concepts.dm.pdsketch.regression_utils#
Utility functions for regression search.
Functions
Create a TensorValue that corresponds to a variable inside a FindExpression. |
|
Create a TensorValue that corresponds to a variable inside a FindExpression. |
|
Convert a single optimistic value stored in a TensorValue to an OptimisticValue. |
|
|
|
|
|
Generated a set of subgoals with placeholders for CSP variables. |
|
|
Ground the given FOL expression with the given variable mapping. |
|
Ground the given FOL expression with the given variable mapping. |
Ground the given operator application expression with the given variable mapping. |
|
Ground the given regression application expression with the given variable mapping. |
|
|
Check if there is a ConstantExpression whose value is an optimistic constant. |
Check if there is any optimistic value in the input TensorValue or a list of TensorValue's. |
|
Check if the input TensorValue is a single optimistic value or a list of TensorValue's that are all single optimistic values. |
|
Compute the number of candidate regression rules. |
|
|
Make a rule applier from a regression rule and a set of bounded variables. |
|
Map the CSP variables in the action to the CSP variables in the placeholder CSP. |
|
Map the CSP variables in the subgoal to the CSP variables in the placeholder CSP. |
Map the CSP variables in the regression rule applier to the CSP variables in the placeholder CSP. |
|
Map the CSP variable mapping to the new variable mapping. |
|
|
Map the CSP variable state to the new variable state. |
|
Mark the solver for the current state. |
|
Trying to downcast the expression_1 to the same form as expression_2. |
Classes
ApplicableRegressionRuleGroup(chain_index, subgoal_index, regression_rules) |
|
ApplicableRegressionRuleItem(regression_rule, bounded_variables) |