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librosa.sequence.transition_local
- librosa.sequence.transition_local(n_states, width, window='triangle', wrap=False)[source]
Construct a localized transition matrix.
The transition matrix will have the following properties:
transition[i, j] = 0
if|i - j| > width
transition[i, i]
is maximaltransition[i, i - width//2 : i + width//2]
has shapewindow
This type of transition matrix is appropriate for state spaces that discretely approximate continuous variables, such as in fundamental frequency estimation.
- Parameters:
- n_statesint > 1
The number of states
- widthint >= 1 or iterable
The maximum number of states to treat as “local”. If iterable, it should have length equal to
n_states
, and specify the width independently for each state.- windowstr, callable, or window specification
The window function to determine the shape of the “local” distribution.
Any window specification supported by filters.get_window will work here.
Note
Certain windows (e.g., ‘hann’) are identically 0 at the boundaries, so and effectively have
width-2
non-zero values. You may have to expandwidth
to get the desired behavior.- wrapbool
If
True
, then state locality|i - j|
is computed modulon_states
. IfFalse
(default), then locality is absolute.
- Returns:
- transitionnp.ndarray [shape=(n_states, n_states)]
The transition matrix
See also
Examples
Triangular distributions with and without wrapping
>>> librosa.sequence.transition_local(5, 3, window='triangle', wrap=False) array([[0.667, 0.333, 0. , 0. , 0. ], [0.25 , 0.5 , 0.25 , 0. , 0. ], [0. , 0.25 , 0.5 , 0.25 , 0. ], [0. , 0. , 0.25 , 0.5 , 0.25 ], [0. , 0. , 0. , 0.333, 0.667]])
>>> librosa.sequence.transition_local(5, 3, window='triangle', wrap=True) array([[0.5 , 0.25, 0. , 0. , 0.25], [0.25, 0.5 , 0.25, 0. , 0. ], [0. , 0.25, 0.5 , 0.25, 0. ], [0. , 0. , 0.25, 0.5 , 0.25], [0.25, 0. , 0. , 0.25, 0.5 ]])
Uniform local distributions with variable widths and no wrapping
>>> librosa.sequence.transition_local(5, [1, 2, 3, 3, 1], window='ones', wrap=False) array([[1. , 0. , 0. , 0. , 0. ], [0.5 , 0.5 , 0. , 0. , 0. ], [0. , 0.333, 0.333, 0.333, 0. ], [0. , 0. , 0.333, 0.333, 0.333], [0. , 0. , 0. , 0. , 1. ]])