From 980a5350b5a4845db2bd5d6feb9f463a3c1a3aa6 Mon Sep 17 00:00:00 2001
From: sanine <sanine.not@pm.me>
Date: Sun, 11 Jun 2023 22:50:42 -0500
Subject: add hidden neuron state
---
src/mind/topology.js | 24 ++++++++++++++++++++----
src/mind/topology.test.js | 39 +++++++++++++++++++++++++++++++++++++++
2 files changed, 59 insertions(+), 4 deletions(-)
(limited to 'src/mind')
diff --git a/src/mind/topology.js b/src/mind/topology.js
index 1cd52d3..19eb399 100644
--- a/src/mind/topology.js
+++ b/src/mind/topology.js
@@ -72,6 +72,8 @@ function incident_edges(n, adj) {
}
+// 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)
@@ -79,7 +81,13 @@ function edge_ends(n, edge) {
ends.sort((a, b) => n.adjacency[a][edge] < n.adjacency[b][edge] ? -1 : 1);
- return ends;
+ 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");
+ }
}
@@ -91,8 +99,7 @@ function get_value(n, index, input) {
const incident = incident_edges(n, adj);
const weight = incident.map(x => n.weight[x]);
const sources = incident
- .map(x => edge_ends(n, x))
- .map(x => x.length === 2 ? x[1] : x[0]);
+ .map(x => edge_ends(n, x).source);
const sum = sources
.reduce((acc, x, i) => acc + (weight[i] * get_value(n, x, input)), 0);
@@ -102,6 +109,13 @@ function get_value(n, index, input) {
function network_compute(n, input, state) {
+ const hidden = n.adjacency
+ .map((x, i) =>
+ (
+ (!(is_input(n, i))) &&
+ (!is_output(n, i))) ? i : null)
+ .filter(i => i !== null);
+
const outputs = n.adjacency
.map((x, i) => is_output(n, i) ? i : null)
.filter(i => i !== null);
@@ -110,7 +124,9 @@ function network_compute(n, input, state) {
outputs.map(x => get_value(n, x, input))
);
- const newstate = Object.freeze([]);
+ const newstate = Object.freeze(
+ hidden.map(x => get_value(n, x, input))
+ );
return Object.freeze([output, newstate]);
}
diff --git a/src/mind/topology.test.js b/src/mind/topology.test.js
index e1c5f87..7612c3d 100644
--- a/src/mind/topology.test.js
+++ b/src/mind/topology.test.js
@@ -137,3 +137,42 @@ test('multiple input network', () => {
[],
]);
});
+
+
+test('multiple outputs', () => {
+ const n = network(4, 0, 2)
+ .connect(0, 4, -1)
+ .connect(1, 4, 1)
+ .connect(2, 5, -1)
+ .connect(3, 5, 1);
+
+ expect(n.compute([1,2,3,5], [])).toEqual([
+ [ Math.tanh(2-1), Math.tanh(5-3) ],
+ [],
+ ]);
+});
+
+
+test('hidden neurons', () => {
+ const n = network(4, 2, 1)
+ .connect(0, 4, -1)
+ .connect(1, 4, 1)
+ .connect(2, 5, -1)
+ .connect(3, 5, 1)
+ .connect(4, 6, -1)
+ .connect(5, 6, 1);
+
+ expect(n.compute([1,2,3,5], [ 0, 0 ])).toEqual([
+ [ Math.tanh( Math.tanh(5-3) - Math.tanh(2-1) ) ],
+ [ Math.tanh(2-1), Math.tanh(5-3) ],
+ ]);
+});
+
+
+//test('arbitrary hidden neurons', () => {
+// const n = network(1, 2, 1)
+// .connect(0, 1, 1)
+// .connect(1, 2, -1)
+// .connect(2, 3, 2)
+// .connect(3, 4, -2);
+//});
--
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