7.00.02 - HMMUnsupervisedLearner Arguments - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Aster Analytics
Release Number
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
English (United States)
[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.
Specifies the name of the input column that contains the sequence attribute. The sequence_column must be a sequence attribute in the PARTITION BY clause and contain more than two observation symbols.
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.
Specifies the number of hidden states.
The number of hidden states can influence model quality and performance, so choose the number appropriately.
[Optional] Specifies the number of iterations that the training process runs before the function completes. Default: 10.
[Optional] Specifies the threshold value that determines the convergence of HMM training. If the parameter value difference is less than the threshold, the training process converges. Default behavior: Only MaxIterNum determines when the training process converges.
[Optional] Specifies the name of the input column whose Boolean values determine whether the function skips the row.
[Optional] Specifies the method for generating the initial parameters for the initial state probabilities, state transition probabilities, and observation emission probabilities:
  • 'random' (Default)

    The initial parameters are based on uniform distribution.

  • 'flat'

    The probabilities are equal. Each cell holds the same probability in the matrix or vector.

  • 'input'

    The function takes the initial parameters from the InitParams argument.

The seed is meaningful only when the initialization method is 'random'.
[Required with InitMethods('input')] When InitMethods specifies 'input', this argument specifies the initial parameters for the models.

For example, if the NumberHiddenStates argument specifies three hidden states and two observed symbols ('yes' and 'no'), the InitParams values are:

  • init_state_probability_vector (the initial state probabilities):

    '0.3333333333 0.3333333333 0.3333333333'

  • state_transition_probability_matrix (the state transition probabilities):

    '0.3333333333 0.3333333333 0.3333333333;

    0.3333333333 0.3333333333 0.3333333333;

    0.3333333333 0.3333333333 0.3333333333'

  • observation_emission_probability_matrix (the observation emission probabilities):

    'no:0.25 yes:0.75; no:0.35 yes:0.65; no:0.45 yes:0.55'

The sum of the probabilities in each row for the initial state probabilities, state transition probabilities, or observation emission probabilities parameters must round to 1.0. The number of states and the number of observed symbols must be consistent with the NumberHiddenStates argument and the observed symbols in the input table; otherwise, the function displays error messages.

[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".