lasdi.param
Attributes
Classes
Functions
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Module Contents
- lasdi.param.get_1dspace_from_list(config)
- lasdi.param.create_uniform_1dspace(config)
- lasdi.param.getParam1DSpace
- class lasdi.param.ParameterSpace(config)
- param_list = []
- param_name = []
- n_param = 0
- train_space = None
- test_space = None
- n_init = 0
- test_grid_sizes = []
- test_meshgrid = None
- n_train()
- n_test()
- createInitialTrainSpace(param_list)
- createInitialTrainSpaceForHull(param_list)
Concatenates the provided lists of training points into a 2D array.
- Parameters:
param_list (
list(dict)) – A list of parameter dictionaries- Returns:
mesh_grids – np.array of size [d, k], where d is the number of points provided on the exterior of the training space and k is the number of parameters (k == len(param_list)).
- Return type:
numpy.array
- createTestGridSpace(param_list)
- createTestGridSpaceForHull(param_list)
Builds an initial uniform grid for the testing parameters when the test_space is ‘hull’.
- Parameters:
param_list (
list(dict)) – A list of parameter dictionaries- Returns:
gridSizes (
list(int)) – A list containing the number of elements on the grid in each parameter.mesh_grids (
numpy.array) – tuple of numpy nd arrays, corresponding to each parameter. Dimension of the array equals to the number of parameters.param_grid (
numpy.array) – numpy 2d array of size (grid size x number of parameters).
- createTestHullSpace(param_list)
This function builds an initial uniform grid for the testing parameters, and then returns any testing points which are within the convex hull of the provided training parameters.
- Parameters:
param_list (
list(dict)) – A list of parameter dictionaries- Returns:
gridSizes (
list(int)) – A list containing the number of elements on the grid in each parameter.mesh_grids (
numpy.array) – tuple of numpy nd arrays, corresponding to each parameter. Dimension of the array equals to the number of parameters.test_space (
numpy.array) – numpy 2d array of size [d, k], where d is the number of testing points within convex hull of the training space and k is the number of parameters (k == len(param_list)).
- getParameter(param_vector)
convert numpy array parameter vector to a dict. Physics class takes the dict for solve/initial_condition.
- createHyperMeshGrid(param_ranges)
- param_ranges: list of numpy 1d arrays, each corresponding to 1d parameter grid space.
The list size is equal to the number of parameters.
- Output: paramSpaces
tuple of numpy nd arrays, corresponding to each parameter. Dimension of the array equals to the number of parameters
- createHyperGridSpace(mesh_grids)
- mesh_grids: tuple of numpy nd arrays, corresponding to each parameter.
Dimension of the array equals to the number of parameters
- Output: param_grid
numpy 2d array of size (grid size x number of parameters).
grid size is the size of a numpy nd array.
- appendTrainSpace(param)
- export()
- load(dict_)