GridFunctionalData#
- class FDApy.representation.GridFunctionalData(argvals, values)[source]#
Represent discretised functional data.
- Parameters:
- Attributes:
argvals_stand (Type[Argvals]) – Standardized sampling points of the functional data.
n_obs (int) – Number of observations of the functional data.
n_dimension (int) – Number of input dimension of the functional data.
n_points (Tuple[int, …] | Dict[int, Tuple[int, …]]) – Number of sampling points.
Methods
center([mean, method_smoothing])Center the data.
concatenate(*fdata)Concatenate FunctionalData objects.
covariance([points, method_smoothing])Compute an estimate of the covariance.
inner_product([method_integration, ...])Compute an estimate of the inner product matrix.
mean([points, method_smoothing])Compute an estimate of the mean.
noise_variance([order])Estimate the variance of the noise.
norm([squared, method_integration, ...])Norm of each observation of the data.
normalize(**kwargs)Normalize the data.
rescale([weights, method_integration, ...])Rescale the data.
smooth([points, method, bandwidth, penalty])Smooth the data.
standardize([center])Standardize the data.
to_basis([points, method])Convert the data to basis format.
to_long([reindex])Convert the data to long format.
- abstract center(mean=None, method_smoothing=None, **kwargs)[source]#
Center the data.
- Parameters:
mean (DenseFunctionalData | None)
method_smoothing (str | None)
- Return type:
- abstract static concatenate(*fdata)[source]#
Concatenate FunctionalData objects.
- Parameters:
fdata (Type[FunctionalData]) – Functional data to concatenate.
- Raises:
ValueError – When all fdata do not have the same dimension.
TypeError – When all fdata do not have the same type.
- Return type:
- abstract covariance(points=None, method_smoothing=None, **kwargs)[source]#
Compute an estimate of the covariance.
- Parameters:
points (DenseArgvals | None)
method_smoothing (str | None)
- Return type:
- abstract inner_product(method_integration='trapz', method_smoothing=None, noise_variance=None, **kwargs)[source]#
Compute an estimate of the inner product matrix.
- abstract mean(points=None, method_smoothing=None, **kwargs)[source]#
Compute an estimate of the mean.
- Parameters:
points (DenseArgvals | None)
method_smoothing (str | None)
- Return type:
- abstract norm(squared=False, method_integration='trapz', use_argvals_stand=False)[source]#
Norm of each observation of the data.
For each observation in the data, it computes its norm defined as
\[\| X \| = \left\{\int_{\mathcal{T}} X(t)^2dt\right\}^{\frac12}.\]
- abstract rescale(weights=0.0, method_integration='trapz', use_argvals_stand=False, **kwargs)[source]#
Rescale the data.
- abstract smooth(points=None, method='PS', bandwidth=None, penalty=None, **kwargs)[source]#
Smooth the data.
- Parameters:
points (DenseArgvals | None)
method (str)
bandwidth (float | None)
penalty (float | None)
- Return type:
- abstract standardize(center=True, **kwargs)[source]#
Standardize the data.
- Parameters:
center (bool)
- Return type:
- abstract to_basis(points=None, method='PS', **kwargs)[source]#
Convert the data to basis format.
- Parameters:
points (DenseArgvals | None)
method (str)
- Return type:
Examples using FDApy.representation.GridFunctionalData#
Representation of univariate and dense functional data
Representation of univariate and irregular functional data
