/// <inheritdoc />
public INormalizationStrategy SuggestNormalizationStrategy(VersatileMLDataSet dataset, String architecture)
{
double inputLow = -1;
double inputHigh = 1;
double outputLow = -1;
double outputHigh = 1;
// Create a basic neural network, just to examine activation functions.
var methodFactory = new MLMethodFactory();
var network = (BasicNetwork)methodFactory.Create(MethodName, architecture, 1, 1);
if (network.LayerCount < 1)
{
throw new EncogError("Neural network does not have an output layer.");
}
IActivationFunction outputFunction = network.GetActivation(network.LayerCount - 1);
double[] d = { -1000, -100, -50 };
outputFunction.ActivationFunction(d, 0, d.Length);
if (d[0] > 0 && d[1] > 0 && d[2] > 0)
{
inputLow = 0;
}
INormalizationStrategy result = new BasicNormalizationStrategy(
inputLow,
inputHigh,
outputLow,
outputHigh);
return(result);
}
/// <inheritdoc />
public INormalizationStrategy SuggestNormalizationStrategy(VersatileMLDataSet dataset, String architecture)
{
var result = new BasicNormalizationStrategy();
result.AssignInputNormalizer(ColumnType.Continuous, new RangeNormalizer(0, 1));
result.AssignInputNormalizer(ColumnType.Nominal, new OneOfNNormalizer(0, 1));
result.AssignInputNormalizer(ColumnType.Ordinal, new OneOfNNormalizer(0, 1));
result.AssignOutputNormalizer(ColumnType.Continuous, new RangeNormalizer(0, 1));
result.AssignOutputNormalizer(ColumnType.Nominal, new OneOfNNormalizer(0, 1));
result.AssignOutputNormalizer(ColumnType.Ordinal, new OneOfNNormalizer(0, 1));
return(result);
}
/// <inheritdoc />
public INormalizationStrategy SuggestNormalizationStrategy(VersatileMLDataSet dataset, string architecture)
{
int outputColumns = dataset.NormHelper.OutputColumns.Count;
ColumnType ct = dataset.NormHelper.OutputColumns[0].DataType;
var result = new BasicNormalizationStrategy();
result.AssignInputNormalizer(ColumnType.Continuous, new RangeNormalizer(0, 1));
result.AssignInputNormalizer(ColumnType.Nominal, new OneOfNNormalizer(0, 1));
result.AssignInputNormalizer(ColumnType.Ordinal, new OneOfNNormalizer(0, 1));
result.AssignOutputNormalizer(ColumnType.Continuous, new RangeNormalizer(0, 1));
result.AssignOutputNormalizer(ColumnType.Nominal, new OneOfNNormalizer(0, 1));
result.AssignOutputNormalizer(ColumnType.Ordinal, new OneOfNNormalizer(0, 1));
return(result);
}
/// <inheritdoc />
public INormalizationStrategy SuggestNormalizationStrategy(VersatileMLDataSet dataset, string architecture)
{
int outputColumns = dataset.NormHelper.OutputColumns.Count;
if (outputColumns > 1)
{
throw new EncogError("SVM does not support multiple output columns.");
}
ColumnType ct = dataset.NormHelper.OutputColumns[0].DataType;
var result = new BasicNormalizationStrategy();
result.AssignInputNormalizer(ColumnType.Continuous, new RangeNormalizer(0, 1));
result.AssignInputNormalizer(ColumnType.Nominal, new OneOfNNormalizer(0, 1));
result.AssignInputNormalizer(ColumnType.Ordinal, new OneOfNNormalizer(0, 1));
result.AssignOutputNormalizer(ColumnType.Continuous, new RangeNormalizer(0, 1));
result.AssignOutputNormalizer(ColumnType.Nominal, new IndexedNormalizer());
result.AssignOutputNormalizer(ColumnType.Ordinal, new OneOfNNormalizer(0, 1));
return(result);
}
