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authorsanine <sanine.not@pm.me>2023-11-16 14:50:00 -0600
committersanine <sanine.not@pm.me>2023-11-16 14:50:00 -0600
commitf9fc4d26ec5fca9ee175c8a6fbcdd0fa36f10947 (patch)
treef190e6e465bb563c608a916f41fc8bf686ea2897 /src/mind/topology.js
parent7825b92ce3be95a0ce1dfea9388adbaadce83b1f (diff)
clear out js files
Diffstat (limited to 'src/mind/topology.js')
-rw-r--r--src/mind/topology.js183
1 files changed, 0 insertions, 183 deletions
diff --git a/src/mind/topology.js b/src/mind/topology.js
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--- a/src/mind/topology.js
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-'use strict';
-
-import { create } from '../util.js';
-
-
-const DEFAULT_WEIGHT_MAX = 4;
-
-
-// prototype for network objects
-const network_proto = {
- connect: function(source, sink, weight) {
- return network_connect(this, source, sink, weight);
- },
- compute: function(inputs, state) {
- return network_compute(this, inputs, state);
- },
-};
-
-
-// create a new network
-export function network(input_count, internal_count, output_count, weight_max = 4) {
- const count = input_count + internal_count + output_count;
- const n = create({
- input_count,
- output_count,
- adjacency: new Array(count).fill([]),
- weight: [],
- }, network_proto);
- return n;
-}
-
-
-// check index is an input
-function is_input(n, index) {
- return index < n.input_count;
-}
-// check if index is an output
-function is_output(n, index) {
- return index >= (n.adjacency.length - n.output_count);
-}
-// check if index is a hidden neuron
-function is_hidden(n, index) {
- return (!is_input(n, index)) && (!is_output(n, index));
-}
-
-
-// returns a new network with an edge between the given nodes
-// with the given weight
-export function network_connect(n, source, sink, weight) {
- if (is_input(n, sink)) {
- // inputs cannot be sinks
- throw new Error(`attempt to use input as sink (${source} -> ${sink})`);
- }
- if (is_output(n, source)) {
- // outputs cannot be sources
- throw new Error(`attempt to use output as source (${source} -> ${sink})`);
- }
-
- return create({
- ...n,
- adjacency: n.adjacency.map((row, i) => {
- if (i === source && i === sink) {
- // self-loop
- return [...row, 2];
- } else if (i === source) {
- return [...row, 1];
- } else if (i === sink) {
- return [...row, -1];
- } else {
- return [...row, 0];
- }
- }),
- weight: [...n.weight, weight],
- }, network_proto);
-}
-
-
-// gets the indices of the edges incident on the given adjacency list
-function incident_edges(n, adj) {
- const incident = adj
- .map((edge, index) => (edge < 0) || (edge === 2) ? index : null)
- .filter(index => index !== null);
-
- return incident;
-}
-
-
-// get the indices of the ends of an edge
-// in the case of self-loops, both values are the same
-function edge_ends(n, edge) {
- const ends = n.adjacency
- .map((adj, index) => adj[edge] !== 0 ? index : null)
- .filter(index => index != null);
-
- ends.sort((a, b) => n.adjacency[a][edge] < n.adjacency[b][edge] ? -1 : 1);
-
- if (ends.length === 1) {
- return { source: ends[0], sink: ends[0] };
- } else if (ends.length === 2) {
- return { source: ends[1], sink: ends[0] };
- } else {
- throw new Error("something bad happened with the ends");
- }
-}
-
-
-// recursively get the value of a node from the input nodes,
-// optionally caching the computed values
-function get_value(n, index, input, prev, cache) {
- // check if value is cached
- if (cache !== undefined && cache[index]) {
- return cache[index];
- }
- // check if value is input
- if (is_input(n, index)) {
- return input[index];
- }
-
- const adj = n.adjacency[index]; // get adjacency list
- const incident = incident_edges(n, adj); // get incident edges
- const weight = incident.map(x => n.weight[x]); // edge weights
- const sources = incident // get ancestor nodes
- .map(x => edge_ends(n, x).source);
-
- // get the value of each ancestor
- const values = sources
- .map(x => x === index // if the ancestor is this node
- ? prev[x - n.input_count] // then the value is the previous value
- : get_value(n, x, input, prev, cache)); // else recurse
-
- const sum = values // compute the weighted sum of the values
- .reduce((acc, x, i) => acc + (weight[i] * x), 0);
-
- if (sum !== sum) { // NaN test
- console.log(n);
- console.log(sources);
- console.log(input);
- throw new Error(`failed to get output for index ${index}`);
- }
-
- // compute result
- const value = Math.tanh(sum);
-
- // !!! impure caching !!!
- // cache result
- if (cache !== undefined) {
- cache[index] = value;
- }
-
- return value;
-}
-
-
-// compute a network's output and new hidden state
-// given the input and previous hidden state
-export function network_compute(n, input, state) {
- // validate input
- if (input.length !== n.input_count) {
- throw new Error("incorrect number of input elements");
- }
- // validate state
- const hidden_count = n.adjacency.length - n.input_count - n.output_count;
- if (state.length !== hidden_count) {
- throw new Error("incorrect number of state elements");
- }
-
- // !!! impure caching !!!
- const value_cache = {};
-
- const result = Object.freeze(n.adjacency
- .map((x, i) => is_output(n, i) ? i : null) // output index or null
- .filter(i => i !== null) // remove nulls
- .map(x => get_value(n, x, input, state, value_cache)) // map to computed value
- );
-
- const newstate = Object.freeze(n.adjacency
- .map((x, i) => is_hidden(n, i) ? i : null) // hidden index or null
- .filter(i => i !== null) // remove nulls
- .map(x => get_value(n, x, input, state, value_cache)) // map to computed value (using cache)
- );
-
- return Object.freeze([result, newstate]);
-}