cbr_fox.custom_distance.cci_distance

cbr_fox.custom_distance.cci_distance(input_data_dictionary, punished_sum_factor)

Compute a combined correlation and distance measure using Pearson correlation and Euclidean distance, with a normalization factor applied.

This function first computes the Pearson correlation and the Euclidean distance between training windows and target windows using the sktime_interface. Then, it normalizes the Euclidean distance and combines both the correlation and distance measures into a final value. The result is further scaled and returned.

Parameters:

• input_data_dictionary (dict) – A dictionary containing processed input data, including training windows, target training windows, and any other necessary components for distance calculations.

• punished_sum_factor (float) – A factor applied to the sum of the normalized correlation to adjust the final computed correlation.

Returns:

A 2D array of shape (n_windows, 1) representing the normalized and scaled correlation per window.

Return type:

numpy.ndarray