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'use strict';
import {
mutation_type, mutate,
get_size,
parse_genome,
} from './genome';
test('basic gene mutations', () => {
expect(mutate([0, 1, 2], mutation_type.none, 0)).toEqual([0, 1, 2]);
expect(mutate([0, 1, 2], mutation_type.source, 0.2)).toEqual([0, 1, 2]);
expect(mutate([1, 1, 2], mutation_type.source, 0.2)).toEqual([0, 1, 2]);
expect(mutate([0, 1, 2], mutation_type.source, 0.8)).toEqual([1, 1, 2]);
expect(mutate([0, 1, 2], mutation_type.sink, 0.2)).toEqual([0, 0, 2]);
expect(mutate([0, 1, 2], mutation_type.sink, 0.8)).toEqual([0, 2, 2]);
expect(mutate([0, 0, 2], mutation_type.sink, 0.2)).toEqual([0, 0, 2]);
expect(mutate([0, 1, 2], mutation_type.weight, 0.5)).toEqual([0, 1, 1]);
expect(mutate([0, 1, 2], mutation_type.weight, 0.0)).toEqual([0, 1, -1]);
expect(mutate([0, 1, 2], mutation_type.weight, 1.0)).toEqual([0, 1, 3]);
});
test('genome validation and size', () => {
expect(get_size(0, 0, [ [ 0, 0, 1.0 ] ])).toBe(1);
expect(get_size(2, 1, [ [ 0, 2, 1 ] ])).toBe(3);
expect(get_size(2, 1, [ [ 0, 1, 1 ] ])).toBe(-1);
expect(get_size(2, 1, [ [ 0, 2, 5 ] ])).toBe(-1);
});
test('parse a genome into a neural net', () => {
const n = parse_genome(1, 1, [
[0, 1, 1],
[1, 1, 1],
[1, 2, 1]
]);
console.log(n);
expect(n.input_count).toBe(1);
expect(n.output_count).toBe(1);
expect(n.compute([2], [-1])).toEqual([
[ Math.tanh( Math.tanh( 2-1 ) ) ],
[ Math.tanh( 2-1 ) ],
]);
});
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