pyts.metrics.itakura_parallelogram(n_timestamps_1, n_timestamps_2=None, max_slope=2.0)[source]

Compute the Itakura parallelogram.

n_timestamps_1 : int

The size of the first time series.

n_timestamps_2 : int (optional, default None)

The size of the second time series. If None, set to n_timestamps_1.

max_slope : float (default = 2.)

Maximum slope for the parallelogram. Must be >= 1.

region : array, shape = (2, n_timestamps_1)

Constraint region. The first row consists of the starting indices (included) and the second row consists of the ending indices (excluded) of the valid rows for each column.


[1]F. Itakura, “Minimum prediction residual principle applied to speech recognition”. IEEE Transactions on Acoustics, Speech, and Signal Processing, 23(1), 67–72 (1975).


>>> from pyts.metrics import itakura_parallelogram
>>> print(itakura_parallelogram(5))
[[0 1 1 2 4]
 [1 3 4 4 5]]

Examples using pyts.metrics.itakura_parallelogram

Dynamic Time Warping

Dynamic Time Warping

Dynamic Time Warping
Itakura parallelogram

Itakura parallelogram

Itakura parallelogram