aepsych.utils¶
aepsych.utils module¶
- aepsych.utils.dim_grid(lower, upper, 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) –
gridsize (int) –
slice_dims (Optional[Mapping[int, float]]) –
- aepsych.utils.interpolate_monotonic(x, y, z, min_x=- inf, max_x=inf)[source]¶
- Parameters:
x (Tensor) –
y (Tensor) –
z (Union[Tensor, float]) –
min_x (Union[Tensor, float]) –
max_x (Union[Tensor, float]) –
- Return type:
Tensor
- 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]¶
- Parameters:
mono_grid (Union[Tensor, ndarray]) –
target_level (float) –
cred_level (Optional[float]) –
mono_dim (int) –
n_samps (int) –
lb (float) –
ub (float) –
gridsize (int) –
- Return type:
Union[Tuple[Tensor, Tensor, Tensor], Tensor]
- aepsych.utils.get_lse_contour(post_mean, mono_grid, level, mono_dim=- 1, lb=- inf, ub=inf)[source]¶
- Parameters:
post_mean (Tensor) –
mono_grid (Union[Tensor, ndarray]) –
level (float) –
mono_dim (int) –
lb (Union[Tensor, float]) –
ub (Union[Tensor, float]) –
- Return type:
Tensor
- aepsych.utils.get_jnd_1d(post_mean, mono_grid, df=1, mono_dim=- 1, lb=- inf, ub=inf)[source]¶
- Parameters:
post_mean (Tensor) –
mono_grid (Tensor) –
df (int) –
mono_dim (int) –
lb (Union[Tensor, float]) –
ub (Union[Tensor, float]) –
- Return type:
Tensor