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============================================================\n// Pure TypeScript statistics & regression engine (no native deps)\n// ============================================================\n\n// ---------- basic stats ----------\nexport function mean(arr: number[]): number {\n return arr.reduce((s, v) => s + v, 0) / arr.length;\n}\nexport function median(arr: number[]): number {\n const sorted = [...arr].sort((a, b) => a - b);\n const mid = Math.floor(sorted.length / 2);\n return sorted.length % 2 ? sorted[mid] : (sorted[mid - 1] + sorted[mid]) / 2;\n}\nexport function mode(arr: number[]): number {\n const freq: Record = {};\n arr.forEach((v) => (freq[v] = (freq[v] || 0) + 1));\n return +Object.entries(freq).sort((a, b) => b[1] - a[1])[0][0];\n}\nexport function variance(arr: number[]): number {\n const m = mean(arr);\n return arr.reduce((s, v) => s + (v - m) ** 2, 0) / (arr.length - 1);\n}\nexport function std(arr: number[]): number {\n return Math.sqrt(variance(arr));\n}\n\n// ---------- matrix helpers ----------\ntype Mat = number[][];\ntype Vec = number[];\n\nfunction zeros(r: number, c: number): Mat {\n return Array.from({ length: r }, () => new Array(c).fill(0));\n}\nfunction transpose(A: Mat): Mat {\n const [r, c] = [A.length, A[0].length];\n const T = zeros(c, r);\n for (let i = 0; i < r; i++) for (let j = 0; j < c; j++) T[j][i] = A[i][j];\n return T;\n}\nfunction matMul(A: Mat, B: Mat): Mat {\n const [ar, ac, bc] = [A.length, A[0].length, B[0].length];\n const C = zeros(ar, bc);\n for (let i = 0; i < ar; i++)\n for (let j = 0; j < bc; j++)\n for (let k = 0; k < ac; k++) C[i][j] += A[i][k] * B[k][j];\n return C;\n}\nfunction matVecMul(A: Mat, v: Vec): Vec {\n return A.map((row) => row.reduce((s, val, j) => s + val * v[j], 0));\n}\n\n// LU decomposition based inverse\nfunction invertMatrix(M: Mat): Mat {\n const n = M.length;\n const aug = M.map((row, i) => [...row, ...Array.from({ length: n }, (_, j) => (i === j ? 1 : 0))]);\n for (let col = 0; col < n; col++) {\n let maxRow = col;\n for (let row = col + 1; row < n; row++)\n if (Math.abs(aug[row][col]) > Math.abs(aug[maxRow][col])) maxRow = row;\n [aug[col], aug[maxRow]] = [aug[maxRow], aug[col]];\n const pivot = aug[col][col];\n if (Math.abs(pivot) < 1e-12) throw new Error(\"Singular matrix\");\n for (let j = 0; j < 2 * n; j++) aug[col][j] /= pivot;\n for (let row = 0; row < n; row++) {\n if (row === col) continue;\n const factor = aug[row][col];\n for (let j = 0; j < 2 * n; j++) aug[row][j] -= factor * aug[col][j];\n }\n }\n return aug.map((row) => row.slice(n));\n}\n\n// ---------- OLS regression ----------\nexport interface RegressionResult {\n coefficients: number[];\n intercept: number;\n rSquared: number;\n adjRSquared: number;\n mse: number;\n mae: number;\n predictions: number[];\n}\n\nexport function olsRegression(X: Mat, y: Vec): RegressionResult {\n const n = X.length;\n const p = X[0].length;\n // Add intercept column\n const Xa = X.map((row) => [1, ...row]);\n const Xt = transpose(Xa);\n const XtX = matMul(Xt, Xa);\n const XtXinv = invertMatrix(XtX);\n const Xty = matVecMul(Xt, y);\n const beta = matVecMul(XtXinv, Xty);\n\n const intercept = beta[0];\n const coefficients = beta.slice(1);\n const predictions = Xa.map((row) => row.reduce((s, v, j) => s + v * beta[j], 0));\n const yMean = mean(y);\n const ssTot = y.reduce((s, v) => s + (v - yMean) ** 2, 0);\n const ssRes = y.reduce((s, v, i) => s + (v - predictions[i]) ** 2, 0);\n const rSquared = 1 - ssRes / ssTot;\n const adjRSquared = 1 - ((1 - rSquared) * (n - 1)) / (n - p - 1);\n const mse = ssRes / n;\n const mae = y.reduce((s, v, i) => s + Math.abs(v - predictions[i]), 0) / n;\n\n return { coefficients, intercept, rSquared, adjRSquared, mse, mae, predictions };\n}\n\n// ---------- Ridge regression ----------\nexport function ridgeRegression(X: Mat, y: Vec, lambda = 1.0): RegressionResult {\n const n = X.length;\n const p = X[0].length;\n const Xa = X.map((row) => [1, ...row]);\n const Xt = transpose(Xa);\n const XtX = matMul(Xt, Xa);\n // Add lambda * I (skip intercept penalty)\n for (let i = 1; i < XtX.length; i++) XtX[i][i] += lambda;\n const XtXinv = invertMatrix(XtX);\n const Xty = matVecMul(Xt, y);\n const beta = matVecMul(XtXinv, Xty);\n\n const intercept = beta[0];\n const coefficients = beta.slice(1);\n const predictions = Xa.map((row) => row.reduce((s, v, j) => s + v * beta[j], 0));\n const yMean = mean(y);\n const ssTot = y.reduce((s, v) => s + (v - yMean) ** 2, 0);\n const ssRes = y.reduce((s, v, i) => s + (v - predictions[i]) ** 2, 0);\n const rSquared = 1 - ssRes / ssTot;\n const adjRSquared = 1 - ((1 - rSquared) * (n - 1)) / (n - p - 1);\n const mse = ssRes / n;\n const mae = y.reduce((s, v, i) => s + Math.abs(v - predictions[i]), 0) / n;\n\n return { coefficients, intercept, rSquared, adjRSquared, mse, mae, predictions };\n}\n\n// ---------- LASSO regression (coordinate descent) ----------\nexport function lassoRegression(X: Mat, y: Vec, lambda = 1.0, maxIter = 1000, tol = 1e-6): RegressionResult {\n const n = X.length;\n const p = X[0].length;\n\n // Standardize features\n const xMeans = Array.from({ length: p }, (_, j) => mean(X.map((row) => row[j])));\n const xStds = Array.from({ length: p }, (_, j) => {\n const s = std(X.map((row) => row[j]));\n return s < 1e-10 ? 1 : s;\n });\n const yMean = mean(y);\n const Xs = X.map((row) => row.map((v, j) => (v - xMeans[j]) / xStds[j]));\n const ys = y.map((v) => v - yMean);\n\n const beta = new Array(p).fill(0);\n const residual = [...ys];\n\n for (let iter = 0; iter < maxIter; iter++) {\n let maxChange = 0;\n for (let j = 0; j < p; j++) {\n // Add back current beta_j contribution\n for (let i = 0; i < n; i++) residual[i] += Xs[i][j] * beta[j];\n // Compute rho = X_j^T * residual / n\n let rho = 0;\n for (let i = 0; i < n; i++) rho += Xs[i][j] * residual[i];\n rho /= n;\n // Compute norm\n let xjNorm = 0;\n for (let i = 0; i < n; i++) xjNorm += Xs[i][j] ** 2;\n xjNorm /= n;\n // Soft threshold\n const newBeta = softThreshold(rho, lambda) / xjNorm;\n const change = Math.abs(newBeta - beta[j]);\n if (change > maxChange) maxChange = change;\n beta[j] = newBeta;\n // Update residual\n for (let i = 0; i < n; i++) residual[i] -= Xs[i][j] * beta[j];\n }\n if (maxChange < tol) break;\n }\n\n // Convert back to original scale\n const coefficients = beta.map((b, j) => b / xStds[j]);\n const intercept = yMean - coefficients.reduce((s, c, j) => s + c * xMeans[j], 0);\n\n const predictions = X.map((row) => intercept + row.reduce((s, v, j) => s + v * coefficients[j], 0));\n const ssTot = y.reduce((s, v) => s + (v - yMean) ** 2, 0);\n const ssRes = y.reduce((s, v, i) => s + (v - predictions[i]) ** 2, 0);\n const rSquared = ssTot > 0 ? 1 - ssRes / ssTot : 0;\n const pActive = coefficients.filter((c) => Math.abs(c) > 1e-10).length;\n const adjRSquared = n > pActive + 1 ? 1 - ((1 - rSquared) * (n - 1)) / (n - pActive - 1) : rSquared;\n const mse = ssRes / n;\n const mae = y.reduce((s, v, i) => s + Math.abs(v - predictions[i]), 0) / n;\n\n return { coefficients, intercept, rSquared, adjRSquared, mse, mae, predictions };\n}\n\nfunction softThreshold(x: number, lambda: number): number {\n if (x > lambda) return x - lambda;\n if (x < -lambda) return x + lambda;\n return 0;\n}\n\n// ---------- Elastic Net ----------\nexport function elasticNetRegression(X: Mat, y: Vec, lambda = 1.0, l1Ratio = 0.5, maxIter = 1000, tol = 1e-6): RegressionResult {\n const n = X.length;\n const p = X[0].length;\n const xMeans = Array.from({ length: p }, (_, j) => mean(X.map((row) => row[j])));\n const xStds = Array.from({ length: p }, (_, j) => {\n const s = std(X.map((row) => row[j]));\n return s < 1e-10 ? 1 : s;\n });\n const yMean = mean(y);\n const Xs = X.map((row) => row.map((v, j) => (v - xMeans[j]) / xStds[j]));\n const ys = y.map((v) => v - yMean);\n\n const beta = new Array(p).fill(0);\n const residual = [...ys];\n const l1 = lambda * l1Ratio;\n const l2 = lambda * (1 - l1Ratio);\n\n for (let iter = 0; iter < maxIter; iter++) {\n let maxChange = 0;\n for (let j = 0; j < p; j++) {\n for (let i = 0; i < n; i++) residual[i] += Xs[i][j] * beta[j];\n let rho = 0;\n for (let i = 0; i < n; i++) rho += Xs[i][j] * residual[i];\n rho /= n;\n let xjNorm = 0;\n for (let i = 0; i < n; i++) xjNorm += Xs[i][j] ** 2;\n xjNorm /= n;\n const newBeta = softThreshold(rho, l1) / (xjNorm + l2);\n const change = Math.abs(newBeta - beta[j]);\n if (change > maxChange) maxChange = change;\n beta[j] = newBeta;\n for (let i = 0; i < n; i++) residual[i] -= Xs[i][j] * beta[j];\n }\n if (maxChange < tol) break;\n }\n\n const coefficients = beta.map((b, j) => b / xStds[j]);\n const intercept = yMean - coefficients.reduce((s, c, j) => s + c * xMeans[j], 0);\n const predictions = X.map((row) => intercept + row.reduce((s, v, j) => s + v * coefficients[j], 0));\n const ssTot = y.reduce((s, v) => s + (v - yMean) ** 2, 0);\n const ssRes = y.reduce((s, v, i) => s + (v - predictions[i]) ** 2, 0);\n const rSquared = ssTot > 0 ? 1 - ssRes / ssTot : 0;\n const pActive = coefficients.filter((c) => Math.abs(c) > 1e-10).length;\n const adjRSquared = n > pActive + 1 ? 1 - ((1 - rSquared) * (n - 1)) / (n - pActive - 1) : rSquared;\n const mse = ssRes / n;\n const mae = y.reduce((s, v, i) => s + Math.abs(v - predictions[i]), 0) / n;\n\n return { coefficients, intercept, rSquared, adjRSquared, mse, mae, predictions };\n}\n\n// ---------- K-Fold cross validation ----------\nexport function kFoldCV(\n X: Mat,\n y: Vec,\n k: number,\n regressFn: (X: Mat, y: Vec) => RegressionResult,\n): { scores: number[]; meanScore: number } {\n const n = X.length;\n const indices = Array.from({ length: n }, (_, i) => i);\n // Shuffle\n for (let i = n - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [indices[i], indices[j]] = [indices[j], indices[i]];\n }\n const foldSize = Math.floor(n / k);\n const scores: number[] = [];\n\n for (let fold = 0; fold < k; fold++) {\n const testStart = fold * foldSize;\n const testEnd = fold === k - 1 ? n : testStart + foldSize;\n const testIdx = indices.slice(testStart, testEnd);\n const trainIdx = [...indices.slice(0, testStart), ...indices.slice(testEnd)];\n\n const Xtrain = trainIdx.map((i) => X[i]);\n const ytrain = trainIdx.map((i) => y[i]);\n const Xtest = testIdx.map((i) => X[i]);\n const ytest = testIdx.map((i) => y[i]);\n\n try {\n const result = regressFn(Xtrain, ytrain);\n // Compute R² on test set\n const preds = Xtest.map(\n (row) => result.intercept + row.reduce((s, v, j) => s + v * result.coefficients[j], 0),\n );\n const yMean = mean(ytest);\n const ssTot = ytest.reduce((s, v) => s + (v - yMean) ** 2, 0);\n const ssRes = ytest.reduce((s, v, i) => s + (v - preds[i]) ** 2, 0);\n scores.push(ssTot > 0 ? 1 - ssRes / ssTot : 0);\n } catch {\n scores.push(0);\n }\n }\n\n return { scores, meanScore: mean(scores) };\n}\n\n// ---------- Format equation ----------\nexport function formatEquation(\n target: 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{ parseVal, serveFile } from \"https://esm.town/v/std/utils/index.ts\";\nimport { Hono } from \"npm:hono\";\nimport { dataRoutes } from \"./backend/routes/data.ts\";\nimport { preprocessRoutes } from \"./backend/routes/preprocess.ts\";\nimport { trainRoutes } from \"./backend/routes/train.ts\";\nimport { regressionRoutes } from \"./backend/routes/regression.ts\";\nimport { predictRoutes } from \"./backend/routes/predict.ts\";\nimport { initDB } from \"./backend/db.ts\";\n\nconst app = new Hono();\n\n// Initialize database on startup\nawait initDB();\n\n// API routes\napp.route(\"/api/data\", dataRoutes);\napp.route(\"/api/preprocess\", preprocessRoutes);\napp.route(\"/api/train\", trainRoutes);\napp.route(\"/api/regression\", regressionRoutes);\napp.route(\"/api/predict\", predictRoutes);\n\n// Serve frontend\napp.get(\"/\", () => serveFile(\"/frontend/index.html\"));\napp.get(\"/frontend/**/*\", (c) => serveFile(c.req.path));\napp.get(\"/source\", (c) => c.redirect(parseVal().links.self.val));\n\napp.onError((err) => Promise.reject(err));\n\nexport default app.fetch;\n","label",[157],"67cdbe78-56ae-11f1-8e14-ee650bb23af1","README.md","019e551a-5303-7301-b8d9-cf7bf407ea4d",["D",1779544249028],"# React + Hono Starter\n\nA minimal starter template for building client-side React apps with a Hono\nbackend on Val Town. Click **Remix** to get your own copy.\n\n**Want to build UI?** Start writing React components in\n[`frontend/components/`](/frontend/components/). Import them into `App.tsx` and\nyou're good to go. Styling is [Twind](https://twind.style/) — just use Tailwind\nutility classes, zero config.\n\n**Want to add an API?** Add routes in [`index.ts`](/index.ts) (there's a\ncommented-out example). Fetch them from your React components.\n\n## Architecture\n\n```mermaid\ngraph LR\n Browser -->|\"GET /\"| Hono[\"index.ts
(Hono server)\"]\n Hono -->|serves| HTML[\"index.html\"]\n HTML -->|loads| Entry[\"index.tsx\"]\n Entry -->|renders| App[\"App.tsx\"]\n App -->|renders| Counter[\"Counter.tsx\"]\n```\n\n## File structure\n\n```\nindex.ts ← Hono backend (serves frontend + API routes)\nfrontend/\n index.html ← HTML shell (loads Twind + React)\n index.tsx ← React entrypoint (mounts )\n favicon.svg ← App icon\n components/\n App.tsx ← Root component (layout + view source link)\n Counter.tsx ← Example component with state\n```\n\n## How it works\n\n1. **`index.ts`** is the HTTP entrypoint — a Hono server that serves the\n frontend and any API routes you add.\n2. **`frontend/index.html`** loads Twind and the React entrypoint.\n3. **`frontend/index.tsx`** mounts the React app.\n4. **`frontend/components/App.tsx`** is the root layout — add new components\n alongside it.\n5. **`frontend/components/Counter.tsx`** is an example stateful component —\n replace it with your own!\n\n## Next steps\n\n- Add API routes in `index.ts`\n- Add new components in `frontend/components/`\n- Add a SQLite database — see the\n [React Hono Example](https://www.val.town/x/stevekrouse/reactHonoExample) for\n a full-stack example with database, queries, and shared types",[168],"67cdbc66-56ae-11f1-8e14-ee650bb23af1","frontend","019e551a-5303-7301-b8d9-c990a2e05367",["D",1779544249028],"directory",[174],"709cfbc2-56ae-11f1-913f-ee650bb23af1","backend","709cfc62-56ae-11f1-913f-ee650bb23af1",3,["D",1779544263876],[180],"pulls","branches","forks","envVars",{"_192":68,"_194":68},"connections",{"_192":68,"_193":68},"count","unfilledCount","emptyCount",{"_48":179,"_37":180,"_35":181,"_158":-5,"_46":47,"_45":182,"_160":64,"_43":278,"_13":68,"_39":177,"_41":-5,"_29":30,"_165":-5,"_209":279,"_32":280},{"_48":271,"_37":272,"_35":273,"_158":181,"_46":47,"_45":274,"_160":64,"_43":275,"_13":68,"_39":177,"_41":-5,"_29":30,"_165":-5,"_209":276,"_32":277},{"_48":263,"_37":264,"_35":265,"_158":181,"_46":47,"_45":266,"_160":64,"_43":267,"_13":68,"_39":40,"_41":268,"_29":30,"_165":-5,"_209":269,"_32":270},{"_48":259,"_37":38,"_35":36,"_158":181,"_46":47,"_45":14,"_160":64,"_43":260,"_13":68,"_39":40,"_41":42,"_29":30,"_165":-5,"_209":261,"_32":262},{"_48":173,"_37":174,"_35":175,"_158":-5,"_46":47,"_45":162,"_160":64,"_43":256,"_13":68,"_39":177,"_41":-5,"_29":30,"_165":-5,"_209":257,"_32":258},{"_48":252,"_37":244,"_35":239,"_158":175,"_46":47,"_45":162,"_160":64,"_43":253,"_13":68,"_39":177,"_41":-5,"_29":30,"_165":-5,"_209":254,"_32":255},{"_48":245,"_37":246,"_35":247,"_158":239,"_46":47,"_45":162,"_160":30,"_43":248,"_13":68,"_39":40,"_41":249,"_29":30,"_165":-5,"_209":250,"_32":251},{"_48":236,"_37":237,"_35":238,"_158":239,"_46":47,"_45":162,"_160":30,"_43":240,"_13":68,"_39":40,"_41":241,"_29":30,"_165":-5,"_209":242,"_32":243},{"_48":229,"_37":230,"_35":231,"_158":175,"_46":47,"_45":162,"_160":64,"_43":232,"_13":68,"_39":26,"_41":233,"_29":30,"_165":-5,"_209":234,"_32":235},{"_48":222,"_37":223,"_35":224,"_158":175,"_46":47,"_45":162,"_160":30,"_43":225,"_13":68,"_39":26,"_41":226,"_29":30,"_165":-5,"_209":227,"_32":228},{"_48":215,"_37":216,"_35":217,"_158":175,"_46":47,"_45":162,"_160":30,"_43":218,"_13":68,"_39":40,"_41":219,"_29":30,"_165":-5,"_209":220,"_32":221},{"_48":167,"_37":168,"_35":169,"_158":-5,"_46":47,"_45":162,"_160":64,"_43":212,"_13":68,"_39":26,"_41":171,"_29":30,"_165":-5,"_209":213,"_32":214},{"_48":156,"_37":157,"_35":62,"_158":-5,"_46":47,"_45":159,"_160":64,"_43":208,"_13":162,"_39":163,"_41":164,"_29":30,"_165":-5,"_209":210,"_32":211},["D",1779544263276],"children",[],[157],["D",1779544249028],[],[168],"67cdb75c-56ae-11f1-8e14-ee650bb23af1","index.tsx","019e551a-5303-7301-b8d9-c4a05227290d",["D",1779544249028],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { createRoot } from \"https://esm.sh/react-dom@18.2.0/client\";\nimport { App } from \"./components/App.tsx\";\n\nconst root = document.getElementById(\"root\");\nif (!root) {\n throw new Error(\"No root element found\");\n}\n\ncreateRoot(root).render();",[],[174,216],"67cdb1da-56ae-11f1-8e14-ee650bb23af1","index.html","019e551a-5303-7301-b8d9-c0cc68bae9d4",["D",1779544249028],"\n\n \n \n \n React Hono Val Town Starter\n \n \n \n \n

\n \n \n \n",[],[174,223],"67cdaf0a-56ae-11f1-8e14-ee650bb23af1","favicon.svg","019e551a-5303-7301-b8d9-bc0e8f5aabf9",["D",1779544249028],"\n",[],[174,230],"67cda8b6-56ae-11f1-8e14-ee650bb23af1","Counter.tsx","019e551a-5303-7301-b8d9-b7a8d40bac4e","019e551a-5303-7301-b8d9-b9274695fab5",["D",1779544249028],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState } from \"https://esm.sh/react@18.2.0\";\n\nexport function Counter() {\n const [count, setCount] = useState(0);\n return (\n

\n Clicked\n setCount((c) => c + 1)}\n class=\"bg-blue-500 hover:bg-blue-600 text-white font-medium px-4 py-2 rounded-lg transition-colors cursor-pointer\"\n >\n {count}\n \n times\n

\n );\n}\n",[],[174,244,237],"components","67cd954c-56ae-11f1-8e14-ee650bb23af1","App.tsx","019e551a-5302-73ad-8589-62cc6807635a",["D",1779544249028],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { Counter } from \"./Counter.tsx\";\n\nexport function App() {\n return (\n

React + Hono Starter

\n \n

\n View source\n

\n );\n}\n",[],[174,244,246],"67cdad8e-56ae-11f1-8e14-ee650bb23af1",["D",1779544249028],[201,202],[174,244],["D",1779544249028],[200,203,204,205],[174],"950e41dc-56ae-11f1-9ddb-ee650bb23af1",["D",1779544325014],[],[180,38],"76bd199c-56ae-11f1-8e14-ee650bb23af1","db.ts","76bd1b5e-56ae-11f1-8e14-ee650bb23af1",5,["D",1779544274151],"import { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport { sqlite };\n\nexport async function initDB() {\n await sqlite.batch([\n {\n sql: `CREATE TABLE IF NOT EXISTS datasets (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n name TEXT NOT NULL,\n source TEXT NOT NULL DEFAULT 'upload',\n row_count INTEGER DEFAULT 0,\n col_count INTEGER DEFAULT 0,\n columns_json TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS dataset_rows (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n dataset_id INTEGER NOT NULL,\n row_index INTEGER NOT NULL,\n data_json TEXT NOT NULL,\n FOREIGN KEY (dataset_id) REFERENCES datasets(id) ON DELETE CASCADE\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS training_results (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n dataset_id INTEGER NOT NULL,\n target_column TEXT NOT NULL,\n method TEXT NOT NULL DEFAULT 'lasso',\n alpha REAL,\n selected_features TEXT,\n cv_scores TEXT,\n mean_cv_score REAL,\n coefficients TEXT,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS regression_models (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n training_id INTEGER,\n dataset_id INTEGER NOT NULL,\n method TEXT NOT NULL,\n equation TEXT,\n coefficients TEXT,\n intercept REAL,\n r_squared REAL,\n adj_r_squared REAL,\n mse REAL,\n mae REAL,\n features TEXT,\n target TEXT,\n is_recommended INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS prediction_pages (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n model_id INTEGER NOT NULL,\n name TEXT NOT NULL,\n description TEXT,\n is_active INTEGER DEFAULT 1,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS predictions (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n page_id INTEGER NOT NULL,\n model_id INTEGER NOT NULL,\n input_json TEXT NOT NULL,\n predicted_value REAL NOT NULL,\n actual_value REAL,\n error REAL,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n ]);\n}\n",[],[180,264],"72270d2a-56ae-11f1-8e14-ee650bb23af1","routes","72270dc0-56ae-11f1-8e14-ee650bb23af1",4,["D",1779544266458],[],[180,272],["D",1779544263876],[196,197,198],[180],["D",1779544249028],["D",1789492855745],{"_109":108,"_116":112},{"_109":108,"_110":-5,"_111":112,"_19":60,"_113":114,"_115":30},{"_13":94,"_37":95,"_48":47,"_96":49,"_97":98,"_99":100,"_101":55,"_102":103,"_104":105,"_106":50,"_107":108},"ctx",{"_87":-5,"_138":319,"_320":321,"_322":323,"_324":30,"_325":30,"_326":30,"_327":64,"_328":-5,"_329":-7,"_330":-7,"_331":68,"_332":30,"_333":30,"_334":-5,"_335":30,"_336":30,"_337":30,"_338":-5},"ENV",{"_290":291,"_292":293,"_294":95,"_295":296,"_297":298,"_299":300,"_301":302,"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v6