IrregularArgvals

class FDApy.representation.IrregularArgvals(dict=None, /, **kwargs)

Represent the argvals of irregular functional data.

This class extends the Argvals class to represent a dictionary where the keys are strings and the values are DenseArgvals. It provides additional functionality for working with argument values in scientific computing.

Attributes:

• n_points (Dict[int, Tuple[int, …]]) – Number of sampling points of each dimension.

• n_dimension (int) – Number of input dimension of the data.

• min_max (Dict[str, Tuple[float, float]]) – Minimum and maximum sampling points for each dimension.

Methods

clear()
compatible_with(values)	Raise an error if Argvals is not compatible with Values.
concatenate(*argvals)	Concatenate IrregularArgvals objects.
copy()
fromkeys(iterable[, value])
get(k[,d])
items()
keys()
normalization()	Normalize the IrregularArgvals.
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised.
popitem()	as a 2-tuple; but raise KeyError if D is empty.
setdefault(k[,d])
switch()	Switch the dictionary.
to_dense()	Concatenate IrregularArgvals to DenseArgvals.
update([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
values()

clear() → None.  Remove all items from D.

compatible_with(values)

Raise an error if Argvals is not compatible with Values.

Parameters:

values (Type[Values]) – A Values object.

Raises:

ValueError – When self and values do not have coherent common sampling points. The first dimension of values is assumed to represented the number of observations.

Return type:

None

static concatenate(*argvals)

Concatenate IrregularArgvals objects.

This function concatenates the IrregularArgvals objects by adding the one at the end of the other. The keys are updated to avoid duplicates.

Parameters:

argvals – The IrregularArgvals objects to concatenate.

Returns:

The concatenated IrregularArgvals.

Return type:

IrregularArgvals

copy()

classmethod fromkeys(iterable, value=None)

get(k[, d]) → D[k] if k in D, else d.  d defaults to None.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

normalization()

Normalize the IrregularArgvals.

This function normalizes the Argvals by applying the following transformation to each observation:

\[X_{norm} = \frac{X - \min{X}}{\max{X} - \min{X}}.\]

Returns:

Normalized argvals.

Return type:

IrregularArgvals

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D

switch()

Switch the dictionary.

This function switches nested dictionaries. It convert an IrregularArgvals object (with signature dict[int, dict[str, npt.NDArray[np.float64]]]) into a dictionary with signature dict[str, dict[int, npt.NDArray[np.float64]]].

Returns:

The switched dictionnary.

Return type:

dict[str, dict[int, npt.NDArray[np.float64]]]

Examples

>>> argvals_1 = DenseArgvals(
...     {
...         'input_dim_0': np.array([1, 2, 3]),
...         'input_dim_1': np.array([4, 5, 6])
...     }
... )
>>> argvals_2 = DenseArgvals(
...     {
...         'input_dim_0': np.array([2, 4, 6]),
...         'input_dim_1': np.array([1, 3, 5])
...     }
... )
>>> argvals = IrregularArgvals({0: argvals_1, 1: argvals_2})

>>> argvals.switch()
{
    'input_dim_0': {0: array([1, 2, 3]), 1: array([2, 4, 6])},
    'input_dim_1': {0: array([4, 5, 6]), 1: array([1, 3, 5])}
}

to_dense()

Concatenate IrregularArgvals to DenseArgvals.

This function concatenates the sampling points of IrregularArgvals into a DenseArgvals object. The duplicated sampling points are dropped.

Returns:

The concatenation of the IrregularArgvals.

Return type:

DenseArgvals

Examples

>>> argvals_1 = DenseArgvals(
...     {
...         'input_dim_0': np.array([1, 2, 3]),
...         'input_dim_1': np.array([4, 5, 6])
...     }
... )
>>> argvals_2 = DenseArgvals(
...     {
...         'input_dim_0': np.array([2, 4, 6]),
...         'input_dim_1': np.array([1, 3, 5])
...     }
... )
>>> argvals = IrregularArgvals({0: argvals_1, 1: argvals_2})

>>> argvals.to_dense()
{
    'input_dim_0': array([1, 2, 3, 4, 6]),
    'input_dim_1': array([1, 3, 4, 5, 6])
}

update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values

Examples using FDApy.representation.IrregularArgvals

Representation of univariate and irregular functional data

Representation of univariate and irregular functional data

Representation of multivariate functional data

Representation of multivariate functional data