Libraries/Microsoft.ML.Transforms.xml

<?xml version="1.0"?>
<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&lt;int&gt;.
            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]&quot;;.
             </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
            email
            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]&quot;;.
             </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]&quot;;.
             </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]&quot;;.
             </summary>
        </member>
        <member name="P:Microsoft.ML.Transforms.Properties.Resources.French">
             <summary>
               Looks up a localized string similar to de
            des
            d&apos;
            la
            du
            l&apos;
            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&apos;
            sans
            vous
            avec
            ne
            ce
            son
            ses
            mon
            moins
            se
            qu&apos;
            moi
            j&apos;
            c&apos;
            si
            ma
            s&apos;
            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&apos;
            aussi
            donc
            ces
            toute
             [rest of string was truncated]&quot;;.
             </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]&quot;;.
             </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]&quot;;.
             </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]&quot;;.
             </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]&quot;;.
             </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]&quot;;.
             </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]&quot;;.
             </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]&quot;;.
             </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]&quot;;.
             </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]&quot;;.
             </summary>
        </member>
    </members>
</doc>