 +++++++++ 9 files changed, 767 insertions(+), 44 deletions(-) create mode 100644 stat/src/GoNumStat.go create mode 100644 stat/src/utils.go create mode 100644 stat/types.ts diff --git a/Makefile b/Makefile index aa13c6a..b036614 100644 --- a/Makefile +++ b/Makefile @@ -1,2 +1,5 @@ -wasm: - GOOS=js GOARCH=wasm tinygo build -o stat/mod.wasm ./stat/main.go \ No newline at end of file +dev: + GOOS=js GOARCH=wasm tinygo build -o stat/mod.wasm ./stat/main.go + +prod: + GOOS=js GOARCH=wasm tinygo build -o stat/mod.wasm -no-debug ./stat/main.go \ No newline at end of file diff --git a/deno_tinygo_wasm.ipynb b/deno_tinygo_wasm.ipynb index 6eac91d..e5027a4 100644 --- a/deno_tinygo_wasm.ipynb +++ b/deno_tinygo_wasm.ipynb @@ -2,24 +2,60 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Stats initialized\n", - "Estimated offset is: 0.988572\n", - "Estimated slope is: 3.000154\n", - "R^2: 0.999999\n" + "Estimated offset is: 0.982618\n", + "Estimated slope is: 3.000173\n", + "R^2: 0.999999\n", + "2 \n" ] + }, + { + "data": { + "text/plain": [ + "\u001b[1mnull\u001b[22m" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "import stats from \"./stat/mod.ts\";\n", + "\n", "stats.example();" ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 \u0001\u0002\u0003\n" + ] + }, + { + "data": { + "text/plain": [ + "\u001b[1mnull\u001b[22m" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] } ], "metadata": { diff --git a/split.ts b/split.ts index 68468a9..ca915f5 100644 --- a/split.ts +++ b/split.ts @@ -23,16 +23,16 @@ export function sliceK( `k value is too large, max k value is ${df.shape.height / testSize}`, ); } - let trainSize = df.shape.height - testSize; - let result: DfSplit[] = []; + const trainSize = df.shape.height - testSize; + const result: DfSplit[] = []; let data = df; for (let i = 0; i < k; i++) { - let [train, test] = [data.head(trainSize), data.tail(testSize)]; - let [trainY, testY] = [ + const [train, test] = [data.head(trainSize), data.tail(testSize)]; + const [trainY, testY] = [ train.select(...yFeatures), test.select(...yFeatures), ]; - let [trainX, testX] = [train.drop(...yFeatures), test.drop(...yFeatures)]; + const [trainX, testX] = [train.drop(yFeatures), test.drop(yFeatures)]; result.push({ trainX, trainY, @@ -51,7 +51,7 @@ export function trainTestSplit( shuffle = true, ...yFeatures: string[] ) { - let data = shuffle ? df.sample(df.height - 1) : df; + const data = shuffle ? df.sample(df.height - 1) : df; const result = sliceK(data, size, 1, ...yFeatures); return result[0]; } @@ -62,6 +62,6 @@ export function kFold( shuffle = true, ...yFeatures: string[] ): DfSplit[] { - let data = shuffle ? df.sample(df.height - 1) : df; + const data = shuffle ? df.sample(df.height - 1) : df; return sliceK(data, 1 / k, k, ...yFeatures); } diff --git a/stat/main.go b/stat/main.go index af7e2cf..a5066b4 100644 --- a/stat/main.go +++ b/stat/main.go @@ -1,42 +1,65 @@ +//go:build js && wasm +// +build js,wasm + package main import ( "fmt" - "golang.org/x/exp/rand" + "l12.xyz/x/shortcuts/stat/src" - "gonum.org/v1/gonum/stat" + "syscall/js" ) -//go:export example -func Example() { - var ( - xs = make([]float64, 100) - ys = make([]float64, 100) - weights []float64 - ) - - line := func(x float64) float64 { - return 1 + 3*x - } - - for i := range xs { - xs[i] = float64(i) - ys[i] = line(xs[i]) + 0.1*rand.NormFloat64() - } - - // Do not force the regression line to pass through the origin. - origin := false - - alpha, beta := stat.LinearRegression(xs, ys, weights, origin) - r2 := stat.RSquared(xs, ys, weights, alpha, beta) - - fmt.Printf("Estimated offset is: %.6f\n", alpha) - fmt.Printf("Estimated slope is: %.6f\n", beta) - fmt.Printf("R^2: %.6f\n", r2) - +func InitStatExports(this js.Value, args []js.Value) interface{} { + exports := args[0] + exports.Set("Bhattacharyya", js.FuncOf(src.Bhattacharyya)) + exports.Set("BivariateMoment", js.FuncOf(src.BivariateMoment)) + exports.Set("ChiSquare", js.FuncOf(src.ChiSquare)) + exports.Set("CircularMean", js.FuncOf(src.CircularMean)) + exports.Set("Correlation", js.FuncOf(src.Correlation)) + exports.Set("Covariance", js.FuncOf(src.Covariance)) + exports.Set("CrossEntropy", js.FuncOf(src.CrossEntropy)) + exports.Set("Entropy", js.FuncOf(src.Entropy)) + exports.Set("ExKurtosis", js.FuncOf(src.ExKurtosis)) + exports.Set("GeometricMean", js.FuncOf(src.GeometricMean)) + exports.Set("HarmonicMean", js.FuncOf(src.HarmonicMean)) + exports.Set("Hellinger", js.FuncOf(src.Hellinger)) + exports.Set("Histogram", js.FuncOf(src.Histogram)) + exports.Set("JensenShannon", js.FuncOf(src.JensenShannon)) + exports.Set("Kendall", js.FuncOf(src.Kendall)) + exports.Set("KolmogorovSmirnov", js.FuncOf(src.KolmogorovSmirnov)) + exports.Set("KullbackLeibler", js.FuncOf(src.KullbackLeibler)) + exports.Set("LinearRegression", js.FuncOf(src.LinearRegression)) + exports.Set("Mean", js.FuncOf(src.Mean)) + exports.Set("MeanStdDev", js.FuncOf(src.MeanStdDev)) + exports.Set("MeanVariance", js.FuncOf(src.MeanVariance)) + exports.Set("Mode", js.FuncOf(src.Mode)) + exports.Set("Moment", js.FuncOf(src.Moment)) + exports.Set("MomentAbout", js.FuncOf(src.MomentAbout)) + exports.Set("PopMeanStdDev", js.FuncOf(src.PopMeanStdDev)) + exports.Set("PopMeanVariance", js.FuncOf(src.PopMeanVariance)) + exports.Set("PopStdDev", js.FuncOf(src.PopStdDev)) + exports.Set("PopVariance", js.FuncOf(src.PopVariance)) + exports.Set("Quantile", js.FuncOf(src.Quantile)) + exports.Set("RNoughtSquared", js.FuncOf(src.RNoughtSquared)) + exports.Set("ROC", js.FuncOf(src.ROC)) + exports.Set("RSquared", js.FuncOf(src.RSquared)) + exports.Set("RSquaredFrom", js.FuncOf(src.RSquaredFrom)) + exports.Set("Skew", js.FuncOf(src.Skew)) + exports.Set("SortWeighted", js.FuncOf(src.SortWeighted)) + exports.Set("SortWeightedLabeled", js.FuncOf(src.SortWeightedLabeled)) + exports.Set("StdDev", js.FuncOf(src.StdDev)) + exports.Set("StdErr", js.FuncOf(src.StdErr)) + exports.Set("StdScore", js.FuncOf(src.StdScore)) + exports.Set("TOC", js.FuncOf(src.TOC)) + exports.Set("Variance", js.FuncOf(src.Variance)) + return nil } func main() { + wait := make(chan struct{}, 0) + js.Global().Set("__InitStatExports", js.FuncOf(InitStatExports)) fmt.Println("Stats initialized") + <-wait } diff --git a/stat/mod.ts b/stat/mod.ts index 02c81cf..6e49a25 100644 --- a/stat/mod.ts +++ b/stat/mod.ts @@ -1,4 +1,5 @@ import "../lib/wasm.js"; +import type { Stat } from "./types.ts"; // @ts-expect-error: no types const go = new Go(); @@ -6,7 +7,15 @@ const go = new Go(); const code = await (await fetch(import.meta.url.replace("/mod.ts", "/mod.wasm"))) .arrayBuffer(); + const wasmMmodule = await WebAssembly.instantiate(code, go.importObject); const wasm = wasmMmodule.instance; + go.run(wasm); -export default wasm.exports as Record unknown>; + +const _exports = {} as Record unknown> & Stat; + +// @ts-ignore: no types +__InitStatExports(_exports); + +export default _exports; diff --git a/stat/mod.wasm b/stat/mod.wasm index fbe5ba26ff05c66fb93c86ee06779566b37c8eb7..d4107dbb6aeb0d30c19290fec06ebe3fdd078a5b 100755 GIT binary patch literal 240836 zcmeFa3!Gh7ec!tu=Q(F)bR>;rStIOyjzu!Y5(seoii2hg$&zjPk;dWDga*r!42+Di 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zMT=;6klC1VAwRQ!yyLk!W;t!n!p=FJb2C$i>h;muQL|d)4u}p-}G|z}BaMEA;0azSl^j&b!{3#`8w)@O{e_ qxc number; + BivariateMoment: ( + q: number, + p: number, + xs: number[], + ys: number[], + weights: number[], + ) => number; + CDF: (q: number, xs: number[], weights: number[]) => number; + ChiSquare: (xs: number[], ys: number[]) => number; + CircularMean: (xs: number[], weights: number[]) => number; + Correlation: (xs: number[], ys: number[], weights: number[]) => number; + Covariance: (xs: number[], ys: number[], weights: number[]) => number; + CrossEntropy: (xs: number[], ys: number[]) => number; + Entropy: (xs: number[]) => number; + ExKurtosis: (xs: number[], weights: number[]) => number; + GeometricMean: (xs: number[], weights: number[]) => number; + HarmonicMean: (xs: number[], weights: number[]) => number; + Hellinger: (xs: number[], ys: number[]) => number; + Histogram: ( + counts: number[], + divs: number[], + xs: number[], + bins: number, + ) => number[]; + JensenShannon: (xs: number[], ys: number[]) => number; + Kendall: (xs: number[], ys: number[], weights: number[]) => number; + KolmogorovSmirnov: ( + xs: number[], + xw: number[], + ys: number[], + yw: number[], + ) => number; + KullbackLeibler: (xs: number[], ys: number[]) => number; + LinearRegression: ( + xs: number[], + ys: number[], + weights: number[], + origin: boolean, + ) => { alpha: number; beta: number }; + Mean: (xs: number[], weights: number[]) => number; + MeanStdDev: ( + xs: number[], + weights: number[], + ) => { mean: number; stdDev: number }; + MeanVariance: ( + xs: number[], + weights: number[], + ) => { mean: number; variance: number }; + Mode: (xs: number[], weights: number[]) => { value: number; count: number }; + Moment: (q: number, xs: number[], weights: number[]) => number; + MomentAbout: ( + q: number, + xs: number[], + mean: number, + weights: number[], + ) => number; + PopMeanStdDev: ( + xs: number[], + weights: number[], + ) => { mean: number; stdDev: number }; + PopMeanVariance: ( + xs: number[], + weights: number[], + ) => { mean: number; variance: number }; + PopStdDev: (xs: number[], weights: number[]) => number; + PopVariance: (xs: number[], weights: number[]) => number; + Quantile: ( + q: number, + type: "linear" | "empirical", + xs: number[], + weights: number[], + ) => number; + RNoughtSquared: ( + xs: number[], + ys: number[], + weights: number[], + beta: number, + ) => number; + ROC: ( + cutoffs: number[], + ys: number[], + classes: boolean[], + weights: number[], + ) => { tpr: number[]; fpr: number[]; tresh: number[] }; + RSquared: ( + xs: number[], + ys: number[], + weights: number[], + alpha: number, + beta: number, + ) => number; + RSquaredFrom: ( + estimates: number[], + ys: number[], + weights: number[], + ) => number; + Skew: (xs: number[], weights: number[]) => number; + SortWeighted: (xs: number[], weights: number[]) => number[]; + SortWeightedLabeled: ( + xs: number[], + labels: boolean[], + weights: number[], + ) => { xs: number[]; labels: boolean[] }; + StdDev: (xs: number[], weights: number[]) => number; + StdErr: (std: number, size: number) => number; + StdScore: (x: number, mean: number, stdDev: number) => number; + TOC: ( + classes: boolean[], + ys: number[], + ) => { min: number[]; ntp: number[]; max: number[] }; + Variance: (xs: number[], weights: number[]) => number; +}