handwritten-mathematics-recogniser/utility/forwarder.js

Easy and abstracted way to recognise handwritten mathematics in a browser or in a web view.

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
class Forwarder {
    static relu(value) {
        return Math.max(value, 0);
    }
    static matrixAddition(matrix1, matrix2) {
        return;
    }
    static matrixMultiplication(matrix1, matrix2) {
        if (matrix1.length !== matrix2[0].length) {
            throw new Error('Illegal matrix dimensions!');
        }
        const matrix = Array.from({ length: matrix1[0].length }, () => Array.from({ length: matrix2.length }, () => 0));
        for (let y = 0; y < matrix.length; y++) {
            for (let x = 0; x < matrix[0].length; x++) {
                for (let z = 0; z < matrix[0].length; z++) {
                    matrix[y][x] += matrix1[y][z] * matrix2[z][x];
                }
            }
        }
        return matrix;
    }
    static convolution(image, kernel, padding = false) {
        const convolution = Array.from({ length: image.length }, () => Array.from({ length: image[0].length }, () => 0));
        for (let y = 0; y < image.length; y++) {
            for (let x = 0; x < image[y].length; x++) {
                for (let j = 0; j < kernel.length; j++) {
                    for (let i = 0; i < kernel[j].length; i++) {
                        convolution[y][x] += image[y + j][x + i] * kernel[y + j][x + i];
                    }
                }
            }
        }
        return convolution;
    }
    static maxPooling(image, windowX, windowY, strideX, strideY) {
        return;
    }
    static softmax(value) {
        return;
    }
    static softmaxProbabilities(probabilities) {
        return;
    }
}
exports.Forwarder = Forwarder;

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