public FloatColumn normalizeFrequencyMinMax()
{
float[] newValues = new float[values.Count];
float[] frequencies = Functions.map((x => (float)x), ((StringValueSet)valueSet).valueOccurences.ToArray());
float max = (float)Functions.max(frequencies);
float min = (float)Functions.min(frequencies);
float mid = (max + min) / 2.0f;
max = max - mid;
if (max != 0.0f)
{
frequencies = Functions.map((x => (x - mid) / max), frequencies);
}
else
{
frequencies = Functions.initArray(frequencies.Length, 0.0f);
}
newValues = Functions.map((x => frequencies[((StringValueSet)valueSet).values.IndexOf(x)]), values.ToArray());
FloatValueSet normalizedValueSet = new FloatValueSet(attributeName);
normalizedValueSet.max = 1.0f;
normalizedValueSet.mid = 0.0f;
normalizedValueSet.min = -1.0f;
normalizedValueSet.range = 2.0f;
return(new FloatColumn(attributeName, newValues, normalizedValueSet));
}
public override FloatColumn normalizeMinMax()
{
float[] newValues = new float[values.Count];
List <string> columnValues = ((StringValueSet)valueSet).values;
float max = columnValues.Count - 1;
float mid = max / 2.0f;
max = max - mid;
newValues = Functions.map((x => (float)columnValues.IndexOf(x)), values.ToArray());
if (max != 0.0f)
{
newValues = Functions.map((x => (x - mid) / max), newValues);
}
else
{
newValues = Functions.initArray(newValues.Length, 0.0f);
}
FloatValueSet normalizedValueSet = new FloatValueSet(attributeName);
normalizedValueSet.max = 1.0f;
normalizedValueSet.mid = 0.0f;
normalizedValueSet.min = -1.0f;
normalizedValueSet.range = 2.0f;
return(new FloatColumn(attributeName, newValues, normalizedValueSet));
}
public override FloatColumn normalizeZScore()
{
float[] newValues = Functions.map((x => x ? 1.0f : -1.0f), values.ToArray());
FloatValueSet normalizedValueSet = new FloatValueSet(attributeName);
normalizedValueSet.mean = Functions.mean(newValues);
normalizedValueSet.standardDeviation = Functions.standardDeviation(newValues, normalizedValueSet.mean);
float trueZscore = (1.0f - normalizedValueSet.mean) / normalizedValueSet.standardDeviation;
float falseZscore = (-1.0f - normalizedValueSet.mean) / normalizedValueSet.standardDeviation;
newValues = Functions.map((x => (x) ? trueZscore : falseZscore), values.ToArray());
return(new FloatColumn(attributeName, newValues, normalizedValueSet));
}
public FloatColumn(string attributeName, float[] values)
{
this.attributeName = attributeName;
FloatValueSet arrayValueSet = new FloatValueSet(attributeName);
arrayValueSet.max = Functions.max(values);
arrayValueSet.min = Functions.min(values);
arrayValueSet.mid = (arrayValueSet.max + arrayValueSet.min) / 2.0f;
arrayValueSet.range = arrayValueSet.max - arrayValueSet.min;
arrayValueSet.mean = Functions.mean(values);
arrayValueSet.standardDeviation = Functions.standardDeviation(values, arrayValueSet.mean);
this.valueSet = arrayValueSet;
this.values = Vector <float> .Build.DenseOfArray(values);
}
public override FloatColumn normalizeMinMax()
{
float[] newValues = Functions.map((x => x ? 1.0f : -1.0f), values.ToArray());
FloatValueSet normalizedValueSet = new FloatValueSet(attributeName);
normalizedValueSet.max = 1.0f;
normalizedValueSet.mid = 0.0f;
normalizedValueSet.min = -1.0f;
normalizedValueSet.range = 2.0f;
normalizedValueSet.mean = Functions.mean(newValues);
normalizedValueSet.standardDeviation = Functions.standardDeviation(newValues, normalizedValueSet.mean);
return(new FloatColumn(attributeName, newValues, normalizedValueSet));
}
public override FloatColumn normalizeZScore()
{
FloatValueSet normalizedValueSet = new FloatValueSet(attributeName);
normalizedValueSet.mean = Functions.mean(values.ToArray());
normalizedValueSet.standardDeviation = Functions.standardDeviation(values.ToArray(), normalizedValueSet.mean);
float[] newValues = Functions.map((x => (x - normalizedValueSet.mean) / normalizedValueSet.standardDeviation), values.ToArray());
normalizedValueSet.max = Functions.max(newValues);
normalizedValueSet.min = Functions.min(newValues);
normalizedValueSet.mid = (normalizedValueSet.max + normalizedValueSet.min) / 2.0f;
normalizedValueSet.range = normalizedValueSet.max - normalizedValueSet.min;
return(new FloatColumn(attributeName, newValues, normalizedValueSet));
}
public override FloatColumn normalizeZScore()
{
List <string> columnValues = ((StringValueSet)valueSet).values;
float[] frequencies = Functions.map((x => (float)x), (((StringValueSet)valueSet).valueOccurences.ToArray()));
float mean = Functions.mean(frequencies);
float standardDeviation = Functions.standardDeviation(frequencies, mean);
float[] zscores = Functions.map((x => (x - mean) / standardDeviation), frequencies);
FloatValueSet normalizedValueSet = new FloatValueSet(attributeName);
normalizedValueSet.max = Functions.max(zscores);
normalizedValueSet.min = Functions.min(zscores);
normalizedValueSet.mid = (normalizedValueSet.max + normalizedValueSet.min) / 2.0f;
normalizedValueSet.range = normalizedValueSet.max - normalizedValueSet.min;
normalizedValueSet.mean = mean;
normalizedValueSet.standardDeviation = standardDeviation;
return(new FloatColumn(attributeName, Functions.map((x => zscores[columnValues.IndexOf(x)]), values.ToArray()), normalizedValueSet));
}
public override FloatColumn normalizeMinMax()
{
FloatValueSet normalizedValueSet = new FloatValueSet(attributeName);
float[] newValues = values.ToArray();
float max = Functions.max(newValues);
float min = Functions.min(newValues);
float mid = (max + min) / 2.0f;
max = max - mid;
newValues = Functions.map((x => (x - mid) / max), newValues);
normalizedValueSet.max = 1.0f;
normalizedValueSet.min = -1.0f;
normalizedValueSet.mid = 0.0f;
normalizedValueSet.range = 2.0f;
normalizedValueSet.mean = Functions.mean(newValues);
normalizedValueSet.standardDeviation = Functions.standardDeviation(newValues, normalizedValueSet.mean);
return(new FloatColumn(attributeName, newValues, normalizedValueSet));
}
public FloatColumn(string attributeName, float[] values, FloatValueSet valueSet)
{
this.attributeName = attributeName;
this.valueSet = valueSet;
this.values = Vector <float> .Build.DenseOfArray(values);
}
