clustering-tfjs/dist/tf-backend.js

High-performance TypeScript clustering algorithms (K-Means, Spectral, Agglomerative) with TensorFlow.js acceleration and scikit-learn compatibility

"use strict";
/**
 * TensorFlow.js backend manager
 *
 * Manages a singleton instance of TensorFlow.js with support for
 * multiple backends and environments.
 */
var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
    if (k2 === undefined) k2 = k;
    var desc = Object.getOwnPropertyDescriptor(m, k);
    if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
      desc = { enumerable: true, get: function() { return m[k]; } };
    }
    Object.defineProperty(o, k2, desc);
}) : (function(o, m, k, k2) {
    if (k2 === undefined) k2 = k;
    o[k2] = m[k];
}));
var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {
    Object.defineProperty(o, "default", { enumerable: true, value: v });
}) : function(o, v) {
    o["default"] = v;
});
var __importStar = (this && this.__importStar) || (function () {
    var ownKeys = function(o) {
        ownKeys = Object.getOwnPropertyNames || function (o) {
            var ar = [];
            for (var k in o) if (Object.prototype.hasOwnProperty.call(o, k)) ar[ar.length] = k;
            return ar;
        };
        return ownKeys(o);
    };
    return function (mod) {
        if (mod && mod.__esModule) return mod;
        var result = {};
        if (mod != null) for (var k = ownKeys(mod), i = 0; i < k.length; i++) if (k[i] !== "default") __createBinding(result, mod, k[i]);
        __setModuleDefault(result, mod);
        return result;
    };
})();
Object.defineProperty(exports, "__esModule", { value: true });
exports.initializeBackend = initializeBackend;
exports.getTensorFlow = getTensorFlow;
exports.isInitialized = isInitialized;
exports.resetBackend = resetBackend;
// Singleton storage
let tfInstance = null;
let initializationPromise = null;
/**
 * Initialize the TensorFlow.js backend
 */
async function initializeBackend(config = {}) {
    // Return existing instance if already initialized
    if (tfInstance) {
        return tfInstance;
    }
    // Return existing initialization promise if in progress
    if (initializationPromise) {
        return initializationPromise;
    }
    // Start initialization
    initializationPromise = loadBackend(config);
    try {
        tfInstance = await initializationPromise;
        return tfInstance;
    }
    catch (error) {
        // Reset on error to allow retry
        initializationPromise = null;
        throw error;
    }
}
/**
 * Get the current TensorFlow instance
 * @throws Error if not initialized
 */
function getTensorFlow() {
    if (!tfInstance) {
        throw new Error('TensorFlow.js not initialized. Please call Clustering.init() first.');
    }
    return tfInstance;
}
/**
 * Check if TensorFlow is initialized
 */
function isInitialized() {
    return tfInstance !== null;
}
/**
 * Reset the backend (mainly for testing)
 */
function resetBackend() {
    tfInstance = null;
    initializationPromise = null;
}
/**
 * Load the appropriate backend based on environment and config
 */
async function loadBackend(config) {
    // Detect environment
    const isNode = typeof window === 'undefined' &&
        typeof process !== 'undefined' &&
        process.versions &&
        process.versions.node;
    let tf;
    if (isNode) {
        // Node.js environment
        const loader = await Promise.resolve().then(() => __importStar(require('./tf-loader.node')));
        tf = await loader.loadTensorFlow();
    }
    else {
        // Browser environment
        const loader = await Promise.resolve().then(() => __importStar(require('./tf-loader.browser')));
        tf = await loader.loadTensorFlow();
    }
    // Set custom flags if provided
    if (config.flags) {
        Object.entries(config.flags).forEach(([flag, value]) => {
            tf.env().setFlags({ [flag]: value });
        });
    }
    // Set specific backend if requested
    if (config.backend) {
        await tf.setBackend(config.backend);
    }
    // Wait for backend to be ready
    await tf.ready();
    return tf;
}