From f9fc4d26ec5fca9ee175c8a6fbcdd0fa36f10947 Mon Sep 17 00:00:00 2001
From: sanine <sanine.not@pm.me>
Date: Thu, 16 Nov 2023 14:50:00 -0600
Subject: clear out js files

---
 src/genome/README.md      |  12 --
 src/genome/genome.js      | 218 -----------------------------------
 src/genome/genome.test.js | 282 ----------------------------------------------
 src/genome/trial.js       |  37 ------
 4 files changed, 549 deletions(-)
 delete mode 100644 src/genome/README.md
 delete mode 100644 src/genome/genome.js
 delete mode 100644 src/genome/genome.test.js
 delete mode 100644 src/genome/trial.js

(limited to 'src/genome')

diff --git a/src/genome/README.md b/src/genome/README.md
deleted file mode 100644
index 1135ed0..0000000
--- a/src/genome/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-src/genome
-==========
-
-Genomes represent the neural network that underlies a creature. They are an array of tuples of the form `[ source, sink, weight ]`, where:
-
-  * `source` is an integer in the range `[0, N - num_outputs)`, representing the index of the source neuron
-  * `sink` is an integer in the range `[num_inputs, N)`, representing the index of the sink neuron
-  * `weight` is a floating-point value in the range `[-4, 4]`, representing the weight of the connection
-
-`num_input` and `num_output` are fixed by the environment (as they are the input senses and output actions of the creature, respectively). 
-`N` is not fixed, but is instead determined by the maximum index present in the genome. As long as the maximum index is greater than or 
-equal to `num_input + num_output`, the genome is considered valid.
diff --git a/src/genome/genome.js b/src/genome/genome.js
deleted file mode 100644
index 20974fc..0000000
--- a/src/genome/genome.js
+++ /dev/null
@@ -1,218 +0,0 @@
-'use strict';
-
-import { random_choice } from '../util.js';
-import { network } from '../mind/topology.js';
-
-
-// check if a given genome is valid and compute its size
-export function validate_genome(genome) {
-  const { n_input, n_internal, n_output } = genome;
-  console.log(n_input + n_internal);
-  return genome.genes.reduce(
-    (acc, [source, sink, weight]) => acc && (
-      (source < n_input+n_internal) &&
-      (sink >= n_input)
-    ),
-    true
-  );
-}
-
-// parse a genome into a useable neural net
-export function parse_genome(genome) {
-  const { n_input, n_internal, n_output } = genome;
-
-  const n = genome.genes.reduce(
-    (acc, [source, sink, weight]) => acc.connect(source, sink, weight),
-    network(n_input, n_internal, n_output)
-  );
-
-  return n;
-}
-
-
-// --===== mutations =====--
-
-function clamp(value, min, max) {
-  if (value > max) { return max; }
-  if (value < min) { return min; }
-  return value;
-}
-
-// adjust the source input of a gene
-export function mut_gene_source(n_input, n_internal, n_output, gene, r) {
-  const [source, sink, weight] = gene;
-
-  const new_source = r < 0.5 ? source-1 : source+1;
-
-  return [
-    clamp(new_source, 0, n_input+n_internal-1), 
-    sink, 
-    weight,
-  ];
-}
-
-
-// adjust the sink target of a gene
-export function mut_gene_sink(n_input, n_internal, n_output, gene, r) {
-  const [source, sink, weight] = gene;
-
-  const new_sink = r < 0.5 ? sink-1 : sink+1;
-
-  return [
-    source, 
-    clamp(new_sink,  n_input+n_internal, n_input+n_internal+n_output-1), 
-    weight,
-  ];
-}
-
-
-// modify a gene's weight
-// only adjusts the weight by performing a weighted average, so as to
-// more gently modify the generated net
-export function mut_gene_weight(weight_max, gene, r) {
-  const [source, sink, weight] = gene;
-  
-  const rr = (2*r)-1;
-  const move = weight_max * rr;
-  const new_weight = (2*weight + move)/3;
-
-  return [
-    source,
-    sink,
-    clamp(new_weight, -weight_max, weight_max),
-  ];
-}
-
-
-
-// expand the size of the neural net encoded by the genome
-// relabels internal indices so that there is one extra internal neuron
-export function mut_genome_expand(genome, r) {
-  const expand_index = Math.floor(genome.n_internal * r) + genome.n_input;
-  const new_genes = genome.genes.map(([source, sink, weight]) => [
-    source >= expand_index ? source+1 : source,
-    sink >= expand_index ? sink+1 : sink,
-    weight,
-  ]);
-
-  return {
-    ...genome, 
-    n_internal: genome.n_internal+1, 
-    genes: new_genes,
-  };
-}
-
-
-// contract the size of the neural net encoded by the genome
-// relabels internal indices so that there is one less internal neuron
-export function mut_genome_contract(genome, r) {
-  const { n_input, n_internal, n_output } = genome;
-  const contract_idx = Math.floor(n_internal * r) + n_input;
-
-  // decrement sources on the contract index too, to prevent invalid genomes
-  const new_source = (source) => source >= contract_idx ? source-1 : source;
-  // decrement sinks only after the contract index
-  const new_sink   = (sink)   => sink > contract_idx ? sink-1 : sink;
-
-  const new_genes = genome.genes.map(([source, sink, weight]) => [
-    new_source(source), 
-    new_sink(sink), 
-    weight,
-  ]);
-
-  return {
-    ...genome,
-    n_internal: n_internal-1,
-    genes: new_genes,
-  };
-}
-
-
-// append a newly generated gene to the end of the genome
-export function mut_genome_insert(genome, weight_max, r1=Math.random(), r2=Math.random(), r3=Math.random()) {
-  const { n_input, n_internal, n_output } = genome;
-  const source = Math.floor((n_input + n_internal) * r1);
-  const sink   = Math.floor((n_internal + n_output) * r2) + n_input;
-  const weight = weight_max * ((2*r3)-1);
-
-  return {
-    ...genome,
-    genes: [...genome.genes, [source, sink, weight]],
-  };
-}
-
-
-// delete a gene from the genome
-export function mut_genome_delete(genome, r) {
-  const del_idx = Math.floor(r * genome.genes.length);
-  const genes = genome.genes.filter((_, idx) => idx != del_idx);
-  return { ...genome, genes };
-}
-
-
-function mut_gene(
-  [n_input, n_internal, n_output, genome], 
-  weight_max, r1, r2, r3
-) {
-  const gene_idx = Math.floor(genome.length * r1);
-  const mod = random_choice(['source', 'sink', 'weight'], r2);
-  let new_gene;
-  if (mod == 'source') {
-    new_gene = mut_gene_source(
-      n_input, n_internal, n_output, 
-      genome[gene_idx],
-      r3
-    );
-  } else if (mod == 'sink') {
-    new_gene = mut_gene_sink(
-      n_input, n_internal, n_output, 
-      genome[gene_idx],
-      r3
-    );
-  } else {
-    new_gene = mut_gene_weight(
-      weight_max, genome[gene_idx], r3
-    );
-  }
-
-  const new_genome = genome.map((gene, idx) => {
-    if (idx == gene_idx) { return new_gene; }
-    return gene;
-  });
-
-  return [
-    n_input, n_internal, n_output, new_genome
-  ];
-}
-
-
-export function mutate_genome(obj, weight_max) {
-  const mut = random_choice([
-    'gene', 'gene', 'gene',
-    'gene', 'gene', 'gene',
-    'gene', 'gene', 'gene',
-    'insert', 'delete',
-    'insert', 'delete',
-    'expand', 'contract',
-  ], Math.random());
-
-  if (mut == 'gene') {
-    return mut_gene(
-      obj, weight_max, 
-      Math.random(), Math.random(), Math.random()
-    );
-  } else if (mut == 'insert') {
-    return mut_genome_insert(
-      obj, weight_max, 
-      Math.random(), Math.random(), Math.random()
-    );
-  } else if (mut == 'delete') {
-    return mut_genome_delete(obj, Math.random());
-  } else if (mut == 'expand') {
-    return mut_genome_expand(obj, Math.random());
-  } else if (mut == 'contract') {
-    return mut_genome_contract(obj, Math.random());
-  } else {
-    throw new Error(`bad mut value: ${mut}`);
-  }
-}
diff --git a/src/genome/genome.test.js b/src/genome/genome.test.js
deleted file mode 100644
index bc64e4e..0000000
--- a/src/genome/genome.test.js
+++ /dev/null
@@ -1,282 +0,0 @@
-'use strict';
-
-// genome structure
-// {
-//   genes: gene[]
-//   n_input, n_internal, n_output
-// }
-
-
-import { 
-  validate_genome,
-  parse_genome,
-  mut_gene_source,
-  mut_gene_sink,
-  mut_gene_weight,
-  mut_genome_expand,
-  mut_genome_contract,
-  mut_genome_insert,
-  mut_genome_delete,
-} from './genome';
-
-
-test('genome validation', () => {
-  expect(validate_genome({
-    n_input: 0, n_internal: 1, n_output: 0,
-    genes: [[0, 0, 1.0]],
-  })).toBe(true);
-  expect(validate_genome({
-    n_input: 2, n_internal: 0, n_output: 1,
-    genes: [[0, 2, 1]],
-  })).toBe(true);
-  expect(validate_genome({
-    n_input: 2, n_internal: 0, n_output: 1,
-    genes: [[2, 0, 1]],
-  })).toBe(false);
-  expect(validate_genome({
-    n_input: 2, n_internal: 0, n_output: 1,
-    genes: [[2, 2, 1]],
-  })).toBe(false);
-  expect(validate_genome({
-    n_input: 2, n_internal: 1, n_output: 1,
-    genes: [[3, 2, 1]],
-  })).toBe(false);
-});
-
-
-test('parse a genome into a neural net', () => {
-  const n = parse_genome({
-    n_input: 1, n_internal: 1, n_output: 1,
-    genes: [
-      [0, 1, 1],
-      [1, 1, 1],
-      [1, 2, 1]
-    ]
-  });
-
-  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 ) ],
-	]);
-});
-
-
-test('mutate gene source', () => {
-  const n_input = 3;
-  const n_internal = 4;
-  const n_output = 5;
-
-  expect(mut_gene_source(
-    n_input, n_internal, n_output, 
-    [0, 4, 0],
-    0.0
-  )).toEqual([0, 4, 0]);
-
-  expect(mut_gene_source(
-    n_input, n_internal, n_output, 
-    [0, 4, 0],
-    1.0
-  )).toEqual([1, 4, 0]);
-
-  expect(mut_gene_source(
-    n_input, n_internal, n_output, 
-    [6, 4, 0],
-    0.0
-  )).toEqual([5, 4, 0]);
-
-  expect(mut_gene_source(
-    n_input, n_internal, n_output, 
-    [6, 4, 0],
-    1.0
-  )).toEqual([6, 4, 0]);
-});
-
-
-test('mutate gene sink', () => {
-  const n_input = 3;
-  const n_internal = 4;
-  const n_output = 5;
-
-  expect(mut_gene_sink(
-    n_input, n_internal, n_output, 
-    [0, 7, 0],
-    0.0
-  )).toEqual([0, 7, 0]);
-
-  expect(mut_gene_sink(
-    n_input, n_internal, n_output, 
-    [0, 7, 0],
-    1.0
-  )).toEqual([0, 8, 0]);
-
-  expect(mut_gene_sink(
-    n_input, n_internal, n_output, 
-    [6, 11, 0],
-    0.0
-  )).toEqual([6, 10, 0]);
-
-  expect(mut_gene_sink(
-    n_input, n_internal, n_output, 
-    [6, 11, 0],
-    1.0
-  )).toEqual([6, 11, 0]);
-});
-
-
-test('mutate gene weight', () => {
-  const weight_max = 4.0;
-
-  expect(mut_gene_weight(
-    weight_max, [0, 0, 1], 0.0
-  )).toEqual([0, 0, (2 - 4)/3]);
-
-  expect(mut_gene_weight(
-    weight_max, [0, 0, -4], 1.0
-  )).toEqual([0, 0, (-8 + 4)/3]);
-
-  expect(mut_gene_weight(
-    weight_max, [0, 0, 3], 0.5
-  )).toEqual([0, 0, (6+0)/3]);
-});
-
-
-test('expand genome', () => {
-  const n_input = 1;
-  const n_internal = 3;
-  const n_output = 1;
-
-  const genome = {
-    n_input, n_internal, n_output,
-    genes: [
-      [0, 1, 0],
-      [1, 2, 0],
-      [2, 3, 0],
-      [3, 4, 0],
-    ],
-  };
-
-  expect(mut_genome_expand(genome, 0.0)).toEqual({
-    n_input, n_internal: n_internal+1, n_output,
-    genes: [
-      [0, 2, 0],
-      [2, 3, 0],
-      [3, 4, 0],
-      [4, 5, 0],
-    ],
-  });
-
-  expect(mut_genome_expand(genome, 0.5)).toEqual({
-    n_input, n_internal: n_internal+1, n_output,
-    genes: [
-      [0, 1, 0],
-      [1, 3, 0],
-      [3, 4, 0],
-      [4, 5, 0],
-    ],
-  });
-
-  expect(mut_genome_expand(genome, 0.99)).toEqual({
-    n_input, n_internal: n_internal+1, n_output,
-    genes: [
-      [0, 1, 0],
-      [1, 2, 0],
-      [2, 4, 0],
-      [4, 5, 0],
-    ],
-  });
-});
-
-
-test('contract genome', () => {
-  const n_input = 1;
-  const n_internal = 3;
-  const n_output = 1;
-
-  const genome = {
-    n_input, n_internal, n_output,
-    genes: [
-      [0, 1, 0],
-      [1, 2, 1],
-      [2, 3, 2],
-      [3, 4, 3],
-    ],
-  };
-
-  expect(mut_genome_contract(genome, 0.0)).toEqual({
-    n_input, n_internal: n_internal-1, n_output,
-    genes: [
-      [0, 1, 0],
-      [0, 1, 1],
-      [1, 2, 2],
-      [2, 3, 3],
-    ],
-  });
-
-  expect(mut_genome_contract(genome, 0.5)).toEqual({
-    n_input, n_internal: n_internal-1, n_output,
-    genes: [
-      [0, 1, 0],
-      [1, 2, 1],
-      [1, 2, 2],
-      [2, 3, 3],
-    ],
-  });
-
-  expect(mut_genome_contract(genome, 0.99)).toEqual({
-    n_input, n_internal: n_internal-1, n_output,
-    genes: [
-      [0, 1, 0],
-      [1, 2, 1],
-      [2, 3, 2],
-      [2, 3, 3],
-    ],
-  });
-});
-
-
-
-test('insert new genes', () => {
-  const n_input = 1;
-  const n_internal = 2;
-  const n_output = 1;
-  const weight_max = 4;
-
-  expect(mut_genome_insert({
-    n_input, n_internal, n_output,
-    genes: []
-  }, weight_max, 0, 0.5, 0)).toEqual({
-    n_input, n_internal, n_output,
-    genes: [[0, 2, -4]]
-  });
-
-  expect(mut_genome_insert({
-    n_input, n_internal, n_output,
-    genes: [[0, 2, -4]]
-  }, weight_max, 0.99, 0, 1)).toEqual({
-    n_input, n_internal, n_output,
-    genes: [[0, 2, -4], [2, 1, 4]]
-  });
-});
-
-
-test('remove genes', () => {
-  const n_input = 0;
-  const n_output = 0;
-  const n_internal = 3;
-  const genome = {
-    n_input, n_internal, n_output,
-    genes: [[0, 1, 0], [1, 2, 0]],
-  };
-
-  expect(mut_genome_delete(genome, 0.0)).toEqual({
-    n_input, n_internal, n_output, 
-    genes: [[1, 2, 0]],
-  });
-
-  expect(mut_genome_delete(genome, 0.99)).toEqual({
-    n_input, n_internal, n_output, 
-    genes: [[0, 1, 0]],
-  });
-});
diff --git a/src/genome/trial.js b/src/genome/trial.js
deleted file mode 100644
index 2ce23bf..0000000
--- a/src/genome/trial.js
+++ /dev/null
@@ -1,37 +0,0 @@
-import { 
-  get_size,
-  mut_genome_insert, mutate_genome,
-} from './genome.js';
-
-const recurse = (f, x0, n) => {
-  if (n == 0) { 
-    return x0; 
-  } else {
-    return f(recurse(f, x0, n-1));
-  }
-};
-
-
-const n_input = 5;
-const n_output = 5;
-
-
-const [_1, _2, _3, genome] = recurse(
-  s => mut_genome_insert(
-    s, 4, 
-    Math.random(), Math.random(), Math.random()
-  ), 
-  [n_input, 10, n_output, []], 
-  20);
-
-
-const n_internal = get_size(n_input, n_output, genome) - n_input - n_output;
-console.log([n_input, n_internal, n_output, genome]);
-
-const mutation = recurse(
-  s => mutate_genome(s, 4),
-  [n_input, n_internal, n_output, genome],
-  40
-);
-
-console.log(mutation);
-- 
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