From 94c7abdda55178109d114b3ea80c711a6633f0fb Mon Sep 17 00:00:00 2001
From: TreetopFlyer <seth111@gmail.com>
Date: Thu, 29 Jul 2021 20:47:17 -0400
Subject: [PATCH] remove old files

---
 index.html | 717 -----------------------------------------------------
 methods.md |  14 --
 nn_old.js  | 211 ----------------
 3 files changed, 942 deletions(-)
 delete mode 100644 index.html
 delete mode 100644 methods.md
 delete mode 100644 nn_old.js

diff --git a/index.html b/index.html
deleted file mode 100644
index 9fa0878..0000000
--- a/index.html
+++ /dev/null
@@ -1,717 +0,0 @@
-<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>
\ No newline at end of file
diff --git a/methods.md b/methods.md
deleted file mode 100644
index e365088..0000000
--- a/methods.md
+++ /dev/null
@@ -1,14 +0,0 @@
-box(boundingBox, count) // done
-transpose(inMatrix) // done
-outer(inv1, inv2) // done
-clone(inCloud) // done
-
-pad(inCloud) // done
-unpad(inCloud) // done
-
-transform(inCloud, inMatrix) // done
-sigmoid(inCloud) // 1/(1+e^x) // done
-derivative(inCloud) // x*(1-x) // done
-scale(inCloud1, inV) // done
-subtract(inCloud1, inCloud2) // done
-multiply(inCloud1, inCloud2) // done
\ No newline at end of file
diff --git a/nn_old.js b/nn_old.js
deleted file mode 100644
index 262aec3..0000000
--- a/nn_old.js
+++ /dev/null
@@ -1,211 +0,0 @@
-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 = 0.8;
-    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);
-        }
-    }
-};
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