protected override Schema.DetachedColumn[] GetOutputColumnsCore()
{
var result = new Schema.DetachedColumn[_parent.ColumnPairs.Length];
for (int i = 0; i < _parent.ColumnPairs.Length; i++)
{
InputSchema.TryGetColumnIndex(_parent.ColumnPairs[i].input, out int colIndex);
Host.Assert(colIndex >= 0);
result[i] = new Schema.DetachedColumn(_parent.ColumnPairs[i].output, _types[i], null);
}
return result;
}
/// <summary>
/// For PCA, the transform equation is y=U^Tx, where "^T" denotes matrix transpose, x is an 1-D vector (i.e., the input column), and U=[u_1, ..., u_PcaNum]
/// is a n-by-PcaNum matrix. The symbol u_k is the k-th largest (in terms of the associated eigenvalue) eigenvector of (1/m)*\sum_{i=1}^m x_ix_i^T,
/// where x_i is the whitened column at the i-th row and we have m rows in the training data.
/// For ZCA, the transform equation is y = US^{-1/2}U^Tx, where U=[u_1, ..., u_n] (we retain all eigenvectors) and S is a diagonal matrix whose i-th
/// diagonal element is the eigenvalues of u_i. The first U^Tx rotates x to another linear space (bases are u_1, ..., u_n), then S^{-1/2} is applied
/// to ensure unit variance, and finally we rotate the scaled result back to the original space using U (note that UU^T is identity matrix so U is
/// the inverse rotation of U^T).
/// </summary>
protected override Schema.DetachedColumn[] GetOutputColumnsCore()
{
var result = new Schema.DetachedColumn[_parent.ColumnPairs.Length];
for (int iinfo = 0; iinfo < _parent.ColumnPairs.Length; iinfo++)
{
InputSchema.TryGetColumnIndex(_parent.ColumnPairs[iinfo].input, out int colIndex);
Host.Assert(colIndex >= 0);
var info = _parent._columns[iinfo];
ColumnType outType = (info.Kind == WhiteningKind.Pca && info.PcaNum > 0) ? new VectorType(NumberType.Float, info.PcaNum) : _srcTypes[iinfo];
result[iinfo] = new Schema.DetachedColumn(_parent.ColumnPairs[iinfo].output, outType, null);
}
return(result);
}
/// <summary>
/// For PCA, the transform equation is y=U^Tx, where "^T" denotes matrix transpose, x is an 1-D vector (i.e., the input column), and U=[u_1, ..., u_PcaNum]
/// is a n-by-PcaNum matrix. The symbol u_k is the k-th largest (in terms of the associated eigenvalue) eigenvector of (1/m)*\sum_{i=1}^m x_ix_i^T,
/// where x_i is the whitened column at the i-th row and we have m rows in the training data.
/// For ZCA, the transform equation is y = US^{-1/2}U^Tx, where U=[u_1, ..., u_n] (we retain all eigenvectors) and S is a diagonal matrix whose i-th
/// diagonal element is the eigenvalues of u_i. The first U^Tx rotates x to another linear space (bases are u_1, ..., u_n), then S^{-1/2} is applied
/// to ensure unit variance, and finally we rotate the scaled result back to the original space using U (note that UU^T is identity matrix so U is
/// the inverse rotation of U^T).
/// </summary>
protected override DataViewSchema.DetachedColumn[] GetOutputColumnsCore()
{
var result = new DataViewSchema.DetachedColumn[_parent.ColumnPairs.Length];
for (int iinfo = 0; iinfo < _parent.ColumnPairs.Length; iinfo++)
{
InputSchema.TryGetColumnIndex(_parent.ColumnPairs[iinfo].inputColumnName, out int colIndex);
Host.Assert(colIndex >= 0);
var info = _parent._columns[iinfo];
DataViewType outType = (info.Kind == WhiteningKind.PrincipalComponentAnalysis && info.Rank > 0) ? new VectorDataViewType(NumberDataViewType.Single, info.Rank) : _srcTypes[iinfo];
result[iinfo] = new DataViewSchema.DetachedColumn(_parent.ColumnPairs[iinfo].outputColumnName, outType, null);
}
return(result);
}
protected override Schema.DetachedColumn[] GetOutputColumnsCore()
{
var result = new Schema.DetachedColumn[_parent.ColumnPairs.Length];
for (int iinfo = 0; iinfo < _infos.Length; iinfo++)
{
InputSchema.TryGetColumnIndex(_infos[iinfo].Input, out int colIndex);
Host.Assert(colIndex >= 0);
var builder = new MetadataBuilder();
builder.Add(InputSchema[colIndex].Metadata, x => x == MetadataUtils.Kinds.SlotNames);
ValueGetter <bool> getter = (ref bool dst) =>
{
dst = true;
};
builder.Add(MetadataUtils.Kinds.IsNormalized, BoolType.Instance, getter);
result[iinfo] = new Schema.DetachedColumn(_infos[iinfo].Output, _infos[iinfo].OutputType, builder.GetMetadata());
}
return(result);
}
public Mapper(VectorWhiteningTransformer parent, Schema inputSchema)
: base(parent.Host.Register(nameof(Mapper)), parent, inputSchema)
{
_parent = parent;
_cols = new int[_parent.ColumnPairs.Length];
_srcTypes = new ColumnType[_parent.ColumnPairs.Length];
for (int i = 0; i < _parent.ColumnPairs.Length; i++)
{
if (!InputSchema.TryGetColumnIndex(_parent.ColumnPairs[i].input, out _cols[i]))
{
throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", _parent.ColumnPairs[i].input);
}
_srcTypes[i] = inputSchema[_cols[i]].Type;
ValidateModel(Host, _parent._models[i], _srcTypes[i]);
if (_parent._columns[i].SaveInv)
{
ValidateModel(Host, _parent._invModels[i], _srcTypes[i]);
}
}
}
