scalar-autograd/dist/Value.edge-cases.spec.js

Scalar-based reverse-mode automatic differentiation in TypeScript.

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const Value_1 = require("./Value");
// Edge cases and error handling
describe('Value edge cases and error handling', () => {
    it('throws on invalid numeric inputs', () => {
        expect(() => new Value_1.Value(NaN)).toThrow();
        expect(() => new Value_1.Value(Infinity)).toThrow();
        expect(() => new Value_1.Value(-Infinity)).toThrow();
    });
    it('handles gradient accumulation correctly', () => {
        const x = new Value_1.Value(2, 'x', true);
        const y = x.mul(3);
        const z = x.mul(4);
        const out = y.add(z);
        out.backward();
        expect(x.grad).toBe(7); // 3 + 4
    });
    it('handles repeated use of same value in expression', () => {
        const x = new Value_1.Value(3, 'x', true);
        const y = x.mul(x).mul(x); // x^3
        y.backward();
        expect(x.grad).toBeCloseTo(27); // 3*x^2 = 27
    });
    it('throws on division by zero', () => {
        const a = new Value_1.Value(1);
        const b = new Value_1.Value(0);
        expect(() => a.div(b)).toThrow();
    });
    it('throws on log of negative number', () => {
        const x = new Value_1.Value(-1);
        expect(() => x.log()).toThrow();
    });
    it('throws on negative base with fractional exponent', () => {
        const x = new Value_1.Value(-2);
        expect(() => x.pow(0.5)).toThrow();
    });
});
// Complex expressions
describe('Complex mathematical expressions', () => {
    it('computes gradient of complex expression', () => {
        const x = new Value_1.Value(0.5, 'x', true);
        const y = x.sin().mul(x.cos()).add(x.exp());
        y.backward();
        const expected = Math.cos(0.5) ** 2 - Math.sin(0.5) ** 2 + Math.exp(0.5);
        expect(x.grad).toBeCloseTo(expected, 4);
    });
    it('handles nested activation functions', () => {
        const x = new Value_1.Value(0.5, 'x', true);
        const y = x.tanh().sigmoid().relu();
        y.backward();
        expect(x.grad).toBeGreaterThan(0);
    });
});