HMMSupervisedLearner Arguments - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2
Product
Aster Analytics
Release Number
7.00.02
Published
September 2017
Language
English (United States)
Last Update
2018-04-17
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B700-1022
lifecycle
previous
Product Category
Software
ModelColumn
[Optional] Specifies the name of the input column that contains the model attribute. If you specify this argument, model_column must match a model_key in the PARTITION BY clause. The values in the column can be either integers or strings.
SeqColumn
The name of the column that contains the sequence attribute. The sequence_attribute must be a sequence attribute in the PARTITION BY clause. A sequence must contain more than two observation symbols.
ObsColumn
Specifies the name of the input column that contains the observed symbols. The function scans the input table to find all possible observed symbols.
Observed symbols are case-sensitive.
StateColumn
Specifies the names of the input column that contains the state attributes. The state attributes are case-sensitive.
SkipColumn
[Optional] Specifies the name of the input column whose Boolean values determine whether the function skips the row.
OutputTables
[Optional] Specifies the names of the output tables:
  • init_state_prob

    Name of the initial state probability table. Default: "Pi".

  • state_transition_prob

    Name of the state transition probabilities table. Default: "A".

  • emit_prob

    Name of the emission probability table. Default: "B".

BatchSize
[Optional] Specifies the number of models to process. The batch_size must be a positive INTEGER value. If you specify batch_size and there is insufficient free memory, the function reduces the batch size. Default behavior: The function determines the batch size dynamically, based on memory conditions.