The GMM function outputs a message and a table. The OutputTable schema depends on the PackOutput syntax element.
Output Message Properties
Property | Value |
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Output Table | Table name specified in OutputTable syntax element. |
Algorithm Used | Algorithm that function used—Basic GMM or DP-DMM. |
Stopping Criterion | Why function stopped—reached iteration limit or convergence. |
Delta Log Likelihood | Change in mean log-likelihood for each data point between next-to-final and final iterations. |
Number of Iterations | Number of iterations that function performed before stopping. |
Number of Clusters Found | Number of clusters in GMM. |
Covariance Type | Spherical, diagonal, tied, or full. |
Number of Data Points | Number of data points in data set. |
Global Mean | Mean of data set. |
Global Covariance | Covariance of data set. |
Log Likelihood | Log-likelihood of data, given GMM. |
Akaike Information Criterion | Akaike Information Criterion. |
Bayesian Information Criterion | Bayesian Information Criterion. |
OutputTable Schema, PackOutput ('false') (Default)
Column | Data Type | Description | ||||||||
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cluster_id | INTEGER | Cluster identification number. | ||||||||
points_assigned | INTEGER | Number of points in training data set assigned to cluster. | ||||||||
covariance_type | VARCHAR | Covariance type. | ||||||||
weight | DOUBLE PRECISION | Weight assigned to cluster. | ||||||||
dim_n | DOUBLE PRECISION | [Column appears once for each dimension.] Mean of cluster n. | ||||||||
cov_n | DOUBLE PRECISION | [Column appears once for each dimension.] Covariance of cluster n. Depends on CovarianceType:
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log_determinant | DOUBLE PRECISION | Log determinant of covariance matrix. | ||||||||
prec | VARCHAR | Precision matrix, inverse of covariance matrix. Precision matrix is serialized and stored to improve performance of GMMPredict (ML Engine) function. |
OutputTable Schema, PackOutput ('true')
Column | Data Type | Description | ||||||
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cluster_id | INTEGER | Cluster identification number. | ||||||
points_assigned | INTEGER | Number of points in training data set assigned to cluster. | ||||||
covariance_type | VARCHAR | Number of points in training data set assigned to cluster. | ||||||
weight | DOUBLE PRECISION | Weight assigned to cluster. | ||||||
mean | VARCHAR | Vector of D DOUBLE PRECISION values that specify mean of each cluster (for example, [4.5, 2.3, 1.3]). | ||||||
covariance | VARCHAR | Depends on CovarianceType:
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log_determinant | DOUBLE PRECISION | Log determinant of covariance matrix. | ||||||
prec | VARCHAR | Precision matrix, inverse of covariance matrix. Precision matrix is serialized and stored to improve performance of GMMPredict (ML Engine) function. |