def tonal_space_alignment(sem1, sem2, distance=False):
"""
Performs the same algorithm as L{tonal_space_distance} and
L{tonal_space_alignment_costs}, but returns a list of the operations
that produce the optimal alignment: "I" - insertion; "D" - deletion;
"A" - alignment; "S" - full substitution; or anything else beginning with
"S" to indicate a partial substitution.
Returns the operation list and the two sequences that were compared.
If distance=True, also includes the distance metric. Not included by
default for backward compatibility.
"""
from jazzparser.utils.distance import levenshtein_distance_with_pointers
# 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)
dists, pointers = levenshtein_distance_with_pointers(
steps1, steps2, delins_cost=2, subst_cost_fun=_subst_cost)
# We now have the matrix of costs and the pointers that generated
# those costs.
# Trace back to find out what produces the optimal alignment
# Start at the top right corner
i, j = (len(dists) - 1), (len(dists[0]) - 1)
oplist = []
while not i == j == 0:
if pointers[i][j] == "I":
oplist.append("I")
j -= 1
elif pointers[i][j] == "D":
oplist.append("D")
i -= 1
else:
# Substitution: find out what kind
step1 = steps1[i - 1]
step2 = steps2[j - 1]
subst_type = _subst_type(step1, step2)
if subst_type == "both":
oplist.append("S")
elif subst_type == "fun":
oplist.append("Sf")
elif subst_type == "root":
oplist.append("Sr")
else:
oplist.append("A")
j -= 1
i -= 1
oplist = list(reversed(oplist))
if distance:
dist = float(dists[-1][-1]) / 2.0
return oplist, steps1, steps2, dist
else:
return oplist, steps1, steps2
def tonal_space_alignment(sem1, sem2, distance=False):
"""
Performs the same algorithm as L{tonal_space_distance} and
L{tonal_space_alignment_costs}, but returns a list of the operations
that produce the optimal alignment: "I" - insertion; "D" - deletion;
"A" - alignment; "S" - full substitution; or anything else beginning with
"S" to indicate a partial substitution.
Returns the operation list and the two sequences that were compared.
If distance=True, also includes the distance metric. Not included by
default for backward compatibility.
"""
from jazzparser.utils.distance import levenshtein_distance_with_pointers
# 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)
dists,pointers = levenshtein_distance_with_pointers(
steps1,
steps2,
delins_cost=2,
subst_cost_fun=_subst_cost)
# We now have the matrix of costs and the pointers that generated
# those costs.
# Trace back to find out what produces the optimal alignment
# Start at the top right corner
i,j = (len(dists)-1), (len(dists[0])-1)
oplist = []
while not i == j == 0:
if pointers[i][j] == "I":
oplist.append("I")
j -= 1
elif pointers[i][j] == "D":
oplist.append("D")
i -= 1
else:
# Substitution: find out what kind
step1 = steps1[i-1]
step2 = steps2[j-1]
subst_type = _subst_type(step1, step2)
if subst_type == "both":
oplist.append("S")
elif subst_type == "fun":
oplist.append("Sf")
elif subst_type == "root":
oplist.append("Sr")
else:
oplist.append("A")
j -= 1
i -= 1
oplist = list(reversed(oplist))
if distance:
dist = float(dists[-1][-1]) / 2.0
return oplist,steps1,steps2,dist
else:
return oplist,steps1,steps2
def tonal_space_alignment_costs(sem1, sem2):
"""
Performs the same algorithm as tonal_space_distance, but instead
of returning the score returns the counts of deletions,
insertions, root (only) substitutions, function (only)
substitutions, full substitutions and alignments.
"""
from jazzparser.utils.distance import levenshtein_distance_with_pointers
# 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)
dists, pointers = levenshtein_distance_with_pointers(
steps1, steps2, delins_cost=2, subst_cost_fun=_subst_cost)
# We now have the matrix of costs and the pointers that generated
# those costs.
# Trace back to find out what costs were incurred in the optimal
# alignment
insertions = 0
deletions = 0
function_subs = 0
root_subs = 0
full_subs = 0
alignments = 0
# Start at the top right corner
i, j = (len(dists) - 1), (len(dists[0]) - 1)
while not i == j == 0:
if pointers[i][j] == "I":
insertions += 1
j -= 1
elif pointers[i][j] == "D":
deletions += 1
i -= 1
else:
# Substitution: find out what kind
step1 = steps1[i - 1]
step2 = steps2[j - 1]
subst_type = _subst_type(step1, step2)
if subst_type == "both":
full_subs += 1
elif subst_type == "fun":
function_subs += 1
elif subst_type == "root":
root_subs += 1
else:
alignments += 1
j -= 1
i -= 1
return {
'deletions': deletions,
'insertions': insertions,
'root_subs': root_subs,
'function_subs': function_subs,
'full_subs': full_subs,
'substitutions': root_subs + function_subs + full_subs,
'alignments': alignments,
'steps1': steps1,
'steps2': steps2,
}
def tonal_space_alignment_costs(sem1, sem2):
"""
Performs the same algorithm as tonal_space_distance, but instead
of returning the score returns the counts of deletions,
insertions, root (only) substitutions, function (only)
substitutions, full substitutions and alignments.
"""
from jazzparser.utils.distance import levenshtein_distance_with_pointers
# 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)
dists,pointers = levenshtein_distance_with_pointers(
steps1,
steps2,
delins_cost=2,
subst_cost_fun=_subst_cost)
# We now have the matrix of costs and the pointers that generated
# those costs.
# Trace back to find out what costs were incurred in the optimal
# alignment
insertions = 0
deletions = 0
function_subs = 0
root_subs = 0
full_subs = 0
alignments = 0
# Start at the top right corner
i,j = (len(dists)-1), (len(dists[0])-1)
while not i == j == 0:
if pointers[i][j] == "I":
insertions += 1
j -= 1
elif pointers[i][j] == "D":
deletions += 1
i -= 1
else:
# Substitution: find out what kind
step1 = steps1[i-1]
step2 = steps2[j-1]
subst_type = _subst_type(step1, step2)
if subst_type == "both":
full_subs += 1
elif subst_type == "fun":
function_subs += 1
elif subst_type == "root":
root_subs += 1
else:
alignments += 1
j -= 1
i -= 1
return {
'deletions' : deletions,
'insertions' : insertions,
'root_subs' : root_subs,
'function_subs' : function_subs,
'full_subs' : full_subs,
'substitutions' : root_subs+function_subs+full_subs,
'alignments' : alignments,
'steps1' : steps1,
'steps2' : steps2,
}
