Conditioned Synthetic Data Generation in Computer-Based Reasoning Systems

A non-transitory computer readable medium storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform a process of:

• receiving a request for generation of synthetic data based on a set of training data cases;
• determining one or more conditions for the synthetic data;
• for each synthetic data case in the synthetic data,
• for each undetermined feature in the synthetic data case,
• determining one or more focal training data cases from among the set of training data cases based at least in part on the one or more conditions and any already-determined value for features in the synthetic data case;
• determining a value for the undetermined feature in the synthetic data case based at least in part on the focal training data cases;
• using the value for the undetermined feature in the synthetic data case;
• continue to determine undetermined features until there are no more undetermined features;
• causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data.

A system for performing a machine-executed operation involving instructions, wherein said instructions are instructions which, when executed by one or more computing devices, cause performance of a process comprising:

• receiving a request for generation of synthetic data based on a set of training data cases;
• determining one or more conditions for the synthetic data;
• for each synthetic data case in the synthetic data,
• for each undetermined feature in the synthetic data case,
• determining one or more focal training data cases from among the set of training data cases based at least in part on the one or more conditions and any already-determined value for features in the synthetic data case;
• determining a value for the undetermined feature in the synthetic data case based at least in part on the focal training data cases;
• using the value for the undetermined feature in the synthetic data case;
• continue to determine undetermined features until there are no more undetermined features;
• causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data.