ref: exlude notebooks from stats
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4
.gitattributes
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4
.gitattributes
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@ -0,0 +1,4 @@
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notebooks/* linguist-vendored
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lib/* linguist-vendored
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Makefile linguist-vendored
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*.json linguist-vendored
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17
encoding.ts
17
encoding.ts
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@ -1,9 +1,10 @@
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// deno-lint-ignore-file no-explicit-any
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import pl from "npm:nodejs-polars";
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import pl from "npm:nodejs-polars";
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export function oneHotEncoding(dataframe: pl.DataFrame): pl.DataFrame {
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export function oneHotEncoding(dataframe: pl.DataFrame): pl.DataFrame {
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let df = pl.DataFrame();
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let df = pl.DataFrame();
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for (const columnName of dataframe.columns) {
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for (const columnName of dataframe.columns) {
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const column = dataframe[columnName];
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const column = (dataframe as any)[columnName];
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if (!column.isNumeric()) {
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if (!column.isNumeric()) {
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df = df.hstack(column.toDummies());
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df = df.hstack(column.toDummies());
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} else {
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} else {
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@ -19,7 +20,7 @@ export function polynomialTransform(
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interaction_only = false,
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interaction_only = false,
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include_bias = true,
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include_bias = true,
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): pl.DataFrame {
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): pl.DataFrame {
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let polyRecords: number[][] = [];
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const polyRecords: number[][] = [];
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dataframe.map((X: number[]) => {
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dataframe.map((X: number[]) => {
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polyRecords.push(
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polyRecords.push(
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polynomialFeatures(X, degree, interaction_only, include_bias),
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polynomialFeatures(X, degree, interaction_only, include_bias),
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@ -38,7 +39,7 @@ export function polynomialFeatures(
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let prev_chunk = [...X];
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let prev_chunk = [...X];
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const indices = Array.from({ length: X.length }, (_, i) => i);
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const indices = Array.from({ length: X.length }, (_, i) => i);
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for (let d = 1; d < degree; d++) {
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for (let d = 1; d < degree; d++) {
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const new_chunk: any[] = [];
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const new_chunk: number[] = [];
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for (let i = 0; i < (interaction_only ? X.length - d : X.length); i++) {
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for (let i = 0; i < (interaction_only ? X.length - d : X.length); i++) {
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const v = X[i];
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const v = X[i];
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const next_index = new_chunk.length;
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const next_index = new_chunk.length;
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@ -71,13 +72,13 @@ export function augmentMeanForward(
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df: pl.DataFrame,
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df: pl.DataFrame,
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interval = 100,
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interval = 100,
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) {
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) {
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let sorted = df.sort(feature);
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const sorted = df.sort(feature);
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let featIdx = sorted.findIdxByName(feature);
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const featIdx = sorted.findIdxByName(feature);
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let result = sorted.head(1);
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let result = sorted.head(1);
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for (let i = 0; i < sorted.height; i++) {
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for (let i = 0; i < sorted.height; i++) {
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let p1 = sorted.row(i).at(featIdx);
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const p1 = sorted.row(i).at(featIdx);
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let k = (i + 1) % sorted.height;
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const k = (i + 1) % sorted.height;
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let p2 = sorted.row(k).at(featIdx);
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const p2 = sorted.row(k).at(featIdx);
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if (p2 - p1 > interval) {
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if (p2 - p1 > interval) {
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for (let j = 0; j < Math.round((p2 - p1) / interval) - 1; j++) {
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for (let j = 0; j < Math.round((p2 - p1) / interval) - 1; j++) {
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result = pl.concat([
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result = pl.concat([
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6
notebooks/deno_tinygo_wasm.ipynb
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6
notebooks/deno_tinygo_wasm.ipynb
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@ -24,8 +24,8 @@
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"const ys = [];\n",
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"const ys = [];\n",
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"\n",
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"\n",
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"for (let i = 0; i < 100; i++) {\n",
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"for (let i = 0; i < 100; i++) {\n",
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" xs.push(i);\n",
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" xs.push(i);\n",
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" ys.push((1 + 3 * i) + Math.random());\n",
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" ys.push((1 + 3 * i) + Math.random());\n",
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"}\n",
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"}\n",
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"\n",
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"\n",
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"const linreg = stats.LinearRegression(xs, ys, [], false);\n",
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"const linreg = stats.LinearRegression(xs, ys, [], false);\n",
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@ -34,7 +34,7 @@
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"console.log(\"\\nLinear Regression Line:\");\n",
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"console.log(\"\\nLinear Regression Line:\");\n",
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"console.log(\"\\tEstimated offset is: \", linreg.alpha.toFixed(6));\n",
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"console.log(\"\\tEstimated offset is: \", linreg.alpha.toFixed(6));\n",
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"console.log(\"\\tEstimated slope is: \", linreg.beta.toFixed(6));\n",
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"console.log(\"\\tEstimated slope is: \", linreg.beta.toFixed(6));\n",
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"console.log(\"\\tR^2 is: \", r.toFixed(6));\n"
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"console.log(\"\\tR^2 is: \", r.toFixed(6));"
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]
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]
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}
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}
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],
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],
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2
package.json
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2
package.json
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@ -8,4 +8,4 @@
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"peerDependencies": {
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"peerDependencies": {
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"typescript": "^5.0.0"
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"typescript": "^5.0.0"
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}
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}
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}
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}
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