fsu_cvt
- pydantic model dakota.spec.method.fsu_cvt.FsuCvtSelection
Generated model for FsuCvtSelection
Show JSON schema
{ "title": "FsuCvtSelection", "description": "Generated model for FsuCvtSelection", "type": "object", "properties": { "fsu_cvt": { "$ref": "#/$defs/FsuCvtConfig", "x-materialization": [ { "ir_key": "method.algorithm", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "FSU_CVT" } ] } }, "$defs": { "Binned": { "additionalProperties": false, "description": "Computes Sobol' main effects using a binned approach", "properties": { "binned": { "$ref": "#/$defs/BinnedConfig", "x-materialization": [ { "ir_key": "method.vbd_via_sampling_method", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "VBD_BINNED" } ] } }, "required": [ "binned" ], "title": "Binned", "type": "object" }, "BinnedConfig": { "additionalProperties": false, "description": "Computes Sobol' main effects using a binned approach", "properties": { "num_bins": { "default": -1, "description": "Number of bins used to compute the variance-based decomposition", "title": "Num Bins", "type": "integer", "x-materialization": [ { "ir_key": "method.vbd_via_sampling_num_bins", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] } }, "title": "BinnedConfig", "type": "object" }, "Debug": { "additionalProperties": false, "description": "Level 5 of 5 - maximum", "properties": { "debug": { "const": true, "default": true, "description": "Level 5 of 5 - maximum", "title": "Debug", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEBUG_OUTPUT" } ] } }, "title": "Debug", "type": "object" }, "FsuCvtConfig": { "additionalProperties": false, "description": "Design of Computer Experiments - Centroidal Voronoi Tessellation", "properties": { "model_pointer": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Identifier for model block to be used by a method", "title": "Model Pointer", "x-block-pointer": "model", "x-materialization": [ { "ir_key": "method.model_pointer", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "max_iterations": { "default": 9223372036854775807, "description": "Number of iterations allowed for optimizers and adaptive UQ methods", "minimum": 0, "title": "Max Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.max_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "variance_based_decomp": { "anyOf": [ { "$ref": "#/$defs/VbdSamplingVarianceBasedDecomp" }, { "type": "null" } ], "default": null, "description": "Activates global sensitivity analysis based on decomposition of response variance into contributions from variables", "x-materialization": [ { "ir_key": "method.variance_based_decomp", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "seed": { "anyOf": [ { "exclusiveMinimum": 0, "type": "integer" }, { "type": "null" } ], "default": null, "description": "Seed of the random number generator", "title": "Seed", "x-materialization": [ { "ir_key": "method.random_seed", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "fixed_seed": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Reuses the same seed value for multiple random sampling sets", "title": "Fixed Seed", "x-materialization": [ { "ir_key": "method.fixed_seed", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "id_method": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Name the method block; helpful when there are multiple", "title": "Id Method", "x-materialization": [ { "ir_key": "method.id", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "output": { "anyOf": [ { "$ref": "#/$defs/Debug" }, { "$ref": "#/$defs/Verbose" }, { "$ref": "#/$defs/Normal" }, { "$ref": "#/$defs/Quiet" }, { "$ref": "#/$defs/Silent" } ], "description": "Control how much method information is written to the screen and output file", "title": "Output", "x-model-default": "Normal", "x-union-pattern": 1 }, "final_solutions": { "default": 0, "description": "Number of designs returned as the best solutions", "minimum": 0, "title": "Final Solutions", "type": "integer", "x-materialization": [ { "ir_key": "method.final_solutions", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "samples": { "default": 0, "description": "Number of samples for sampling-based methods", "title": "Samples", "type": "integer", "x-materialization": [ { "ir_key": "method.samples", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "latinize": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Adjust samples to improve the discrepancy of the marginal distributions", "title": "Latinize", "x-materialization": [ { "ir_key": "method.latinize", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "quality_metrics": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Calculate metrics to assess the quality of quasi-Monte Carlo samples", "title": "Quality Metrics", "x-materialization": [ { "ir_key": "method.quality_metrics", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "trial_type": { "anyOf": [ { "$ref": "#/$defs/TrialTypeGrid" }, { "$ref": "#/$defs/TrialTypeHalton" }, { "$ref": "#/$defs/TrialTypeRandom" }, { "type": "null" } ], "default": null, "description": "Specify how the trial samples are generated", "title": "Trial Type", "x-union-pattern": 2 }, "num_trials": { "default": 10000, "description": "The number of secondary sample points generated to adjust the location of the primary sample points", "title": "Num Trials", "type": "integer", "x-materialization": [ { "ir_key": "method.fsu_cvt.num_trials", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] } }, "title": "FsuCvtConfig", "type": "object" }, "Normal": { "additionalProperties": false, "description": "Level 3 of 5 - default", "properties": { "normal": { "const": true, "default": true, "description": "Level 3 of 5 - default", "title": "Normal", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NORMAL_OUTPUT" } ] } }, "title": "Normal", "type": "object" }, "PickAndFreeze": { "additionalProperties": false, "description": "Use the pick-and-freeze variance-based decomposition method", "properties": { "pick_and_freeze": { "const": true, "default": true, "description": "Use the pick-and-freeze variance-based decomposition method", "title": "Pick And Freeze", "type": "boolean", "x-materialization": [ { "ir_key": "method.vbd_via_sampling_method", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "VBD_PICK_AND_FREEZE" } ] } }, "title": "PickAndFreeze", "type": "object" }, "Quiet": { "additionalProperties": false, "description": "Level 2 of 5 - less than normal", "properties": { "quiet": { "const": true, "default": true, "description": "Level 2 of 5 - less than normal", "title": "Quiet", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "QUIET_OUTPUT" } ] } }, "title": "Quiet", "type": "object" }, "Silent": { "additionalProperties": false, "description": "Level 1 of 5 - minimum", "properties": { "silent": { "const": true, "default": true, "description": "Level 1 of 5 - minimum", "title": "Silent", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SILENT_OUTPUT" } ] } }, "title": "Silent", "type": "object" }, "TrialTypeGrid": { "additionalProperties": false, "description": "Samples on a regular grid", "properties": { "grid": { "const": true, "default": true, "description": "Samples on a regular grid", "title": "Grid", "type": "boolean", "x-materialization": [ { "ir_key": "method.trial_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "grid" } ] } }, "title": "TrialTypeGrid", "type": "object" }, "TrialTypeHalton": { "additionalProperties": false, "description": "Generate samples from a Halton sequence", "properties": { "halton": { "const": true, "default": true, "description": "Generate samples from a Halton sequence", "title": "Halton", "type": "boolean", "x-materialization": [ { "ir_key": "method.trial_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "halton" } ] } }, "title": "TrialTypeHalton", "type": "object" }, "TrialTypeRandom": { "additionalProperties": false, "description": "Uses purely random Monte Carlo sampling to sample variables", "properties": { "random": { "const": true, "default": true, "description": "Uses purely random Monte Carlo sampling to sample variables", "title": "Random", "type": "boolean", "x-materialization": [ { "ir_key": "method.trial_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "random" } ] } }, "title": "TrialTypeRandom", "type": "object" }, "VbdSamplingVarianceBasedDecomp": { "additionalProperties": false, "description": "Activates global sensitivity analysis based on decomposition of response variance into contributions from variables", "properties": { "drop_tolerance": { "default": -1.0, "description": "Suppresses output of sensitivity indices with values lower than this tolerance", "title": "Drop Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.vbd_drop_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "vbd_sampling_method": { "anyOf": [ { "$ref": "#/$defs/Binned" }, { "$ref": "#/$defs/PickAndFreeze" }, { "type": "null" } ], "default": null, "description": "The method to use for variance-based decomposition", "title": "Vbd Sampling Method", "x-union-pattern": 2 } }, "title": "VbdSamplingVarianceBasedDecomp", "type": "object" }, "Verbose": { "additionalProperties": false, "description": "Level 4 of 5 - more than normal", "properties": { "verbose": { "const": true, "default": true, "description": "Level 4 of 5 - more than normal", "title": "Verbose", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VERBOSE_OUTPUT" } ] } }, "title": "Verbose", "type": "object" } }, "additionalProperties": false, "required": [ "fsu_cvt" ] }
- field fsu_cvt: FsuCvtConfig [Required]
- classmethod get_registry() dict[str, type[MethodSelection]]
Get registry, performing deferred registration on first call
- classmethod get_union()
Generate Union from all registered selections
- pydantic model dakota.spec.method.fsu_cvt.FsuCvtConfig
Design of Computer Experiments - Centroidal Voronoi Tessellation
Show JSON schema
{ "title": "FsuCvtConfig", "description": "Design of Computer Experiments - Centroidal Voronoi Tessellation", "type": "object", "properties": { "model_pointer": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Identifier for model block to be used by a method", "title": "Model Pointer", "x-block-pointer": "model", "x-materialization": [ { "ir_key": "method.model_pointer", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "max_iterations": { "default": 9223372036854775807, "description": "Number of iterations allowed for optimizers and adaptive UQ methods", "minimum": 0, "title": "Max Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.max_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "variance_based_decomp": { "anyOf": [ { "$ref": "#/$defs/VbdSamplingVarianceBasedDecomp" }, { "type": "null" } ], "default": null, "description": "Activates global sensitivity analysis based on decomposition of response variance into contributions from variables", "x-materialization": [ { "ir_key": "method.variance_based_decomp", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "seed": { "anyOf": [ { "exclusiveMinimum": 0, "type": "integer" }, { "type": "null" } ], "default": null, "description": "Seed of the random number generator", "title": "Seed", "x-materialization": [ { "ir_key": "method.random_seed", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "fixed_seed": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Reuses the same seed value for multiple random sampling sets", "title": "Fixed Seed", "x-materialization": [ { "ir_key": "method.fixed_seed", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "id_method": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Name the method block; helpful when there are multiple", "title": "Id Method", "x-materialization": [ { "ir_key": "method.id", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "output": { "anyOf": [ { "$ref": "#/$defs/Debug" }, { "$ref": "#/$defs/Verbose" }, { "$ref": "#/$defs/Normal" }, { "$ref": "#/$defs/Quiet" }, { "$ref": "#/$defs/Silent" } ], "description": "Control how much method information is written to the screen and output file", "title": "Output", "x-model-default": "Normal", "x-union-pattern": 1 }, "final_solutions": { "default": 0, "description": "Number of designs returned as the best solutions", "minimum": 0, "title": "Final Solutions", "type": "integer", "x-materialization": [ { "ir_key": "method.final_solutions", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "samples": { "default": 0, "description": "Number of samples for sampling-based methods", "title": "Samples", "type": "integer", "x-materialization": [ { "ir_key": "method.samples", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "latinize": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Adjust samples to improve the discrepancy of the marginal distributions", "title": "Latinize", "x-materialization": [ { "ir_key": "method.latinize", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "quality_metrics": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Calculate metrics to assess the quality of quasi-Monte Carlo samples", "title": "Quality Metrics", "x-materialization": [ { "ir_key": "method.quality_metrics", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "trial_type": { "anyOf": [ { "$ref": "#/$defs/TrialTypeGrid" }, { "$ref": "#/$defs/TrialTypeHalton" }, { "$ref": "#/$defs/TrialTypeRandom" }, { "type": "null" } ], "default": null, "description": "Specify how the trial samples are generated", "title": "Trial Type", "x-union-pattern": 2 }, "num_trials": { "default": 10000, "description": "The number of secondary sample points generated to adjust the location of the primary sample points", "title": "Num Trials", "type": "integer", "x-materialization": [ { "ir_key": "method.fsu_cvt.num_trials", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] } }, "$defs": { "Binned": { "additionalProperties": false, "description": "Computes Sobol' main effects using a binned approach", "properties": { "binned": { "$ref": "#/$defs/BinnedConfig", "x-materialization": [ { "ir_key": "method.vbd_via_sampling_method", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "VBD_BINNED" } ] } }, "required": [ "binned" ], "title": "Binned", "type": "object" }, "BinnedConfig": { "additionalProperties": false, "description": "Computes Sobol' main effects using a binned approach", "properties": { "num_bins": { "default": -1, "description": "Number of bins used to compute the variance-based decomposition", "title": "Num Bins", "type": "integer", "x-materialization": [ { "ir_key": "method.vbd_via_sampling_num_bins", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] } }, "title": "BinnedConfig", "type": "object" }, "Debug": { "additionalProperties": false, "description": "Level 5 of 5 - maximum", "properties": { "debug": { "const": true, "default": true, "description": "Level 5 of 5 - maximum", "title": "Debug", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEBUG_OUTPUT" } ] } }, "title": "Debug", "type": "object" }, "Normal": { "additionalProperties": false, "description": "Level 3 of 5 - default", "properties": { "normal": { "const": true, "default": true, "description": "Level 3 of 5 - default", "title": "Normal", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NORMAL_OUTPUT" } ] } }, "title": "Normal", "type": "object" }, "PickAndFreeze": { "additionalProperties": false, "description": "Use the pick-and-freeze variance-based decomposition method", "properties": { "pick_and_freeze": { "const": true, "default": true, "description": "Use the pick-and-freeze variance-based decomposition method", "title": "Pick And Freeze", "type": "boolean", "x-materialization": [ { "ir_key": "method.vbd_via_sampling_method", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "VBD_PICK_AND_FREEZE" } ] } }, "title": "PickAndFreeze", "type": "object" }, "Quiet": { "additionalProperties": false, "description": "Level 2 of 5 - less than normal", "properties": { "quiet": { "const": true, "default": true, "description": "Level 2 of 5 - less than normal", "title": "Quiet", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "QUIET_OUTPUT" } ] } }, "title": "Quiet", "type": "object" }, "Silent": { "additionalProperties": false, "description": "Level 1 of 5 - minimum", "properties": { "silent": { "const": true, "default": true, "description": "Level 1 of 5 - minimum", "title": "Silent", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SILENT_OUTPUT" } ] } }, "title": "Silent", "type": "object" }, "TrialTypeGrid": { "additionalProperties": false, "description": "Samples on a regular grid", "properties": { "grid": { "const": true, "default": true, "description": "Samples on a regular grid", "title": "Grid", "type": "boolean", "x-materialization": [ { "ir_key": "method.trial_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "grid" } ] } }, "title": "TrialTypeGrid", "type": "object" }, "TrialTypeHalton": { "additionalProperties": false, "description": "Generate samples from a Halton sequence", "properties": { "halton": { "const": true, "default": true, "description": "Generate samples from a Halton sequence", "title": "Halton", "type": "boolean", "x-materialization": [ { "ir_key": "method.trial_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "halton" } ] } }, "title": "TrialTypeHalton", "type": "object" }, "TrialTypeRandom": { "additionalProperties": false, "description": "Uses purely random Monte Carlo sampling to sample variables", "properties": { "random": { "const": true, "default": true, "description": "Uses purely random Monte Carlo sampling to sample variables", "title": "Random", "type": "boolean", "x-materialization": [ { "ir_key": "method.trial_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "random" } ] } }, "title": "TrialTypeRandom", "type": "object" }, "VbdSamplingVarianceBasedDecomp": { "additionalProperties": false, "description": "Activates global sensitivity analysis based on decomposition of response variance into contributions from variables", "properties": { "drop_tolerance": { "default": -1.0, "description": "Suppresses output of sensitivity indices with values lower than this tolerance", "title": "Drop Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.vbd_drop_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "vbd_sampling_method": { "anyOf": [ { "$ref": "#/$defs/Binned" }, { "$ref": "#/$defs/PickAndFreeze" }, { "type": "null" } ], "default": null, "description": "The method to use for variance-based decomposition", "title": "Vbd Sampling Method", "x-union-pattern": 2 } }, "title": "VbdSamplingVarianceBasedDecomp", "type": "object" }, "Verbose": { "additionalProperties": false, "description": "Level 4 of 5 - more than normal", "properties": { "verbose": { "const": true, "default": true, "description": "Level 4 of 5 - more than normal", "title": "Verbose", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VERBOSE_OUTPUT" } ] } }, "title": "Verbose", "type": "object" } }, "additionalProperties": false }
- field final_solutions: int = 0
Number of designs returned as the best solutions
- Constraints:
ge = 0
- field fixed_seed: Literal[True] | None = None
Reuses the same seed value for multiple random sampling sets
- field id_method: str | None = None
Name the method block; helpful when there are multiple
- field latinize: Literal[True] | None = None
Adjust samples to improve the discrepancy of the marginal distributions
- field max_iterations: int = 9223372036854775807
Number of iterations allowed for optimizers and adaptive UQ methods
- Constraints:
ge = 0
- field model_pointer: str | None = None
Identifier for model block to be used by a method
- field num_trials: int = 10000
The number of secondary sample points generated to adjust the location of the primary sample points
- field output: Debug | Verbose | Normal | Quiet | Silent [Optional]
Control how much method information is written to the screen and output file
- field quality_metrics: Literal[True] | None = None
Calculate metrics to assess the quality of quasi-Monte Carlo samples
- field samples: int = 0
Number of samples for sampling-based methods
- field seed: int | None = None
Seed of the random number generator
- Constraints:
gt = 0
- field trial_type: TrialTypeGrid | TrialTypeHalton | TrialTypeRandom | None = None
Specify how the trial samples are generated
- field variance_based_decomp: VbdSamplingVarianceBasedDecomp | None = None
Activates global sensitivity analysis based on decomposition of response variance into contributions from variables
Generated Pydantic models for method.fsu_cvt
- pydantic model dakota.spec.method.fsu_cvt.TrialTypeGrid
Samples on a regular grid
Show JSON schema
{ "title": "TrialTypeGrid", "description": "Samples on a regular grid", "type": "object", "properties": { "grid": { "const": true, "default": true, "description": "Samples on a regular grid", "title": "Grid", "type": "boolean", "x-materialization": [ { "ir_key": "method.trial_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "grid" } ] } }, "additionalProperties": false }
- Fields:
- field grid: Literal[True] = True
Samples on a regular grid
- pydantic model dakota.spec.method.fsu_cvt.TrialTypeHalton
Generate samples from a Halton sequence
Show JSON schema
{ "title": "TrialTypeHalton", "description": "Generate samples from a Halton sequence", "type": "object", "properties": { "halton": { "const": true, "default": true, "description": "Generate samples from a Halton sequence", "title": "Halton", "type": "boolean", "x-materialization": [ { "ir_key": "method.trial_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "halton" } ] } }, "additionalProperties": false }
- Fields:
- field halton: Literal[True] = True
Generate samples from a Halton sequence
- pydantic model dakota.spec.method.fsu_cvt.TrialTypeRandom
Uses purely random Monte Carlo sampling to sample variables
Show JSON schema
{ "title": "TrialTypeRandom", "description": "Uses purely random Monte Carlo sampling to sample variables", "type": "object", "properties": { "random": { "const": true, "default": true, "description": "Uses purely random Monte Carlo sampling to sample variables", "title": "Random", "type": "boolean", "x-materialization": [ { "ir_key": "method.trial_type", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "random" } ] } }, "additionalProperties": false }
- Fields:
- field random: Literal[True] = True
Uses purely random Monte Carlo sampling to sample variables
