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librosa.sequence.transition_loop

librosa.sequence.transition_loop(n_states, prob)[source]

Construct a self-loop transition matrix over n_states.

The transition matrix will have the following properties:

transition[i, i] = p for all i

transition[i, j] = (1 - p) / (n_states - 1) for all j != i

This type of transition matrix is appropriate when states tend to be locally stable, and there is no additional structure between different states. This is primarily useful for de-noising frame-wise predictions.

Parameters:

n_statesint > 1

The number of states

probfloat in [0, 1] or iterable, length=n_states

If a scalar, this is the probability of a self-transition.

If a vector of length n_states, p[i] is the probability of self-transition in state i

Returns:

transitionnp.ndarray [shape=(n_states, n_states)]: The transition matrix

Examples

>>> librosa.sequence.transition_loop(3, 0.5)
array([[0.5 , 0.25, 0.25],
       [0.25, 0.5 , 0.25],
       [0.25, 0.25, 0.5 ]])

>>> librosa.sequence.transition_loop(3, [0.8, 0.5, 0.25])
array([[0.8  , 0.1  , 0.1  ],
       [0.25 , 0.5  , 0.25 ],
       [0.375, 0.375, 0.25 ]])