add memory

2 files changed, 36 insertions, 9 deletions

diff --git a/src/mind/topology.js b/src/mind/topology.js
index 320b499..56bc498 100644
--- a/src/mind/topology.js
+++ b/src/mind/topology.js
@@ -65,8 +65,10 @@ function network_connect(n, source, sink, weight) {
 
 function incident_edges(n, adj) {
 	const incident = adj
-		.map((edge, index) => edge < 0 ? index : null)
+		.map((edge, index) => (edge < 0) || (edge === 2) ? index : null)
 		.filter(index => index !== null);
+	
+	console.log(incident);
 
 	return incident;
 }
@@ -91,7 +93,7 @@ function edge_ends(n, edge) {
 }
 
 
-function get_value(n, index, input, cache) {
+function get_value(n, index, input, prev, cache) {
 	if (cache !== undefined && cache[index]) {
 		return cache[index];
 	}
@@ -103,12 +105,14 @@ function get_value(n, index, input, cache) {
 	const weight = incident.map(x => n.weight[x]);
 	const sources = incident
 		.map(x => edge_ends(n, x).source);
+	
+	const values = sources
+		.map(x => x === index ? prev[x - n.input_count] : get_value(n, x, input, prev, cache));
+	
+	console.log(n, index, sources, values);
 
-	const sum = sources
-		.reduce((acc, x, i) => 
-			acc + (weight[i] * get_value(n, x, input, cache)), 
-			0
-		);
+	const sum = values
+		.reduce((acc, x, i) => acc + (weight[i] * x), 0);
 
 	const value = Math.tanh(sum);
 
@@ -137,11 +141,11 @@ function network_compute(n, input, state) {
 		.filter(i => i !== null);
 
 	const output = Object.freeze(
-		outputs.map(x => get_value(n, x, input, value_cache))
+		outputs.map(x => get_value(n, x, input, state, value_cache))
 	);
 
 	const newstate = Object.freeze(
-		hidden.map(x => get_value(n, x, input, value_cache))
+		hidden.map(x => get_value(n, x, input, state, value_cache))
 	);
 	
 	return Object.freeze([output, newstate]);
diff --git a/src/mind/topology.test.js b/src/mind/topology.test.js
index 5867763..fbe1862 100644
--- a/src/mind/topology.test.js
+++ b/src/mind/topology.test.js
@@ -187,3 +187,26 @@ test('arbitrary hidden neurons', () => {
 		],
 	]);
 });
+
+
+test('memory', () => {
+	const n = network(0, 1, 1).connect(0, 0, -0.5).connect(0, 1, 2);
+
+	expect(n.compute([], [1])).toEqual([
+		[ Math.tanh( 2 * Math.tanh( -0.5 * 1 ) ) ],
+		[ Math.tanh( -0.5 * 1) ],
+	]);
+});
+
+
+test('memory and input', () => {
+	const n = network(1, 1, 1)
+		.connect(0, 1, 1)
+		.connect(1, 1, 1)
+		.connect(1, 2, 1);
+	
+	expect(n.compute([2], [-1])).toEqual([
+		[ Math.tanh( Math.tanh( 2-1 ) ) ],
+		[ Math.tanh( 2-1 ) ],
+	]);
+});