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Commit a506dd44 authored by MARMORET Axel's avatar MARMORET Axel
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Adding a scikit-learn wrapper for the CBM and moving the sandbox code into a dedicated file

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as_seg/CBM_algorithm.py
+ 80
− 201
View file @ a506dd44
......@@ -172,52 +172,6 @@ def convolutionnal_cost(cropped_autosimilarity, kernels):
#return np.mean(np.multiply(kern,cropped_autosimilarity))
return np.sum(np.multiply(kern,cropped_autosimilarity)) / p**2
def compute_cbm_sum_normalization(autosimilarity, min_size = 1, max_size = 32, penalty_weight = 1, penalty_func = "modulo8", bands_number = None):
scores = [-math.inf for i in range(len(autosimilarity))]
segments_best_starts = [None for i in range(len(autosimilarity))]
segments_best_starts[0] = 0
scores[0] = 0
kernels = compute_all_kernels(max_size, bands_number = bands_number)
max_conv_eight = np.amax(convolution_entire_matrix_computation(autosimilarity, kernels))
for current_idx in range(1, len(autosimilarity)): # Parse all indexes of the autosimilarity
for possible_start_idx in possible_segment_start(current_idx, min_size = min_size, max_size = max_size):
if possible_start_idx < 0:
raise err.ToDebugException("Invalid value of start index, shouldn't happen.") from None
# Convolutionnal cost between the possible start of the segment and the current index (entire segment)
cropped_autosimilarity = autosimilarity[possible_start_idx:current_idx,possible_start_idx:current_idx]
kern = kernels[len(cropped_autosimilarity)]
if np.sum(kern) == 0:
conv_cost = 0
else:
conv_cost = np.sum(np.multiply(kern,cropped_autosimilarity)) / (np.sum(kern) + len(cropped_autosimilarity))
segment_length = current_idx - possible_start_idx
penalty_cost = penalty_cost_from_arg(penalty_func, segment_length)
this_segment_cost = conv_cost * segment_length - penalty_cost * penalty_weight * max_conv_eight
# Note: conv_eight is not normalized by its size (not a problem in itself as size is contant, but generally not specified in formulas).
if possible_start_idx == 0: # Avoiding errors, as scores values are initially set to -inf.
if this_segment_cost > scores[current_idx]: # This segment is of larger score
scores[current_idx] = this_segment_cost
segments_best_starts[current_idx] = 0
else:
if scores[possible_start_idx] + this_segment_cost > scores[current_idx]: # This segment is of larger score
scores[current_idx] = scores[possible_start_idx] + this_segment_cost
segments_best_starts[current_idx] = possible_start_idx
segments = [(segments_best_starts[len(autosimilarity) - 1], len(autosimilarity) - 1)]
precedent_frontier = segments_best_starts[len(autosimilarity) - 1] # Because a segment's start is the previous one's end.
while precedent_frontier > 0:
segments.append((segments_best_starts[precedent_frontier], precedent_frontier))
precedent_frontier = segments_best_starts[precedent_frontier]
if precedent_frontier == None:
raise err.ToDebugException("Well... The dynamic programming algorithm took an impossible path, so it failed. Understand why.") from None
return segments[::-1], scores[-1]
def convolution_entire_matrix_computation(autosimilarity_array, kernels, kernel_size = 8):
"""
Computes the convolution measure on the entire autosimilarity matrix, with a defined and fixed kernel size.
......@@ -331,163 +285,88 @@ def possible_segment_start(idx, min_size = 1, max_size = None):
else:
return []
# %% Sandbox
def dynamic_convolution_computation_test_line(autosimilarity, line_conv_weight = 1, min_size = 2, max_size = 36, novelty_kernel_size = 16, penalty_weight = 1, penalty_func = "modulo8", convolution_type = "eight_bands"):
"""
Segmentation algo with inline convolution test, doesn't work that much in practice.
"""
costs = [-math.inf for i in range(len(autosimilarity))]
segments_best_starts = [None for i in range(len(autosimilarity))]
segments_best_starts[0] = 0
costs[0] = 0
kernels = compute_all_kernels(max_size, convolution_type = convolution_type)
full_kernels = compute_full_kernels(max_size, convolution_type = convolution_type)
#novelty = novelty_computation(autosimilarity, novelty_kernel_size)
conv_eight = convolution_entire_matrix_computation(autosimilarity, kernels)
for current_idx in range(1, len(autosimilarity)): # Parse all indexes of the autosimilarity
for possible_start_idx in possible_segment_start(current_idx, min_size = min_size, max_size = max_size):
if possible_start_idx < 0:
raise err.ToDebugException("Invalid value of start index.")
# Convolutionnal cost between the possible start of the segment and the current index (entire segment)
conv_cost = convolutionnal_cost(autosimilarity[possible_start_idx:current_idx,possible_start_idx:current_idx], kernels)
# Novelty cost, computed with a fixed kernel (doesn't make sense otherwise), on the end of the segment
#nov_cost = novelty[current_idx]
# %% Scikit-learn class
# Author: Axel Marmoret
#
# Adapted from: https://scikit-learn.org/stable/auto_examples/developing_estimators/sklearn_is_fitted.html, Author: Kushan <kushansharma1@gmail.com>
#
# License: BSD 3 clause
segment_length = current_idx - possible_start_idx
penalty_cost = penalty_cost_from_arg(penalty_func, segment_length)
current_line_conv_max = 0
# if possible_start_idx >= segment_length:
# for before_start in range(0, possible_start_idx - segment_length + 1):
# line_conv_cost = convolutionnal_cost(autosimilarity[possible_start_idx:current_idx,before_start:before_start + segment_length], full_kernels)
# if line_conv_cost > current_line_conv_max:
# current_line_conv_max = line_conv_cost
# if current_idx + segment_length < len(autosimilarity):
# for after_start in range(current_idx, len(autosimilarity) - segment_length):
# line_conv_cost = convolutionnal_cost(autosimilarity[possible_start_idx:current_idx,after_start:after_start + segment_length], full_kernels)
# if line_conv_cost > current_line_conv_max:
# current_line_conv_max = line_conv_cost
mat_vec = []
if possible_start_idx >= segment_length:
for before_start in range(0, possible_start_idx - segment_length + 1):
mat_vec.append(autosimilarity[possible_start_idx:current_idx,before_start:before_start + segment_length].flatten())
if current_idx + segment_length < len(autosimilarity):
for after_start in range(current_idx, len(autosimilarity) - segment_length):
mat_vec.append(autosimilarity[possible_start_idx:current_idx,after_start:after_start + segment_length].flatten())
if mat_vec == []:
current_line_conv_max = 0
else:
kern = full_kernels[segment_length]
convs_on_line = np.matmul(kern.reshape(1,segment_length**2), np.array(mat_vec).T)
current_line_conv_max = np.amax(convs_on_line) / segment_length**2
from sklearn.base import BaseEstimator, ClassifierMixin
this_segment_cost = (conv_cost + line_conv_weight * current_line_conv_max) * segment_length - penalty_cost * penalty_weight * np.max(conv_eight)
# Note: the length of the segment does not appear in conv_eight (not a problem in itself as size is contant, but generally not specified in formulas).
# Avoiding errors, as segment_cost are initially set to -inf.
if possible_start_idx == 0:
if this_segment_cost > costs[current_idx]:
costs[current_idx] = this_segment_cost
segments_best_starts[current_idx] = 0
else:
if costs[possible_start_idx] + this_segment_cost > costs[current_idx]:
costs[current_idx] = costs[possible_start_idx] + this_segment_cost
segments_best_starts[current_idx] = possible_start_idx
import as_seg.autosimilarity_computation as as_comp
import as_seg.data_manipulation as dm
segments = [(segments_best_starts[len(autosimilarity) - 1], len(autosimilarity) - 1)]
precedent_frontier = segments_best_starts[len(autosimilarity) - 1] # Because a segment's start is the previous one's end.
while precedent_frontier > 0:
segments.append((segments_best_starts[precedent_frontier], precedent_frontier))
precedent_frontier = segments_best_starts[precedent_frontier]
if precedent_frontier == None:
raise err.ToDebugException("Well... Viterbi took an impossible path, so it failed. Understand why.") from None
return segments[::-1], costs[-1]
class CBMEstimator(BaseEstimator, ClassifierMixin):
"""
Scikit-learn class for the CBM algorithm. May be used for practicity, following the scikit-learn API.
"""
def __init__(self, similarity_function="cosine", max_size=32, penalty_weight=1, penalty_func="modulo8", bands_number=7):
"""
Constructor of the CBM estimator.
Parameters
----------
similarity_function : string, optional
The similarity function to use for computing the autosimilarity.
The default is "cosine".
max_size : integer, optional
The maximal size of segments.
The default is 32.
penalty_weight : float, optional
The ponderation parameter for the penalty function.
The default is 1.
penalty_func : string, optional
The type of penalty function to use.
The default is "modulo8".
bands_number : positive integer or None, optional
The number of bands in the kernel.
For the full kernel, bands_number must be set to None
(or higher than the maximal size, but cumbersome)
See [1] for details.
The default is 7.
"""
self.similarity_function = similarity_function
self.max_size = max_size
self.penalty_weight = penalty_weight
self.penalty_func = penalty_func
self.bands_number = bands_number
self.algorithm_name = "CBM"
def predict(self, barwise_features):
"""
Perform Predictions
def compute_all_kernels_oldway(max_size, convolution_type = "full"):
If the estimator is not fitted, then raise NotFittedError
"""
DEPRECATED but some ideas may be worth the shot.
ssm_matrix = as_comp.switch_autosimilarity(barwise_features, similarity_type=self.similarity_function)
segments = compute_cbm(ssm_matrix, max_size=self.max_size, penalty_weight=self.penalty_weight,
penalty_func=self.penalty_func, bands_number = self.bands_number)[0]
return segments
Precomputes all kernels of size 0 ([0]) to max_size, and feed them to the Dynamic Progamming algorithm.
def predict_in_seconds(self, barwise_features, bars):
"""
Perform Predictions, and convert the segments from bars to seconds.
This is used for acceleration purposes.
If the estimator is not fitted, then raise NotFittedError
"""
segments = self.predict(barwise_features)
return dm.segments_from_bar_to_time(segments, bars)
Parameters
----------
max_size : integer
The maximal size (included) for kernels.
convolution_type: string
The type of convolution. (to explicit)
Possibilities are :
- "full" : squared matrix entirely composed of one, except on the diagonal where it's zero.
The associated convolution cost for a segment (b_1, b_2) will be
.. math::
c_{b_1,b_2} = \\frac{1}{b_2 - b_1 + 1}\\sum_{i,j = 0, i \\ne j}^{n - 1} a_{i + b_1, j + b_1}
- "4_bands" : squared matrix where the only nonzero values are ones on the
4 upper- and 4 sub-diagonals surrounding the main diagonal.
The associated convolution cost for a segment (b_1, b_2) will be
.. math::
c_{b_1,b_2} = \\frac{1}{b_2 - b_1 + 1}\\sum_{i,j = 0, 1 \\leq |i - j| \\leq 4}^{n - 1} a_{i + b_1, j + b_1}
- "mixed" : sum of both previous kernels, i.e. values are zero on the diagonal,
2 on the 4 upper- and 4 sub-diagonals surrounding the main diagonal, and 1 elsewhere.
The associated convolution cost for a segment (b_1, b_2) will be
.. math::
c_{b_1,b_2} = \\frac{1}{b_2 - b_1 + 1}(2*\\sum_{i,j = 0, 1 \\leq |i - j| \\leq 4}^{n - 1} a_{i + b_1, j + b_1} \\ + \sum_{i,j = 0, |i - j| > 4}^{n - 1} a_{i + b_1, j + b_1})
def predict_in_seconds_this_autosimilarity(self, ssm_matrix, bars):
"""
Perform Predictions on a given autosimilarity matrix, and convert the segments from bars to seconds.
Returns
-------
kernels : array of arrays (which are kernels)
All the kernels, of size 0 ([0]) to max_size.
If the estimator is not fitted, then raise NotFittedError
"""
segments = compute_cbm(ssm_matrix, max_size=self.max_size, penalty_weight=self.penalty_weight,
penalty_func=self.penalty_func, bands_number = self.bands_number)[0]
return dm.segments_from_bar_to_time(segments, bars)
def score(self, predictions, annotations):
"""
kernels = [[0]]
for p in range(1,max_size + 1):
if p < 4:
kern = np.ones((p,p)) - np.identity(p)
# elif convolution_type == "7_bands" or convolution_type == "mixed_7_bands":
# if p < 8:
# kern = np.ones((p,p)) - np.identity(p)
# else:
# # Diagonal where only the six subdiagonals surrounding the main diagonal is one
# k = np.array([np.ones(p-7),np.ones(p-6),np.ones(p-5),np.ones(p-4),np.ones(p-3),np.ones(p-2),np.ones(p-1),np.zeros(p),np.ones(p-1),np.ones(p-2),np.ones(p-3),np.ones(p-4),np.ones(p-5),np.ones(p-6),np.ones(p-7)], dtype=object)
# offset = [-7,-6,-5,-4,-3,-2,-1,0,1,2,3, 4, 5, 6, 7]
# if convolution_type == "14_bands":
# kern = diags(k,offset).toarray()
# else:
# kern = np.ones((p,p)) - np.identity(p) + diags(k,offset).toarray()
else:
if convolution_type == "full":
# Full kernel (except for the diagonal)
kern = np.ones((p,p)) - np.identity(p)
elif convolution_type == "4_bands":
# Diagonal where only the eight subdiagonals surrounding the main diagonal is one
k = np.array([np.ones(p-4),np.ones(p-3),np.ones(p-2),np.ones(p-1),np.zeros(p),np.ones(p-1),np.ones(p-2),np.ones(p-3),np.ones(p-4)],dtype=object)
offset = [-4,-3,-2,-1,0,1,2,3,4]
kern = diags(k,offset).toarray()
elif convolution_type == "mixed":
# Sum of both previous kernels
k = np.array([np.ones(p-4),np.ones(p-3),np.ones(p-2),np.ones(p-1),np.zeros(p),np.ones(p-1),np.ones(p-2),np.ones(p-3),np.ones(p-4)],dtype=object)
offset = [-4,-3,-2,-1,0,1,2,3,4]
kern = np.ones((p,p)) - np.identity(p) + diags(k,offset).toarray()
elif convolution_type == "3_bands":
# Diagonal where only the six subdiagonals surrounding the main diagonal is one
k = np.array([np.ones(p-3),np.ones(p-2),np.ones(p-1),np.zeros(p),np.ones(p-1),np.ones(p-2),np.ones(p-3)],dtype=object)
offset = [-3,-2,-1,0,1,2,3]
kern = diags(k,offset).toarray()
elif convolution_type == "mixed_3_bands":
# Sum of both previous kernels
k = np.array([np.ones(p-3),np.ones(p-2),np.ones(p-1),np.zeros(p),np.ones(p-1),np.ones(p-2),np.ones(p-3)],dtype=object)
offset = [-3,-2,-1,0,1,2,3]
kern = np.ones((p,p)) - np.identity(p) + diags(k,offset).toarray()
else:
raise err.InvalidArgumentValueException(f"Convolution type not understood: {convolution_type}.")
kernels.append(kern)
return kernels
Compute the score of the predictions
"""
close_tolerance = dm.compute_score_of_segmentation(annotations, predictions, window_length=0.5)
large_tolerance = dm.compute_score_of_segmentation(annotations, predictions, window_length=3)
return close_tolerance, large_tolerance
\ No newline at end of file
as_seg/sandbox/regularized_cbm.py 0 → 100644
+ 224
− 0
View file @ a506dd44
"""
Sandbox from the main code
"""
import math
from CBM_algorithm import *
# %% ###################################################################### Sandbox ######################################################################
def compute_cbm_normalization_nb_nonzero_kernel_elements(autosimilarity, min_size = 1, max_size = 32, penalty_weight = 1, penalty_func = "modulo8", bands_number = None):
"""
Normalizing the convolutionnal cost by the number of non-zero elements in the kernel instead of the size of the kernel. As in [SJK06].
References
----------
[SJK06] Shiu, Y., Jeong, H., & Kuo, C. C. J. (2006, October). Similarity matrix processing for music structure analysis. In Proceedings of the 1st ACM workshop on Audio and music computing multimedia (pp. 69-76).
"""
scores = [-math.inf for i in range(len(autosimilarity))]
segments_best_starts = [None for i in range(len(autosimilarity))]
segments_best_starts[0] = 0
scores[0] = 0
kernels = compute_all_kernels(max_size, bands_number = bands_number)
max_conv_eight = np.amax(convolution_entire_matrix_computation(autosimilarity, kernels))
for current_idx in range(1, len(autosimilarity)): # Parse all indexes of the autosimilarity
for possible_start_idx in possible_segment_start(current_idx, min_size = min_size, max_size = max_size):
if possible_start_idx < 0:
raise err.ToDebugException("Invalid value of start index, shouldn't happen.") from None
# Convolutionnal cost between the possible start of the segment and the current index (entire segment)
cropped_autosimilarity = autosimilarity[possible_start_idx:current_idx,possible_start_idx:current_idx]
kern = kernels[len(cropped_autosimilarity)]
if np.sum(kern) == 0:
conv_cost = 0
else:
# THE MAIN DIFFERENCE WITH THE PREVIOUS FUNCTION IS HERE: NORMALIZATION BY THE NUMBER OF NON-ZERO ELEMENTS IN THE KERNEL INSTAD OF SIZE OF KERNEL
conv_cost = np.sum(np.multiply(kern,cropped_autosimilarity)) / (np.sum(kern) + len(cropped_autosimilarity))
segment_length = current_idx - possible_start_idx
penalty_cost = penalty_cost_from_arg(penalty_func, segment_length)
this_segment_cost = conv_cost * segment_length - penalty_cost * penalty_weight * max_conv_eight
# Note: conv_eight is not normalized by its size (not a problem in itself as size is contant, but generally not specified in formulas).
if possible_start_idx == 0: # Avoiding errors, as scores values are initially set to -inf.
if this_segment_cost > scores[current_idx]: # This segment is of larger score
scores[current_idx] = this_segment_cost
segments_best_starts[current_idx] = 0
else:
if scores[possible_start_idx] + this_segment_cost > scores[current_idx]: # This segment is of larger score
scores[current_idx] = scores[possible_start_idx] + this_segment_cost
segments_best_starts[current_idx] = possible_start_idx
segments = [(segments_best_starts[len(autosimilarity) - 1], len(autosimilarity) - 1)]
precedent_frontier = segments_best_starts[len(autosimilarity) - 1] # Because a segment's start is the previous one's end.
while precedent_frontier > 0:
segments.append((segments_best_starts[precedent_frontier], precedent_frontier))
precedent_frontier = segments_best_starts[precedent_frontier]
if precedent_frontier == None:
raise err.ToDebugException("Well... The dynamic programming algorithm took an impossible path, so it failed. Understand why.") from None
return segments[::-1], scores[-1]
def dynamic_convolution_computation_test_line(autosimilarity, line_conv_weight = 1, min_size = 2, max_size = 36, novelty_kernel_size = 16, penalty_weight = 1, penalty_func = "modulo8", convolution_type = "eight_bands"):
"""
Segmentation algo with inline convolution test, doesn't work that much in practice.
"""
costs = [-math.inf for i in range(len(autosimilarity))]
segments_best_starts = [None for i in range(len(autosimilarity))]
segments_best_starts[0] = 0
costs[0] = 0
kernels = compute_all_kernels(max_size, convolution_type = convolution_type)
full_kernels = compute_full_kernels(max_size, convolution_type = convolution_type)
#novelty = novelty_computation(autosimilarity, novelty_kernel_size)
conv_eight = convolution_entire_matrix_computation(autosimilarity, kernels)
for current_idx in range(1, len(autosimilarity)): # Parse all indexes of the autosimilarity
for possible_start_idx in possible_segment_start(current_idx, min_size = min_size, max_size = max_size):
if possible_start_idx < 0:
raise err.ToDebugException("Invalid value of start index.")
# Convolutionnal cost between the possible start of the segment and the current index (entire segment)
conv_cost = convolutionnal_cost(autosimilarity[possible_start_idx:current_idx,possible_start_idx:current_idx], kernels)
# Novelty cost, computed with a fixed kernel (doesn't make sense otherwise), on the end of the segment
#nov_cost = novelty[current_idx]
segment_length = current_idx - possible_start_idx
penalty_cost = penalty_cost_from_arg(penalty_func, segment_length)
current_line_conv_max = 0
# if possible_start_idx >= segment_length:
# for before_start in range(0, possible_start_idx - segment_length + 1):
# line_conv_cost = convolutionnal_cost(autosimilarity[possible_start_idx:current_idx,before_start:before_start + segment_length], full_kernels)
# if line_conv_cost > current_line_conv_max:
# current_line_conv_max = line_conv_cost
# if current_idx + segment_length < len(autosimilarity):
# for after_start in range(current_idx, len(autosimilarity) - segment_length):
# line_conv_cost = convolutionnal_cost(autosimilarity[possible_start_idx:current_idx,after_start:after_start + segment_length], full_kernels)
# if line_conv_cost > current_line_conv_max:
# current_line_conv_max = line_conv_cost
mat_vec = []
if possible_start_idx >= segment_length:
for before_start in range(0, possible_start_idx - segment_length + 1):
mat_vec.append(autosimilarity[possible_start_idx:current_idx,before_start:before_start + segment_length].flatten())
if current_idx + segment_length < len(autosimilarity):
for after_start in range(current_idx, len(autosimilarity) - segment_length):
mat_vec.append(autosimilarity[possible_start_idx:current_idx,after_start:after_start + segment_length].flatten())
if mat_vec == []:
current_line_conv_max = 0
else:
kern = full_kernels[segment_length]
convs_on_line = np.matmul(kern.reshape(1,segment_length**2), np.array(mat_vec).T)
current_line_conv_max = np.amax(convs_on_line) / segment_length**2
this_segment_cost = (conv_cost + line_conv_weight * current_line_conv_max) * segment_length - penalty_cost * penalty_weight * np.max(conv_eight)
# Note: the length of the segment does not appear in conv_eight (not a problem in itself as size is contant, but generally not specified in formulas).
# Avoiding errors, as segment_cost are initially set to -inf.
if possible_start_idx == 0:
if this_segment_cost > costs[current_idx]:
costs[current_idx] = this_segment_cost
segments_best_starts[current_idx] = 0
else:
if costs[possible_start_idx] + this_segment_cost > costs[current_idx]:
costs[current_idx] = costs[possible_start_idx] + this_segment_cost
segments_best_starts[current_idx] = possible_start_idx
segments = [(segments_best_starts[len(autosimilarity) - 1], len(autosimilarity) - 1)]
precedent_frontier = segments_best_starts[len(autosimilarity) - 1] # Because a segment's start is the previous one's end.
while precedent_frontier > 0:
segments.append((segments_best_starts[precedent_frontier], precedent_frontier))
precedent_frontier = segments_best_starts[precedent_frontier]
if precedent_frontier == None:
raise err.ToDebugException("Well... Viterbi took an impossible path, so it failed. Understand why.") from None
return segments[::-1], costs[-1]
def compute_all_kernels_oldway(max_size, convolution_type = "full"):
"""
DEPRECATED but some ideas may be worth the shot.
Precomputes all kernels of size 0 ([0]) to max_size, and feed them to the Dynamic Progamming algorithm.
This is used for acceleration purposes.
Parameters
----------
max_size : integer
The maximal size (included) for kernels.
convolution_type: string
The type of convolution. (to explicit)
Possibilities are :
- "full" : squared matrix entirely composed of one, except on the diagonal where it's zero.
The associated convolution cost for a segment (b_1, b_2) will be
.. math::
c_{b_1,b_2} = \\frac{1}{b_2 - b_1 + 1}\\sum_{i,j = 0, i \\ne j}^{n - 1} a_{i + b_1, j + b_1}
- "4_bands" : squared matrix where the only nonzero values are ones on the
4 upper- and 4 sub-diagonals surrounding the main diagonal.
The associated convolution cost for a segment (b_1, b_2) will be
.. math::
c_{b_1,b_2} = \\frac{1}{b_2 - b_1 + 1}\\sum_{i,j = 0, 1 \\leq |i - j| \\leq 4}^{n - 1} a_{i + b_1, j + b_1}
- "mixed" : sum of both previous kernels, i.e. values are zero on the diagonal,
2 on the 4 upper- and 4 sub-diagonals surrounding the main diagonal, and 1 elsewhere.
The associated convolution cost for a segment (b_1, b_2) will be
.. math::
c_{b_1,b_2} = \\frac{1}{b_2 - b_1 + 1}(2*\\sum_{i,j = 0, 1 \\leq |i - j| \\leq 4}^{n - 1} a_{i + b_1, j + b_1} \\ + \sum_{i,j = 0, |i - j| > 4}^{n - 1} a_{i + b_1, j + b_1})
Returns
-------
kernels : array of arrays (which are kernels)
All the kernels, of size 0 ([0]) to max_size.
"""
kernels = [[0]]
for p in range(1,max_size + 1):
if p < 4:
kern = np.ones((p,p)) - np.identity(p)
# elif convolution_type == "7_bands" or convolution_type == "mixed_7_bands":
# if p < 8:
# kern = np.ones((p,p)) - np.identity(p)
# else:
# # Diagonal where only the six subdiagonals surrounding the main diagonal is one
# k = np.array([np.ones(p-7),np.ones(p-6),np.ones(p-5),np.ones(p-4),np.ones(p-3),np.ones(p-2),np.ones(p-1),np.zeros(p),np.ones(p-1),np.ones(p-2),np.ones(p-3),np.ones(p-4),np.ones(p-5),np.ones(p-6),np.ones(p-7)], dtype=object)
# offset = [-7,-6,-5,-4,-3,-2,-1,0,1,2,3, 4, 5, 6, 7]
# if convolution_type == "14_bands":
# kern = diags(k,offset).toarray()
# else:
# kern = np.ones((p,p)) - np.identity(p) + diags(k,offset).toarray()
else:
if convolution_type == "full":
# Full kernel (except for the diagonal)
kern = np.ones((p,p)) - np.identity(p)
elif convolution_type == "4_bands":
# Diagonal where only the eight subdiagonals surrounding the main diagonal is one
k = np.array([np.ones(p-4),np.ones(p-3),np.ones(p-2),np.ones(p-1),np.zeros(p),np.ones(p-1),np.ones(p-2),np.ones(p-3),np.ones(p-4)],dtype=object)
offset = [-4,-3,-2,-1,0,1,2,3,4]
kern = diags(k,offset).toarray()
elif convolution_type == "mixed":
# Sum of both previous kernels
k = np.array([np.ones(p-4),np.ones(p-3),np.ones(p-2),np.ones(p-1),np.zeros(p),np.ones(p-1),np.ones(p-2),np.ones(p-3),np.ones(p-4)],dtype=object)
offset = [-4,-3,-2,-1,0,1,2,3,4]
kern = np.ones((p,p)) - np.identity(p) + diags(k,offset).toarray()
elif convolution_type == "3_bands":
# Diagonal where only the six subdiagonals surrounding the main diagonal is one
k = np.array([np.ones(p-3),np.ones(p-2),np.ones(p-1),np.zeros(p),np.ones(p-1),np.ones(p-2),np.ones(p-3)],dtype=object)
offset = [-3,-2,-1,0,1,2,3]
kern = diags(k,offset).toarray()
elif convolution_type == "mixed_3_bands":
# Sum of both previous kernels
k = np.array([np.ones(p-3),np.ones(p-2),np.ones(p-1),np.zeros(p),np.ones(p-1),np.ones(p-2),np.ones(p-3)],dtype=object)
offset = [-3,-2,-1,0,1,2,3]
kern = np.ones((p,p)) - np.identity(p) + diags(k,offset).toarray()
else:
raise err.InvalidArgumentValueException(f"Convolution type not understood: {convolution_type}.")
kernels.append(kern)
return kernels
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