public static IEstimator <ITransformer> _NormalizeLpNorm(this MLContext MLContext, JToken componentObject)
{
string outputColumn = componentObject.Value <string>("OutputColumnName");
string inputColumn = componentObject.Value <string>("InputColumnName");
NormFunction normFunction = Enum.Parse <NormFunction>(componentObject.Value <string>("NormFunction"));
bool ensureZeroMean = componentObject.Value <bool>("EnsureZeroMean");
return(MLContext.Transforms.NormalizeLpNorm(outputColumn, inputColumn, normFunction, ensureZeroMean));
}
public TransformApplierParams(TextFeaturizingEstimator parent)
{
var host = parent._host;
host.Check(Enum.IsDefined(typeof(Language), parent.OptionalSettings.Language));
host.Check(Enum.IsDefined(typeof(CaseMode), parent.OptionalSettings.CaseMode));
WordExtractorFactory = parent._wordFeatureExtractor?.CreateComponent(host, parent._dictionary);
CharExtractorFactory = parent._charFeatureExtractor?.CreateComponent(host, parent._dictionary);
Norm = parent.OptionalSettings.Norm;
Language = parent.OptionalSettings.Language;
UsePredefinedStopWordRemover = parent.OptionalSettings.UsePredefinedStopWordRemover;
TextCase = parent.OptionalSettings.CaseMode;
KeepDiacritics = parent.OptionalSettings.KeepDiacritics;
KeepPunctuations = parent.OptionalSettings.KeepPunctuations;
KeepNumbers = parent.OptionalSettings.KeepNumbers;
OutputTextTokens = parent.OptionalSettings.OutputTokens;
Dictionary = parent._dictionary;
}
internal static List <List <double> > NormalizeArray(List <List <double> > inArray, NormFunction func, bool DaedalusTrainingSet = true)
{
if (func == null)
{
func = MinMaxNorm;
}
List <List <Double> > ret = new List <List <double> >(inArray.Count);
if (DaedalusTrainingSet || Stats == null)///get the stats from the array, and use it for all resulting data (eg, get stats from training set and then apply it to testing set)
{
Stats = new List <List <double> >();
for (int i = 0; i < inArray[0].Count; ++i)
{
double[] column = Util.Flatten2dArray(pullColumnFromArray(inArray, i));
Stats.Add(GetStats(column));
}
}
else
{
//Testing set- get min and max from testing set
for (int i = 0; i < inArray[0].Count; ++i)
{
double[] column = Util.Flatten2dArray(pullColumnFromArray(inArray, i));
List <Double> temp = GetStats(column);
Stats[i][Mins] = Math.Min(temp[Mins], Stats[i][Mins]);
Stats[i][Maxes] = Math.Max(temp[Maxes], Stats[i][Maxes]);
}
}
foreach (List <Double> l in inArray)
{
ret.Add(func(l, Stats));
}
return(ret);
}
