public FCMSolution(MethodInputResponse input, int clustersNumber)
: base(input, clustersNumber)
{
// random initialization:
Random gen = new Random();
// initialize random cluster prototypes:
for (int j = 0; j < clustersNumber; j++)
{
InputObject prototype = new InputObject("");
foreach (string dimension in input.dimensionsLowerBounds.Keys)
{
prototype.Dimensions.Add(dimension, input.dimensionsLowerBounds[dimension] + gen.NextDouble() * (input.dimensionsUpperBounds[dimension] - input.dimensionsLowerBounds[dimension]));
}
clusterPrototypes[j] = prototype;
}
// initialize random primary memberships:
for (int j = 0; j < input.InputObjects.Count; j++)
{
double[] fuzzyMarkers = new double[clustersNumber + 1];
fuzzyMarkers[0] = 0;
fuzzyMarkers[clustersNumber] = 1;
for (int mcount = 1; mcount < clustersNumber; mcount++)
do
{
fuzzyMarkers[mcount] = gen.NextDouble();
} while (fuzzyMarkers[mcount] <= fuzzyMarkers[mcount - 1]);
for (int mcount = 0; mcount < clustersNumber; mcount++)
primaryMemberships[mcount, j] = fuzzyMarkers[mcount + 1] - fuzzyMarkers[mcount];
}
setTarget();
}
public InputObject Clone()
{
InputObject cloneObject = new InputObject(Name);
foreach (string dimensionName in Dimensions.Keys)
{
cloneObject.Dimensions.Add(dimensionName, Dimensions[dimensionName]);
}
return cloneObject;
}
public PCMSolution(FCMSolution init, MethodInputResponse input, int clustersCount)
: base(input, clustersCount)
{
eta = new double[clustersNumber];
D = new double[clustersNumber, methodInput.InputObjects.Count];
// initialize cluster prototypes (as a result of FCM):
for (int i = 0; i < clustersNumber; i++)
{
clusterPrototypes[i] = new InputObject("");
clusterPrototypes[i].Dimensions = init.ClusterPrototypes[i].Dimensions;
}
// initialize primary memberships (the same way):
for (int i = 0; i < methodInput.InputObjects.Count; i++)
for (int j = 0; j < clustersNumber; j++)
primaryMemberships[j, i] = init.PrimaryMemberships[j, i];
setD();
setTarget();
}
private InputObject VectorXnumber(InputObject vector, double number)
{
InputObject result = new InputObject("");
foreach (string dimension in vector.Dimensions.Keys)
{
result.Dimensions.Add(dimension, vector.Dimensions[dimension] * number);
}
return result;
}
private InputObject VectorPlusVector(InputObject vector1, InputObject vector2)
{
InputObject result = new InputObject("");
foreach (string dimension in vector1.Dimensions.Keys)
{
result.Dimensions.Add(dimension, vector1.Dimensions[dimension] + vector2.Dimensions[dimension]);
}
return result;
}
protected double GetSqrEuclideanDistance(InputObject x, InputObject centre)
{
double dist = 0;
foreach (string dimension in x.Dimensions.Keys)
{
dist += Math.Pow(x.Dimensions[dimension] - centre.Dimensions[dimension], 2);
}
return dist;
}
public double GetSignedEuclidean(InputObject x, InputObject centre)
{
double dist = 0;
foreach (string dimension in x.Dimensions.Keys)
{
double unsignedDistance = Math.Pow(x.Dimensions[dimension] - centre.Dimensions[dimension], 2);
if (x.Dimensions[dimension] > centre.Dimensions[dimension])
{
dist += unsignedDistance;
}
else
{
dist -= unsignedDistance;
}
}
return dist;
}
private MethodInputResponse GetInput()
{
try
{
MethodInputResponse inputParameters = new MethodInputResponse();
//count objects:
myExcel.Range nameCell = m_ObjWorkSheet1.get_Range("A2", "A2");
int rowNumber = 2;
while (nameCell.Text.ToString() != "")
{
//inputParameters.ObjectsQuantity++;
InputObject newInputObject = new InputObject(nameCell.Text.ToString());// What's in the A column starting row 2
//get the dimensions now; start with the B1 (C1?) and go on along row 1 till run into an empty cell
// myExcel.Range dimensionCell = m_ObjWorkSheet1.get_Range("B2", "B2");
int columnNumber = 2;
myExcel.Range dimensionCell = m_ObjWorkSheet1.get_Range(MethodInputResponse.ALPHABET[columnNumber] + rowNumber.ToString(), MethodInputResponse.ALPHABET[columnNumber] + rowNumber.ToString());
while (!String.IsNullOrEmpty(dimensionCell.Text.ToString()))
{
string dimensionName = "Dimension" + (columnNumber-1);
newInputObject.Dimensions.Add(dimensionName, double.Parse(dimensionCell.Text.ToString()));
//handle bounds
double dimensionLowestValue, dimensionHighestValue;
if (inputParameters.dimensionsLowerBounds.TryGetValue(dimensionName, out dimensionLowestValue))
{
if (dimensionLowestValue > newInputObject.Dimensions[dimensionName])
{
inputParameters.dimensionsLowerBounds[dimensionName] = newInputObject.Dimensions[dimensionName];
}
}
else
{
inputParameters.dimensionsLowerBounds.Add(dimensionName, double.PositiveInfinity);
}
if (inputParameters.dimensionsUpperBounds.TryGetValue(dimensionName, out dimensionHighestValue))
{
if (dimensionHighestValue < newInputObject.Dimensions[dimensionName])
{
inputParameters.dimensionsUpperBounds[dimensionName] = newInputObject.Dimensions[dimensionName];
}
}
else
{
inputParameters.dimensionsUpperBounds.Add(dimensionName, double.NegativeInfinity);
}
columnNumber++;
dimensionCell = m_ObjWorkSheet1.get_Range(MethodInputResponse.ALPHABET[columnNumber] + rowNumber.ToString(), MethodInputResponse.ALPHABET[columnNumber] + rowNumber.ToString());
}
inputParameters.InputObjects.Add(newInputObject);
rowNumber++;
nameCell = m_ObjWorkSheet1.get_Range("A" + rowNumber.ToString(), "A" + rowNumber.ToString());
}
return inputParameters;
}
catch (Exception ex)
{
MessageBox.Show("Exception occured while reading input data " + ex.ToString());
return new MethodInputResponse();
}
}
public double GetKwonIndex()
{
InputObject meanPrototypes = new InputObject("");
foreach ( string dimension in methodInput.dimensionsLowerBounds.Keys)
{
double tempDimension = 0;
for (int i = 0; i < clustersNumber; i++)
tempDimension += clusterPrototypes[i].Dimensions[dimension];
meanPrototypes.Dimensions[dimension] = tempDimension / clustersNumber;
}
double minDist = double.PositiveInfinity;
for (int i = 0; i < clustersNumber; i++)
for (int j = 0; j < clustersNumber; j++)
if ((i != j) && (GetSqrEuclideanDistance(clusterPrototypes[i], clusterPrototypes[j]) < minDist))
minDist = GetSqrEuclideanDistance(clusterPrototypes[i], clusterPrototypes[j]);
double summand1 = 0;
for (int i = 0; i < clustersNumber; i++)
for (int j = 0; j < methodInput.InputObjects.Count; j++)
summand1 += Math.Pow(primaryMemberships[i, j], m) * GetSqrEuclideanDistance(methodInput.InputObjects[j], clusterPrototypes[i]);
double summand2 = 0;
for (int i = 0; i < clustersNumber; i++)
summand2 += GetSqrEuclideanDistance(clusterPrototypes[i], meanPrototypes);
summand2 /= clustersNumber;
KwonIndex = (summand1 + summand2) / minDist;
return KwonIndex;
}
