Libraries/Microsoft.ML.Transforms.xml
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<doc> <assembly> <name>Microsoft.ML.Transforms</name> </assembly> <members> <member name="T:Microsoft.ML.Runtime.Data.BootstrapSampleTransform"> <summary> This class approximates bootstrap sampling of a dataview. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.CategoricalHashTransform.Arguments"> <summary> This class is a merger of <see cref="T:Microsoft.ML.Runtime.Data.HashTransform.Arguments"/> and <see cref="T:Microsoft.ML.Runtime.Data.KeyToVectorTransform.Arguments"/> with join option removed </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.CategoricalTransform"> <summary> Categorical trans. Each column can specify an output kind, Bag, Ind, or Key. Notes: * Each column builds/uses exactly one "vocabulary" (dictionary). * The Key output kind produces integer values and KeyType columns. * The Key value is the one-based index of the slot set in the Ind/Bag options. * In the Key option, not found is assigned the value zero. * In the Ind/Bag options, not found results in an all zero bit vector. * Ind and Bag differ simply in how the bit-vectors generated from individual slots are aggregated: for Ind they are concatenated and for Bag they are added. * When the source column is a singleton, the Ind and Bag options are identical. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.CountFeatureSelectionTransform"> <summary> Selects the slots for which the count of non-default values is greater than a threshold. Uses a set of aggregators to count the number of non-default values for each slot and instantiates a DropSlots transform to actually drop the slots. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.CountFeatureSelectionTransform.Create(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.CountFeatureSelectionTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Create method corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.CountFeatureSelectionUtils.Train(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.IDataView,System.String[],System.Int32[]@)"> <summary> Returns the feature selection scores for each slot of each column. </summary> <param name="env">The host environment.</param> <param name="input">The input dataview.</param> <param name="columns">The columns for which to compute the feature selection scores.</param> <param name="colSizes">Outputs an array containing the vector sizes of the input columns</param> <returns>A list of scores.</returns> </member> <member name="T:Microsoft.ML.Runtime.Data.SignatureFourierDistributionSampler"> <summary> Signature for an IFourierDistributionSampler constructor. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.LpNormNormalizerTransform"> <summary> Lp-Norm (vector/row-wise) normalization transform. Has the following two set of arguments: 1- Lp-Norm normalizer arguments: Normalize rows individually by rescaling them to unit norm (L2, L1 or LInf). Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D is either L2 norm, L1 norm or LInf norm. Scaling inputs to unit norms is a common operation for text classification or clustering. 2- Global contrast normalization (GCN) arguments: Performs the following operation on a vector X: Y = (s * X - M) / D, where s is a scale, M is mean and D is either L2 norm or standard deviation. Usage examples and Matlab code: <see href="http://www.cs.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf"/> </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.LpNormNormalizerTransform.NormalizerKind"> <summary> The kind of unit norm vectors are rescaled to. This enumeration is serialized. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.LpNormNormalizerTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.LpNormNormalizerTransform.GcnArguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.LpNormNormalizerTransform.StdDev(System.Single[],System.Int32,System.Int32)"> <summary> Compute Standard Deviation. In case of both subMean and useStd are true, we technically need to compute variance based on centered values (i.e. after subtracting the mean). But since the centered values mean is approximately zero, we can use variance of non-centered values. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.LpNormNormalizerTransform.StdDev(System.Single[],System.Int32,System.Int32,System.Single)"> <summary> Compute Standard Deviation. We have two overloads of StdDev instead of one with <see cref="T:System.Nullable`1"/> mean for perf reasons. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.LpNormNormalizerTransform.L2Norm(System.Single[],System.Int32,System.Single)"> <summary> Compute L2-norm. L2-norm computation doesn't subtract the mean from the source values. However, we substract the mean here in case subMean is true (if subMean is false, mean is zero). </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.LpNormNormalizerTransform.L1Norm(System.Single[],System.Int32,System.Single)"> <summary> Compute L1-norm. L1-norm computation doesn't subtract the mean from the source values. However, we substract the mean here in case subMean is true (if subMean is false, mean is zero). </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.LpNormNormalizerTransform.LInfNorm(System.Single[],System.Int32,System.Single)"> <summary> Compute LInf-norm. LInf-norm computation doesn't subtract the mean from the source values. However, we substract the mean here in case subMean is true (if subMean is false, mean is zero). </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.GroupTransform"> <summary> This transform essentially performs the following SQL-like operation: SELECT GroupKey1, GroupKey2, ... GroupKeyK, LIST(Value1), LIST(Value2), ... LIST(ValueN) FROM Data GROUP BY GroupKey1, GroupKey2, ... GroupKeyK. It assumes that the group keys are contiguous (if a new group key sequence is encountered, the group is over). The GroupKeyN and ValueN columns can be of any primitive types. The code requires that every raw type T of the group key column is an <see cref="T:System.IEquatable`1"/>, which is currently true for all existing primitive types. The produced ValueN columns will be variable-length vectors of the original value column types. The order of ValueN entries in the lists is preserved. Example: User Item Pete Book Tom Table Tom Kitten Pete Chair Pete Cup Result: User Item Pete [Book] Tom [Table, Kitten] Pete [Chair, Cup] </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.GroupTransform.GroupSchema"> <summary> For group columns, the schema information is intact. For keep columns, the type is Vector of original type and variable length. The only metadata preserved is the KeyNames and IsNormalized. All other columns are dropped. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.GroupTransform.Cursor"> <summary> This cursor will create two cursors on the input data view: - The leading cursor will activate all the group columns, and will advance until it hits the end of the contiguous group. - The trailing cursor will activate all the requested columns, and will go through the group (as identified by the leading cursor), and aggregate the keep columns. The getters are as follows: - The group column getters are taken directly from the trailing cursor. - The keep column getters are provided by the aggregators. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.GroupTransform.Cursor.GroupKeyColumnChecker"> <summary> This class keeps track of the previous group key and tests the current group key against the previous one. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.GroupTransform.Cursor.KeepColumnAggregator"> <summary> This class handles the aggregation of one 'keep' column into a vector. It wraps around an <see cref="T:Microsoft.ML.Runtime.Data.IRow"/>'s column, reads the data and aggregates. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.HashJoinTransform"> <summary> This transform hashes its input columns. Each column is hashed separately, and within each column there is an option to specify which slots should be hashed together into one output slot. This transform can be applied either to single valued columns or to known length vector columns. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.HashJoinTransform.ColumnInfoEx.GetItemType(System.Int32)"> <summary> Constructs the correct KeyType for the given hash bits. Because of array size limitation, if hashBits = 31, the key type is not contiguous (not transformable into indicator array) </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.HashJoinTransform.ComposeGetterOneToOne``1(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Getter generator for inputs of type <typeparamref name="TSrc"/> </summary> <typeparam name="TSrc">Input type. Must be a non-vector</typeparam> <param name="input">Row inout</param> <param name="iinfo">Index of the getter</param> </member> <member name="M:Microsoft.ML.Runtime.Data.HashJoinTransform.ComposeGetterVecToVec``1(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Getter generator for inputs of type <typeparamref name="TSrc"/>, where output type is a vector of hashes </summary> <typeparam name="TSrc">Input type. Must be a vector</typeparam> <param name="input">Row input</param> <param name="iinfo">Index of the getter</param> </member> <member name="M:Microsoft.ML.Runtime.Data.HashJoinTransform.ComposeGetterVecToOne``1(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Getter generator for inputs of type <typeparamref name="TSrc"/>, where output type is a single hash </summary> <typeparam name="TSrc">Input type. Must be a vector</typeparam> <param name="input">Row input</param> <param name="iinfo">Index of the getter</param> </member> <member name="M:Microsoft.ML.Runtime.Data.HashJoinTransform.ComposeHashDelegate``1"> <summary> Generic hash function </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.HashJoinTransform.ComposeFloatHashDelegate"> <summary> Generate a specialized hash function for floats </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.HashJoinTransform.ComposeDoubleHashDelegate"> <summary> Generate a specialized hash function for doubles </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.KeyToBinaryVectorTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.KeyToBinaryVectorTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.KeyToBinaryVectorTransform.ComputeType(Microsoft.ML.Runtime.Data.KeyToBinaryVectorTransform,Microsoft.ML.Runtime.Data.ISchema,System.Int32,Microsoft.ML.Runtime.Data.OneToOneTransformBase.ColInfo,Microsoft.ML.Runtime.Data.MetadataDispatcher,Microsoft.ML.Runtime.Data.VectorType@,System.Boolean@,System.Int32@)"> <summary> Computes the column type and whether multiple indicator vectors need to be concatenated. Also populates the metadata. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.KeyToBinaryVectorTransform.MakeGetterOne(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> This is for the scalar case. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.KeyToBinaryVectorTransform.MakeGetterInd(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> This is for the indicator case - vector input and outputs should be concatenated. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.LearnerFeatureSelectionTransform"> <summary> Selects the slots for which the absolute value of the corresponding weight in a linear learner is greater than a threshold. Instantiates a DropSlots transform to actually drop the slots. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.LearnerFeatureSelectionTransform.Create(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.LearnerFeatureSelectionTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Create method corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.LearnerFeatureSelectionTransform.Train(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.IDataView,Microsoft.ML.Runtime.Data.LearnerFeatureSelectionTransform.Arguments,Microsoft.ML.Runtime.Data.VBuffer{System.Single}@)"> <summary> Returns a score for each slot of the features column. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.LoadTransform"> <summary> Load specific transforms from the specified model file. Allows one to 'cherry pick' transforms from a serialized chain, or to apply a pre-trained transform to a different (but still compatible) data view. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MissingValueIndicatorTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.MissingValueIndicatorTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionTransform"> <summary> Selects the top k slots ordered by their mutual information with the label column. Instantiates a DropSlots transform to actually drop the slots. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionTransform.Create(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Create method corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionTransform.ComputeThreshold(System.Single[][],System.Int32,System.Int32@)"> <summary> Computes the threshold for the scores such that the top k slots are preserved. If there are less than k scores greater than zero, the threshold is set to zero and the tiedScoresToKeep is set to zero, so that we only keep scores strictly greater than zero. </summary> <param name="scores">The score for each column and each slot.</param> <param name="topk">How many slots to preserve.</param> <param name="tiedScoresToKeep">If there are ties, how many of them to keep.</param> <returns>The threshold.</returns> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.Train(Microsoft.ML.Runtime.IHost,Microsoft.ML.Runtime.Data.IDataView,System.String,System.String[],System.Int32)"> <summary> Returns the feature selection scores for each slot of each column. </summary> <param name="host">The host.</param> <param name="input">The input dataview.</param> <param name="labelColumnName">The label column.</param> <param name="columns">The columns for which to compute the feature selection scores.</param> <param name="numBins">The number of bins to use for numeric features.</param> <returns>A list of scores for each column and each slot.</returns> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.Impl.ComputeMutualInformation(Microsoft.ML.Runtime.Data.Transposer,System.Int32)"> <summary> Computes the mutual information for one column. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.Impl.ComputeMutualInformation``1(Microsoft.ML.Runtime.Data.Transposer,System.Int32,Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.Impl.Mapper{``0})"> <summary> Computes the mutual information for one column. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.Impl.ComputeMutualInformation(Microsoft.ML.Runtime.Data.VBuffer{System.Int32}@,System.Int32,System.Int32)"> <summary> Computes the mutual information for one slot. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.Impl.FillTable(Microsoft.ML.Runtime.Data.VBuffer{System.Int32}@,System.Int32,System.Int32)"> <summary> Fills the contingency table. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.Impl.BinKeys``1(Microsoft.ML.Runtime.Data.ColumnType)"> <summary> Maps from keys to ints. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.Impl.BinInts(Microsoft.ML.Runtime.Data.VBuffer{Microsoft.ML.Runtime.Data.DvInt4}@,Microsoft.ML.Runtime.Data.VBuffer{System.Int32}@,System.Int32,System.Int32@,System.Int32@)"> <summary> Maps from DvInt4 to ints. NaNs (and only NaNs) are mapped to the first bin. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.Impl.BinSingles(Microsoft.ML.Runtime.Data.VBuffer{System.Single}@,Microsoft.ML.Runtime.Data.VBuffer{System.Int32}@,System.Int32,System.Int32@,System.Int32@)"> <summary> Maps from Singles to ints. NaNs (and only NaNs) are mapped to the first bin. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.Impl.BinDoubles(Microsoft.ML.Runtime.Data.VBuffer{System.Double}@,Microsoft.ML.Runtime.Data.VBuffer{System.Int32}@,System.Int32,System.Int32@,System.Int32@)"> <summary> Maps from Doubles to ints. NaNs (and only NaNs) are mapped to the first bin. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionUtils.CreateVectorMapper``2(Microsoft.ML.Runtime.Data.ValueMapper{``0,``1})"> <summary> Given a mapper from T to int, creates a mapper from VBuffer{T} to VBuffer<int>. Assumes that the mapper maps default(TSrc) to default(TDst) so that the returned mapper preserves sparsity. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NADropTransform"> <summary> Transform to drop NAs from vector columns. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NADropTransform.GetIsNADelegate(Microsoft.ML.Runtime.Data.ColumnType)"> <summary> Returns the isNA predicate for the respective type. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NAHandleTransform"> <summary> This transform handles missing values in the input columns. For each input column, it creates an output column where the missing values are replaced by one of these specified values: - The default value of the appropriate type. - The mean value of the appropriate type. - The max value of the appropriate type. - The min value of the appropriate type. (The last three work only for numeric/time span/ DateTime columns). The output column can also optionally include an indicator vector for which slots were missing in the input column (this can be done only when the indicator vector type can be converted to the input column type, i.e. only for numeric columns). When computing the mean/max/min value, there is also an option to compute it over the whole column instead of per slot. This option has a default value of true for variable length vectors, and false for known length vectors. It can be changed to true for known length vectors, but it results in an error if changed to false for variable length vectors. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NAIndicatorTransform"> <summary> This transform can transform either scalars or vectors (both fixed and variable size), creating output columns that indicate corresponding NA values. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAIndicatorTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.NAIndicatorTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAIndicatorTransform.ComposeGetterOne(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Getter generator for single valued inputs. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAIndicatorTransform.ComposeGetterOne``1(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Tests if a value is NA for scalars. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAIndicatorTransform.ComposeGetterVec(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Getter generator for vector valued inputs. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAIndicatorTransform.ComposeGetterVec``1(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Tests if a value is NA for vectors. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAIndicatorTransform.FindNAs``1(Microsoft.ML.Runtime.Data.VBuffer{``0}@,Microsoft.ML.Runtime.Data.RefPredicate{``0},System.Boolean,System.Collections.Generic.List{System.Int32},System.Boolean@)"> <summary> Adds all NAs (or non-NAs) to the indices List. Whether NAs or non-NAs have been added is indicated by the bool sense. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAIndicatorTransform.FillValues(System.Int32,Microsoft.ML.Runtime.Data.VBuffer{Microsoft.ML.Runtime.Data.DvBool}@,System.Collections.Generic.List{System.Int32},System.Boolean)"> <summary> Fills indicator values for vectors. The indices is a list that either holds all of the NAs or all of the non-NAs, indicated by sense being true or false respectively. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NAReplaceTransform"> <summary> This transform can transform either scalars or vectors (both fixed and variable size), creating output columns that are identical to the input columns except for replacing NA values with either the default value, user input, or imputed values (min/max/mean are currently supported). Imputation modes are supported for vectors both by slot and across all slots. </summary> REVIEW: May make sense to implement the transform template interface. </member> <member name="M:Microsoft.ML.Runtime.Data.NAReplaceTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.NAReplaceTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAReplaceTransform.GetReplacementValues(Microsoft.ML.Runtime.Data.NAReplaceTransform.Arguments,System.Object[]@,System.Collections.BitArray[]@)"> <summary> Fill the repValues array with the correct replacement values based on the user-given replacement kinds. Vectors default to by-slot imputation unless otherwise specified, except for unknown sized vectors which force across-slot imputation. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAReplaceTransform.GetIsNADelegate(Microsoft.ML.Runtime.Data.ColumnType)"> <summary> Returns the isNA predicate for the respective type. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAReplaceTransform.GetSpecifiedValue(System.String,Microsoft.ML.Runtime.Data.ColumnType,System.Delegate)"> <summary> Converts a string to its respective value in the corresponding type. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAReplaceTransform.ComposeGetterOne(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Getter generator for single valued inputs. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAReplaceTransform.ComposeGetterOne``1(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Replaces NA values for scalars. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAReplaceTransform.ComposeGetterVec(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Getter generator for vector valued inputs. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAReplaceTransform.ComposeGetterVec``1(Microsoft.ML.Runtime.Data.IRow,System.Int32)"> <summary> Replaces NA values for vectors. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAReplaceTransform.FillValues``1(Microsoft.ML.Runtime.Data.VBuffer{``0}@,Microsoft.ML.Runtime.Data.VBuffer{``0}@,Microsoft.ML.Runtime.Data.RefPredicate{``0},``0,System.Boolean)"> <summary> Fills values for vectors where there is one replacement value. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NAReplaceTransform.FillValues``1(Microsoft.ML.Runtime.Data.VBuffer{``0}@,Microsoft.ML.Runtime.Data.VBuffer{``0}@,Microsoft.ML.Runtime.Data.RefPredicate{``0},``0[],System.Collections.BitArray)"> <summary> Fills values for vectors where there is slot-wise replacement values. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NAReplaceTransform.StatAggregator"> <summary> The base class for stat aggregators for imputing mean, min, and max for the NAReplaceTransform. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NAReplaceTransform.StatAggregator`2"> <summary> The base class for stat aggregators with knowledge of types. </summary> <typeparam name="TValue">The type for the column being aggregated.</typeparam> <typeparam name="TStat">The type of the stat being computed by the stat aggregator.</typeparam> </member> <member name="P:Microsoft.ML.Runtime.Data.NAReplaceTransform.StatAggregator`2.RowCount"> <summary> Returns the number of times that ProcessRow has been called. </summary> </member> <member name="P:Microsoft.ML.Runtime.Data.NAReplaceTransform.StatAggregatorAcrossSlots`2.ValueCount"> <summary> Returns the number of values that have been processed so far. </summary> </member> <member name="P:Microsoft.ML.Runtime.Data.NAReplaceTransform.MinMaxAggregatorAcrossSlots`2.ValuesProcessed"> <summary> Returns the number of times that ProcessValue has been called. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NAReplaceTransform.MeanStatDouble"> <summary> This is a mutable struct (so is evil). However, its scope is restricted and the only instances are in a field or an array, so the mutation does the right thing. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NAReplaceTransform.MeanStatInt"> <summary> A mutable struct for keeping the appropriate statistics for mean calculations for IX types, TS, and DT, whose scope is restricted and only exists as an instance in a field or an array, utilizing the mutation of the struct correctly. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NAReplaceTransform.Long.Converter"> <summary> The base class for conversions from types to long. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NormalizeTransform.Create(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.NormalizeTransform.MinMaxArguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public create method corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NormalizeTransform.Create(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.NormalizeTransform.MeanVarArguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public create method corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NormalizeTransform.Create(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.NormalizeTransform.LogMeanVarArguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public create method corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NormalizeTransform.Create(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.NormalizeTransform.BinArguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public create method corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NormalizeTransform.Create(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.NormalizeTransform.SupervisedBinArguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public create method corresponding to SignatureDataTransform. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.MinMaxDblAggregator"> <summary> Base class for tracking min and max values for a vector valued column. It tracks min, max, number of non-sparse values (vCount) and number of ProcessValue() calls (trainCount). NaNs are ignored when updating min and max. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.MeanVarDblAggregator"> <summary> Class for computing the mean and variance for a vector valued column. It tracks the current mean and the M2 (sum of squared diffs of the values from the mean), the number of NaNs and the number of non-zero elements. Uses the algorithm described here: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online_algorithm </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.MinMaxSngAggregator"> <summary> Base class for tracking min and max values for a vector valued column. It tracks min, max, number of non-sparse values (vCount) and number of ProcessValue() calls (trainCount). NaNs are ignored when updating min and max. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.MeanVarSngAggregator"> <summary> Class for computing the mean and variance for a vector valued column. It tracks the current mean and the M2 (sum of squared diffs of the values from the mean), the number of NaNs and the number of non-zero elements. Uses the algorithm described here: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online_algorithm </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.SignatureLoadColumnFunction"> <summary> Signature for a repository based loader of a IColumnFunction </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.IColumnFunctionBuilder.ProcessValue"> <summary> Trains on the current value. </summary> <returns>True if it can use more values for training.</returns> </member> <member name="M:Microsoft.ML.Runtime.Data.IColumnFunctionBuilder.CreateColumnFunction"> <summary> Finishes training and returns a column function. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.IColumnAggregator`1"> <summary> Interface to define an aggregate function over values </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.IColumnAggregator`1.ProcessValue(`0@)"> <summary> Updates the aggregate function with a value </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.IColumnAggregator`1.Finish"> <summary> Finishes the aggregation </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.ProduceIdTransform"> <summary> Produces a column with the cursor's ID as a column. This can be useful for diagnostic purposes. This class will obviously generate different data given different IDs. So, if you save data to some other file, then apply this transform to that dataview, it may of course have a different result. This is distinct from most transforms that produce results based on data alone. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.RffTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.RffTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to <see cref="T:Microsoft.ML.Runtime.Data.SignatureDataTransform"/>. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.TermLookupTransform"> <summary> This transform maps text values columns to new columns using a map dataset provided through its arguments. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.TermLookupTransform.ValueMap"> <summary> Holds the values that the terms map to. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.TermLookupTransform.ValueMap`1"> <summary> Holds the values that the terms map to - where the destination type is TRes. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.TermLookupTransform.ValueMap`1.Train(Microsoft.ML.Runtime.IExceptionContext,Microsoft.ML.Runtime.Data.IRowCursor,System.Int32,System.Int32)"> <summary> Bind this value map to the given cursor for "training". </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.TermLookupTransform.ValueMap`1.GetGetter(Microsoft.ML.Runtime.Data.ValueGetter{Microsoft.ML.Runtime.Data.DvText})"> <summary> Given the term getter, produce a value getter from this value map. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.TermLookupTransform.OneValueMap`1"> <summary> Holds the values that the terms map to when the destination type is a PrimitiveType (non-vector). </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.TermLookupTransform.VecValueMap`1"> <summary> Holds the values that the terms map to when the destination type is a VectorType. TItem is the represtation type for the vector's ItemType. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.TermLookupTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.TermLookupTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NgramHashTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.NgramHashTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NgramHashTransform.InvertHashHelper.CallAllGetters(Microsoft.ML.Runtime.Data.IRow)"> <summary> Construct an action that calls all the getters for a row, so as to easily force computation of lazily computed values. This will have the side effect of calling the decorator. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NgramTransform.WeightingCriteria"> <summary> Weighting criteria: a statistical measure used to evaluate how important a word is to a document in a corpus. This enumeration is serialized. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NgramTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.NgramTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NgramIdFinder"> <summary> This delegate represents a function that gets an ngram as input, and outputs the id of the ngram and whether or not to continue processing ngrams. </summary> <param name="ngram">The array containing the ngram</param> <param name="lim">The ngram is stored in ngram[0],...ngram[lim-1].</param> <param name="icol">The index of the column the transform is applied to.</param> <param name="more">True if processing should continue, false if it should stop. It is true on input, so only needs to be set to false.</param> <returns>The ngram slot if it was found, -1 otherwise.</returns> </member> <member name="T:Microsoft.ML.Runtime.Data.TextTransform"> <summary> A transform that turns a collection of text documents into numerical feature vectors. The feature vectors are counts of (word or character) ngrams in a given text. It offers ngram hashing (finding the ngram token string name to feature integer index mapping through hashing) as an option. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.TextTransform.Language"> <summary> Text language. This enumeration is serialized. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.TextTransform.TextNormKind"> <summary> Text vector normalizer kind. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.TextTransform.Arguments"> <summary> This class exposes <see cref="T:Microsoft.ML.Runtime.Data.NgramExtractorTransform"/>/<see cref="T:Microsoft.ML.Runtime.Data.NgramHashExtractorTransform"/> arguments. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.TextTransform.TransformApplierParams"> <summary> A distilled version of the TextTransform Arguments, with all fields marked readonly and only the exact set of information needed to construct the transforms preserved. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.SignatureNgramExtractorFactory"> <summary> Signature for creating an INgramExtractorFactory. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.ExtractorColumn"> <summary> A many-to-one column common to both <see cref="T:Microsoft.ML.Runtime.Data.NgramExtractorTransform"/> and <see cref="T:Microsoft.ML.Runtime.Data.NgramHashExtractorTransform"/>. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.WordBagTransform.TokenizeColumn"> <summary> A vanilla implementation of OneToOneColumn that is used to represent the input of any tokenize transform (a transform that implements ITokenizeTransform interface). Note: Since WordBagTransform is a many-to-one column transform, for each WordBagTransform.Column with multiple sources, ConcatTransform is applied first. The output of ConcatTransform is a one-to-one column which is in turn the input to a tokenize transform. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.WordBagTransform.Arguments"> <summary> This class is a merger of <see cref="T:Microsoft.ML.Runtime.Data.ITokenizeTransform"/> and <see cref="T:Microsoft.ML.Runtime.Data.NgramExtractorTransform.ArgumentsBase"/> options. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NgramExtractorTransform"> <summary> A transform that turns a collection of tokenized text (vector of DvText), or vectors of keys into numerical feature vectors. The feature vectors are counts of ngrams (sequences of consecutive *tokens* -words or keys- of length 1-n). </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NgramExtractorTransform.ArgumentsBase"> <summary> This class is a merger of <see cref="T:Microsoft.ML.Runtime.Data.TermTransform.Arguments"/> and <see cref="T:Microsoft.ML.Runtime.Data.NgramTransform.Arguments"/>, with the allLength option removed. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.TermLoaderArguments"> <summary> Arguments for defining custom list of terms or data file containing the terms. The class includes a subset of <see cref="T:Microsoft.ML.Runtime.Data.TermTransform"/>'s arguments. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.INgramExtractorFactory"> <summary> An ngram extractor factory interface to create an ngram extractor transform. </summary> </member> <member name="P:Microsoft.ML.Runtime.Data.INgramExtractorFactory.UseHashingTrick"> <summary> Whether the extractor transform created by this factory uses the hashing trick (by using <see cref="T:Microsoft.ML.Runtime.Data.HashTransform"/> or <see cref="T:Microsoft.ML.Runtime.Data.NgramHashTransform"/>, for example). </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NgramExtractorFactory"> <summary> An implementation of <see cref="T:Microsoft.ML.Runtime.Data.INgramExtractorFactory"/> to create <see cref="T:Microsoft.ML.Runtime.Data.NgramExtractorTransform"/>. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NgramHashExtractorFactory"> <summary> An implementation of <see cref="T:Microsoft.ML.Runtime.Data.INgramExtractorFactory"/> to create <see cref="T:Microsoft.ML.Runtime.Data.NgramHashExtractorTransform"/>. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.NgramExtractionUtils.GenerateUniqueSourceNames(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.ManyToOneColumn[],Microsoft.ML.Runtime.Data.ISchema)"> <summary> Generates and returns unique names for columns source. Each element of the returned array is an array of unique source names per specific column. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.WordHashBagTransform.Arguments"> <summary> This class is a merger of <see cref="T:Microsoft.ML.Runtime.Data.ITokenizeTransform"/> and <see cref="T:Microsoft.ML.Runtime.Data.NgramExtractorTransform.ArgumentsBase"/> options. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NgramHashExtractorTransform"> <summary> A transform that turns a collection of tokenized text (vector of DvText) into numerical feature vectors using the hashing trick. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.NgramHashExtractorTransform.ArgumentsBase"> <summary> This class is a merger of <see cref="T:Microsoft.ML.Runtime.Data.HashTransform.Arguments"/> and <see cref="T:Microsoft.ML.Runtime.Data.NgramHashTransform.Arguments"/>, with the ordered option, the rehashUnigrams option and the allLength option removed. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.SignatureTokenizeTransform"> <summary> Signature for creating an ITokenizeTransform. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.DelimitedTokenizeTransform"> <summary> The input for this transform is a DvText or a vector of DvTexts, and its output is a vector of DvTexts, corresponding to the tokens in the input text, split using a set of user specified separator characters. Empty strings and strings containing only spaces are dropped. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.DelimitedTokenizeTransform.ColInfoEx"> <summary> Extra information for each column (in addition to ColumnInfo). </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.DelimitedTokenizeTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.DelimitedTokenizeTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.DelimitedTokenizeTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.DelimitedTokenizeTransform.TokenizeArguments,Microsoft.ML.Runtime.Data.IDataView,Microsoft.ML.Runtime.Data.OneToOneColumn[])"> <summary> Public constructor corresponding to SignatureTokenizeTransform. It accepts arguments of type ArgumentsBase, and a separate array of columns (constructed from the caller -WordBag/WordHashBag- arguments). </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.UngroupTransform"> <summary> This can be thought of as an inverse of <see cref="T:Microsoft.ML.Runtime.Data.GroupTransform"/>. For all specified vector columns ("pivot" columns), performs the "ungroup" (or "unroll") operation as outlined below. If the only pivot column is called P, and has size K, then for every row of the input we will produce K rows, that are identical in all columns except P. The column P will become a scalar column, and this column will hold all the original values of input's P, one value per row, in order. The order of columns will remain the same. Variable-length pivot columns are supported (including zero, which will eliminate the row from the result). Multiple pivot columns are also supported: * A number of output rows is controlled by the 'mode' parameter. - outer: it is equal to the maximum length of pivot columns, - inner: it is equal to the minimum length of pivot columns, - first: it is equal to the length of the first pivot column. * If a particular pivot column has size that is different than the number of output rows, the extra slots will be ignored, and the missing slots will be 'padded' with default values. All metadata is preserved for the retained columns. For 'unrolled' columns, all known metadata except slot names is preserved. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.UngroupTransform.SchemaImpl.GetActiveInput(System.Func{System.Int32,System.Boolean})"> <summary> Return an array of active input columns given the target predicate. </summary> </member> <member name="T:Microsoft.ML.Runtime.Data.WhiteningTransform"> <summary> Implements PCA (Principal Component Analysis) and ZCA (Zero phase Component Analysis) whitening. The whitening process consists of 2 steps: 1. Decorrelation of the input data. Input data is assumed to have zero mean. 2. Rescale decorrelated features to have unit variance. That is, PCA whitening is essentially just a PCA + rescale. ZCA whitening tries to make resulting data to look more like input data by rotating it back to the original input space. More information: <see href="http://ufldl.stanford.edu/wiki/index.php/Whitening"/> </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.WhiteningTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.WhiteningTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="M:Microsoft.ML.Runtime.Data.WhiteningTransform.DotProduct(System.Single[],System.Int32,System.Single[],System.Int32[],System.Int32)"> <summary> Returns a dot product of dense vector 'a' starting from offset 'aOffset' and sparse vector 'b' with first 'count' valid elements and their corresponding 'indices'. </summary> </member> <member name="T:Microsoft.ML.Runtime.Transforms.TextAnalytics"> <summary> Entry points for text anylytics transforms. </summary> </member> <member name="M:Microsoft.ML.Runtime.DataPipe.OptionalColumnTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.DataPipe.OptionalColumnTransform.Arguments,Microsoft.ML.Runtime.Data.IDataView)"> <summary> Public constructor corresponding to SignatureDataTransform. </summary> </member> <member name="T:Microsoft.ML.Runtime.TextAnalytics.CharTokenizeTransform"> <summary> Character-oriented tokenizer where text is considered a sequence of characters. </summary> </member> <member name="M:Microsoft.ML.Runtime.TextAnalytics.CharTokenizeTransform.GetKeyValues(System.Int32,Microsoft.ML.Runtime.Data.VBuffer{Microsoft.ML.Runtime.Data.DvText}@)"> <summary> Get the key values (chars) corresponding to keys in the output columns. </summary> </member> <member name="M:Microsoft.ML.Runtime.TextAnalytics.SentimentAnalyzingTransform.AliasIfNeeded(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.Data.IDataView,System.String[],System.Collections.Generic.KeyValuePair{System.String,System.String}[]@)"> <summary> If any column names in <param name="colNames" /> are present in <param name="input" />, this method will create a transform that copies them to temporary columns. It will populate <param name="hiddenNames" /> with an array of string pairs containing the original name and the generated temporary column name, respectively. </summary> <param name="env"></param> </member> <member name="T:Microsoft.ML.Runtime.TextAnalytics.SignatureStopWordsRemoverTransform"> <summary> Signature for creating an IStopWordsRemoverTransform. </summary> </member> <member name="T:Microsoft.ML.Runtime.TextAnalytics.StopWordsRemoverTransform"> <summary> A Stopword remover transform based on language-specific lists of stop words (most common words) from Office Named Entity Recognition project. The transform is usually applied after tokenizing text, so it compares individual tokens (case-insensitive comparison) to the stopwords. </summary> </member> <member name="T:Microsoft.ML.Runtime.TextAnalytics.StopWordsRemoverTransform.Language"> <summary> Stopwords language. This enumeration is serialized. </summary> </member> <member name="M:Microsoft.ML.Runtime.TextAnalytics.StopWordsRemoverTransform.ColInfoEx.Bind(Microsoft.ML.Runtime.Data.ISchema,System.String,System.Int32@,System.Boolean)"> <summary> Binds a text column with the given name using input schema and returns the column index. Fails if there is no column with the given name or if the column type is not text. </summary> </member> <member name="M:Microsoft.ML.Runtime.TextAnalytics.CustomStopWordsRemoverTransform.#ctor(Microsoft.ML.Runtime.IHostEnvironment,Microsoft.ML.Runtime.TextAnalytics.CustomStopWordsRemoverTransform.LoaderArguments,Microsoft.ML.Runtime.Data.IDataView,Microsoft.ML.Runtime.Data.OneToOneColumn[])"> <summary> Public constructor corresponding to SignatureStopWordsRemoverTransform. It accepts arguments of type LoaderArguments, and a separate array of columns (constructed by the caller -TextTransform- arguments). </summary> </member> <member name="T:Microsoft.ML.Runtime.TextAnalytics.TextNormalizerTransform"> <summary> A text normalization transform that allows normalizing text case, removing diacritical marks, punctuation marks and/or numbers. The transform operates on text input as well as vector of tokens/text (vector of DvText). </summary> </member> <member name="T:Microsoft.ML.Runtime.TextAnalytics.TextNormalizerTransform.CaseNormalizationMode"> <summary> Case normalization mode of text. This enumeration is serialized. </summary> </member> <member name="M:Microsoft.ML.Runtime.TextAnalytics.TextNormalizerTransform.IsCombiningDiacritic(System.Char)"> <summary> Whether a character is a combining diacritic character or not. Combining diacritic characters are the set of diacritics intended to modify other characters. The list is provided by Office NL team. </summary> </member> <member name="T:Microsoft.ML.Transforms.Properties.Resources"> <summary> A strongly-typed resource class, for looking up localized strings, etc. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.ResourceManager"> <summary> Returns the cached ResourceManager instance used by this class. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Culture"> <summary> Overrides the current thread's CurrentUICulture property for all resource lookups using this strongly typed resource class. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Arabic"> <summary> Looks up a localized string similar to ب ب بعد حتى حين دون صباح في قبل لكن مساء مع من نحو واكد وفي ومن اطار و اثر اجل احد اذا اكثر اكد التي الثاني الثانية الذاتي الذي الذين السابق الف الماضي المقبل الوقت اليوم امس انه باسم بان برس بسبب بشكل بعض بن به بها بين تم ثلاثة ثم جميع حاليا حوالى حول حيث خلال ذلك زيارة سنة سنوات شخصا صفر ضد ضمن عام عاما عدة عدد عدم عشر عشرة على عليه عليها عن عند عندما غدا غير فان فيه فيها قال قد قوة كان كانت كل كلم كما لا لدى لقا [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Czech"> <summary> Looks up a localized string similar to bude budeš byl byla byli bylo je jsem jsme jsou jste aby aj ale ani asi až bez bude-li budeme budeme-li budete budete-li budeš-li budou budou-li budu budu-li buď buďme buďte by byl-li byla-li bylas byli-li bylo-li bylos byls byly byly-li byt byv byvše byvši být býti co což cz další design dnes do ho jak jako je-li jeho jej jejich její jen ještě ji jine již jsa jsem-li jsi jsi-li jsme-li jsou-li jsouc jsouce jste-li kam kde [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Danish"> <summary> Looks up a localized string similar to af andre at da de deres dette din ej en ene et han hans i ind lille ni ny otte stor store syv alle andet begge den denne der det dig dog du eller end eneste enhver fem fire flere fleste for fordi forrige fra få før god har hendes her hun hvad hvem hver hvilken hvis hvor hvordan hvorfor hvornår ikke ingen intet jeg jeres kan kom kommer lav lidt man mand mange med meget men mens mere mig ned nogen noget nyt nær næste næsten [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Dutch"> <summary> Looks up a localized string similar to aan af al als bij dan dat die dit een en er had heb hem het hij hoe hun ik in is je kan me men met mij nog nu of ons ook te tot uit van was wat we wel wij zal ze zei zij zo zou . </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.English"> <summary> Looks up a localized string similar to a about above above across after afterwards again against all almost alone along already also although always am among amongst amoungst amount an and another any anyhow anyone anything anyway anywhere are around as at back be became because become becomes becoming been before beforehand behind being below beside besides between beyond both bottom but by call can cannot cant co con could couldnt cry de describe detail do done down due du [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.French"> <summary> Looks up a localized string similar to de des d' la du l' et à en sur aux par pour au dans un est été a une sous ou pas entre qui nº lès plus il y que contre je non n' sans vous avec ne ce son ses mon moins se qu' moi j' c' si ma s' être tout comme sa sont ai elle autres pendant chez mais avant nous cette après vers était tous autre tu très même ont anti puis leur où lui ça suis depuis ni mes près hors outre ils votre toi lors t' aussi donc ces toute [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.German"> <summary> Looks up a localized string similar to a ab aber aber ach acht achte achten achter achtes ag alle allein allem allen aller allerdings alles allgemeinen als als also am an andere anderen andern anders au auch auch auf aus ausser außer ausserdem außerdem b bald bei beide beiden beim beispiel bekannt bereits besonders besser besten bin bis bisher bist c d da dabei dadurch dafür dagegen daher dahin dahinter damals damit danach daneben dank dann daran darauf daraus darf darfst [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Italian"> <summary> Looks up a localized string similar to a adesso ai al alla allo allora altre altri altro anche ancora avere aveva avevano ben buono che chi cinque comprare con consecutivi consecutivo cosa cui da del della dello dentro deve devo di doppio due e ecco fare fine fino fra gente giù ha hai hanno ho il indietro invece io la lavoro le lei lo loro lui lungo ma me meglio molta molti molto nei nella no noi nome nostro nove nuovi nuovo o oltre ora otto peggio pero persone più [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Japanese"> <summary> Looks up a localized string similar to これ それ あれ この その あの ここ そこ あそこ こちら どこ だれ なに なん 何 私 貴方 貴方方 我々 私達 あの人 あのかた 彼女 彼 です あります おります います は が の に を で え から まで より も どの と し それで しかし . </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Norwegian_Bokmal"> <summary> Looks up a localized string similar to alle andre arbeid av begge bort bra bruke da denne der deres det din disse du eller en ene eneste enhver enn er et folk for fordi forsøke fra få før først gjorde gjøre god gå ha hadde han hans hennes her hva hvem hver hvilken hvis hvor hvordan hvorfor i ikke inn innen kan kunne lage lang lik like makt mange med meg meget men mens mer mest min mye må måte navn nei ny nå når og også om opp oss over part punkt på rett rikti [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Polish"> <summary> Looks up a localized string similar to ach aj albo bardzo bez bo być ci ciebie cię co czy daleko dla dlaczego dlatego do dobrze dokąd dość dużo dwa dwaj dwie dwoje dzisiaj dziś gdyby gdzie go ich ile im inny ja jak jakby jaki je jeden jedna jedno jego jej jemu jest jestem jeśli jeżeli już ją każdy kiedy kierunku kto ku lub ma mają mam mi mnie mną moi moja moje może mu my mój na nam nami nas nasi nasz nasza nasze natychmiast nic nich nie niego niej niemu nigdy n [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Portuguese"> <summary> Looks up a localized string similar to último é acerca agora algumas alguns ali ambos antes apontar aquela aquelas aquele aqueles aqui atrás bem bom cada caminho cima com como comprido conhecido corrente das debaixo dentro desde desligado deve devem deverá direita diz dizer dois dos e ela ele eles em enquanto então está estão estado estar estará este estes esteve estive estivemos estiveram eu fará faz fazer fazia fez fim foi fora horas iniciar inicio ir irá isto ligado maio [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Portuguese_Brazilian"> <summary> Looks up a localized string similar to último é acerca agora algumas alguns ali ambos antes apontar aquela aquelas aquele aqueles aqui atrás bem bom cada caminho cima com como comprido conhecido corrente das debaixo dentro desde desligado deve devem deverá direita diz dizer dois dos e ela ele eles em enquanto então está estão estado estar estará este estes esteve estive estivemos estiveram eu fará faz fazer fazia fez fim foi fora horas iniciar inicio ir irá isto ligado maio [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Russian"> <summary> Looks up a localized string similar to а е и ж м о на не ни об но он мне мои мож она они оно мной много многочисленное многочисленная многочисленные многочисленный мною мой мог могут можно может можхо мор моя моё мочь над нее оба нам нем нами ними мимо немного одной одного менее однажды однако меня нему меньше ней наверху него ниже мало надо один одиннадцать одиннадцатый назад наиболее недавно миллионов недалеко между низко меля нельзя нибудь непрерывно наконец никогда ник [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Spanish"> <summary> Looks up a localized string similar to ésa ésta éste última últimas último últimos aún a la vez a.m. abierto abunda acaba además ahora al algún alguna alguno alrededor alta altas alto altos am ambas ambos antes aquél aquélla aquí aquel aquella aquellas aquello aquellos así atardecer aunque básicamente b c cada casi celebran centro cercanía cierta ciertas cierto ciertos común comúnmente como complemento complementos completamente comunes con conforme considerable considerada consider [rest of string was truncated]";. </summary> </member> <member name="P:Microsoft.ML.Transforms.Properties.Resources.Swedish"> <summary> Looks up a localized string similar to aderton adertonde adjö aldrig alla allas allt alltid alltså än andra andras annan annat ännu artonde arton åtminstone att åtta åttio åttionde åttonde av även båda bådas bakom bara bäst bättre behöva behövas behövde behövt beslut beslutat beslutit bland blev bli blir blivit bort borta bra då dag dagar dagarna dagen där därför de del delen dem den deras dess det detta dig din dina dit ditt dock du efter eftersom elfte eller elva en enk [rest of string was truncated]";. </summary> </member> </members> </doc> |