normalised_squared_distance_profile¶

normalised_squared_distance_profile(X: ndarray | List, q: ndarray, mask: ndarray, X_means: ndarray, X_stds: ndarray, q_means: ndarray, q_stds: ndarray) → ndarray[source]¶

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.

Parameters:

Xnp.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 2D array 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_timepoints - query_length + 1): Boolean mask of the shape of the distance profile indicating for which part of it the distance should be computed.
X_meansnp.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501: Means of each subsequences of X of size query_length
X_stdsnp.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501: Stds of each subsequences of X of size query_length
q_meansnp.ndarray, 1D array of shape (n_channels): Means of the query q
q_stdsnp.ndarray, 1D array of shape (n_channels): Stds of the query q

Returns:

distance_profilesnp.ndarray: 3D array of shape (n_cases, n_timepoints - query_length + 1) The distance profile between q and the input time series X.