add training set and tests

nn.test.js

@@ -1,14 +1,61 @@
 import { assert, assertEquals } from "https://deno.land/std@0.102.0/testing/asserts.ts";
-import * as NN from "./nn.ts";
+import { Label, Forward, Backward } from "./nn.ts";
+import { default as M } from "./m.ts";
 
-Deno.test("NN.Observe", ()=>
+const input = [
-{
+    [ 0.1,  0.05],
-    console.log(NN.Observe([[[1, 2, 3]]], [[[0.4, 0.5, 0.6]]]));
+    [ 0.0, -0.06]
-});
+    [ 0.99, 0.85],
-Deno.test("NN.Train", ()=>
+    [ 1.2,  1.05]
-{
+];
-    const stages = [[[1, 2, 3]]];
-    const layers = [[[0.4, 0.5, 0.6]]];
 
-    console.log(NN.Train(stages, layers, [[0, 0, 1]], 0.1));
+const training = [];
+const stages = [];
+const layers = [];
+
+Deno.test("NN.Label", ()=>
+{
+    Label(training,
+    [
+        [ 0.1,  0.05],
+        [ 0.0, -0.06]
+    ],
+    [1]);
+    Label(training,
+    [
+        [ 0.99, 0.85],
+        [ 1.2,  1.05]
+    ],
+    [0]);
+    stages.push(training[0]);
+    console.log(training);
+    assertEquals(training.length, 2, "input and output sets created");
+    assertEquals(training[0].length, training[1].length, "both sets have same length");
+    assertEquals(training[0][0].length, 3, "padded input component");
+    assertEquals(training[1][0].length, 1, "unchanged label vector");
 });
+
+Deno.test("NN.Backward", ()=>
+{
+
+    layers.push(M.Create.Box([0, 0, 0], [1, 1, 1], 4));
+    layers.push(M.Create.Box([0, 0, 0, 0, 0], [1, 1, 1, 1, 1], 1));
+
+    let copy1 = M.Create.Clone(layers[0]);
+    let copy2 = M.Create.Clone(layers[1]);
+
+    for(let i=0; i<1000; i++)
+    {
+        Backward(stages, layers, training[1], 0.1);
+    }
+
+    assert(layers[0][0][0] != copy1[0][0][0], "first matrix has changed");
+    assert(layers[1][0][0] != copy2[0][0][0], "second matrix has changed");
+});
+
+Deno.test("NN.Forward", ()=>
+{
+    console.log(Forward(stages, layers));
+    console.log(training[1]);
+});
+

nn.ts

@@ -1,6 +1,22 @@
 import { default as M, Cloud } from "./m.ts";
+export type N = Array<Array<Array<number>>>
 
-const Observe = (inStages:Array<Cloud.M>, inLayers:Array<Cloud.M>):Cloud.M =>
+const Label = (inSet:any, inData:Cloud.M, inLabel:Cloud.V):N =>
+{
+    if(!inSet){inSet = [[], []];}
+    if(inSet.length == 0){inSet.push([]);}
+    if(inSet.length == 1){inSet.push([]);}
+
+    inData.forEach((row:Cloud.V) =>
+    {
+        row.push(1);
+        inSet[0].push(row);
+        inSet[1].push(inLabel);
+    });
+    return inSet;
+};
+
+const Forward = (inStages:N, inLayers:N):Cloud.M =>
 {
     let i:number;
     let process = (index:number):Cloud.M => M.Batch.Sigmoid(M.Batch.Affine(inStages[index], inLayers[index]));
@@ -12,16 +28,17 @@ const Observe = (inStages:Array<Cloud.M>, inLayers:Array<Cloud.M>):Cloud.M =>
     inStages[i+1] = process(i);
     return inStages[i+1];
 };
-const Train = (inStages:Array<Cloud.M>, inLayers:Array<Cloud.M>, inGoals:Cloud.M, inRate:number):void =>
+const Backward = (inStages:N, inLayers:N, inGoals:Cloud.M, inRate:number):void =>
 {
     let i:number;
-    let errorBack:Cloud.M = M.Batch.Subtract(Observe(inStages, inLayers), inGoals);
+    let errorBack:Cloud.M = M.Batch.Subtract(Forward(inStages, inLayers), inGoals);
 
-    for(i=inLayers.length-1; i>=0; i++)
+    for(i=inLayers.length-1; i>=0; i--)
     {
         let layerMatrix:Cloud.M = inLayers[i];
         let  layerInput:Cloud.M = inStages[i];
         let layerOutput:Cloud.M = inStages[i+1];
+
         let errorScaled:Cloud.M = M.Batch.Multiply(errorBack, M.Batch.Derivative(layerOutput));
 
         errorBack = M.Batch.Affine(errorScaled, M.Create.Transpose(layerMatrix));
@@ -32,5 +49,5 @@ const Train = (inStages:Array<Cloud.M>, inLayers:Array<Cloud.M>, inGoals:Cloud.M
     }
 };
 
-export { Observe, Train };
+export { Label, Forward, Backward };
 export type { Cloud };