naive_distance_profile

naive_distance_profile(X: ndarray | List, q: ndarray, mask: ndarray | List, distance_function, distance_args: dict | None = None) → ndarray | List

Compute a distance profile in a brute force way.

It computes the distance profiles between the input time series and the query using the specified distance. The search is made in a brute force way without any optimizations and can thus be slow.

A distance profile between a (univariate) time series \(X_i = {x_1, ..., x_m}\) and a query \(Q = {q_1, ..., q_m}\) is defined as a vector of size \(m-( l-1)\), such as \(P(X_i, Q) = {d(C_1, Q), ..., d(C_m-(l-1), Q)}\) with d the distance function, and \(C_j = {x_j, ..., x_{j+(l-1)}}\) the j-th candidate subsequence of size \(l\) in \(X_i\).

Parameters:

X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints)

The input samples. If X is an unquel length collection, expect a numba TypedList of 2D arrays of shape (n_channels, n_timepoints)

qnp.ndarray, 2D array of shape (n_channels, query_length)

The query used for similarity search.

masknp.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501

Boolean mask of the shape of the distance profile indicating for which part of it the distance should be computed. Should be a numba TypedList if X is unequal length.

distance_functionfunc

A python function or a numba njit function used to compute the distance between two 1D vectors.

distance_argsdict, default=None

Dictionary containing keywords arguments to use for the distance_function

Returns:

distance_profilesnp.ndarray

3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) The distance profile between q and the input time series X independently for each channel. Returns a TypedList if X is unequal length.