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 GMMFit function outputs a message and output_table. The message describes these properties:

GMMFit Output Message Properties
Property Value
Output Table Name of the output table to which the function outputs cluster information (output_table).
Algorithm Used Algorithm that the function used—Basic GMM or DP-DMM.
Stopping Criterion Why the function stopped—maximum iterations reached or convergence reached.
Delta Log Likelihood Change in the mean log-likelihood for each data point between the next-to-last and the final iterations.
Number of Iterations Number of iterations that the function performed before stopping.
Number of Clusters Number of clusters in the GMM.
Covariance Type Spherical, diagonal, tied, or full.
Number of Data Points Number of data points in the data set.
Global Mean Mean of the data set.
Global Covariance Covariance of the data set.
Log Likelihood Log-likelihood of the data, given the GMM.
Akaike Information Criterion Akaike Information Criterion.
Bayesian Information Criterion Bayesian Information Criterion.

The output_table format depends on the PackOutput argument. For PackOutput('false'), the default, the following table describes output_table.

GMMFit output_table Schema, PackOutput('false')
Column Name Data Type Description
cluster_id INTEGER Identification number of the cluster
points_assigned INTEGER Number of points in the training data set assigned to the cluster
covariance_type VARCHAR Covariance type (specified by the CovarianceType argument)
weight DOUBLE PRECISION Weight assigned to the cluster
dim_n DOUBLE PRECISION Mean of the cluster n.

The table has one such column for each dimension (n has the values 1 through D).

cov_n DOUBLE PRECISION Covariance of the cluster n.

Depends on the covariance type:

'spherical': Each row in a single covariance column contains a single DOUBLE PRECISION value.

'diagonal': There are D covariance rows, each containing a DOUBLE PRECISION value.

'tied' or 'full': There are D(D-1) covariance rows, each containing a DOUBLE PRECISION value.

The table has one such column for each dimension (n has the values 1 through D).

determinant DOUBLE PRECISION Determinant of the covariance matrix
precision VARCHAR Precision matrix, the inverse of the covariance matrix. The precision matrix is serialized and stored to improve the performance of the function GMMPredict.

For PackOutput('true'), the following table describes output_table.

GMMFit output_table Schema, PackOutput('true')
Column Name Data Type Description
cluster_id INTEGER Identification number of the cluster
points_assigned INTEGER Number of points in the training data set assigned to the cluster
covariance_type VARCHAR Covariance type (specified by the CovarianceType argument)
weight DOUBLE PRECISION Weight assigned to the cluster
mean VARCHAR A vector of D DOUBLE PRECISION values that specify the mean of each cluster (for example, [4.5, 2.3, 1.3)].
covariance VARCHAR Depends on the covariance type:

'spherical': Each row in a single covariance column contains a single DOUBLE PRECISION value.

'diagonal': Each row contains a white-space separated list of D DOUBLE PRECISION values.

'tied' or 'full': Each row contains a white-space separated list of D*D DOUBLE PRECISION values.

determinant DOUBLE PRECISION Determinant of the covariance matrix
precision VARCHAR Precision matrix, the inverse of the covariance matrix. The precision matrix is serialized and stored to improve the performance of the function GMMPredict.