Merge pull request 'network' (#1) from network into master

Reviewed-on: #1

index.html

@@ -0,0 +1,717 @@
+<script>
+    /* Vector Library */
+/*
+	Works with n-dimensional vectors: represented as arrays of numbers
+*/
+var V = {};
+V.Subtract = function(inV1, inV2)
+{
+	var out = [];
+	for(var i=0; i<inV1.length; i++)
+	{
+		out[i] = inV1[i] - inV2[i];
+	}
+	return out;
+};
+V.Add = function(inV1, inV2)
+{
+	var out = [];
+	for(var i=0; i<inV1.length; i++)
+	{
+		out[i] = inV1[i] + inV2[i];
+	}
+	return out;
+};
+V.Distance = function(inV1, inV2)
+{
+	return V.Length(V.Subtract(inV1, inV2))
+};
+V.Dot = function(inV1, inV2)
+{
+	var out = 0;
+	for(var i=0; i<inV1.length; i++)
+	{
+		out += inV1[i] * inV2[i];
+	}
+	return out;
+};
+V.Multiply = function(inV1, inV2)
+{
+	var out = [];
+	for(var i=0; i<inV1.length; i++)
+	{
+		out[i] = inV1[i] * inV2[i];
+	}
+	return out;
+};
+V.Length = function(inV1)
+{
+	return Math.sqrt(V.Dot(inV1, inV1));
+};
+V.Scale = function(inV1, inScalar)
+{
+	var out = [];
+	for(var i=0; i<inV1.length; i++)
+	{
+		out[i] = inV1[i] * inScalar;
+	}
+	return out;
+};
+V.Normalize = function(inV1)
+{
+    return V.Scale(inV1, 1/V.Length(inV1));
+};
+V.Clone = function(inV1)
+{
+	var out = [];
+	var i;
+	for(i=0; i<inV1.length; i++)
+	{
+		out[i] = inV1[i];
+	}
+	return out;
+};
+
+
+
+
+
+
+
+
+
+
+
+var M = {};
+
+/**************************
+M A T R I X
+*/
+// transform inC with inM
+// returns the transformed inC
+M.Transform = function(inM, inC)
+{
+	var outM = [];
+	var outV = [];
+	var i, j;
+	
+	for(i=0; i<inC.length; i++)
+	{
+		outV = [];
+		for(j=0; j<inM.length; j++)
+		{
+			outV[j] = V.Dot(inM[j], inC[i]);
+		}
+		outM.push(outV);
+	}
+	return outM;
+};
+
+
+// flip rows for columns in inM
+// returns the modified Matrix
+M.Transpose = function(inM)
+{
+	var dimensions = inM[0].length;
+	var i, j;
+	var outM = [];
+	var outV = [];
+	for(i=0; i<dimensions; i++)
+	{
+		outV = [];
+		for(j=0; j<inM.length; j++)
+		{
+			//the Ith componenth of the Jth member
+			outV[j] = inM[j][i];
+		}
+		outM.push(outV);
+	}
+	return outM;
+}
+
+// returns a matrix that is the result of the outer product of inV1 and inV2
+// where the Nth member of outM is a copy of V1, scaled by the Nth component of V2
+M.Outer = function(inV1, inV2)
+{
+	var outM = [];
+	
+	var i;
+	for(i=0; i<inV2.length; i++)
+	{
+		outM.push(V.Scale(inV1, inV2[i]));
+	}
+	
+	return outM;
+};
+
+
+
+
+
+/**************************
+B A T C H
+*/
+//smash the members of inM with a softmax
+M.Sigmoid = function(inM)
+{
+	var i, j;
+	var outM = [];
+	var outV = [];
+	for(i=0; i<inM.length; i++)
+	{
+		outV = [];
+		for(j=0; j<inM[i].length; j++)
+		{
+			outV[j] = 1/(1 + Math.pow(Math.E, -inM[i][j]));
+		}
+		outM.push(outV);
+	}
+	return outM;
+};
+// return the derivatives of the members of inM (that have been run through the softmax)
+M.Derivative = function(inM)
+{
+	var i, j;
+	var component;
+	var outM = [];
+	var outV = [];
+	for(i=0; i<inM.length; i++)
+	{
+		outV = [];
+		for(j=0; j<inM[i].length; j++)
+		{
+			component = inM[i][j];
+			outV[j] = component*(1 - component);
+		}
+		outM.push(outV);
+	}
+	return outM;
+};
+// batch multiply these pairs of vectors
+M.Multiply = function(inCloud1, inCloud2)
+{
+	var i;
+	var outM = [];
+	for(i=0; i<inCloud1.length; i++)
+	{
+		outM.push(V.Multiply(inCloud1[i], inCloud2[i]));
+	};
+	return outM;
+};
+// batch add
+M.Add = function(inCloud1, inCloud2)
+{
+    var outM = [];
+    
+    var i;
+    for(i=0; i<inCloud1.length; i++)
+    {
+        outM.push(V.Add(inCloud1[i], inCloud2[i]));
+    }
+    return outM;
+};
+M.Subtract = function(inCloud1, inCloud2)
+{
+    var outM = [];
+    
+    var i;
+    for(i=0; i<inCloud1.length; i++)
+    {
+        outM.push(V.Subtract(inCloud1[i], inCloud2[i]));
+    }
+    return outM;
+};
+M.Scale = function(inCloud1, inScalar)
+{
+    var outM = [];
+    
+    var i;
+    for(i=0; i<inCloud1.length; i++)
+    {
+        outM.push(V.Scale(inCloud1[i], inScalar));
+    }
+    return outM;
+};
+M.Clone = function(inM)
+{
+    var i;
+    var outM;
+    var outV;
+    
+    outM =[];
+    for(i=0; i<inM.length; i++)
+    {
+        outM.push(V.Clone(inM[i]));
+    }
+    return outM;
+};
+
+
+/**************************
+B O U N D S
+*/
+// return the bounding box of inM as a two-member Matrix
+M.Bounds = function(inM)
+{
+	var dimensions = inM[0].length;
+	var i, j;
+	var min = [];
+	var max = [];
+	for(i=0; i<dimensions; i++)
+	{
+		min[i] = 9999999;
+		max[i] = -999999;
+	}
+	for(i=0; i<inM.length; i++)
+	{
+		for(j=0; j<dimensions; j++)
+		{
+			if(inM[i][j] < min[j])
+			{
+				min[j] = inM[i][j];
+			}
+			if(inM[i][j] > max[j])
+			{
+				max[j] = inM[i][j];
+			}			
+		}
+	}
+	return [min, max];
+};
+
+// find the local coordinates for all the members of inM, within the bounding box inB
+// returns a new Matrix of relative vectors
+M.GlobalToLocal = function(inM, inB)
+{
+	var dimensions = inB[0].length;
+	var i, j;
+	var outM = [];
+	var outV = [];
+	var size;
+	var min;
+	var denominator;
+	for(i=0; i<inM.length; i++)
+	{
+		outV = [];
+		for(j=0; j<dimensions; j++)
+		{
+			denominator = inB[1][j] - inB[0][j];
+			if(denominator == 0)
+			{
+				outV[j] = inB[1][j];// if min and max are the same, just output max
+			}
+			else
+			{
+				outV[j] = (inM[i][j] - inB[0][j])/denominator;	
+			}
+		}
+		outM.push(outV);
+	}
+	return outM;
+};
+// find the global coordinates for all the members of inM, within the bounding box inB
+// returns a new Matrix of global vectors
+M.LocalToGlobal = function(inM, inB)
+{
+	var dimensions = inB[0].length;
+	var i, j;
+	var outM = [];
+	var outV = [];
+	var size;
+	var min;
+	for(i=0; i<inM.length; i++)
+	{
+		outV = [];
+		for(j=0; j<dimensions; j++)
+		{
+			outV[j] = inB[0][j] + inM[i][j] * (inB[1][j] - inB[0][j]);
+		}
+		outM.push(outV);
+	}
+	return outM;
+};
+
+
+/**************************
+C L O U D
+*/
+// return some number of points from inM as a new Matrix
+M.Reduce = function(inM, inCount)
+{
+	var largeGroupSize;
+	var largeGroupCount;
+	var smallGroupSize;
+	var outM = [];
+	
+	largeGroupSize = Math.floor(inM.length/inM);
+	smallGroupSize = inM.length%inCount
+	for(i=0; i<inM-1; i++)
+	{
+		index = i*largeGroupSize + Math.floor(Math.random()*largeGroupSize);
+		outM.push( V.Clone(inM[index]) );
+	}
+	if(smallGroupSize != 0)
+	{
+		index = i*largeGroupSize + Math.floor(Math.random()*smallGroupSize)
+		outM.push( V.Clone(inM[index]) );
+	}
+	return outM;
+};
+
+// return a Matrix of length inCount, where all the members fall within the circle paramemters, including a bias
+M.Circle = function(inCenter, inRadius, inBias, inCount)
+{
+	var i, j;
+	var vector;
+	var length;
+	var outM = [];
+	
+	for(i=0; i<inCount; i++)
+	{
+		//generate a random vector
+		vector = [];
+		for(j=0; j<inCenter.length; j++)
+		{
+			vector[j] = (Math.random() - 0.5);
+		}
+		
+		//normalize the vector
+		vector = V.Scale(vector, 1/V.Length(vector));
+		
+		//set a random length (with a bias)
+		length = Math.pow(Math.random(), Math.log(inBias)/Math.log(0.5))*inRadius;
+		vector = V.Scale(vector, length);
+		
+		//move the vector to the center
+		vector = V.Add(vector, inCenter);
+		
+		outM.push(vector);
+	}
+	return outM;
+};
+
+// return a Matrix of length inCount, where all the members fall within inBounds
+M.Box = function(inBounds, inCount)
+{
+	var vector;
+	var dimensions = inBounds[0].length;
+	var i, j;
+	var min, max;
+	var outM = [];
+	
+	for(i=0; i<inCount; i++)
+	{
+		vector = [];
+		for(j=0; j<dimensions; j++)
+		{
+			min = inBounds[0][j];
+			max = inBounds[1][j];
+			
+			vector[j] = min + Math.random()*(max - min);
+		}
+		outM.push(vector);
+	}
+	return outM;
+};
+
+//combine all the matricies in inList into one long Matrix
+M.Combine = function(inList)
+{
+	var i, j;
+	var outM = [];
+	for(i=0; i<inList.length; i++)
+	{
+		for(j=0; j<inList[i].length; j++)
+		{
+			outM.push(V.Clone(inList[i][j]));
+		}
+	}
+	return outM;
+};
+
+/*
+PLEASE NOTE: These padding routines are unique to this library in that they
+actually modify the input object(s) rather than returning modified copies!
+*/
+// add a new component (set to '1') to each member of inM
+M.Pad = function(inM)
+{
+	var i;
+	for(i=0; i<inM.length; i++)
+	{
+		inM[i].push(1);
+	}
+    	return inM;
+};
+// remove the last component of each memeber of inM
+M.Unpad = function(inM)
+{
+	var i;
+    	for(i=0; i<inM.length; i++)
+	{
+        	inM[i].pop();
+	}
+	return inM;
+};
+// set the last component of each member of inM to 1
+M.Repad = function(inM)
+{
+	var i;
+	var last = inM[0].length-1;
+    	for(i=0; i<inM.length; i++)
+	{
+        	inM[i][last] = 1;
+	}
+	return inM;
+};
+</script>
+
+<script>
+var NN = {};
+
+NN.TrainingSet = {};
+NN.TrainingSet.Instances = [];
+NN.TrainingSet.Create = function()
+{
+    var obj = {};
+
+    obj.Input = [];
+    obj.Output = [];
+    obj.Order = [];
+    
+    NN.TrainingSet.Instances.push(obj);
+    return obj;
+};
+NN.TrainingSet.AddPoint = function(inTrainingSet, inType, inData)
+{
+    inTrainingSet.Input.push(inData);
+    inTrainingSet.Output.push(inType);
+    inTrainingSet.Order.push(inTrainingSet.Order.length);
+};
+NN.TrainingSet.AddCloud = function(inTrainingSet, inLabel, inCloud)
+{
+    var i;
+    for(i=0; i<inCloud.length; i++)
+    {
+        NN.TrainingSet.AddPoint(inTrainingSet, inLabel, inCloud[i]);
+    }
+};
+NN.TrainingSet.Randomize = function(inTrainingSet)
+{
+      var newOrder = [];
+      var selection;
+      while(inTrainingSet.Order.length != 0)
+      {
+          selection = Math.floor(inTrainingSet.Order.length * Math.random());
+          inTrainingSet.Order.splice(selection, 1);
+          newOrder.push(selection);
+      }
+      inTrainingSet.Order = newOrder;
+};
+
+
+NN.Layer = {};
+NN.Layer.Create = function(sizeIn, sizeOut)
+{
+    var i;
+    var min = [];
+    var max = [];
+    var obj = {};
+    
+    sizeIn++;
+    
+    obj.Forward = {};
+    for(i=0; i<sizeIn; i++)
+    {
+        min.push(-1);
+        max.push(1);
+    }
+    obj.Forward.Matrix = M.Box([min, max], sizeOut);
+    obj.Forward.StageInput = [];
+    obj.Forward.StageAffine = [];
+    obj.Forward.StageSigmoid = [];
+    obj.Forward.StageDerivative = [];
+    
+    obj.Backward = {};
+    obj.Backward.Matrix = M.Transpose(obj.Forward.Matrix);
+    obj.Backward.StageInput = [];
+    obj.Backward.StageDerivative = [];
+    obj.Backward.StageAffine = [];
+    
+    return obj;
+};
+NN.Layer.Forward = function(inLayer, inInput)
+{
+    inLayer.Forward.StageInput = M.Pad(inInput); // Pad the input
+    inLayer.Forward.StageAffine = M.Transform(inLayer.Forward.Matrix, inLayer.Forward.StageInput);
+    inLayer.Forward.StageSigmoid = M.Sigmoid(inLayer.Forward.StageAffine);
+    
+    return inLayer.Forward.StageSigmoid;
+};
+NN.Layer.Error = function(inLayer, inTarget)
+{
+    return M.Subtract(inLayer.Forward.StageSigmoid, inTarget);
+};
+NN.Layer.Backward = function(inLayer, inInput)
+{
+    /* We need the derivative of the forward pass, but only during the backward pass.
+    That's why-- even though it "belongs" to the forward pass-- it is being calculated here. */
+    inLayer.Forward.StageDerivative = M.Derivative(inLayer.Forward.StageSigmoid);
+    
+    /* This transpose matrix is for sending the error back to a previous layer.
+    And again, even though it is derived directly from the forward matrix, it is only needed during the backward pass so we calculate it here.*/
+    inLayer.Backward.Matrix = M.Transpose(inLayer.Forward.Matrix);
+    
+    /* When the error vector arrives at a layer, it always needs to be multiplied (read 'supressed') by the derivative of
+    what the layer output earlier during the forward pass.
+    So despite its name, Backward.StageDerivative contains the result of this *multiplication* and not some new derivative calculation.*/
+    inLayer.Backward.StageInput = inInput;
+    inLayer.Backward.StageDerivative = M.Multiply(inLayer.Backward.StageInput, inLayer.Forward.StageDerivative);
+    inLayer.Backward.StageAffine = M.Transform(inLayer.Backward.Matrix, inLayer.Backward.StageDerivative);
+    
+    return M.Unpad(inLayer.Backward.StageAffine);// Unpad the output
+};
+NN.Layer.Adjust = function(inLayer, inLearningRate)
+{
+    var deltas;
+    var vector;
+    var scalar;
+    var i, j;
+    
+    for(i=0; i<inLayer.Forward.StageInput.length; i++)
+    {
+        deltas = M.Outer(inLayer.Forward.StageInput[i], inLayer.Backward.StageDerivative[i]);
+        deltas = M.Scale(deltas, inLearningRate);
+        
+        inLayer.Forward.Matrix = M.Subtract(inLayer.Forward.Matrix, deltas);
+    }
+};
+NN.Layer.Stochastic = function(inLayer, inTrainingSet, inIterations)
+{
+    /* this method is ONLY for testing individual layers, and does not translate to network-level training */
+    var i, j;
+    var current;
+    var error;
+    for(i=0; i<inIterations; i++)
+    {
+        NN.TrainingSet.Randomize(inTrainingSet);
+        for(j=0; j<inTrainingSet.Order.length; j++)
+        {
+            current = inTrainingSet.Order[j];
+            NN.Layer.Forward(inLayer, [inTrainingSet.Input[current]]);
+            error = M.Subtract(inLayer.Forward.StageSigmoid, [inTrainingSet.Output[current]]);
+            NN.Layer.Backward(inLayer, error);
+            NN.Layer.Adjust(inLayer, 0.1);
+        }
+    }
+};
+
+NN.Network = {};
+NN.Network.Instances = [];
+NN.Network.Create = function()
+{
+    var obj = {};
+    var i;    
+    
+    obj.Layers = [];
+    obj.LearningRate = 0.1;
+    obj.Error = [];
+    
+    for(i=0; i<arguments.length-1; i++)
+    {
+        obj.Layers.push(NN.Layer.Create(arguments[i], arguments[i+1]));
+    }
+    
+    NN.Network.Instances.push(obj);
+    return obj;
+};
+NN.Network.Observe = function(inNetwork, inBatch)
+{
+      var input = M.Clone(inBatch);
+      var i;
+      for(i=0; i<inNetwork.Layers.length; i++)
+      {
+          input = NN.Layer.Forward(inNetwork.Layers[i], input);
+      }
+      return inNetwork.Layers[inNetwork.Layers.length-1].Forward.StageSigmoid;
+};
+NN.Network.Error = function(inNetwork, inTraining)
+{
+      return M.Subtract(inNetwork.Layers[inNetwork.Layers.length-1].Forward.StageSigmoid, inTraining);
+};
+NN.Network.Learn = function(inNetwork, inError)
+{
+      var input = inError;
+      var i;
+      for(i=inNetwork.Layers.length-1; i>=0; i--)
+      {
+          input = NN.Layer.Backward(inNetwork.Layers[i], input);
+          NN.Layer.Adjust(inNetwork.Layers[i], inNetwork.LearningRate);
+      }
+};
+
+
+NN.Network.Batch = function(inNetwork, inTrainingSet, inIterations)
+{
+    var i;
+    for(i=0; i<inIterations; i++)
+    {
+        NN.Network.Observe(inNetwork, inTrainingSet.Input);
+        inNetwork.Error = NN.Network.Error(inNetwork, inTrainingSet.Output)
+        NN.Network.Learn(inNetwork, inNetwork.Error);
+    }
+};
+NN.Network.Stochastic = function(inNetwork, inTrainingSet, inIterations)
+{
+    var i, j;
+    var current;
+    
+    for(i=0; i<inIterations; i++)
+    {
+        NN.TrainingSet.Randomize(inTrainingSet);
+        for(j=0; j<inTrainingSet.Order.length; j++)
+        {
+            current = inTrainingSet.Order[j];
+            NN.Network.Observe(inNetwork, [inTrainingSet.Input[current]]);
+            inNetwork.Error = NN.Network.Error(inNetwork, [inTrainingSet.Output[current]]);
+            NN.Network.Learn(inNetwork, inNetwork.Error);
+        }
+    }
+};
+</script>
+
+<script>
+    let matrix1 = [
+    [-0.43662948305036675, -0.368590640707799, -0.23227179558890843],
+    [-0.004292653969505622, 0.38670055222186317, -0.2478421495365568],
+    [0.738181366836224, 0.3389203747353555, 0.4920200816404332]
+    ];
+
+    let matrix2 = [
+    [0.7098703863463034, 0.35485944251238033, 0.7642849892333241, 0.03046174288491077],
+	[-0.30655426258144347, 0.45509633551425077, -0.5013795222004322, -0.3421292736637427]
+    ];
+
+    let input = [
+    [ 0.1,  0.05],
+    [ 0.0, -0.06],
+    [ 0.99, 0.85],
+    [ 1.2,  1.05]
+    ];
+    let output = [
+    [1, 0],
+    [1, 0],
+    [0, 1],
+    [0, 1]
+    ];
+
+	let nn1 = NN.Network.Create(2, 3, 2);
+	nn1.Layers[0].Forward.Matrix = matrix1;
+	nn1.Layers[1].Forward.Matrix = matrix2;
+	nn1.LearningRate = 0.1;
+	//let logLayers = inNN => inNN.Layers.forEach(L=>console.log(L.Forward.Matrix));
+
+	NN.Network.Batch(nn1, {Input:input, Output:output}, 1000);
+	console.log(NN.Network.Observe(nn1, input));
+
+</script>

m.test.js

@@ -98,6 +98,7 @@ Deno.test("Single.Affine", ()=>
     assertEquals(t.length, 2, "correct dimensions");
     assertEquals(t[0], 0.5)
     assertEquals(t[1], 1.1, "correct placement");
+    console.log(t);
 });
 Deno.test("Single.Subtract", ()=>
 {

m.ts

@@ -38,11 +38,18 @@ const Methods = {
           Pad: (inCloud:Cloud.M):Cloud.M=> {inCloud.forEach((row:Cloud.V)=> row.push(1)); return inCloud; },
         Unpad: (inCloud:Cloud.M):Cloud.M=> {inCloud.forEach((row:Cloud.V)=> row.pop());   return inCloud; }
     },
+    Test:
+    {
+        Dot:(v1:Cloud.V, v2:Cloud.V):number=> 
+        {
+            return v1.reduce((sum, current, index)=> sum + current*v2[index]);
+        }
+    },
     Single:
     {
         Subtract:    (inV1:Cloud.V, inV2:Cloud.V):Cloud.V=> inV1.map((component, i)=> component-inV2[i]),
         Multiply:    (inV1:Cloud.V, inV2:Cloud.V):Cloud.V=> inV1.map((component, i)=> component*inV2[i]),
-          Affine: (inV:Cloud.V, inMatrix:Cloud.M):Cloud.V=> inMatrix.map((row:Cloud.V)=> row.reduce((sum, current, index)=> sum + current*inV[index]))
+          Affine: (inV:Cloud.V, inMatrix:Cloud.M):Cloud.V=> inMatrix.map((row:Cloud.V)=> row.reduce((sum, current, index)=> sum + current*inV[index], 0))
     },
     Batch:
     {

nn.test.js

@@ -1,58 +1,52 @@
 import { assert, assertEquals } from "https://deno.land/std@0.102.0/testing/asserts.ts";
-import { Label, Forward, Backward } from "./nn.ts";
-import { default as M } from "./m.ts";
+import { Split, Build, Label, Learn, Check } from "./nn.ts";
 
-let training = [];
-let stages = [];
+let data = [
+    [ 0.10,  0.05, 0, 1],
+    [ 0.00, -0.06, 0, 1],
+    [ 0.99,  0.85, 1, 0],
+    [ 1.20,  1.05, 1, 0]
+];
+let columns = [2, 3];
+let input, output;
 let layers = [];
 
+Deno.test("NN.Split", ()=>
+{
+    [input, output] = Split(data, columns);
+    assert(input);
+    assert(output);
+    assertEquals(input.length, output.length, "data split into equal input and output");
+
+    assertEquals(input[0].length, 3, "padded input");
+    assertEquals(output[0].length, 2, "unpadded output");
+});
+
+Deno.test("NN.Build", ()=>
+{
+    layers = Build(2, 5, 2);
+
+    assertEquals(layers.length, 2, "correct number of matrices");
+    assertEquals(layers[0][0].length, input[0].length, "input: padded input");
+    assertEquals(layers[0].length, 5, "input: unpadded output");
+
+    assertEquals(layers[1][0].length, 6, "hidden: padded input");
+    assertEquals(layers[1].length, output[0].length, "hidden: unpadded output");
+});
+
 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");
+    let labels = Label(input, layers);
+    assertEquals(labels.length, output.length);
+    assertEquals(labels[0].length, output[0].length);
 });
 
-Deno.test("NN.Backward", ()=>
+Deno.test("NN.Learn", ()=>
 {
-
-    let layer1 = M.Create.Box([0, 0, 0], [1, 1, 1], 2);
-    let layer2 = M.Create.Box([0, 0, 0], [1, 1, 1], 1);
-    let copy1 = M.Create.Clone(layer1);
-    let copy2 = M.Create.Clone(layer2);
-
-    layers.push(layer1);
-    layers.push(layer2);
-
-    for(let i=0; i<100; i++)
-    {
-        Backward(stages, layers, training[1], 0.1);
-    }
-
-    assert(layers[0][0][0] != copy1[0][0], "first matrix has changed");
-    assert(layers[1][0][0] != copy2[0][0], "second matrix has changed");
-});
-
-
-Deno.test("NN.Forward", ()=>
-{
-    console.log(Forward(stages, layers));
-    console.log(training[1]);
-});
-
-
+    let error = Learn(input, layers, output, 1000, 0.1);
+    assertEquals(error.length, output.length);
+    let total = 0;
+    let count = error.length*error[0].length;
+    error.forEach(row=> row.forEach(component=> total+=Math.abs(component)));
+    assert(total/count < 0.3);
+});

nn.ts

@@ -1,54 +1,78 @@
 import { default as M, Cloud } from "./m.ts";
 export type N = Array<Array<Array<number>>>
 
-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 =>
+const Forward = (inData:Cloud.M, inLayers:N):N =>
 {
     let i:number;
-    let process = (index:number):Cloud.M => M.Batch.Sigmoid(M.Batch.Affine(inStages[index], inLayers[index]));
+    let stages:N = [inData];
+    let process = (index:number):Cloud.M => M.Batch.Sigmoid(M.Batch.Affine(stages[index], inLayers[index]));
 
-    for(i=0; i<inLayers.length-1; i++)
-    {
-        inStages[i+1] = M.Mutate.Pad(process(i));
-    }
-    inStages[i+1] = process(i);
-    return inStages[i+1];
+    for(i=0; i<inLayers.length-1; i++){ stages[i+1] = M.Mutate.Pad(process(i)); }
+    stages[i+1] = process(i);
+    return stages;
 };
 const Backward = (inStages:N, inLayers:N, inGoals:Cloud.M, inRate:number):N =>
 {
     let i:number;
-    let errorBack:Cloud.M = M.Batch.Subtract(Forward(inStages, inLayers), inGoals);
+    let errorBack:Cloud.M = M.Batch.Subtract(inStages[inStages.length-1], inGoals);
 
     for(i=inLayers.length-1; i>=0; 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));
-
+        let errorScaled:Cloud.M = M.Batch.Multiply(errorBack, M.Batch.Derivative(inStages[i+1]));
         errorBack = M.Batch.Affine(errorScaled, M.Create.Transpose(inLayers[i]));
-
-        errorScaled.forEach((inScaledError:Cloud.V, inIndex:number)=> {
-            const deltas = M.Batch.Scale(M.Create.Outer(layerInput[inIndex], inScaledError), inRate);
-            inLayers[i] = M.Batch.Subtract(inLayers[i], deltas);
+        errorScaled.forEach((inScaledError:Cloud.V, inIndex:number)=>
+        {
+            inLayers[i] = M.Batch.Subtract(
+                inLayers[i],
+                M.Batch.Scale(M.Create.Outer(inStages[i][inIndex], inScaledError), inRate)
+            );
         });
-
     }
     return inLayers;
 };
+const Split = (inTrainingSet:Cloud.M, inHeaderLabel:Cloud.V, inHeaderKeep:Cloud.V = []):N =>
+{
+    let data:Cloud.M = [];
+    let label:Cloud.M = [];
+    if(!inHeaderKeep.length)
+    {
+        inTrainingSet[0].forEach( (item:number, index:number)=> inHeaderLabel.includes(index) ? false : inHeaderKeep.push(index) );
+    }
+    inTrainingSet.forEach((row:Cloud.V):void =>
+    {
+        let vectorData = [ ...inHeaderKeep.map((i:number)=>row[i]), 1];
+        let vectorLabel = inHeaderLabel.map((i:number)=>row[i]) 
+         data.push( vectorData );
+        label.push( vectorLabel );
+    });
+    return [ data, label ];
+};
+const Build = (...inLayers:Array<number>):N =>
+{
+    let i:number;
+    let output:N = [];
+    let rand = (inDimensions:number, inCount:number):Cloud.M => M.Create.Box( new Array(inDimensions).fill(-1), new Array(inDimensions).fill(1), inCount);
+    for(i=0; i<inLayers.length-1; i++)
+    {
+        output.push(rand( inLayers[i]+1, inLayers[i+1]));
+    }
+    return output;
+};
+const Label = (inData:Cloud.M, inLayers:N):Cloud.M =>
+{
+    let stages:N = Forward(inData, inLayers);
+    return stages[stages.length-1];
+};
+const Learn = (inData:Cloud.M, inLayers:N, inLabels:Cloud.M, inIterations:number, inRate:number):Cloud.M =>
+{
+    let stages:N = [];
+    for(let i=0; i<inIterations; i++)
+    {
+        stages = Forward(inData, inLayers);
+        Backward(stages, inLayers, inLabels, inRate);
+    }
+    return M.Batch.Subtract(stages[stages.length-1], inLabels);
+};
+const Check = (inData:Cloud.M, inLayers:N, inLabels:Cloud.M):Cloud.M => Learn(inData, inLayers, inLabels, 1, 0);
 
-export { Label, Forward, Backward };
-export type { Cloud };
+export { Split, Build, Label, Learn, Check, Forward, Backward };