add hidden neuron state

2 files changed, 59 insertions, 4 deletions

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);
+//});