参考：http://gpdb.docs.pivotal.io/4390/admin_guide/query/topics/functions-operators.html
| Function | Return Type | Full Syntax | Description |
|---|---|---|---|
| matrix_add(array[], array[]) | smallint[], int[], bigint[], float[] | matrix_add( array[[1,1],[2,2]], array[[3,4],[5,6]]) | Adds two two-dimensional matrices. The matrices must be conformable. |
| matrix_multiply( array[], array[]) | smallint[]int[], bigint[], float[] | matrix_multiply( array[[2,0,0],[0,2,0],[0,0,2]], array[[3,0,3],[0,3,0],[0,0,3]] ) | Multiplies two, three- dimensional arrays. The matrices must be conformable. |
| matrix_multiply( array[], expr) | int[], float[] | matrix_multiply( array[[1,1,1], [2,2,2], [3,3,3]], 2) | Multiplies a two-dimensional array and a scalar numeric value. |
| matrix_transpose( array[]) | Same as input arraytype. | matrix_transpose( array [[1,1,1],[2,2,2]]) | Transposes a two-dimensional array. |
| pinv(array []) | smallint[]int[], bigint[], float[] | pinv(array[[2.5,0,0],[0,1,0],[0,0,.5]]) | Calculates the Moore-Penrose pseudoinverse of a matrix. |
| unnest (array[]) | set of anyelement | unnest( array['one', 'row', 'per', 'item']) | Transforms a one dimensional array into rows. Returns a set ofanyelement, a polymorphic pseudotype in PostgreSQL. |
| Function | Return Type | Full Syntax | Description |
|---|---|---|---|
| MEDIAN (expr) | timestamp, timestampz, interval, float | MEDIAN (expression)
Example: SELECT department_id, MEDIAN(salary) FROM employees GROUP BY department_id; |
Can take a two-dimensional array as input. Treats such arrays as matrices. |
| PERCENTILE_CONT (expr) WITHIN GROUP (ORDER BYexpr [DESC/ASC]) | timestamp, timestampz, interval, float | PERCENTILE_CONT(percentage) WITHIN GROUP (ORDER BY expression)
Example: SELECT department_id, PERCENTILE_CONT (0.5) WITHIN GROUP (ORDER BY salary DESC) "Median_cont"; FROM employees GROUP BY department_id; |
Performs an inverse function that assumes a continuous distribution model. It takes a percentile value and a sort specification and returns the same datatype as the numeric datatype of the argument. This returned value is a computed result after performing linear interpolation. Null are ignored in this calculation. |
| PERCENTILE_DISC (expr) WITHIN GROUP (ORDER BYexpr [DESC/ASC]) | timestamp, timestampz, interval, float | PERCENTILE_DISC(percentage) WITHIN GROUP (ORDER BY expression)
Example: SELECT department_id, PERCENTILE_DISC (0.5) WITHIN GROUP (ORDER BY salary DESC) "Median_desc"; FROM employees GROUP BY department_id; |
Performs an inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort specification. This returned value is an element from the set. Null are ignored in this calculation. |
| sum(array[]) | smallint[]int[], bigint[], float[] | sum(array[[1,2],[3,4]])
Example: CREATE TABLE mymatrix (myvalue int[]);
INSERT INTO mymatrix VALUES (array[[1,2],[3,4]]);
INSERT INTO mymatrix VALUES (array[[0,1],[1,0]]);
SELECT sum(myvalue) FROM mymatrix;
sum
---------------
{{1,3},{4,4}}
|
Performs matrix summation. Can take as input a two-dimensional array that is treated as a matrix. |
| pivot_sum (label[], label, expr) | int[], bigint[], float[] | pivot_sum( array['A1','A2'], attr, value) | A pivot aggregation using sum to resolve duplicate entries. |
| mregr_coef(expr, array[]) | float[] | mregr_coef(y, array[1, x1, x2]) | The four mregr_*aggregates perform linear regressions using the ordinary-least-squares method. mregr_coefcalculates the regression coefficients. The size of the return array formregr_coef is the same as the size of the input array of independent variables, since the return array contains the coefficient for each independent variable. |
| mregr_r2 (expr, array[]) | float | mregr_r2(y, array[1, x1, x2]) | The four mregr_*aggregates perform linear regressions using the ordinary-least-squares method. mregr_r2calculates the r-squared error value for the regression. |
| mregr_pvalues(expr, array[]) | float[] | mregr_pvalues(y, array[1, x1, x2]) | The four mregr_*aggregates perform linear regressions using the ordinary-least-squares method. mregr_pvaluescalculates the p-values for the regression. |
| mregr_tstats(expr, array[]) | float[] | mregr_tstats(y, array[1, x1, x2]) | The four mregr_*aggregates perform linear regressions using the ordinary-least-squares method. mregr_tstatscalculates the t-statistics for the regression. |
| nb_classify(text[], bigint, bigint[], bigint[]) | text | nb_classify(classes, attr_count, class_count, class_total) | Classify rows using a Naive Bayes Classifier. This aggregate uses a baseline of training data to predict the classification of new rows and returns the class with the largest likelihood of appearing in the new rows. |
| nb_probabilities(text[], bigint, bigint[], bigint[]) | text | nb_probabilities(classes, attr_count, class_count, class_total) | Determine probability for each class using a Naive Bayes Classifier. This aggregate uses a baseline of training data to predict the classification of new rows and returns the probabilities that each class will appear in new rows. |