7.00.02 - Output - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
B700-1022-700K
Language
English (United States)

The GMMPredict function has one output table, whose format depends on the OutputFormat argument.

The following table describes the output table for OutputFormat('sparse'), the default. The table has D+3 columns, where D is the number of dimensions of the input data.

GMMPredict Output Table Schema, OutputFormat('sparse')
Column Name Data Type Description
accumulate_column Same as in input table Column copied from the testdata table. Typically, one accumulate_column contains the unique identifier of a data point.
data_point NUMERIC Input data point. The table has D such columns, which are copied from input_table to the output table. Their names are the same in input_table and the output table.
cluster_rank INTEGER Probability that the data point belongs to the cluster.
cluster_id INTEGER Identification number of the cluster
prob DOUBLE PRECISION Probability that the cluster assigned to the data point

The following table describes the output table for OutputFormat('dense'). The table has D+2k columns, where D is the number of dimensions of the input data and n is the the number of cluster weights that the function outputs (the value of the TopNClusters argument).

GMMPredict Output Table Schema, OutputFormat('dense')
Column Name Data Type Description
accumulate_column Same as in input table Column copied from the testdata table. Typically, one accumulate_column contains the unique identifier of a data point.
id Any Identification of the data point
data_point NUMERIC Input data point. The table has D such columns, which are copied from input_table to the output table. Their names are the same in input_table and the output table.
cluster_rank_i INTEGER Probability that data point i belongs to the cluster.
cluster_id_i INTEGER Identification number of the cluster with the greatest probability assigned to data point i. The table has n such columns (cluster_id_1, ..., cluster_id_n).
prob_i DOUBLE PRECISION Probability assigned to data point i by the cluster named in cluster_id_i. The table has n such columns (prob_1, ..., prob_n).

For each n, the column prob_n immediately follows the column cluster_id_n.