8839075805
Implements Phases 1-8 of the TFTSR implementation plan. Rust backend (Tauri 2.x, src-tauri/): - Multi-provider AI: OpenAI-compatible, Anthropic, Gemini, Mistral, Ollama - PII detection engine: 11 regex patterns with overlap resolution - SQLCipher AES-256 encrypted database with 10 versioned migrations - 28 Tauri IPC commands for triage, analysis, document, and system ops - Ollama: hardware probe, model recommendations, pull/delete with events - RCA and blameless post-mortem Markdown document generators - PDF export via printpdf - Audit log: SHA-256 hash of every external data send - Integration stubs for Confluence, ServiceNow, Azure DevOps (v0.2) Frontend (React 18 + TypeScript + Vite, src/): - 9 pages: full triage workflow NewIssue→LogUpload→Triage→Resolution→RCA→Postmortem→History+Settings - 7 components: ChatWindow, TriageProgress, PiiDiffViewer, DocEditor, HardwareReport, ModelSelector, UI primitives - 3 Zustand stores: session, settings (persisted), history - Type-safe tauriCommands.ts matching Rust backend types exactly - 8 IT domain system prompts (Linux, Windows, Network, K8s, DB, Virt, HW, Obs) DevOps: - .woodpecker/test.yml: rustfmt, clippy, cargo test, tsc, vitest on every push - .woodpecker/release.yml: linux/amd64 + linux/arm64 builds, Gogs release upload Verified: - cargo check: zero errors - tsc --noEmit: zero errors - vitest run: 13/13 unit tests passing Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
112 lines
2.0 KiB
JavaScript
112 lines
2.0 KiB
JavaScript
/*
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Fraction.js v5.0.0 10/1/2024
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https://raw.org/article/rational-numbers-in-javascript/
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Copyright (c) 2024, Robert Eisele (https://raw.org/)
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Licensed under the MIT license.
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*/
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const Fraction = require('fraction.js');
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/*
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We have the polynom f(x) = 1/3x_1^2 + x_2^2 + x_1 * x_2 + 3
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The gradient of f(x):
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grad(x) = | x_1^2+x_2 |
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| 2x_2+x_1 |
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And thus the Hesse-Matrix H:
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| 2x_1 1 |
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| 1 2 |
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The inverse Hesse-Matrix H^-1 is
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| -2 / (1-4x_1) 1 / (1 - 4x_1) |
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| 1 / (1 - 4x_1) -2x_1 / (1 - 4x_1) |
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We now want to find lim ->oo x[n], with the starting element of (3 2)^T
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*/
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// Get the Hesse Matrix
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function H(x) {
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var z = Fraction(1).sub(Fraction(4).mul(x[0]));
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return [
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Fraction(-2).div(z),
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Fraction(1).div(z),
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Fraction(1).div(z),
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Fraction(-2).mul(x[0]).div(z),
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];
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}
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// Get the gradient of f(x)
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function grad(x) {
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return [
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Fraction(x[0]).mul(x[0]).add(x[1]),
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Fraction(2).mul(x[1]).add(x[0])
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];
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}
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// A simple matrix multiplication helper
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function matrMult(m, v) {
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return [
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Fraction(m[0]).mul(v[0]).add(Fraction(m[1]).mul(v[1])),
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Fraction(m[2]).mul(v[0]).add(Fraction(m[3]).mul(v[1]))
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];
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}
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// A simple vector subtraction helper
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function vecSub(a, b) {
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return [
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Fraction(a[0]).sub(b[0]),
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Fraction(a[1]).sub(b[1])
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];
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}
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// Main function, gets a vector and the actual index
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function run(V, j) {
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var t = H(V);
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//console.log("H(X)");
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for (var i in t) {
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// console.log(t[i].toFraction());
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}
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var s = grad(V);
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//console.log("vf(X)");
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for (var i in s) {
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// console.log(s[i].toFraction());
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}
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//console.log("multiplication");
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var r = matrMult(t, s);
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for (var i in r) {
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// console.log(r[i].toFraction());
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}
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var R = (vecSub(V, r));
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console.log("X" + j);
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console.log(R[0].toFraction(), "= " + R[0].valueOf());
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console.log(R[1].toFraction(), "= " + R[1].valueOf());
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console.log("\n");
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return R;
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}
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// Set the starting vector
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var v = [3, 2];
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for (var i = 0; i < 15; i++) {
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v = run(v, i);
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}
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