public Bitmap AddCountData(int cellWidth, Bitmap img)
{
Graphics g = Graphics.FromImage(img);
int[,] count = new int[dimensions[0], dimensions[1]];
foreach (var p in data.PointList)
{
SOMNeuron bmu = GetBestMatchingUnit(p);
var pos = utilGetPosFromID(bmu.id);
count[pos.Item1, pos.Item2]++;
}
int max = 0;
for (int x = 0; x < dimensions[0]; x++)
{
for (int y = 0; y < dimensions[1]; y++)
{
max = Math.Max(max, count[x, y]);
}
}
for (int x = 0; x < dimensions[0]; x++)
{
for (int y = 0; y < dimensions[1]; y++)
{
int intensity = (int)(255.0 * (double)count[x, y] / (double)max);
Color c = Color.FromArgb(intensity, 255, 0, 0);
Brush b = new SolidBrush(c);
g.FillRectangle(b, new Rectangle(new Point(x * cellWidth, y * cellWidth), new Size(cellWidth, cellWidth)));
}
}
return(img);
}
public SelfOrganizingMap(PointSet data, List <int> dimensions, int totalItt, double learningRate)
{
//Get initial learning rate
this.initialLearningRate = learningRate;
this.learningRate = learningRate;
influence = 0;
this.totalItterations = totalItt;
itterations = 1;
this.dimensions = dimensions;
this.data = data;
rng = new Random((int)DateTime.Now.Ticks);
int numNeurons = 1;
foreach (int d in dimensions)
{
numNeurons *= d;
}
int[] pos = new int[dimensions.Count];
for (int i = 0; i < dimensions.Count; i++)
{
pos[i] = 0;
}
//Scale each attribute from [0,1] and store the conversion matrix
conversionMatrix = data.GetMinMaxWeights().Max.Coordinates;
foreach (KPoint d in data.PointList)
{
d.Normalize(conversionMatrix);
}
//Here We will initialize Our Neurons
neurons = new List <SOMNeuron>();
KPoint zero = KPoint.Zero(data[0].Dimensions);
KPoint one = KPoint.One(data[0].Dimensions);
for (int i = 0; i < numNeurons; i++)
{
//Generate our neurons
double[] posNeuron = new double[dimensions.Count];
for (int d = 0; d < dimensions.Count; d++)
{
posNeuron[d] = (double)pos[d];
}
SOMNeuron neuron = new SOMNeuron(zero, one, rng, new KPoint(posNeuron), i);
neurons.Add(neuron);
addOneCarry(ref pos, dimensions);
}
//Get the max radius
setRadius();
this.timeConstant = (double)totalItterations / Math.Log(radius);
}
private SOMNeuron GetBestMatchingUnit(KPoint data)
{
SOMNeuron best = null;
double dist = Double.MaxValue;
foreach (SOMNeuron n in neurons)
{
double d = n.weights.distanceSquared(data);
if (d < dist)
{
dist = d;
best = n;
}
}
return best;
}
public void Epoch(KPoint p)
{
//get a random item
SOMNeuron bmu = GetBestMatchingUnit(p);
neighborhoodRadius = radius * Math.Exp(-(double)itterations / timeConstant);
double neighborSqr = neighborhoodRadius * neighborhoodRadius;
QuadTreePointStruct center = new QuadTreePointStruct()
{
Index = -1,
X = bmu.position[0],
Y = bmu.position[1]
};
QuadTreePointStruct halfLength = new QuadTreePointStruct()
{
Index = -1,
X = neighborhoodRadius,
Y = neighborhoodRadius
};
List <int> neuronsInArea =
neuronQuadTree.QueryRange(new QuadTreeBoundingBox()
{
Center = center, HalfLength = halfLength
});
//Now we need to update each neuron
foreach (int i in neuronsInArea)
{
SOMNeuron n = neurons[i];
double distToBMU = n.position.elucideanDistance(bmu.position);
if (distToBMU <= neighborhoodRadius)
{
influence = Math.Exp(-(distToBMU * distToBMU) / (2 * neighborSqr));
n.updateWeights(p, learningRate, influence);
}
}
learningRate = this.initialLearningRate * Math.Exp(-(double)itterations / (double)this.totalItterations);
itterations++;
}
public void Epoch(KPoint p)
{
//get a random item
SOMNeuron bmu = GetBestMatchingUnit(p);
neighborhoodRadius = radius * Math.Exp(-(double)itterations / timeConstant);
double neighborSqr = neighborhoodRadius * neighborhoodRadius;
//Now we need to update each neuron
foreach (SOMNeuron n in neurons)
{
double distToBMUSqr = n.position.elucideanDistance(bmu.position);
if (distToBMUSqr <= neighborSqr)
{
influence = Math.Exp(-(distToBMUSqr) / (2 * neighborSqr));
n.updateWeights(p, learningRate, influence);
}
}
learningRate = this.initialLearningRate * Math.Exp(-(double)itterations / (double)this.totalItterations);
itterations++;
}
public HexagonalSelfOrganizingMap(PointSet data, int dimension, double learningRate)
{
_dimension = dimension;
//Get initial learning rate
this.initialLearningRate = learningRate;
this.learningRate = learningRate;
influence = 0;
itterations = 1;
this.data = data;
rng = new Random();
int numNeurons = dimension * dimension;
//Scale each attribute from [0,1] and store the conversion matrix
conversionMatrix = data.GetMinMaxWeights().Max.Coordinates;
foreach (KPoint d in data.PointList)
{
d.Normalize(conversionMatrix);
}
//Here We will initialize Our Neurons
neurons = new List <SOMNeuron>();
KPoint zero = KPoint.Zero(data[0].Dimensions);
KPoint one = KPoint.One(data[0].Dimensions);
double halfNeuronDelta = 1 / Math.Sqrt(3.0);
//Initialize our Quadtree for reference
double centerX = (dimension - 0.5) * (2 * halfNeuronDelta) / 2.0;
double centerY = (dimension - 1.0) / 2.0;
QuadTreePointStruct center = new QuadTreePointStruct()
{
Index = -1, X = centerX, Y = centerY
};
QuadTreePointStruct halfDistance = new QuadTreePointStruct()
{
Index = -1, X = centerX + halfNeuronDelta, Y = centerY + 0.5
};
neuronQuadTree = new QuadTree(new QuadTreeBoundingBox()
{
Center = center, HalfLength = halfDistance
});
int neuronIndex = 0;
for (int r = 0; r < dimension; r++)
{
double xPos = (r % 2 == 1) ? halfNeuronDelta : 0.0;
for (int c = 0; c < dimension; c++)
{
//Generate our neurons
double[] posNeuron = { xPos, (double)r };
SOMNeuron neuron = new SOMNeuron(zero, one, rng, new KPoint(posNeuron), neuronIndex);
QuadTreePointStruct neuronQTPos = new QuadTreePointStruct()
{
Index = neuronIndex,
X = posNeuron[0],
Y = posNeuron[1]
};
neuronQuadTree.Insert(neuronQTPos);
neurons.Add(neuron);
xPos += 2 * halfNeuronDelta;
neuronIndex++;
}
}
//Get the max radius
SetRadius();
timeConstant = (double)totalItterations / Math.Log(radius);
}
public List <Bitmap> GetUMatrix(int cellWidth, int labelCount = 0, int[] labels = null)
{
cellWidth += (cellWidth % 2); //make it even
int halfCellWidth = cellWidth / 2;
double verticalOffset = (double)halfCellWidth / Math.Tan(Math.PI / 6);
double halfVerticalHeight = (double)halfCellWidth / Math.Cos(Math.PI / 6);
List <Bitmap> images = new List <Bitmap>();
List <Graphics> imgGraphics = new List <Graphics>();
Size imageSize = new Size((int)((_dimension + .5) * cellWidth), (int)((_dimension - 1) * verticalOffset + 2 * halfVerticalHeight));
Bitmap img = new Bitmap(imageSize.Width, imageSize.Height);
Bitmap imgPointCount = new Bitmap(imageSize.Width, imageSize.Height);
Graphics g = Graphics.FromImage(img);
Graphics gpc = Graphics.FromImage(imgPointCount);
images.Add(img); images.Add(imgPointCount);
imgGraphics.Add(g); imgGraphics.Add(gpc);
if (labels != null)
{
for (int i = 0; i < labelCount; i++)
{
Bitmap classImg = new Bitmap(imageSize.Width, imageSize.Height);
Graphics classG = Graphics.FromImage(classImg);
images.Add(classImg); imgGraphics.Add(classG);
}
}
//Calculate the distance matrix
double maxValue = 0.0;
double minValue = double.MaxValue;
double[,] weights = new double[_dimension, _dimension];
for (int x = 0; x < _dimension; x++)
{
for (int y = 0; y < _dimension; y++)
{
weights[x, y] = 0;
int[] neighborCoordPairsEven = { x - 1, y - 1, x, y - 1, x - 1, y, x + 1, y, x - 1, y + 1, x, y + 1 };
int[] neighborCoordPairsOdd = { x, y - 1, x + 1, y - 1, x - 1, y, x + 1, y, x, y + 1, x + 1, y + 1 };
SOMNeuron n = neurons[y * _dimension + x];
int neighborCount = 0;
for (int i = 0; i < 12; i += 2)
{
int index = 0;
if (y % 2 == 0)
{
index = GetNeuronIndexFromPos(neighborCoordPairsEven[i], neighborCoordPairsEven[i + 1]);
}
else
{
index = GetNeuronIndexFromPos(neighborCoordPairsOdd[i], neighborCoordPairsOdd[i + 1]);
}
if (index >= 0)
{
weights[x, y] += n.weights.elucideanDistance(neurons[index].weights);
neighborCount++;
}
}
weights[x, y] /= (double)neighborCount;
maxValue = Math.Max(maxValue, weights[x, y]);
minValue = Math.Min(minValue, weights[x, y]);
}
}
//now get neuron matching count
int[] matchingCount = new int[_dimension * _dimension];
int[,] matchingCountSpecific = (labelCount > 0) ? new int[_dimension * _dimension, labelCount] : null;
for (int kIndex = 0; kIndex < data.Count; kIndex++)
{
var bmu = GetBestMatchingUnit(data[kIndex]);
matchingCount[bmu.id]++;
if (labelCount > 0)
{
matchingCountSpecific[bmu.id, labels[kIndex]]++;
}
}
int maxMatchingCount = matchingCount.Max();
int[] maxMatchingLabel = (labelCount > 0) ? new int[labelCount] : null;
if (labelCount > 0)
{
for (int n = 0; n < neurons.Count; n++)
{
for (int l = 0; l < labelCount; l++)
{
maxMatchingLabel[l] = Math.Max(maxMatchingLabel[l], matchingCountSpecific[n, l]);
}
}
}
//Now that we have a weight matrix and the maxvalue
//we can print our nodes!
for (int x = 0; x < _dimension; x++)
{
for (int y = 0; y < _dimension; y++)
{
double distanceShifted = weights[x, y] - minValue;
double intensity = distanceShifted / (maxValue - minValue);
int bright = (int)((1.0 - intensity) * 255.0);
Color c = Color.FromArgb(bright, bright, bright);
Brush b = new SolidBrush(c);
//Calculate the brush for the point matching list
Color cCount = Color.White;
if (matchingCount[x + y * _dimension] > 0)
{
double countIntensity = (matchingCount[x + y * _dimension] - 1.0) / (maxMatchingCount - 1.0);
cCount = LerpColor(Color.Aqua, Color.Red, countIntensity);
}
Brush bC = new SolidBrush(cCount);
//get x
double xAdjusted = (double)x * cellWidth + halfCellWidth;
if (y % 2 == 1)
{
xAdjusted += halfCellWidth;
}
double yAdjusted = (double)y * verticalOffset + halfVerticalHeight;
Point[] hexagon = GetCenteredHexagon(xAdjusted, yAdjusted, halfCellWidth, halfVerticalHeight);
g.FillPolygon(b, hexagon, FillMode.Winding);
gpc.FillPolygon(bC, hexagon, FillMode.Winding);
//Color the specific class images
for (int i = 0; i < labelCount; i++)
{
Color classColor = Color.White;
if (matchingCountSpecific[x + y * _dimension, i] > 0)
{
double countIntensity = (matchingCountSpecific[x + y * _dimension, i] - 1.0) / (maxMatchingLabel[i] - 1.0);
classColor = LerpColor(Color.Aqua, Color.Red, countIntensity);
}
Brush classBrush = new SolidBrush(classColor);
imgGraphics[i + 2].FillPolygon(classBrush, hexagon, FillMode.Winding);
}
}
}
foreach (Graphics gr in imgGraphics)
{
gr.Dispose();
}
return(images);
}
public Bitmap GetUMatrix(int cellWidth, bool use8Neighbors)
{
if (dimensions.Count == 2)
{
Bitmap img = new Bitmap(dimensions[0] * cellWidth, dimensions[1] * cellWidth);
Graphics g = Graphics.FromImage(img);
double maxValue = 0.0;
double minValue = double.MaxValue;
double[,] weights = new double[dimensions[0], dimensions[1]];
for (int x = 0; x < dimensions[0]; x++)
{
for (int y = 0; y < dimensions[1]; y++)
{
weights[x, y] = 0;
SOMNeuron n = neurons[utilGet1dIndex(x, y)];
int neighborCount = 0;
for (int dx = -1; dx <= 1; dx++)
{
for (int dy = -1; dy <= 1; dy++)
{
int nx = x + dx;
int ny = y + dy;
if (!(dx == 0 && dy == 0) && (nx >= 0 && nx < dimensions[0]) && (ny >= 0 && ny < dimensions[1]))
{
if ((!use8Neighbors && (Math.Abs(dx) == Math.Abs(dy))) || use8Neighbors)
{
SOMNeuron no = neurons[utilGet1dIndex(nx, ny)];
weights[x, y] += n.weights.elucideanDistance(no.weights);
neighborCount++;
}
}
}
}
weights[x, y] /= (double)neighborCount;
maxValue = Math.Max(maxValue, weights[x, y]);
minValue = Math.Min(minValue, weights[x, y]);
}
}
//Now that we have a weight matrix and the maxvalue
//we can print our nodes!
for (int x = 0; x < dimensions[0]; x++)
{
for (int y = 0; y < dimensions[1]; y++)
{
double distanceShifted = weights[x, y] - minValue;
double intensity = distanceShifted / (maxValue - minValue);
int bright = (int)((1.0 - intensity) * 255.0);
Color c = Color.FromArgb(bright, bright, bright);
Brush b = new SolidBrush(c);
//get x
g.FillRectangle(b, new Rectangle(new Point(x * cellWidth, y * cellWidth), new Size(cellWidth, cellWidth)));
}
}
return(img);
}
else
{
return(new Bitmap(10, 10));
}
}