internal ColumnInfoBase(ModelLoadContext ctx, string input, string output, bool normKindSerialized)
{
Contracts.AssertValue(ctx);
Contracts.CheckNonWhiteSpace(input, nameof(input));
Contracts.CheckNonWhiteSpace(output, nameof(output));
Input = input;
Output = output;
// *** Binary format ***
// byte: SubtractMean
// byte: NormKind
// Float: Scale
SubtractMean = ctx.Reader.ReadBoolByte();
byte normKindVal = ctx.Reader.ReadByte();
Contracts.CheckDecode(Enum.IsDefined(typeof(LpNormalizingEstimatorBase.NormalizerKind), normKindVal));
NormKind = (LpNormalizingEstimatorBase.NormalizerKind)normKindVal;
// Note: In early versions, a bool option (useStd) to whether to normalize by StdDev rather than
// L2 norm was used. normKind was added in version=verVectorNormalizerSupported.
// normKind was defined in a way such that the serialized boolean (0: use StdDev, 1: use L2) is
// still valid.
Contracts.CheckDecode(normKindSerialized ||
(NormKind == LpNormalizingEstimatorBase.NormalizerKind.L2Norm || NormKind == LpNormalizingEstimatorBase.NormalizerKind.StdDev));
Scale = ctx.Reader.ReadFloat();
Contracts.CheckDecode(0 < Scale && Scale < float.PositiveInfinity);
}
internal ColumnInfoBase(string input, string output, bool substractMean, LpNormalizingEstimatorBase.NormalizerKind normalizerKind, float scale)
{
Contracts.CheckNonWhiteSpace(input, nameof(input));
Contracts.CheckNonWhiteSpace(output, nameof(output));
Input = input;
Output = output;
SubtractMean = substractMean;
Contracts.CheckUserArg(0 < scale && scale < float.PositiveInfinity, nameof(scale), "scale must be a positive finite value");
Scale = scale;
NormKind = normalizerKind;
}
/// <include file='doc.xml' path='doc/members/member[@name="LpNormalize"]/*'/>
/// <param name="input">The column to apply to.</param>
/// <param name="normKind">Type of norm to use to normalize each sample.</param>
/// <param name="subMean">Subtract mean from each value before normalizing.</param>
public static Vector <float> LpNormalize(this Vector <float> input,
LpNormalizingEstimatorBase.NormalizerKind normKind = LpNormalizingEstimatorBase.Defaults.NormKind,
bool subMean = LpNormalizingEstimatorBase.Defaults.LpSubstractMean) => new OutPipelineColumn(input, normKind, subMean);
public Reconciler(LpNormalizingEstimatorBase.NormalizerKind normKind, bool subMean)
{
_normKind = normKind;
_subMean = subMean;
}
public OutPipelineColumn(Vector <float> input, LpNormalizingEstimatorBase.NormalizerKind normKind, bool subMean)
: base(new Reconciler(normKind, subMean), input)
{
Input = input;
}
/// <summary>
/// Describes how the transformer handles one LpNorm column pair.
/// </summary>
/// <param name="input">Name of input column.</param>
/// <param name="output">Name of output column.</param>
/// <param name="substractMean">Subtract mean from each value before normalizing.</param>
/// <param name="normalizerKind">The norm to use to normalize each sample.</param>
public LpNormColumnInfo(string input, string output,
bool substractMean = LpNormalizingEstimatorBase.Defaults.LpSubstractMean,
LpNormalizingEstimatorBase.NormalizerKind normalizerKind = LpNormalizingEstimatorBase.Defaults.NormKind)
: base(input, output, substractMean, normalizerKind, 1)
{
}
/// <summary>
/// Takes column filled with a vector of floats and computes L-p norm of it.
/// </summary>
/// <param name="catalog">The transform's catalog.</param>
/// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
/// <param name="normKind">Type of norm to use to normalize each sample.</param>
/// <param name="subMean">Subtract mean from each value before normalizing.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LpNormalizingEstimator LpNormalize(this TransformsCatalog.ProjectionTransforms catalog, string outputColumnName, string inputColumnName = null,
LpNormalizingEstimatorBase.NormalizerKind normKind = LpNormalizingEstimatorBase.Defaults.NormKind, bool subMean = LpNormalizingEstimatorBase.Defaults.LpSubstractMean)
=> new LpNormalizingEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, inputColumnName, normKind, subMean);
/// <summary>
/// Describes how the transformer handles one LpNorm column pair.
/// </summary>
/// <param name="name">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="name"/> will be used as source.</param>
/// <param name="substractMean">Subtract mean from each value before normalizing.</param>
/// <param name="normalizerKind">The norm to use to normalize each sample.</param>
public LpNormColumnInfo(string name, string inputColumnName = null,
bool substractMean = LpNormalizingEstimatorBase.Defaults.LpSubstractMean,
LpNormalizingEstimatorBase.NormalizerKind normalizerKind = LpNormalizingEstimatorBase.Defaults.NormKind)
: base(name, inputColumnName ?? name, substractMean, normalizerKind, 1)
{
}
