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mkdata

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Readme

mkdata

Generate synthetic datasets

Install

npm i mkdata -S or
npm i mkdata -g or run using npx without installation
npx mkdata -d moons -s 1000 -o friedman1.csv

CLI

mkdata -d friedman1 -s 1000 -o friedman1.csv

or using stdout

mkdata -d friedman1 -s 1000 > friedman1.csv

Params:

-d, --dataset - dataset name
-f, --nFeatures - number of features
-s, --nSamples - number of samples
-n, --noise - noise size
-o, --output - output file name
-r, --randomState - seed

API

const make = require('mkdata')
const [X, y] = make.spirals({ nSamples: 1000 })

friedman1, friedman2, friedman3 methods also return data generating functions:

const [X, y, f] = make.friedman3({ nSamples: 1000 })
const yt = X.map(f)

Datasets

Friedman 1 (y = 10 * sin(Pi * x1 * x2) + 20 * (x3 - 0.5) ** 2 + 10 * x4 + 5 * x5 + e)
Friedman 2 (y = sqrt(x1 ** 2 + (x2 * x3 - 1 / (x2 * x4)) ** 2) + e)
Friedman 3 (y = atan(x2 * x3 - 1 / (x2 * x4) / x1) + e)
Hastie (binary classification problem used in Hastie et al 2009)
Moons (two interleaving half circles)
Peak (peak benchmark problem)
Ringnorm (from Breiman 1996)
Spirals (two entangled spirals)
Swissroll (from S. Marsland 2009)