//Initialize by creating random seed centroids and starting the method cascade
public void Init(int k, List <Vector3> input)
{
_simpleDataModel = new SimpleDatamodel();
Clustering = true;
NumberOfClusters();
NumberOfLoops();
k = K;
if (_distanceMatrix != null)
{
Array.Clear(_distanceMatrix, 0, _distanceMatrix.Length);
Array.Clear(_clusterMatrix, 0, _clusterMatrix.Length);
}
_distanceMatrix = new float[k + 1, input.Count + 1];
_clusterMatrix = new Vector3[k + 1, input.Count + 1];
//get the range of the input data
var minVector = Vector3.positiveInfinity;
var maxVector = Vector3.zero;
for (var i = 0; i < input.Count; i++)
{
minVector = input[i].magnitude < minVector.magnitude ? input[i] : minVector;
maxVector = input[i].magnitude > maxVector.magnitude ? input[i] : maxVector;
}
var xMin = minVector.x;
var yMin = minVector.y;
var zMin = minVector.z;
var xMax = maxVector.x;
var yMax = maxVector.y;
var zMax = maxVector.z;
//set the centroids randomly inside the range of the input (minmax)
for (var i = 0; i < K; i++)
{
var randomX = Random.Range(xMin, xMax);
var randomY = Random.Range(yMin, yMax);
var randomZ = Random.Range(zMin, zMax);
var tempRandom = new Vector3(randomX, randomY, randomZ);
_centroids.Add(tempRandom);
}
CalculateDistances();
}
public override bool Process()
{
float[][] data = generateData();
var dataModel = new SimpleDatamodel(); // ReadCsv().MergeDatamodels(_rawInputData);
dataModel.addFloatMatrixColwise(data);
SetOutputData(dataModel);
// Transform data into dataModel here.
// add here data generation
// Analog to Dataloader:
// var dataModel = ReadCsv().MergeDatamodels(_rawInputData);
// SetOutputData(dataModel);
return(true);
}
public void SplitDataset()
{
for (int i = 0; i < _simpleDataModels.Length; i++)
{
_simpleDataModels[i] = new SimpleDatamodel();
}
foreach (var dataItem in _dataItems)
{
if (axis == "X")
{
if (dataItem.GetfirstThreeNumericColsAsVector().x >= threshold)
{
_simpleDataModels[0].Add(dataItem);
}
else
{
_simpleDataModels[1].Add(dataItem);
}
}
else if (axis == "Y")
{
if (dataItem.GetfirstThreeNumericColsAsVector().y >= threshold)
{
_simpleDataModels[0].Add(dataItem);
}
else
{
_simpleDataModels[1].Add(dataItem);
}
}
else if (axis == "Z")
{
if (dataItem.GetfirstThreeNumericColsAsVector().z >= threshold)
{
_simpleDataModels[0].Add(dataItem);
}
else
{
_simpleDataModels[1].Add(dataItem);
}
}
}
//get parent operator index for creating
bool toBreak = false;
foreach (var op in _observer.GetOperatorPrefabs())
{
foreach (var parent in Parents)
{
if (parent.name.Contains(op.name))
{
toBreak = true;
break;
}
}
if (toBreak)
{
break;
}
_parentIndex++;
}
}
private GenericDatamodel ReadCsv()
{
GenericDatamodel dataModel = new SimpleDatamodel();
var pathToData = _path + _filename;
if (File.Exists(pathToData))
{
var fileContent = System.IO.File.ReadAllLines(pathToData);
if (fileContent.Length == 0)
{
throw new FileLoadException("Empty file!");
}
var start = 0;
var attributeTitles = TrimStringArray(fileContent[0].Split(_delimiter.ToCharArray()));
if (!_hasHeader)
{
for (var i = 0; i < attributeTitles.Length; i++)
{
attributeTitles[i] = "Column_" + (i + 1);
}
}
else
{
start++;
}
var datatypes = new DataAttribute.Valuetype[attributeTitles.Length];
var firstRow = TrimStringArray(fileContent[start].Split(_delimiter.ToCharArray()));
for (var i = 0; i < firstRow.Length; i++)
{
datatypes[i] = DataAttribute.GetDataType(firstRow[i]);
}
for (var i = start; i < fileContent.Length; i++)
{
var dataItem = new DataItem();
var attributes = TrimStringArray(fileContent[i].Split(_delimiter.ToCharArray()));
if (attributes.Length != attributeTitles.Length)
{
throw new FileLoadException("Can not load " + pathToData + ". Row " + i + " does not contain the same amount of columns than the first row(" + attributeTitles.Length + ").");
}
;
for (var j = 0; j < attributes.Length; j++)
{
var dataAttribute = new DataAttribute();
dataAttribute.Init(j, attributeTitles[j], attributes[j], datatypes[j]);
dataItem.Add(dataAttribute);
}
dataModel.Add(dataItem);
}
if ((_hasHeader && fileContent.Length - 1 != dataModel.GetDataItems().Count) || (!_hasHeader && fileContent.Length != dataModel.GetDataItems().Count))
{
throw new FileLoadException("Incomplete Parsing! Not all rows were transformed imported as data items!");
}
;
return(dataModel);
}
else
{
throw new FileLoadException("Did not find file '" + pathToData + "'.");
}
}