def tonal_space_align(sem1, sem2):
"""
Like the other alignment functions, but returns the list of aligned
pairs.
"""
from jazzparser.utils.distance import align
# Get a list of (coord,fun) pairs for the logical forms
seq1 = _lf_to_coord_funs(sem1)
seq2 = _lf_to_coord_funs(sem2)
# Produce a version of the paths made up of steps and functions,
# rather than points and functions
steps1 = _steps_list(seq1)
steps2 = _steps_list(seq2)
pairs = align(steps1, steps2, delins_cost=2, subst_cost=_subst_cost)
return pairs
def tonal_space_align(sem1, sem2):
"""
Like the other alignment functions, but returns the list of aligned
pairs.
"""
from jazzparser.utils.distance import align
# Get a list of (coord,fun) pairs for the logical forms
seq1 = _lf_to_coord_funs(sem1)
seq2 = _lf_to_coord_funs(sem2)
# Produce a version of the paths made up of steps and functions,
# rather than points and functions
steps1 = _steps_list(seq1)
steps2 = _steps_list(seq2)
pairs = align(steps1, steps2, delins_cost=2, subst_cost=_subst_cost)
return pairs
def chord_sequence_alignment(seq1, seq2, transposition=0):
"""
Performs the same alignment as L{chord_sequence_match_score}, but
returns the actual alignment of chords.
The alignment assumes that the first sequence is transposed by
C{transposition}.
"""
# Give a half-point to alignments of roots without labels or vice versa
def _subst_cost(crd1, crd2):
cost = 0
if (crd1.root+transposition) % 12 == crd2.root:
cost += -1
if crd1.type == crd2.type:
cost += -1
return cost
# Try aligning the two sequences
alignment = align(seq1, seq2, delins_cost=0, subst_cost=_subst_cost)
return alignment
def chord_sequence_alignment(seq1, seq2, transposition=0):
"""
Performs the same alignment as L{chord_sequence_match_score}, but
returns the actual alignment of chords.
The alignment assumes that the first sequence is transposed by
C{transposition}.
"""
# Give a half-point to alignments of roots without labels or vice versa
def _subst_cost(crd1, crd2):
cost = 0
if (crd1.root + transposition) % 12 == crd2.root:
cost += -1
if crd1.type == crd2.type:
cost += -1
return cost
# Try aligning the two sequences
alignment = align(seq1, seq2, delins_cost=0, subst_cost=_subst_cost)
return alignment
def fscore_match(self, sem1, sem2):
"""
The core computation of the distance metric. Takes care of the tree
comparison and cadence alignment and return the vital statistics.
"""
from jazzparser.formalisms.music_halfspan.harmstruct import \
semantics_to_dependency_trees
from jazzparser.misc.tree.lces import lces_size
from jazzparser.utils.distance import align
res_score = self.options['res_score']
# Get dependency graphs for the two logical forms
if sem1 is None:
trees1 = []
else:
trees1 = semantics_to_dependency_trees(sem1)
if sem2 is None:
trees2 = []
else:
trees2 = semantics_to_dependency_trees(sem2)
if sem1 is None or sem2 is None:
# Empty input: give zero score to everything
alignment_score = 0.0
alignment = []
transpose = None
else:
# Try each possible transposition of the second tree to make this
# metric key independent
distances = []
for x_trans in range(4):
for y_trans in range(3):
def _align(tree1, tree2):
# Transpose the label in the second tree
label2 = ((tree2.root.label[0] + x_trans) % 4,
(tree2.root.label[1] + y_trans) % 3)
# Check the root to find out whether they have the same resolution
same_res = tree1.root.label == label2
# Find out what cadence type each is
same_cad = _cadence_type(tree1) == _cadence_type(tree2)
if same_cad:
# Compare the structure of the cadences
tree_similarity = lces_size(tree1, tree2)
else:
tree_similarity = 0
# Work out how much score to give a matching resolution
if res_score == -1:
res_match = tree_similarity + 1
else:
res_match = res_score
return - tree_similarity - (res_match if same_res else 0)
aligned,dist = align(trees1, trees2, delins_cost=0,
subst_cost=_align,
dist=True)
distances.append((dist,aligned,(x_trans,y_trans)))
alignment_score,alignment,transpose = min(distances,
key=lambda x:x[0])
alignment_score = -float(alignment_score)
def _max_score(trees):
"""
Get the maximum possible score that could be assigned to a match
with this tree set.
"""
score = 0
for tree in trees:
# Do the same things as _align (below), but max possible score
# Maximum similarity is just the size of the tree
tree_sim = len(tree)
if res_score == -1:
res_match = tree_sim + 1
else:
res_match = res_score
# Assume the same resolution and cadence type
score += tree_sim + res_match
return score
max_score1 = _max_score(trees1)
max_score2 = _max_score(trees2)
return alignment_score, max_score1, max_score2, alignment, transpose
