aepsych.utils

aepsych.utils module

aepsych.utils.make_scaled_sobol(lb, ub, size, seed=None)[source]
aepsych.utils.promote_0d(x)[source]
aepsych.utils.dim_grid(lower, upper, dim, gridsize=30, slice_dims=None)[source]

Create a grid Create a grid based on lower, upper, and dim. :param - lower (‘int’) - lower bound: :param - upper (‘int’) - upper bound: :param - dim (‘int) - dimension: :param - gridsize (‘int’) - size for grid: :param - slice_dims (Optional: :type - slice_dims (Optional: value} dict :param dict) - values to use for slicing axes: :type dict) - values to use for slicing axes: value} dict :param as an {index: :type as an {index: value} dict

Returns:

grid – Tensor

Return type:

torch.FloatTensor

Parameters:

  • lower (Tensor) –

  • upper (Tensor) –

  • dim (int) –

  • gridsize (int) –

  • slice_dims (Optional[Mapping[int, float]]) –

aepsych.utils.interpolate_monotonic(x, y, z, min_x=- inf, max_x=inf)[source]
aepsych.utils.get_lse_interval(model, mono_grid, target_level, cred_level=None, mono_dim=- 1, n_samps=500, lb=- inf, ub=inf, gridsize=30, **kwargs)[source]
aepsych.utils.get_lse_contour(post_mean, mono_grid, level, mono_dim=- 1, lb=- inf, ub=inf)[source]
aepsych.utils.get_jnd_1d(post_mean, mono_grid, df=1, mono_dim=- 1, lb=- inf, ub=inf)[source]
aepsych.utils.get_jnd_multid(post_mean, mono_grid, df=1, mono_dim=- 1, lb=- inf, ub=inf)[source]
aepsych.utils.get_parameters(config)[source]
Return type:

List[Dict]

aepsych.utils.get_dim(config)[source]
Return type:

int

aepsych.utils.get_objectives(config)[source]
Return type:

Dict