127 lines
65 KiB
Plaintext
Vendored
127 lines
65 KiB
Plaintext
Vendored
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Regressions\n",
|
|
"\n",
|
|
"$X = (0,1)$\n",
|
|
"\n",
|
|
"$Y = sin(2\\pi X)$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/markdown": [
|
|
""
|
|
]
|
|
},
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"// deno-lint-ignore-file\n",
|
|
"\n",
|
|
"import { display } from \"https://deno.land/x/display@v0.1.1/mod.ts\";\n",
|
|
"import pl from \"npm:nodejs-polars\";\n",
|
|
"import plot from \"../plot/mod.ts\";\n",
|
|
"\n",
|
|
"const data = await Deno.readTextFile(\"assets/X_Y_Sinusoid_Data.csv\");\n",
|
|
"const df = pl.readCSV(data, { sep: \",\" });\n",
|
|
"\n",
|
|
"const real = pl.DataFrame({ x: new Array(100).fill(0).map((_, i) => i / 100)}).select(\n",
|
|
" pl.col('x'),\n",
|
|
" pl.col('x').mul(2).mul(3.14).sin().alias('y')\n",
|
|
");\n",
|
|
"\n",
|
|
"const draw = (x, y, title = \"Sinusoid Data\") => \n",
|
|
" plot.DrawPlot(\n",
|
|
" { \n",
|
|
" title,\n",
|
|
" width: 6,\n",
|
|
" height: 3,\n",
|
|
" XLabel: \"X\", \n",
|
|
" YLabel: \"Y\", \n",
|
|
" }, \n",
|
|
" { type: \"line\", data: [real.x, real.y], legend: \"Sinusoid\", lineDashes: [3, 4], lineColor: \"#ff8888\", lineWidth: 1 },\n",
|
|
" { type: \"scatter\", data: [x, y], legend: \"Data\", lineDashes: [3, 4], lineWidth: 2, glyphColor: \"#4444ff\", glyphShape: \"circle\" },\n",
|
|
" { type: \"trend\", data: [x, y], legend: \"Trend\", lineDashes: [4, 2], lineColor: '#aacccc', lineWidth: .5 },\n",
|
|
" );\n",
|
|
"\n",
|
|
"\n",
|
|
"draw(df.x, df.y);"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/markdown": [
|
|
""
|
|
]
|
|
},
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import { polynomialTransform } from '../encoding.ts';\n",
|
|
"import { trainTestSplit } from \"../split.ts\";\n",
|
|
"import regr from '../regr/mod.ts';\n",
|
|
"\n",
|
|
"const polyDf = df.select('x').hstack(\n",
|
|
" polynomialTransform(df.drop('y'), 36, false, false)\n",
|
|
").hstack(df.select('y'));\n",
|
|
"\n",
|
|
"const [trainX, trainY] = [polyDf.drop('y'), polyDf.select('y')]\n",
|
|
"const linreg = regr.Linear();\n",
|
|
"linreg.fit(trainX.rows(), trainY.rows());\n",
|
|
"const predY = linreg.predict(trainX.rows());\n",
|
|
"\n",
|
|
"\n",
|
|
"plot.DrawPlot(\n",
|
|
" { \n",
|
|
" width: 7,\n",
|
|
" height: 4,\n",
|
|
" XLabel: \"X\", \n",
|
|
" YLabel: \"Y\", \n",
|
|
" }, \n",
|
|
" { type: \"line\", data: [real.x, real.y], legend: \"Sinusoid\", lineDashes: [3, 4], lineColor: \"#ff8888\", lineWidth: 1 },\n",
|
|
" { type: \"linePoints\", data: [trainX.x, df.y], legend: \"Test Data\", lineDashes: [3, 4], lineColor: \"#8888ff\", glyphColor: \"#4444ff\", glyphShape: \"circle\" },\n",
|
|
" { type: \"linePoints\", data: [trainX.x, predY], lineWidth: .5, legend: \"Predicted\", glyphColor: '#f00', glyphShape: \"pyramid\" },\n",
|
|
");\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
|
|
}
|