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Source code for librosa.sequence
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
Sequential modeling
===================
Sequence alignment
------------------
.. autosummary::
:toctree: generated/
dtw
rqa
Viterbi decoding
----------------
.. autosummary::
:toctree: generated/
viterbi
viterbi_discriminative
viterbi_binary
Transition matrices
-------------------
.. autosummary::
:toctree: generated/
transition_uniform
transition_loop
transition_cycle
transition_local
"""
import numpy as np
from scipy.spatial.distance import cdist
from numba import jit
from .util import pad_center, fill_off_diagonal, tiny, expand_to
from .util.exceptions import ParameterError
from .filters import get_window
__all__ = [
"dtw",
"dtw_backtracking",
"rqa",
"viterbi",
"viterbi_discriminative",
"viterbi_binary",
"transition_uniform",
"transition_loop",
"transition_cycle",
"transition_local",
]
[docs]def dtw(
X=None,
Y=None,
*,
C=None,
metric="euclidean",
step_sizes_sigma=None,
weights_add=None,
weights_mul=None,
subseq=False,
backtrack=True,
global_constraints=False,
band_rad=0.25,
return_steps=False,
):
"""Dynamic time warping (DTW).
This function performs a DTW and path backtracking on two sequences.
We follow the nomenclature and algorithmic approach as described in [#]_.
.. [#] Meinard Mueller
Fundamentals of Music Processing — Audio, Analysis, Algorithms, Applications
Springer Verlag, ISBN: 978-3-319-21944-8, 2015.
Parameters
----------
X : np.ndarray [shape=(..., K, N)]
audio feature matrix (e.g., chroma features)
If ``X`` has more than two dimensions (e.g., for multi-channel inputs), all leading
dimensions are used when computing distance to ``Y``.
Y : np.ndarray [shape=(..., K, M)]
audio feature matrix (e.g., chroma features)
C : np.ndarray [shape=(N, M)]
Precomputed distance matrix. If supplied, X and Y must not be supplied and
``metric`` will be ignored.
metric : str
Identifier for the cost-function as documented
in `scipy.spatial.distance.cdist()`
step_sizes_sigma : np.ndarray [shape=[n, 2]]
Specifies allowed step sizes as used by the dtw.
weights_add : np.ndarray [shape=[n, ]]
Additive weights to penalize certain step sizes.
weights_mul : np.ndarray [shape=[n, ]]
Multiplicative weights to penalize certain step sizes.
subseq : bool
Enable subsequence DTW, e.g., for retrieval tasks.
backtrack : bool
Enable backtracking in accumulated cost matrix.
global_constraints : bool
Applies global constraints to the cost matrix ``C`` (Sakoe-Chiba band).
band_rad : float
The Sakoe-Chiba band radius (1/2 of the width) will be
``int(radius*min(C.shape))``.
return_steps : bool
If true, the function returns ``steps``, the step matrix, containing
the indices of the used steps from the cost accumulation step.
Returns
-------
D : np.ndarray [shape=(N, M)]
accumulated cost matrix.
D[N, M] is the total alignment cost.
When doing subsequence DTW, D[N,:] indicates a matching function.
wp : np.ndarray [shape=(N, 2)]
Warping path with index pairs.
Each row of the array contains an index pair (n, m).
Only returned when ``backtrack`` is True.
steps : np.ndarray [shape=(N, M)]
Step matrix, containing the indices of the used steps from the cost
accumulation step.
Only returned when ``return_steps`` is True.
Raises
------
ParameterError
If you are doing diagonal matching and Y is shorter than X or if an
incompatible combination of X, Y, and C are supplied.
If your input dimensions are incompatible.
If the cost matrix has NaN values.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> y, sr = librosa.load(librosa.ex('brahms'), offset=10, duration=15)
>>> X = librosa.feature.chroma_cens(y=y, sr=sr)
>>> noise = np.random.rand(X.shape[0], 200)
>>> Y = np.concatenate((noise, noise, X, noise), axis=1)
>>> D, wp = librosa.sequence.dtw(X, Y, subseq=True)
>>> fig, ax = plt.subplots(nrows=2, sharex=True)
>>> img = librosa.display.specshow(D, x_axis='frames', y_axis='frames',
... ax=ax[0])
>>> ax[0].set(title='DTW cost', xlabel='Noisy sequence', ylabel='Target')
>>> ax[0].plot(wp[:, 1], wp[:, 0], label='Optimal path', color='y')
>>> ax[0].legend()
>>> fig.colorbar(img, ax=ax[0])
>>> ax[1].plot(D[-1, :] / wp.shape[0])
>>> ax[1].set(xlim=[0, Y.shape[1]], ylim=[0, 2],
... title='Matching cost function')
"""
# Default Parameters
default_steps = np.array([[1, 1], [0, 1], [1, 0]], dtype=np.uint32)
default_weights_add = np.zeros(3, dtype=np.float64)
default_weights_mul = np.ones(3, dtype=np.float64)
if step_sizes_sigma is None:
# Use the default steps
step_sizes_sigma = default_steps
# Use default weights if none are provided
if weights_add is None:
weights_add = default_weights_add
if weights_mul is None:
weights_mul = default_weights_mul
else:
# If we have custom steps but no weights, construct them here
if weights_add is None:
weights_add = np.zeros(len(step_sizes_sigma), dtype=np.float64)
if weights_mul is None:
weights_mul = np.ones(len(step_sizes_sigma), dtype=np.float64)
# Make the default step weights infinite so that they are never
# preferred over custom steps
default_weights_add.fill(np.inf)
default_weights_mul.fill(np.inf)
# Append custom steps and weights to our defaults
step_sizes_sigma = np.concatenate((default_steps, step_sizes_sigma))
weights_add = np.concatenate((default_weights_add, weights_add))
weights_mul = np.concatenate((default_weights_mul, weights_mul))
if np.any(step_sizes_sigma < 0):
raise ParameterError("step_sizes_sigma cannot contain negative values")
if len(step_sizes_sigma) != len(weights_add):
raise ParameterError("len(weights_add) must be equal to len(step_sizes_sigma)")
if len(step_sizes_sigma) != len(weights_mul):
raise ParameterError("len(weights_mul) must be equal to len(step_sizes_sigma)")
if C is None and (X is None or Y is None):
raise ParameterError("If C is not supplied, both X and Y must be supplied")
if C is not None and (X is not None or Y is not None):
raise ParameterError("If C is supplied, both X and Y must not be supplied")
c_is_transposed = False
# calculate pair-wise distances, unless already supplied.
# C_local will keep track of whether the distance matrix was supplied
# by the user (False) or constructed locally (True)
C_local = False
if C is None:
C_local = True
# take care of dimensions
X = np.atleast_2d(X)
Y = np.atleast_2d(Y)
# Perform some shape-squashing here
# Put the time axes around front
X = np.swapaxes(X, -1, 0)
Y = np.swapaxes(Y, -1, 0)
# Flatten the remaining dimensions
# Use F-ordering to preserve columns
X = X.reshape((X.shape[0], -1), order="F")
Y = Y.reshape((Y.shape[0], -1), order="F")
try:
C = cdist(X, Y, metric=metric)
except ValueError as exc:
raise ParameterError(
"scipy.spatial.distance.cdist returned an error.\n"
"Please provide your input in the form X.shape=(K, N) "
"and Y.shape=(K, M).\n 1-dimensional sequences should "
"be reshaped to X.shape=(1, N) and Y.shape=(1, M)."
) from exc
# for subsequence matching:
# if N > M, Y can be a subsequence of X
if subseq and (X.shape[0] > Y.shape[0]):
C = C.T
c_is_transposed = True
C = np.atleast_2d(C)
# if diagonal matching, Y has to be longer than X
# (X simply cannot be contained in Y)
if np.array_equal(step_sizes_sigma, np.array([[1, 1]])) and (
C.shape[0] > C.shape[1]
):
raise ParameterError(
"For diagonal matching: Y.shape[-1] >= X.shape[-11] "
"(C.shape[1] >= C.shape[0])"
)
max_0 = step_sizes_sigma[:, 0].max()
max_1 = step_sizes_sigma[:, 1].max()
# check C here for nans before building global constraints
if np.any(np.isnan(C)):
raise ParameterError("DTW cost matrix C has NaN values. ")
if global_constraints:
# Apply global constraints to the cost matrix
if not C_local:
# If C was provided as input, make a copy here
C = np.copy(C)
fill_off_diagonal(C, radius=band_rad, value=np.inf)
# initialize whole matrix with infinity values
D = np.ones(C.shape + np.array([max_0, max_1])) * np.inf
# set starting point to C[0, 0]
D[max_0, max_1] = C[0, 0]
if subseq:
D[max_0, max_1:] = C[0, :]
# initialize step matrix with -1
# will be filled in calc_accu_cost() with indices from step_sizes_sigma
steps = np.zeros(D.shape, dtype=np.int32)
# these steps correspond to left- (first row) and up-(first column) moves
steps[0, :] = 1
steps[:, 0] = 2
# calculate accumulated cost matrix
D, steps = __dtw_calc_accu_cost(
C, D, steps, step_sizes_sigma, weights_mul, weights_add, max_0, max_1
)
# delete infinity rows and columns
D = D[max_0:, max_1:]
steps = steps[max_0:, max_1:]
if backtrack:
if subseq:
if np.all(np.isinf(D[-1])):
raise ParameterError(
"No valid sub-sequence warping path could "
"be constructed with the given step sizes."
)
start = np.argmin(D[-1, :])
wp = __dtw_backtracking(steps, step_sizes_sigma, subseq, start)
else:
# perform warping path backtracking
if np.isinf(D[-1, -1]):
raise ParameterError(
"No valid sub-sequence warping path could "
"be constructed with the given step sizes."
)
wp = __dtw_backtracking(steps, step_sizes_sigma, subseq)
if wp[-1] != (0, 0):
raise ParameterError(
"Unable to compute a full DTW warping path. "
"You may want to try again with subseq=True."
)
wp = np.asarray(wp, dtype=int)
# since we transposed in the beginning, we have to adjust the index pairs back
if subseq and (
(X is not None and Y is not None and X.shape[0] > Y.shape[0])
or c_is_transposed
or C.shape[0] > C.shape[1]
):
wp = np.fliplr(wp)
return_values = [D, wp]
else:
return_values = [D]
if return_steps:
return_values.append(steps)
if len(return_values) > 1:
return tuple(return_values)
else:
return return_values[0]
@jit(nopython=True, cache=True)
def __dtw_calc_accu_cost(
C, D, steps, step_sizes_sigma, weights_mul, weights_add, max_0, max_1
): # pragma: no cover
"""Calculate the accumulated cost matrix D.
Use dynamic programming to calculate the accumulated costs.
Parameters
----------
C : np.ndarray [shape=(N, M)]
pre-computed cost matrix
D : np.ndarray [shape=(N, M)]
accumulated cost matrix
steps : np.ndarray [shape=(N, M)]
Step matrix, containing the indices of the used steps from the cost
accumulation step.
step_sizes_sigma : np.ndarray [shape=[n, 2]]
Specifies allowed step sizes as used by the dtw.
weights_add : np.ndarray [shape=[n, ]]
Additive weights to penalize certain step sizes.
weights_mul : np.ndarray [shape=[n, ]]
Multiplicative weights to penalize certain step sizes.
max_0 : int
maximum number of steps in step_sizes_sigma in dim 0.
max_1 : int
maximum number of steps in step_sizes_sigma in dim 1.
Returns
-------
D : np.ndarray [shape=(N, M)]
accumulated cost matrix.
D[N, M] is the total alignment cost.
When doing subsequence DTW, D[N,:] indicates a matching function.
steps : np.ndarray [shape=(N, M)]
Step matrix, containing the indices of the used steps from the cost
accumulation step.
See Also
--------
dtw
"""
for cur_n in range(max_0, D.shape[0]):
for cur_m in range(max_1, D.shape[1]):
# accumulate costs
for cur_step_idx, cur_w_add, cur_w_mul in zip(
range(step_sizes_sigma.shape[0]), weights_add, weights_mul
):
cur_D = D[
cur_n - step_sizes_sigma[cur_step_idx, 0],
cur_m - step_sizes_sigma[cur_step_idx, 1],
]
cur_C = cur_w_mul * C[cur_n - max_0, cur_m - max_1]
cur_C += cur_w_add
cur_cost = cur_D + cur_C
# check if cur_cost is smaller than the one stored in D
if cur_cost < D[cur_n, cur_m]:
D[cur_n, cur_m] = cur_cost
# save step-index
steps[cur_n, cur_m] = cur_step_idx
return D, steps
@jit(nopython=True, cache=True)
def __dtw_backtracking(steps, step_sizes_sigma, subseq, start=None): # pragma: no cover
"""Backtrack optimal warping path.
Uses the saved step sizes from the cost accumulation
step to backtrack the index pairs for an optimal
warping path.
Parameters
----------
steps : np.ndarray [shape=(N, M)]
Step matrix, containing the indices of the used steps from the cost
accumulation step.
step_sizes_sigma : np.ndarray [shape=[n, 2]]
Specifies allowed step sizes as used by the dtw.
subseq : bool
Enable subsequence DTW, e.g., for retrieval tasks.
start : int
Start column index for backtraing (only allowed for ``subseq=True``)
Returns
-------
wp : list [shape=(N,)]
Warping path with index pairs.
Each list entry contains an index pair
(n, m) as a tuple
See Also
--------
dtw
"""
if start is None:
cur_idx = (steps.shape[0] - 1, steps.shape[1] - 1)
else:
cur_idx = (steps.shape[0] - 1, start)
wp = []
# Set starting point D(N, M) and append it to the path
wp.append((cur_idx[0], cur_idx[1]))
# Loop backwards.
# Stop criteria:
# Setting it to (0, 0) does not work for the subsequence dtw,
# so we only ask to reach the first row of the matrix.
while (subseq and cur_idx[0] > 0) or (not subseq and cur_idx != (0, 0)):
cur_step_idx = steps[(cur_idx[0], cur_idx[1])]
# save tuple with minimal acc. cost in path
cur_idx = (
cur_idx[0] - step_sizes_sigma[cur_step_idx][0],
cur_idx[1] - step_sizes_sigma[cur_step_idx][1],
)
# If we run off the side of the cost matrix, break here
if min(cur_idx) < 0:
break
# append to warping path
wp.append((cur_idx[0], cur_idx[1]))
return wp
def dtw_backtracking(steps, *, step_sizes_sigma=None, subseq=False, start=None):
"""Backtrack a warping path.
Uses the saved step sizes from the cost accumulation
step to backtrack the index pairs for a warping path.
Parameters
----------
steps : np.ndarray [shape=(N, M)]
Step matrix, containing the indices of the used steps from the cost
accumulation step.
step_sizes_sigma : np.ndarray [shape=[n, 2]]
Specifies allowed step sizes as used by the dtw.
subseq : bool
Enable subsequence DTW, e.g., for retrieval tasks.
start : int
Start column index for backtraing (only allowed for ``subseq=True``)
Returns
-------
wp : list [shape=(N,)]
Warping path with index pairs.
Each list entry contains an index pair
(n, m) as a tuple
See Also
--------
dtw
"""
if subseq is False and start is not None:
raise ParameterError(
"start is only allowed to be set if subseq is True "
"(start={}, subseq={})".format(start, subseq)
)
# Default Parameters
default_steps = np.array([[1, 1], [0, 1], [1, 0]], dtype=np.uint32)
if step_sizes_sigma is None:
# Use the default steps
step_sizes_sigma = default_steps
else:
# Append custom steps and weights to our defaults
step_sizes_sigma = np.concatenate((default_steps, step_sizes_sigma))
wp = __dtw_backtracking(steps, step_sizes_sigma, subseq, start)
return np.asarray(wp, dtype=int)
[docs]def rqa(sim, *, gap_onset=1, gap_extend=1, knight_moves=True, backtrack=True):
"""Recurrence quantification analysis (RQA)
This function implements different forms of RQA as described by
Serra, Serra, and Andrzejak (SSA). [#]_ These methods take as input
a self- or cross-similarity matrix ``sim``, and calculate the value
of path alignments by dynamic programming.
Note that unlike dynamic time warping (`dtw`), alignment paths here are
maximized, not minimized, so the input should measure similarity rather
than distance.
The simplest RQA method, denoted as `L` (SSA equation 3) and equivalent
to the method described by Eckman, Kamphorst, and Ruelle [#]_, accumulates
the length of diagonal paths with positive values in the input:
- ``score[i, j] = score[i-1, j-1] + 1`` if ``sim[i, j] > 0``
- ``score[i, j] = 0`` otherwise.
The second method, denoted as `S` (SSA equation 4), is similar to the first,
but allows for "knight moves" (as in the chess piece) in addition to strict
diagonal moves:
- ``score[i, j] = max(score[i-1, j-1], score[i-2, j-1], score[i-1, j-2]) + 1`` if ``sim[i, j] >
0``
- ``score[i, j] = 0`` otherwise.
The third method, denoted as `Q` (SSA equations 5 and 6) extends this by
allowing gaps in the alignment that incur some cost, rather than a hard
reset to 0 whenever ``sim[i, j] == 0``.
Gaps are penalized by two additional parameters, ``gap_onset`` and ``gap_extend``,
which are subtracted from the value of the alignment path every time a gap
is introduced or extended (respectively).
Note that setting ``gap_onset`` and ``gap_extend`` to `np.inf` recovers the second
method, and disabling knight moves recovers the first.
.. [#] Serrà, Joan, Xavier Serra, and Ralph G. Andrzejak.
"Cross recurrence quantification for cover song identification."
New Journal of Physics 11, no. 9 (2009): 093017.
.. [#] Eckmann, J. P., S. Oliffson Kamphorst, and D. Ruelle.
"Recurrence plots of dynamical systems."
World Scientific Series on Nonlinear Science Series A 16 (1995): 441-446.
Parameters
----------
sim : np.ndarray [shape=(N, M), non-negative]
The similarity matrix to use as input.
This can either be a recurrence matrix (self-similarity)
or a cross-similarity matrix between two sequences.
gap_onset : float > 0
Penalty for introducing a gap to an alignment sequence
gap_extend : float > 0
Penalty for extending a gap in an alignment sequence
knight_moves : bool
If ``True`` (default), allow for "knight moves" in the alignment,
e.g., ``(n, m) => (n + 1, m + 2)`` or ``(n + 2, m + 1)``.
If ``False``, only allow for diagonal moves ``(n, m) => (n + 1, m + 1)``.
backtrack : bool
If ``True``, return the alignment path.
If ``False``, only return the score matrix.
Returns
-------
score : np.ndarray [shape=(N, M)]
The alignment score matrix. ``score[n, m]`` is the cumulative value of
the best alignment sequence ending in frames ``n`` and ``m``.
path : np.ndarray [shape=(k, 2)] (optional)
If ``backtrack=True``, ``path`` contains a list of pairs of aligned frames
in the best alignment sequence.
``path[i] = [n, m]`` indicates that row ``n`` aligns to column ``m``.
See Also
--------
librosa.segment.recurrence_matrix
librosa.segment.cross_similarity
dtw
Examples
--------
Simple diagonal path enhancement (L-mode)
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> y, sr = librosa.load(librosa.ex('nutcracker'), duration=30)
>>> chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
>>> # Use time-delay embedding to reduce noise
>>> chroma_stack = librosa.feature.stack_memory(chroma, n_steps=10, delay=3)
>>> # Build recurrence, suppress self-loops within 1 second
>>> rec = librosa.segment.recurrence_matrix(chroma_stack, width=43,
... mode='affinity',
... metric='cosine')
>>> # using infinite cost for gaps enforces strict path continuation
>>> L_score, L_path = librosa.sequence.rqa(rec,
... gap_onset=np.inf,
... gap_extend=np.inf,
... knight_moves=False)
>>> fig, ax = plt.subplots(ncols=2)
>>> librosa.display.specshow(rec, x_axis='frames', y_axis='frames', ax=ax[0])
>>> ax[0].set(title='Recurrence matrix')
>>> librosa.display.specshow(L_score, x_axis='frames', y_axis='frames', ax=ax[1])
>>> ax[1].set(title='Alignment score matrix')
>>> ax[1].plot(L_path[:, 1], L_path[:, 0], label='Optimal path', color='c')
>>> ax[1].legend()
>>> ax[1].label_outer()
Full alignment using gaps and knight moves
>>> # New gaps cost 5, extending old gaps cost 10 for each step
>>> score, path = librosa.sequence.rqa(rec, gap_onset=5, gap_extend=10)
>>> fig, ax = plt.subplots(ncols=2, sharex=True, sharey=True)
>>> librosa.display.specshow(rec, x_axis='frames', y_axis='frames', ax=ax[0])
>>> ax[0].set(title='Recurrence matrix')
>>> librosa.display.specshow(score, x_axis='frames', y_axis='frames', ax=ax[1])
>>> ax[1].set(title='Alignment score matrix')
>>> ax[1].plot(path[:, 1], path[:, 0], label='Optimal path', color='c')
>>> ax[1].legend()
>>> ax[1].label_outer()
"""
if gap_onset < 0:
raise ParameterError("gap_onset={} must be strictly positive")
if gap_extend < 0:
raise ParameterError("gap_extend={} must be strictly positive")
score, pointers = __rqa_dp(sim, gap_onset, gap_extend, knight_moves)
if backtrack:
path = __rqa_backtrack(score, pointers)
return score, path
return score
@jit(nopython=True, cache=True)
def __rqa_dp(sim, gap_onset, gap_extend, knight): # pragma: no cover
"""RQA dynamic programming implementation"""
# The output array
score = np.zeros(sim.shape, dtype=sim.dtype)
# The backtracking array
backtrack = np.zeros(sim.shape, dtype=np.int8)
# These are place-holder arrays to limit the points being considered
# at each step of the DP
#
# If knight moves are enabled, values are indexed according to
# [(-1,-1), (-1, -2), (-2, -1)]
#
# If knight moves are disabled, then only the first entry is used.
#
# Using dummy vectors here makes the code a bit cleaner down below.
sim_values = np.zeros(3)
score_values = np.zeros(3)
vec = np.zeros(3)
if knight:
# Initial limit is for the base case: diagonal + one knight
init_limit = 2
# Otherwise, we have 3 positions
limit = 3
else:
init_limit = 1
limit = 1
# backtracking rubric:
# 0 ==> diagonal move
# 1 ==> knight move up
# 2 ==> knight move left
# -1 ==> reset without inclusion
# -2 ==> reset with inclusion (ie positive value at init)
# Initialize the first row and column with the data
score[0, :] = sim[0, :]
score[:, 0] = sim[:, 0]
# backtracking initialization: the first row and column are all resets
# if there's a positive link here, it's an inclusive reset
for i in range(sim.shape[0]):
if sim[i, 0]:
backtrack[i, 0] = -2
else:
backtrack[i, 0] = -1
for j in range(sim.shape[1]):
if sim[0, j]:
backtrack[0, j] = -2
else:
backtrack[0, j] = -1
# Initialize the 1-1 case using only the diagonal
if sim[1, 1] > 0:
score[1, 1] = score[0, 0] + sim[1, 1]
backtrack[1, 1] = 0
else:
link = sim[0, 0] > 0
score[1, 1] = max(0, score[0, 0] - (link) * gap_onset - (~link) * gap_extend)
if score[1, 1] > 0:
backtrack[1, 1] = 0
else:
backtrack[1, 1] = -1
# Initialize the second row with diagonal and left-knight moves
i = 1
for j in range(2, sim.shape[1]):
score_values[:-1] = (score[i - 1, j - 1], score[i - 1, j - 2])
sim_values[:-1] = (sim[i - 1, j - 1], sim[i - 1, j - 2])
t_values = sim_values > 0
if sim[i, j] > 0:
backtrack[i, j] = np.argmax(score_values[:init_limit])
score[i, j] = score_values[backtrack[i, j]] + sim[i, j] # or + 1 for binary
else:
vec[:init_limit] = (
score_values[:init_limit]
- t_values[:init_limit] * gap_onset
- (~t_values[:init_limit]) * gap_extend
)
backtrack[i, j] = np.argmax(vec[:init_limit])
score[i, j] = max(0, vec[backtrack[i, j]])
# Is it a reset?
if score[i, j] == 0:
backtrack[i, j] = -1
# Initialize the second column with diagonal and up-knight moves
j = 1
for i in range(2, sim.shape[0]):
score_values[:-1] = (score[i - 1, j - 1], score[i - 2, j - 1])
sim_values[:-1] = (sim[i - 1, j - 1], sim[i - 2, j - 1])
t_values = sim_values > 0
if sim[i, j] > 0:
backtrack[i, j] = np.argmax(score_values[:init_limit])
score[i, j] = score_values[backtrack[i, j]] + sim[i, j] # or + 1 for binary
else:
vec[:init_limit] = (
score_values[:init_limit]
- t_values[:init_limit] * gap_onset
- (~t_values[:init_limit]) * gap_extend
)
backtrack[i, j] = np.argmax(vec[:init_limit])
score[i, j] = max(0, vec[backtrack[i, j]])
# Is it a reset?
if score[i, j] == 0:
backtrack[i, j] = -1
# Now fill in the rest of the table
for i in range(2, sim.shape[0]):
for j in range(2, sim.shape[1]):
score_values[:] = (
score[i - 1, j - 1],
score[i - 1, j - 2],
score[i - 2, j - 1],
)
sim_values[:] = (sim[i - 1, j - 1], sim[i - 1, j - 2], sim[i - 2, j - 1])
t_values = sim_values > 0
if sim[i, j] > 0:
# if knight is true, it's max of (-1,-1), (-1, -2), (-2, -1)
# otherwise, it's just the diagonal move (-1, -1)
# for backtracking purposes, if the max is 0 then it's the start of a new sequence
# if the max is non-zero, then we extend the existing sequence
backtrack[i, j] = np.argmax(score_values[:limit])
score[i, j] = (
score_values[backtrack[i, j]] + sim[i, j]
) # or + 1 for binary
else:
# if the max of our options is negative, then it's a hard reset
# otherwise, it's a skip move
vec[:limit] = (
score_values[:limit]
- t_values[:limit] * gap_onset
- (~t_values[:limit]) * gap_extend
)
backtrack[i, j] = np.argmax(vec[:limit])
score[i, j] = max(0, vec[backtrack[i, j]])
# Is it a reset?
if score[i, j] == 0:
backtrack[i, j] = -1
return score, backtrack
def __rqa_backtrack(score, pointers):
"""RQA path backtracking
Given the score matrix and backtracking index array,
reconstruct the optimal path.
"""
# backtracking rubric:
# 0 ==> diagonal move
# 1 ==> knight move up
# 2 ==> knight move left
# -1 ==> reset (sim = 0)
# -2 ==> start of sequence (sim > 0)
# This array maps the backtracking values to the
# relative index offsets
offsets = [(-1, -1), (-1, -2), (-2, -1)]
# Find the maximum to end the path
idx = list(np.unravel_index(np.argmax(score), score.shape))
# Construct the path
path = []
while True:
bt_index = pointers[tuple(idx)]
# A -1 indicates a non-inclusive reset
# this can only happen when sim[idx] == 0,
# and a reset with zero score should not be included
# in the path. In this case, we're done.
if bt_index == -1:
break
# Other bt_index values are okay for inclusion
path.insert(0, idx)
# -2 indicates beginning of sequence,
# so we can't backtrack any further
if bt_index == -2:
break
# Otherwise, prepend this index and continue
idx = [idx[_] + offsets[bt_index][_] for _ in range(len(idx))]
# If there's no alignment path at all, eg an empty cross-similarity
# matrix, return a properly shaped and typed array
if not path:
return np.empty((0, 2), dtype=np.uint)
return np.asarray(path, dtype=np.uint)
@jit(nopython=True, cache=True)
def _viterbi(log_prob, log_trans, log_p_init): # pragma: no cover
"""Core Viterbi algorithm.
This is intended for internal use only.
Parameters
----------
log_prob : np.ndarray [shape=(T, m)]
``log_prob[t, s]`` is the conditional log-likelihood
``log P[X = X(t) | State(t) = s]``
log_trans : np.ndarray [shape=(m, m)]
The log transition matrix
``log_trans[i, j] = log P[State(t+1) = j | State(t) = i]``
log_p_init : np.ndarray [shape=(m,)]
log of the initial state distribution
Returns
-------
None
All computations are performed in-place on ``state, value, ptr``.
"""
n_steps, n_states = log_prob.shape
state = np.zeros(n_steps, dtype=np.uint16)
value = np.zeros((n_steps, n_states), dtype=np.float64)
ptr = np.zeros((n_steps, n_states), dtype=np.uint16)
# factor in initial state distribution
value[0] = log_prob[0] + log_p_init
for t in range(1, n_steps):
# Want V[t, j] <- p[t, j] * max_k V[t-1, k] * A[k, j]
# assume at time t-1 we were in state k
# transition k -> j
# Broadcast over rows:
# Tout[k, j] = V[t-1, k] * A[k, j]
# then take the max over columns
# We'll do this in log-space for stability
trans_out = value[t - 1] + log_trans.T
# Unroll the max/argmax loop to enable numba support
for j in range(n_states):
ptr[t, j] = np.argmax(trans_out[j])
# value[t, j] = log_prob[t, j] + np.max(trans_out[j])
value[t, j] = log_prob[t, j] + trans_out[j, ptr[t][j]]
# Now roll backward
# Get the last state
state[-1] = np.argmax(value[-1])
for t in range(n_steps - 2, -1, -1):
state[t] = ptr[t + 1, state[t + 1]]
logp = value[-1:, state[-1]]
return state, logp
[docs]def viterbi(prob, transition, *, p_init=None, return_logp=False):
"""Viterbi decoding from observation likelihoods.
Given a sequence of observation likelihoods ``prob[s, t]``,
indicating the conditional likelihood of seeing the observation
at time ``t`` from state ``s``, and a transition matrix
``transition[i, j]`` which encodes the conditional probability of
moving from state ``i`` to state ``j``, the Viterbi algorithm [#]_ computes
the most likely sequence of states from the observations.
.. [#] Viterbi, Andrew. "Error bounds for convolutional codes and an
asymptotically optimum decoding algorithm."
IEEE transactions on Information Theory 13.2 (1967): 260-269.
Parameters
----------
prob : np.ndarray [shape=(..., n_states, n_steps), non-negative]
``prob[..., s, t]`` is the probability of observation at time ``t``
being generated by state ``s``.
transition : np.ndarray [shape=(n_states, n_states), non-negative]
``transition[i, j]`` is the probability of a transition from i->j.
Each row must sum to 1.
p_init : np.ndarray [shape=(n_states,)]
Optional: initial state distribution.
If not provided, a uniform distribution is assumed.
return_logp : bool
If ``True``, return the log-likelihood of the state sequence.
Returns
-------
Either ``states`` or ``(states, logp)``:
states : np.ndarray [shape=(..., n_steps,)]
The most likely state sequence.
If ``prob`` contains multiple channels of input, then each channel is
decoded independently.
logp : scalar [float] or np.ndarray
If ``return_logp=True``, the log probability of ``states`` given
the observations.
See Also
--------
viterbi_discriminative : Viterbi decoding from state likelihoods
Examples
--------
Example from https://en.wikipedia.org/wiki/Viterbi_algorithm#Example
In this example, we have two states ``healthy`` and ``fever``, with
initial probabilities 60% and 40%.
We have three observation possibilities: ``normal``, ``cold``, and
``dizzy``, whose probabilities given each state are:
``healthy => {normal: 50%, cold: 40%, dizzy: 10%}`` and
``fever => {normal: 10%, cold: 30%, dizzy: 60%}``
Finally, we have transition probabilities:
``healthy => healthy (70%)`` and
``fever => fever (60%)``.
Over three days, we observe the sequence ``[normal, cold, dizzy]``,
and wish to know the maximum likelihood assignment of states for the
corresponding days, which we compute with the Viterbi algorithm below.
>>> p_init = np.array([0.6, 0.4])
>>> p_emit = np.array([[0.5, 0.4, 0.1],
... [0.1, 0.3, 0.6]])
>>> p_trans = np.array([[0.7, 0.3], [0.4, 0.6]])
>>> path, logp = librosa.sequence.viterbi(p_emit, p_trans, p_init=p_init,
... return_logp=True)
>>> print(logp, path)
-4.19173690823075 [0 0 1]
"""
n_states, n_steps = prob.shape[-2:]
if transition.shape != (n_states, n_states):
raise ParameterError(
"transition.shape={}, must be "
"(n_states, n_states)={}".format(transition.shape, (n_states, n_states))
)
if np.any(transition < 0) or not np.allclose(transition.sum(axis=1), 1):
raise ParameterError(
"Invalid transition matrix: must be non-negative "
"and sum to 1 on each row."
)
if np.any(prob < 0) or np.any(prob > 1):
raise ParameterError("Invalid probability values: must be between 0 and 1.")
# Compute log-likelihoods while avoiding log-underflow
epsilon = tiny(prob)
if p_init is None:
p_init = np.empty(n_states)
p_init.fill(1.0 / n_states)
elif (
np.any(p_init < 0)
or not np.allclose(p_init.sum(), 1)
or p_init.shape != (n_states,)
):
raise ParameterError(
"Invalid initial state distribution: " "p_init={}".format(p_init)
)
log_trans = np.log(transition + epsilon)
log_prob = np.log(prob + epsilon)
log_p_init = np.log(p_init + epsilon)
def _helper(lp):
# Transpose input
_state, logp = _viterbi(lp.T, log_trans, log_p_init)
# Transpose outputs for return
return _state.T, logp
if log_prob.ndim == 2:
states, logp = _helper(log_prob)
else:
# Vectorize the helper
__viterbi = np.vectorize(
_helper, otypes=[np.uint16, np.float64], signature="(s,t)->(t),(1)"
)
states, logp = __viterbi(log_prob)
# Flatten out the trailing dimension introduced by vectorization
logp = logp[..., 0]
if return_logp:
return states, logp
return states
[docs]def viterbi_discriminative(
prob, transition, *, p_state=None, p_init=None, return_logp=False
):
"""Viterbi decoding from discriminative state predictions.
Given a sequence of conditional state predictions ``prob[s, t]``,
indicating the conditional likelihood of state ``s`` given the
observation at time ``t``, and a transition matrix ``transition[i, j]``
which encodes the conditional probability of moving from state ``i``
to state ``j``, the Viterbi algorithm computes the most likely sequence
of states from the observations.
This implementation uses the standard Viterbi decoding algorithm
for observation likelihood sequences, under the assumption that
``P[Obs(t) | State(t) = s]`` is proportional to
``P[State(t) = s | Obs(t)] / P[State(t) = s]``, where the denominator
is the marginal probability of state ``s`` occurring as given by ``p_state``.
Parameters
----------
prob : np.ndarray [shape=(..., n_states, n_steps), non-negative]
``prob[s, t]`` is the probability of state ``s`` conditional on
the observation at time ``t``.
Must be non-negative and sum to 1 along each column.
transition : np.ndarray [shape=(n_states, n_states), non-negative]
``transition[i, j]`` is the probability of a transition from i->j.
Each row must sum to 1.
p_state : np.ndarray [shape=(n_states,)]
Optional: marginal probability distribution over states,
must be non-negative and sum to 1.
If not provided, a uniform distribution is assumed.
p_init : np.ndarray [shape=(n_states,)]
Optional: initial state distribution.
If not provided, it is assumed to be uniform.
return_logp : bool
If ``True``, return the log-likelihood of the state sequence.
Returns
-------
Either ``states`` or ``(states, logp)``:
states : np.ndarray [shape=(..., n_steps,)]
The most likely state sequence.
If ``prob`` contains multiple input channels,
then each channel is decoded independently.
logp : scalar [float] or np.ndarray
If ``return_logp=True``, the log probability of ``states`` given
the observations.
See Also
--------
viterbi :
Viterbi decoding from observation likelihoods
viterbi_binary :
Viterbi decoding for multi-label, conditional state likelihoods
Examples
--------
This example constructs a simple, template-based discriminative chord estimator,
using CENS chroma as input features.
.. note:: this chord model is not accurate enough to use in practice. It is only
intended to demonstrate how to use discriminative Viterbi decoding.
>>> # Create templates for major, minor, and no-chord qualities
>>> maj_template = np.array([1,0,0, 0,1,0, 0,1,0, 0,0,0])
>>> min_template = np.array([1,0,0, 1,0,0, 0,1,0, 0,0,0])
>>> N_template = np.array([1,1,1, 1,1,1, 1,1,1, 1,1,1.]) / 4.
>>> # Generate the weighting matrix that maps chroma to labels
>>> weights = np.zeros((25, 12), dtype=float)
>>> labels = ['C:maj', 'C#:maj', 'D:maj', 'D#:maj', 'E:maj', 'F:maj',
... 'F#:maj', 'G:maj', 'G#:maj', 'A:maj', 'A#:maj', 'B:maj',
... 'C:min', 'C#:min', 'D:min', 'D#:min', 'E:min', 'F:min',
... 'F#:min', 'G:min', 'G#:min', 'A:min', 'A#:min', 'B:min',
... 'N']
>>> for c in range(12):
... weights[c, :] = np.roll(maj_template, c) # c:maj
... weights[c + 12, :] = np.roll(min_template, c) # c:min
>>> weights[-1] = N_template # the last row is the no-chord class
>>> # Make a self-loop transition matrix over 25 states
>>> trans = librosa.sequence.transition_loop(25, 0.9)
>>> # Load in audio and make features
>>> y, sr = librosa.load(librosa.ex('nutcracker'), duration=15)
>>> # Suppress percussive elements
>>> y = librosa.effects.harmonic(y, margin=4)
>>> chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
>>> # Map chroma (observations) to class (state) likelihoods
>>> probs = np.exp(weights.dot(chroma)) # P[class | chroma] ~= exp(template' chroma)
>>> probs /= probs.sum(axis=0, keepdims=True) # probabilities must sum to 1 in each column
>>> # Compute independent frame-wise estimates
>>> chords_ind = np.argmax(probs, axis=0)
>>> # And viterbi estimates
>>> chords_vit = librosa.sequence.viterbi_discriminative(probs, trans)
>>> # Plot the features and prediction map
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(nrows=2)
>>> librosa.display.specshow(chroma, x_axis='time', y_axis='chroma', ax=ax[0])
>>> librosa.display.specshow(weights, x_axis='chroma', ax=ax[1])
>>> ax[1].set(yticks=np.arange(25) + 0.5, yticklabels=labels, ylabel='Chord')
>>> # And plot the results
>>> fig, ax = plt.subplots()
>>> librosa.display.specshow(probs, x_axis='time', cmap='gray', ax=ax)
>>> times = librosa.times_like(chords_vit)
>>> ax.scatter(times, chords_ind + 0.25, color='lime', alpha=0.5, marker='+',
... s=15, label='Independent')
>>> ax.scatter(times, chords_vit - 0.25, color='deeppink', alpha=0.5, marker='o',
... s=15, label='Viterbi')
>>> ax.set(yticks=np.unique(chords_vit),
... yticklabels=[labels[i] for i in np.unique(chords_vit)])
>>> ax.legend()
"""
n_states, n_steps = prob.shape[-2:]
if transition.shape != (n_states, n_states):
raise ParameterError(
"transition.shape={}, must be "
"(n_states, n_states)={}".format(transition.shape, (n_states, n_states))
)
if np.any(transition < 0) or not np.allclose(transition.sum(axis=1), 1):
raise ParameterError(
"Invalid transition matrix: must be non-negative "
"and sum to 1 on each row."
)
if np.any(prob < 0) or not np.allclose(prob.sum(axis=-2), 1):
raise ParameterError(
"Invalid probability values: each column must "
"sum to 1 and be non-negative"
)
# Compute log-likelihoods while avoiding log-underflow
epsilon = tiny(prob)
# Compute marginal log probabilities while avoiding underflow
if p_state is None:
p_state = np.empty(n_states)
p_state.fill(1.0 / n_states)
elif p_state.shape != (n_states,):
raise ParameterError(
"Marginal distribution p_state must have shape (n_states,). "
"Got p_state.shape={}".format(p_state.shape)
)
elif np.any(p_state < 0) or not np.allclose(p_state.sum(axis=-1), 1):
raise ParameterError(
"Invalid marginal state distribution: " "p_state={}".format(p_state)
)
if p_init is None:
p_init = np.empty(n_states)
p_init.fill(1.0 / n_states)
elif (
np.any(p_init < 0)
or not np.allclose(p_init.sum(), 1)
or p_init.shape != (n_states,)
):
raise ParameterError(
"Invalid initial state distribution: " "p_init={}".format(p_init)
)
# By Bayes' rule, P[X | Y] * P[Y] = P[Y | X] * P[X]
# P[X] is constant for the sake of maximum likelihood inference
# and P[Y] is given by the marginal distribution p_state.
#
# So we have P[X | y] \propto P[Y | x] / P[Y]
# if X = observation and Y = states, this can be done in log space as
# log P[X | y] \propto \log P[Y | x] - \log P[Y]
log_p_init = np.log(p_init + epsilon)
log_trans = np.log(transition + epsilon)
log_marginal = np.log(p_state + epsilon)
# reshape to broadcast against prob
log_marginal = expand_to(log_marginal, ndim=prob.ndim, axes=-2)
log_prob = np.log(prob + epsilon) - log_marginal
def _helper(lp):
# Transpose input
_state, logp = _viterbi(lp.T, log_trans, log_p_init)
# Transpose outputs for return
return _state.T, logp
if log_prob.ndim == 2:
states, logp = _helper(log_prob)
else:
# Vectorize the helper
__viterbi = np.vectorize(
_helper, otypes=[np.uint16, np.float64], signature="(s,t)->(t),(1)"
)
states, logp = __viterbi(log_prob)
# Flatten out the trailing dimension
logp = logp[..., 0]
if return_logp:
return states, logp
return states
[docs]def viterbi_binary(prob, transition, *, p_state=None, p_init=None, return_logp=False):
"""Viterbi decoding from binary (multi-label), discriminative state predictions.
Given a sequence of conditional state predictions ``prob[s, t]``,
indicating the conditional likelihood of state ``s`` being active
conditional on observation at time ``t``, and a 2*2 transition matrix
``transition`` which encodes the conditional probability of moving from
state ``s`` to state ``~s`` (not-``s``), the Viterbi algorithm computes the
most likely sequence of states from the observations.
This function differs from `viterbi_discriminative` in that it does not assume the
states to be mutually exclusive. `viterbi_binary` is implemented by
transforming the multi-label decoding problem to a collection
of binary Viterbi problems (one for each *state* or label).
The output is a binary matrix ``states[s, t]`` indicating whether each
state ``s`` is active at time ``t``.
Parameters
----------
prob : np.ndarray [shape=(..., n_steps,) or (..., n_states, n_steps)], non-negative
``prob[s, t]`` is the probability of state ``s`` being active
conditional on the observation at time ``t``.
Must be non-negative and less than 1.
If ``prob`` is 1-dimensional, it is expanded to shape ``(1, n_steps)``.
If ``prob`` contains multiple input channels, then each channel is decoded independently.
transition : np.ndarray [shape=(2, 2) or (n_states, 2, 2)], non-negative
If 2-dimensional, the same transition matrix is applied to each sub-problem.
``transition[0, i]`` is the probability of the state going from inactive to ``i``,
``transition[1, i]`` is the probability of the state going from active to ``i``.
Each row must sum to 1.
If 3-dimensional, ``transition[s]`` is interpreted as the 2x2 transition matrix
for state label ``s``.
p_state : np.ndarray [shape=(n_states,)]
Optional: marginal probability for each state (between [0,1]).
If not provided, a uniform distribution (0.5 for each state)
is assumed.
p_init : np.ndarray [shape=(n_states,)]
Optional: initial state distribution.
If not provided, it is assumed to be uniform.
return_logp : bool
If ``True``, return the log-likelihood of the state sequence.
Returns
-------
Either ``states`` or ``(states, logp)``:
states : np.ndarray [shape=(..., n_states, n_steps)]
The most likely state sequence.
logp : np.ndarray [shape=(..., n_states,)]
If ``return_logp=True``, the log probability of each state activation
sequence ``states``
See Also
--------
viterbi :
Viterbi decoding from observation likelihoods
viterbi_discriminative :
Viterbi decoding for discriminative (mutually exclusive) state predictions
Examples
--------
In this example, we have a sequence of binary state likelihoods that we want to de-noise
under the assumption that state changes are relatively uncommon. Positive predictions
should only be retained if they persist for multiple steps, and any transient predictions
should be considered as errors. This use case arises frequently in problems such as
instrument recognition, where state activations tend to be stable over time, but subject
to abrupt changes (e.g., when an instrument joins the mix).
We assume that the 0 state has a self-transition probability of 90%, and the 1 state
has a self-transition probability of 70%. We assume the marginal and initial
probability of either state is 50%.
>>> trans = np.array([[0.9, 0.1], [0.3, 0.7]])
>>> prob = np.array([0.1, 0.7, 0.4, 0.3, 0.8, 0.9, 0.8, 0.2, 0.6, 0.3])
>>> librosa.sequence.viterbi_binary(prob, trans, p_state=0.5, p_init=0.5)
array([[0, 0, 0, 0, 1, 1, 1, 0, 0, 0]])
"""
prob = np.atleast_2d(prob)
n_states, n_steps = prob.shape[-2:]
if transition.shape == (2, 2):
transition = np.tile(transition, (n_states, 1, 1))
elif transition.shape != (n_states, 2, 2):
raise ParameterError(
"transition.shape={}, must be (2, 2) or "
"(n_states, 2, 2)={}".format(transition.shape, (n_states))
)
if np.any(transition < 0) or not np.allclose(transition.sum(axis=-1), 1):
raise ParameterError(
"Invalid transition matrix: must be non-negative "
"and sum to 1 on each row."
)
if np.any(prob < 0) or np.any(prob > 1):
raise ParameterError("Invalid probability values: prob must be between [0, 1]")
if p_state is None:
p_state = np.empty(n_states)
p_state.fill(0.5)
else:
p_state = np.atleast_1d(p_state)
if p_state.shape != (n_states,) or np.any(p_state < 0) or np.any(p_state > 1):
raise ParameterError(
"Invalid marginal state distributions: p_state={}".format(p_state)
)
if p_init is None:
p_init = np.empty(n_states)
p_init.fill(0.5)
else:
p_init = np.atleast_1d(p_init)
if p_init.shape != (n_states,) or np.any(p_init < 0) or np.any(p_init > 1):
raise ParameterError(
"Invalid initial state distributions: p_init={}".format(p_init)
)
shape_prefix = list(prob.shape[:-2])
states = np.empty(shape_prefix + [n_states, n_steps], dtype=np.uint16)
logp = np.empty(shape_prefix + [n_states])
prob_binary = np.empty(shape_prefix + [2, n_steps])
p_state_binary = np.empty(2)
p_init_binary = np.empty(2)
for state in range(n_states):
prob_binary[..., 0, :] = 1 - prob[..., state, :]
prob_binary[..., 1, :] = prob[..., state, :]
p_state_binary[0] = 1 - p_state[state]
p_state_binary[1] = p_state[state]
p_init_binary[0] = 1 - p_init[state]
p_init_binary[1] = p_init[state]
states[..., state, :], logp[..., state] = viterbi_discriminative(
prob_binary,
transition[state],
p_state=p_state_binary,
p_init=p_init_binary,
return_logp=True,
)
if return_logp:
return states, logp
return states
[docs]def transition_uniform(n_states):
"""Construct a uniform transition matrix over ``n_states``.
Parameters
----------
n_states : int > 0
The number of states
Returns
-------
transition : np.ndarray [shape=(n_states, n_states)]
``transition[i, j] = 1./n_states``
Examples
--------
>>> librosa.sequence.transition_uniform(3)
array([[0.333, 0.333, 0.333],
[0.333, 0.333, 0.333],
[0.333, 0.333, 0.333]])
"""
if not isinstance(n_states, (int, np.integer)) or n_states <= 0:
raise ParameterError("n_states={} must be a positive integer")
transition = np.empty((n_states, n_states), dtype=np.float64)
transition.fill(1.0 / n_states)
return transition
[docs]def transition_loop(n_states, prob):
"""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_states : int > 1
The number of states
prob : float 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
-------
transition : np.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 ]])
"""
if not isinstance(n_states, (int, np.integer)) or n_states <= 1:
raise ParameterError("n_states={} must be a positive integer > 1")
transition = np.empty((n_states, n_states), dtype=np.float64)
# if it's a float, make it a vector
prob = np.asarray(prob, dtype=np.float64)
if prob.ndim == 0:
prob = np.tile(prob, n_states)
if prob.shape != (n_states,):
raise ParameterError(
"prob={} must have length equal to n_states={}".format(prob, n_states)
)
if np.any(prob < 0) or np.any(prob > 1):
raise ParameterError(
"prob={} must have values in the range [0, 1]".format(prob)
)
for i, prob_i in enumerate(prob):
transition[i] = (1.0 - prob_i) / (n_states - 1)
transition[i, i] = prob_i
return transition
[docs]def transition_cycle(n_states, prob):
"""Construct a cyclic transition matrix over ``n_states``.
The transition matrix will have the following properties:
- ``transition[i, i] = p``
- ``transition[i, i + 1] = (1 - p)``
This type of transition matrix is appropriate for state spaces
with cyclical structure, such as metrical position within a bar.
For example, a song in 4/4 time has state transitions of the form
1->{1, 2}, 2->{2, 3}, 3->{3, 4}, 4->{4, 1}.
Parameters
----------
n_states : int > 1
The number of states
prob : float 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
-------
transition : np.ndarray [shape=(n_states, n_states)]
The transition matrix
Examples
--------
>>> librosa.sequence.transition_cycle(4, 0.9)
array([[0.9, 0.1, 0. , 0. ],
[0. , 0.9, 0.1, 0. ],
[0. , 0. , 0.9, 0.1],
[0.1, 0. , 0. , 0.9]])
"""
if not isinstance(n_states, (int, np.integer)) or n_states <= 1:
raise ParameterError("n_states={} must be a positive integer > 1")
transition = np.zeros((n_states, n_states), dtype=np.float64)
# if it's a float, make it a vector
prob = np.asarray(prob, dtype=np.float64)
if prob.ndim == 0:
prob = np.tile(prob, n_states)
if prob.shape != (n_states,):
raise ParameterError(
"prob={} must have length equal to n_states={}".format(prob, n_states)
)
if np.any(prob < 0) or np.any(prob > 1):
raise ParameterError(
"prob={} must have values in the range [0, 1]".format(prob)
)
for i, prob_i in enumerate(prob):
transition[i, np.mod(i + 1, n_states)] = 1.0 - prob_i
transition[i, i] = prob_i
return transition
[docs]def transition_local(n_states, width, *, window="triangle", wrap=False):
"""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 maximal
- ``transition[i, i - width//2 : i + width//2]`` has shape ``window``
This type of transition matrix is appropriate for state spaces
that discretely approximate continuous variables, such as in fundamental
frequency estimation.
Parameters
----------
n_states : int > 1
The number of states
width : int >= 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.
window : str, 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 expand
``width`` to get the desired behavior.
wrap : bool
If ``True``, then state locality ``|i - j|`` is computed modulo ``n_states``.
If ``False`` (default), then locality is absolute.
See Also
--------
librosa.filters.get_window
Returns
-------
transition : np.ndarray [shape=(n_states, n_states)]
The transition matrix
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. ]])
"""
if not isinstance(n_states, (int, np.integer)) or n_states <= 1:
raise ParameterError("n_states={} must be a positive integer > 1")
width = np.asarray(width, dtype=int)
if width.ndim == 0:
width = np.tile(width, n_states)
if width.shape != (n_states,):
raise ParameterError(
"width={} must have length equal to n_states={}".format(width, n_states)
)
if np.any(width < 1):
raise ParameterError("width={} must be at least 1")
transition = np.zeros((n_states, n_states), dtype=np.float64)
# Fill in the widths. This is inefficient, but simple
for i, width_i in enumerate(width):
trans_row = pad_center(
get_window(window, width_i, fftbins=False), size=n_states
)
trans_row = np.roll(trans_row, n_states // 2 + i + 1)
if not wrap:
# Knock out the off-diagonal-band elements
trans_row[min(n_states, i + width_i // 2 + 1) :] = 0
trans_row[: max(0, i - width_i // 2)] = 0
transition[i] = trans_row
# Row-normalize
transition /= transition.sum(axis=1, keepdims=True)
return transition