83 lines
15 KiB
Plaintext
Vendored
83 lines
15 KiB
Plaintext
Vendored
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Stats initialized\n",
|
|
"\n",
|
|
"Linear Regression Line:\n",
|
|
"\tEstimated offset is: 1.474039\n",
|
|
"\tEstimated slope is: 3.000136\n",
|
|
"\tR^2 is: 0.999989\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import stats from \"https://l12.xyz/x/shortcuts/raw/stat/mod.ts\";\n",
|
|
"\n",
|
|
"const xs = [];\n",
|
|
"const ys = [];\n",
|
|
"\n",
|
|
"for (let i = 0; i < 100; i++) {\n",
|
|
" xs.push(i);\n",
|
|
" ys.push((1 + 3 * i) + Math.random());\n",
|
|
"}\n",
|
|
"\n",
|
|
"const linreg = stats.LinearRegression(xs, ys, [], false);\n",
|
|
"const r = stats.RSquared(xs, ys, [], linreg.alpha, linreg.beta);\n",
|
|
"\n",
|
|
"console.log(\"\\nLinear Regression Line:\");\n",
|
|
"console.log(\"\\tEstimated offset is: \", linreg.alpha.toFixed(6));\n",
|
|
"console.log(\"\\tEstimated slope is: \", linreg.beta.toFixed(6));\n",
|
|
"console.log(\"\\tR^2 is: \", r.toFixed(6));"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/markdown": [
|
|
"![name](data:image/png;base64,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)"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import plot from \"../plot/mod.ts?5\";\n",
|
|
"\n",
|
|
"plot.DrawHist(ys, 16, { title : \"Histogram of Y values\" });\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Deno",
|
|
"language": "typescript",
|
|
"name": "deno"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": "typescript",
|
|
"file_extension": ".ts",
|
|
"mimetype": "text/x.typescript",
|
|
"name": "typescript",
|
|
"nbconvert_exporter": "script",
|
|
"pygments_lexer": "typescript",
|
|
"version": "5.6.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|