private float[] NormaliseCol(float[,] dataArray, DataMetadata metadataPreset, int col)
{
//for each dimensions (column) normalise all data
float[] result = GetCol(dataArray, col);
float minDimension = result.Min();
float maxDimension = result.Max();
if (minDimension == maxDimension)
{
// where there are no distinct values, need the dimension to be distinct
// otherwise lots of maths breaks with division by zero, etc.
// this is the most elegant hack I could think of, but should be fixed properly in future
minDimension -= 1.0f;
maxDimension += 1.0f;
}
DataSource.DimensionData.Metadata metadata = dimensionData[col].MetaData;
metadata.minValue = minDimension;
metadata.maxValue = maxDimension;
metadata.categories = result.Distinct().Select(x => normaliseValue(x, minDimension, maxDimension, 0.0f, 1.0f)).ToArray();
metadata.categoryCount = metadata.categories.Count();
metadata.binCount = (int)(maxDimension - minDimension + 1);
if (metadataPreset != null)
{
foreach (var binSizePreset in metadataPreset.BinSizePreset)
{
if (binSizePreset.index == col)
{
metadata.binCount = binSizePreset.binCount;
}
}
}
dimensionData[col].setMetadata(metadata);
for (int j = 0; j < result.Length; j++)
{
if (minDimension < maxDimension)
{
result[j] = normaliseValue(result[j], minDimension, maxDimension, 0f, 1f);
}
else
{
// avoid NaNs or nonsensical normalization
result[j] = 0;
}
}
return result;
}
/// <summary>
/// internal function: normalises all the data input between 0 and 1
/// </summary>
private float[,] normaliseArray(float[,] dataArray, DataMetadata metadataPreset)
{
//1 make a copy of the parsed array
float[,] normArray = new float[dataArray.GetUpperBound(0) + 1, dataArray.GetUpperBound(1) + 1];
//for each dimensions (column) normalise all data
for (int i = 0; i <= normArray.GetUpperBound(1); i++)
{
float[] rawDimension = GetCol(dataArray, i);
float minDimension = rawDimension.Min();
float maxDimension = rawDimension.Max();
DataSource.DimensionData.Metadata metadata = dimensionData[i].MetaData;
metadata.minValue = minDimension;
metadata.maxValue = maxDimension;
metadata.binCount = (int)(maxDimension - minDimension + 1);
if (metadataPreset != null)
{
foreach (var binSizePreset in metadataPreset.BinSizePreset)
{
if (binSizePreset.index == i)
{
metadata.binCount = binSizePreset.binCount;
}
}
}
dimensionData[i].setMetadata(metadata);
float[] normalisedDimension = new float[rawDimension.Length];
// dimensionsRange.Add(i, new Vector2(minDimension, maxDimension));
for (int j = 0; j < rawDimension.Length; j++)
{
if (minDimension < maxDimension)
{
normalisedDimension[j] = normaliseValue(rawDimension[j], minDimension, maxDimension, 0f, 1f);
}
else
{
// avoid NaNs or nonsensical normalization
normalisedDimension[j] = 0;
}
}
SetCol<float>(normArray, i, normalisedDimension);
}
return normArray;
}
private float[] NormaliseCol(float[,] dataArray, DataMetadata metadataPreset, int col)
{
//for each dimensions (column) normalise all data
float[] result = GetCol(dataArray, col);
float minDimension = result.Min();
float maxDimension = result.Max();
DataSource.DimensionData.Metadata metadata = dimensionData[col].MetaData;
metadata.minValue = minDimension;
metadata.maxValue = maxDimension;
metadata.categories = result.Distinct().Select(x => normaliseValue(x, minDimension, maxDimension, 0.0f, 1.0f)).ToArray();
metadata.categoryCount = result.Distinct().Count();
metadata.binCount = (int)(maxDimension - minDimension + 1);
if (metadataPreset != null)
{
foreach (var binSizePreset in metadataPreset.BinSizePreset)
{
if (binSizePreset.index == col)
{
metadata.binCount = binSizePreset.binCount;
}
}
}
dimensionData[col].setMetadata(metadata);
for (int j = 0; j < result.Length; j++)
{
if (minDimension < maxDimension)
{
result[j] = normaliseValue(result[j], minDimension, maxDimension, 0f, 1f);
}
else
{
// avoid NaNs or nonsensical normalization
result[j] = 0;
}
}
return result;
}
