171 lines
99 KiB
Plaintext
Vendored
171 lines
99 KiB
Plaintext
Vendored
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Regressions\n",
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"\n",
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"$X = (0,1)$\n",
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"\n",
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"$Y = sin(2\\pi X)$"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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""
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"// deno-lint-ignore-file\n",
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"\n",
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"import { display } from \"https://deno.land/x/display@v0.1.1/mod.ts\";\n",
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"import pl from \"npm:nodejs-polars\";\n",
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"import plot from \"../plot/mod.ts\";\n",
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"\n",
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"const data = await Deno.readTextFile(\"assets/X_Y_Sinusoid_Data.csv\");\n",
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"const df = pl.readCSV(data, { sep: \",\" });\n",
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"\n",
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"const real = pl.DataFrame({ x: new Array(100).fill(0).map((_, i) => i / 100)}).select(\n",
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" pl.col('x'),\n",
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" pl.col('x').mul(2).mul(3.14).sin().alias('y')\n",
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");\n",
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"\n",
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"const draw = (x, y, title = \"Sinusoid Data\") => \n",
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" plot.DrawPlot(\n",
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" { \n",
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" title,\n",
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" width: 6,\n",
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" height: 3,\n",
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" XLabel: \"X\", \n",
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" YLabel: \"Y\", \n",
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" }, \n",
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" { type: \"line\", data: [real.x, real.y], legend: \"Sinusoid\", lineDashes: [3, 4], lineColor: \"#ff8888\", lineWidth: 1 },\n",
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" { type: \"scatter\", data: [x, y], legend: \"Data\", lineDashes: [3, 4], lineWidth: 2, glyphColor: \"#4444ff\", glyphShape: \"circle\" },\n",
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" { type: \"trend\", data: [x, y], legend: \"Trend\", lineDashes: [4, 2], lineColor: '#aacccc', lineWidth: .5 },\n",
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" );\n",
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"\n",
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" const comparePredicted = (x, y, predicted) => plot.DrawPlot(\n",
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" { \n",
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" width: 7,\n",
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" height: 4,\n",
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" XLabel: \"X\", \n",
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" YLabel: \"Y\", \n",
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" }, \n",
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" { type: \"line\", data: [real.x, real.y], legend: \"Sinusoid\", lineDashes: [3, 4], lineColor: \"#ff8888\", lineWidth: 1 },\n",
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" { type: \"linePoints\", data: [x, y], legend: \"Test Data\", lineDashes: [3, 4], lineColor: \"#8888ff\", glyphColor: \"#4444ff\", glyphShape: \"circle\" },\n",
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" { type: \"linePoints\", data: [x, predicted], lineWidth: .5, legend: \"Predicted\", glyphColor: '#f00', glyphShape: \"pyramid\" },\n",
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");\n",
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"\n",
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"\n",
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"draw(df.x, df.y);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Polynomial Tranformation\n",
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"\n",
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"First we try to predict values without polynomial transformation:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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""
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import regr from '../regr/mod.ts';\n",
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"\n",
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"const linregWoPoly = regr.Linear();\n",
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"\n",
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"linregWoPoly.fit(df.drop('y').rows(), df.select('y').rows());\n",
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"const predWoPoly = linregWoPoly.predict(df.drop('y').rows());\n",
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"\n",
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"comparePredicted(df.x, df.y, predWoPoly);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now augment our dataset with high degree polynomial:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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""
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import { polynomialTransform } from '../encoding.ts';\n",
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"\n",
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"\n",
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"\n",
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"const polyFeatures = polynomialTransform(df.drop('y'), 36, false, false)\n",
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"\n",
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"const [polyX, polyY] = [polyFeatures, df.select('y')]\n",
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"\n",
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"const linregPoly = regr.Linear();\n",
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"\n",
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"linregPoly.fit(polyX.rows(), polyY.rows());\n",
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"const predY = linregPoly.predict(polyX.rows());\n",
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"\n",
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"comparePredicted(df.x, df.y, predY);\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Deno",
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"language": "typescript",
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"name": "deno"
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},
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"language_info": {
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"codemirror_mode": "typescript",
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"file_extension": ".ts",
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"mimetype": "text/x.typescript",
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"name": "typescript",
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"nbconvert_exporter": "script",
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"pygments_lexer": "typescript",
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"version": "5.6.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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