// npm 패키지
@antv/vis-predict-engine
visualization predict engine
주간
288
월간
717
버전
10
메인테이너
51
라이선스
MIT
최초 publish
2020-11-19
publisher
xdddst
tarball
44,498 B
AUTO-PUBLISHED·1개 버전 인덱싱됨·최근 publish: 2021-06-01
// offending code· @0.1.1· no static-pattern hits
llm: benign · 0.85→ 의심 전송지 없음, 원격 실행 형태 없음 — 1 known-vendor host(s).
- @0.1.1··AUTO-PUBLISHED·publisher: xdddstheuristic 75/100static flags 0llm benign (0.85) via ollamamature-packageosv-flagged:MAL-2026-4094
→ 의심 전송지 없음, 원격 실행 형태 없음 — 1 known-vendor host(s).
// offending code· no static-pattern hits
--- package.json (entry) --- { "name": "@antv/vis-predict-engine", "version": "0.1.1", "description": "visualization predict engine", "repository": { "type": "git", "url": "https://github.com/antvis/vis-predict-engine" }, "license": "MIT", "author": "https://github.com/orgs/antvis/people", "main": "dist/index", "files": [ "package.json", "dist", "LICENSE", "README.md" ], "scripts": { "start": "father build --watch", "build": "npm run clean && father build", "clean": "rimraf es esm lib dist", "lint": "eslint --ext .js,.jsx,.ts,.tsx --format=pretty \"./\"", "prettier": "prettier -c --write \"**/*\"" }, "husky": { "hooks": { "pre-commit": "lint-staged" } }, "lint-staged": { "*.{js,jsx,ts,tsx}": [ "prettier --write", "eslint --ext .js,.jsx,.ts,.tsx", "git add" ] }, "devDependencies": { "eslint": "^7.13.0", "father": "^2.29.11", "lint-staged": "^10.5.1", "pre-commit": "^1.2.2", "prettier": "^2.1.2", "rimraf": "^3.0.2" }, "dependencies": { "@tensorflow/tfjs": "^2.7.0" } } --- index.js (entry) --- 'use strict'; Object.defineProperty(exports, '__esModule', { value: true }); var tf = require('@tensorflow/tfjs'); function _interopNamespace(e) { if (e && e.__esModule) return e; var n = Object.create(null); if (e) { Object.keys(e).forEach(function (k) { if (k !== 'default') { var d = Object.getOwnPropertyDescriptor(e, k); Object.defineProperty(n, k, d.get ? d : { enumerable: true, get: function () { return e[k]; } }); } }); } n['default'] = e; return Object.freeze(n); } var tf__namespace = /*#__PURE__*/_interopNamespace(tf); function ownKeys(object, enumerableOnly) { var keys = Object.keys(object); if (Object.getOwnPropertySymbols) { var symbols = Object.getOwnPropertySymbols(object); if (enumerableOnly) { symbols = symbols.filter(function (sym) { return Object.getOwnPropertyDescriptor(object, sym).enumerable; }); } keys.push.apply(keys, symbols); } return keys; } function _objectSpread2(target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i] != null ? arguments[i] : {}; if (i % 2) { ownKe --- bundled output (OSV-MAL flagged — LLM scope expansion) --- --- dist/index.d.ts (bundled) --- import { GraphLayoutPredict } from './graph-predict'; export { GraphLayoutPredict }; --- dist/index.esm.js (bundled) --- import * as tf from '@tensorflow/tfjs'; import { layers, serialization, initializers, tidy, dot, tensor2d, softmax, diag, pow, tensor, loadLayersModel } from '@tensorflow/tfjs'; function ownKeys(object, enumerableOnly) { var keys = Object.keys(object); if (Object.getOwnPropertySymbols) { var symbols = Object.getOwnPropertySymbols(object); if (enumerableOnly) { symbols = symbols.filter(function (sym) { return Object.getOwnPropertyDescriptor(object, sym).enumerable; }); } keys.push.apply(keys, symbols); } return keys; } function _objectSpread2(target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i] != null ? arguments[i] : {}; if (i % 2) { ownKeys(Object(source), true).forEach(function (key) { _defineProperty(target, key, source[key]); }); } else if (Object.getOwnPropertyDescriptors) { Object.defineProperties(target, Object.getOwnPropertyDescriptors(source)); } else { ownKeys(Object(source)).forEach(function (key) { Object.defineProperty(target, key, Object.getOwnPropertyDescriptor(source, key)); }); } } return target; } function asyncGeneratorStep(gen, resolve, reject, _next, _throw, key, arg) { try { var info = gen[key](arg); var value = info.value; } catch (error) { reject(error); return; } if (info.done) { resolve(value); } else { Promise.resolve(value).then(_next, _throw); } } function _asyncToGenerator(fn) { return function () { var self = this, args = arguments; return new Promise(function (resolve, reject) { var gen = fn.apply(self, args); function _next(value) { asyncGeneratorStep(gen, resolve, reject, _next, _throw, "next", value); } function _throw(err) { asyncGeneratorStep(gen, resolve, reject, _next, _throw, "throw", err); } _next(undefined); }); }; } function _classCallCheck(instance, Constr --- dist/index.js (bundled) --- 'use strict'; Object.defineProperty(exports, '__esModule', { value: true }); var tf = require('@tensorflow/tfjs'); function _interopNamespace(e) { if (e && e.__esModule) return e; var n = Object.create(null); if (e) { Object.keys(e).forEach(function (k) { if (k !== 'default') { var d = Object.getOwnPropertyDescriptor(e, k); Object.defineProperty(n, k, d.get ? d : { enumerable: true, get: function () { return e[k]; } }); } }); } n['default'] = e; return Object.freeze(n); } var tf__namespace = /*#__PURE__*/_interopNamespace(tf); function ownKeys(object, enumerableOnly) { var keys = Object.keys(object); if (Object.getOwnPropertySymbols) { var symbols = Object.getOwnPropertySymbols(object); if (enumerableOnly) { symbols = symbols.filter(function (sym) { return Object.getOwnPropertyDescriptor(object, sym).enumerable; }); } keys.push.apply(keys, symbols); } return keys; } function _objectSpread2(target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i] != null ? arguments[i] : {}; if (i % 2) { ownKeys(Object(source), true).forEach(function (key) { _defineProperty(target, key, source[key]); }); } else if (Object.getOwnPropertyDescriptors) { Object.defineProperties(target, Object.getOwnPropertyDescriptors(source)); } else { ownKeys(Object(source)).forEach(function (key) { Object.defineProperty(target, key, Object.getOwnPropertyDescriptor(source, key)); }); } } return target; } function asyncGeneratorStep(gen, resolve, reject, _next, _throw, key, arg) { try { var info = gen[key](arg); var value = info.value; } catch (error) { reject(error); return; } if (info.done) { resolve(value); } else { Promise.resolve(value).then(_next, _throw); } } function _asyncToGenerator(fn) { return function () --- dist/graph-predict/index.d.ts (bundled) --- import GraphLayoutPredict from './layout'; export { GraphLayoutPredict }; --- dist/graph-predict/types/index.d.ts (bundled) --- export interface PlainObject { [key: string]: any; } export interface Node { id: string; label: string; properties?: PlainObject; [key: string]: any; } export interface Edge { id: string; name?: string; label?: string; source: string; target: string; properties?: PlainObject; [key: string]: any; } export interface GraphData { nodes: Node[]; edges: Edge[]; } export declare type Layout = 'force' | 'radial' | 'concentric' | 'circular'; export interface FinalNode { id: string; } export interface FinalEdge { source?: string; target?: string; from: string; to: string; } export interface PredictGraphData { nodes: FinalNode[]; edges: FinalEdge[]; } --- dist/graph-predict/layout/index.d.ts (bundled) --- import './layer/graph-conv-layer'; import './layer/pooling-layer'; import { Layout, GraphData } from '../types'; declare const _default: { predict(data: GraphData, expectLayout?: Layout | undefined, showLog?: boolean | undefined): Promise<{ predictLayout: Layout; confidence: string; } | { predictLayout?: undefined; confidence?: undefined; }>; }; export default _default; --- dist/graph-predict/layout/utils/index.d.ts (bundled) --- import * as tf from '@tensorflow/tfjs'; import { GraphData, Layout, FinalNode, FinalEdge } from '../../types'; export declare const featureProcess: (nodes: FinalNode[], edges: FinalEdge[], featureCount: number) => tf.Tensor<tf.Rank>[]; export declare const transGraphData: (data: GraphData) => { nodes: FinalNode[]; edges: FinalEdge[]; }; export declare const predictLog: (predictedRes: Layout, confidence: string, expectLayout?: Layout | undefined) => void; declare const _default: { featureProcess: (nodes: FinalNode[], edges: FinalEdge[], featureCount: number) => tf.Tensor<tf.Rank>[]; transGraphData: (data: GraphData) => { nodes: FinalNode[]; edges: FinalEdge[]; }; predictLog: (predictedRes: Layout, confidence: string, expectLayout?: Layout | undefined) => void; }; export default _default; --- dist/graph-predict/layout/layer/graph-conv-layer.d.ts (bundled) --- export default GraphConvLayer; declare class GraphConvLayer extends tf.layers.Layer { constructor(options: any); useBias: boolean; biasInitializer: import("@tensorflow/tfjs/node_modules/@tensorflow/tfjs-layers/dist/initializers").Zeros; biasRegularizer: any; kernel: tf.LayerVariable | undefined; bias: tf.LayerVariable | null | undefined; } declare namespace GraphConvLayer { const className: string; } import * as tf from "@tensorflow/tfjs"; --- dist/graph-predict/layout/layer/pooling-layer.d.ts (bundled) --- export default GraphPoolingLayer; declare class GraphPoolingLayer extends tf.layers.Layer { } declare namespace GraphPoolingLayer { const className: string; } import * as tf from "@tensorflow/tfjs";
