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"""
Operations on strings:
- calculate distance between two strings
- transform strings with transformation codes
"""
import unicodedata
import re
from .char_player import distanceBetweenChars, dDistanceBetweenChars
#### N-GRAMS
def getNgrams (sWord, n=2):
"return a list of Ngrams strings"
return [ sWord[i:i+n] for i in range(len(sWord)-n+1) ]
#### WORD NORMALIZATION
_xTransCharsForSpelling = str.maketrans({
'ſ': 's', 'ffi': 'ffi', 'ffl': 'ffl', 'ff': 'ff', 'ſt': 'ft', 'fi': 'fi', 'fl': 'fl', 'st': 'st',
"'": '’'
})
def spellingNormalization (sWord):
"nomalization NFC and removing ligatures"
return unicodedata.normalize("NFC", sWord.translate(_xTransCharsForSpelling))
_xTransCharsForSimplification = str.maketrans({
'à': 'a', 'é': 'é', 'î': 'i', 'ô': 'o', 'û': 'u', 'ÿ': 'y',
'â': 'a', 'è': 'é', 'ï': 'i', 'ö': 'o', 'ù': 'u', 'ŷ': 'y',
'ä': 'a', 'ê': 'é', 'í': 'i', 'ó': 'o', 'ü': 'u', 'ý': 'y',
'á': 'a', 'ë': 'é', 'ì': 'i', 'ò': 'o', 'ú': 'u', 'ỳ': 'y',
'ā': 'a', 'ē': 'é', 'ī': 'i', 'ō': 'o', 'ū': 'u', 'ȳ': 'y',
'ç': 'c', 'ñ': 'n',
'œ': 'oe', 'æ': 'ae',
'ſ': 's', 'ffi': 'ffi', 'ffl': 'ffl', 'ff': 'ff', 'ſt': 'ft', 'fi': 'fi', 'fl': 'fl', 'st': 'st',
"⁰": "0", "¹": "1", "²": "2", "³": "3", "⁴": "4", "⁵": "5", "⁶": "6", "⁷": "7", "⁸": "8", "⁹": "9",
"₀": "0", "₁": "1", "₂": "2", "₃": "3", "₄": "4", "₅": "5", "₆": "6", "₇": "7", "₈": "8", "₉": "9",
"’": "", "'": "", "ʼ": "", "‘": "", "‛": "", "´": "", "`": "", "′": "", "‵": "", "՚": "", "ꞌ": "", "Ꞌ": "",
"-": ""
})
def simplifyWord (sWord):
"word simplication before calculating distance between words"
sWord = sWord.lower().translate(_xTransCharsForSimplification)
sNewWord = ""
for i, c in enumerate(sWord, 1):
if c != sWord[i:i+1] or (c == 'e' and sWord[i:i+2] != "ee"): # exception for <e> to avoid confusion between crée / créai
sNewWord += c
return sNewWord.replace("eau", "o").replace("au", "o").replace("ai", "éi").replace("ei", "é").replace("ph", "f")
def cleanWord (sWord):
"remove letters repeated more than 2 times"
return re.sub("(.)\\1{2,}", '\\1\\1', sWord)
_xTransNumbersToExponent = str.maketrans({
"0": "⁰", "1": "¹", "2": "²", "3": "³", "4": "⁴", "5": "⁵", "6": "⁶", "7": "⁷", "8": "⁸", "9": "⁹"
})
def numbersToExponent (sWord):
"convert numeral chars to exponant chars"
return sWord.translate(_xTransNumbersToExponent)
#### DISTANCE CALCULATIONS
def longestCommonSubstring (s1, s2):
"longest common substring"
# http://en.wikipedia.org/wiki/Longest_common_substring_problem
# http://en.wikibooks.org/wiki/Algorithm_implementation/Strings/Longest_common_substring
lMatrix = [ [0]*(1+len(s2)) for i in range(1+len(s1)) ]
nLongest, nLongestX = 0, 0
for x in range(1, 1+len(s1)):
for y in range(1, 1+len(s2)):
if s1[x-1] == s2[y-1]:
lMatrix[x][y] = lMatrix[x-1][y-1] + 1
if lMatrix[x][y] > nLongest:
nLongest = lMatrix[x][y]
nLongestX = x
else:
lMatrix[x][y] = 0
return s1[nLongestX-nLongest : nLongestX]
def distanceDamerauLevenshtein (s1, s2):
"distance of Damerau-Levenshtein between <s1> and <s2>"
# https://fr.wikipedia.org/wiki/Distance_de_Damerau-Levenshtein
d = {}
nLen1 = len(s1)
nLen2 = len(s2)
for i in range(-1, nLen1+1):
d[i, -1] = i + 1
for j in range(-1, nLen2+1):
d[-1, j] = j + 1
for i in range(nLen1):
for j in range(nLen2):
#nCost = 0 if s1[i] == s2[j] else 1
nCost = distanceBetweenChars(s1[i], s2[j])
d[i, j] = min(
d[i-1, j] + 1, # Deletion
d[i, j-1] + 1, # Insertion
d[i-1, j-1] + nCost, # Substitution
)
if i and j and s1[i] == s2[j-1] and s1[i-1] == s2[j]:
d[i, j] = min(d[i, j], d[i-2, j-2] + nCost) # Transposition
return d[nLen1-1, nLen2-1]
def distanceJaroWinkler (sWord1, sWord2, fBoost = .666):
"distance of Jaro-Winkler between <sWord1> and <sWord2>, returns a float"
# https://github.com/thsig/jaro-winkler-JS
#if (sWord1 == sWord2): return 1.0
nLen1 = len(sWord1)
nLen2 = len(sWord2)
nMax = max(nLen1, nLen2)
aFlags1 = [ None for _ in range(nMax) ]
aFlags2 = [ None for _ in range(nMax) ]
nSearchRange = (max(nLen1, nLen2) // 2) - 1
nMinLen = min(nLen1, nLen2)
# Looking only within the search range, count and flag the matched pairs.
nCommon = 0
yl1 = nLen2 - 1
for i in range(nLen1):
nLowLim = i - nSearchRange if i >= nSearchRange else 0
nHiLim = i + nSearchRange if (i + nSearchRange) <= yl1 else yl1
for j in range(nLowLim, nHiLim+1):
if aFlags2[j] != 1 and sWord1[j:j+1] == sWord2[i:i+1]:
aFlags1[j] = 1
aFlags2[i] = 1
nCommon += 1
break
# Return if no characters in common
if nCommon == 0:
return 0.0
# Count the number of transpositions
k = 0
nTrans = 0
for i in range(nLen1):
if aFlags1[i] == 1:
for j in range(k, nLen2):
if aFlags2[j] == 1:
k = j + 1
break
if sWord1[i] != sWord2[j]:
nTrans += 1
nTrans = nTrans // 2
# Adjust for similarities in nonmatched characters
nSimi = 0
if nMinLen > nCommon:
for i in range(nLen1):
if not aFlags1[i]:
for j in range(nLen2):
if not aFlags2[j]:
if sWord1[i] in dDistanceBetweenChars and sWord2[j] in dDistanceBetweenChars[sWord1[i]]:
nSimi += dDistanceBetweenChars[sWord1[i]][sWord2[j]]
aFlags2[j] = 2
break
fSim = (nSimi / 10.0) + nCommon
# Main weight computation
fWeight = fSim / nLen1 + fSim / nLen2 + (nCommon - nTrans) / nCommon
fWeight = fWeight / 3
# Continue to boost the weight if the strings are similar
if fWeight > fBoost:
# Adjust for having up to the first 4 characters in common
j = 4 if nMinLen >= 4 else nMinLen
i = 0
while i < j and sWord1[i] == sWord2[i]:
i += 1
if i:
fWeight += i * 0.1 * (1.0 - fWeight)
# Adjust for long strings.
# After agreeing beginning chars, at least two more must agree
# and the agreeing characters must be more than half of the
# remaining characters.
if nMinLen > 4 and nCommon > i + 1 and 2 * nCommon >= nMinLen + i:
fWeight += (1 - fWeight) * ((nCommon - i - 1) / (nLen1 * nLen2 - i*2 + 2))
return fWeight
def distanceSift4 (s1, s2, nMaxOffset=5):
"implementation of general Sift4."
# https://siderite.blogspot.com/2014/11/super-fast-and-accurate-string-distance.html
if not s1:
return len(s2)
if not s2:
return len(s1)
nLen1, nLen2 = len(s1), len(s2)
i1, i2 = 0, 0 # Cursors for each string
nLargestCS = 0 # Largest common substring
nLocalCS = 0 # Local common substring
nTrans = 0 # Number of transpositions ('ab' vs 'ba')
lOffset = [] # Offset pair array, for computing the transpositions
while i1 < nLen1 and i2 < nLen2:
if s1[i1] == s2[i2]:
nLocalCS += 1
# Check if current match is a transposition
bTrans = False
i = 0
while i < len(lOffset):
t = lOffset[i]
if i1 <= t[0] or i2 <= t[1]:
bTrans = abs(i2-i1) >= abs(t[1] - t[0])
if bTrans:
nTrans += 1
elif not t[2]:
t[2] = True
nTrans += 1
break
if i1 > t[1] and i2 > t[0]:
del lOffset[i]
else:
i += 1
lOffset.append([i1, i2, bTrans])
else:
nLargestCS += nLocalCS
nLocalCS = 0
if i1 != i2:
i1 = i2 = min(i1, i2)
for i in range(nMaxOffset):
if i1 + i >= nLen1 and i2 + i >= nLen2:
break
if i1 + i < nLen1 and s1[i1+i] == s2[i2]:
i1 += i - 1
i2 -= 1
break
if i2 + i < nLen2 and s1[i1] == s2[i2+i]:
i2 += i - 1
i1 -= 1
break
i1 += 1
i2 += 1
if i1 >= nLen1 or i2 >= nLen2:
nLargestCS += nLocalCS
nLocalCS = 0
i1 = i2 = min(i1, i2)
nLargestCS += nLocalCS
return round(max(nLen1, nLen2) - nLargestCS + nTrans)
def showDistance (s1, s2):
"display Damerau-Levenshtein distance and Sift4 distance between <s1> and <s2>"
print("Jaro-Winkler: " + s1 + "/" + s2 + " = " + distanceJaroWinkler(s1, s2))
print("Damerau-Levenshtein: " + s1 + "/" + s2 + " = " + distanceDamerauLevenshtein(s1, s2))
print("Sift4:" + s1 + "/" + s2 + " = " + distanceSift4(s1, s2))
#### STEMMING OPERATIONS
## No stemming
def noStemming (sFlex, sStem):
"return <sStem>"
return sStem
def rebuildWord (sFlex, sCode1, sCode2):
""" Change <sFlex> with codes (each inserts a char at a defined possition).
<I forgot what purpose it has…>
"""
if sCode1 == "_":
return sFlex
n, c = sCode1.split(":")
sFlex = sFlex[:n] + c + sFlex[n:]
if sCode2 == "_":
return sFlex
n, c = sCode2.split(":")
return sFlex[:n] + c + sFlex[n:]
## Define affixes for stemming
# Note: 48 is the ASCII code for "0"
# Suffix only
def defineSuffixCode (sFlex, sStem):
""" Returns a string defining how to get stem from flexion
"n(sfx)"
with n: a char with numeric meaning, "0" = 0, "1" = 1, ... ":" = 10, etc. (See ASCII table.) Says how many letters to strip from flexion.
sfx [optional]: string to add on flexion
Examples:
"0": strips nothing, adds nothing
"1er": strips 1 letter, adds "er"
"2": strips 2 letters, adds nothing
"""
if sFlex == sStem:
return "0"
jSfx = 0
for i in range(min(len(sFlex), len(sStem))):
if sFlex[i] != sStem[i]:
break
jSfx += 1
return chr(len(sFlex)-jSfx+48) + sStem[jSfx:]
def changeWordWithSuffixCode (sWord, sSfxCode):
"apply transformation code <sSfxCode> on <sWord> and return the result string"
if sSfxCode == "0":
return sWord
return sWord[:-(ord(sSfxCode[0])-48)] + sSfxCode[1:] if sSfxCode[0] != '0' else sWord + sSfxCode[1:]
# Prefix and suffix
def defineAffixCode (sFlex, sStem):
""" Returns a string defining how to get stem from flexion. Examples:
"0" if stem = flexion
"stem" if no common substring
"n(pfx)/m(sfx)"
with n and m: chars with numeric meaning, "0" = 0, "1" = 1, ... ":" = 10, etc. (See ASCII table.) Says how many letters to strip from flexion.
pfx [optional]: string to add before the flexion
sfx [optional]: string to add after the flexion
"""
if sFlex == sStem:
return "0"
# is stem a substring of flexion?
n = sFlex.find(sStem)
if n >= 0:
return "{}/{}".format(chr(n+48), chr(len(sFlex)-(len(sStem)+n)+48))
# no, so we are looking for common substring
sSubs = longestCommonSubstring(sFlex, sStem)
if len(sSubs) > 1:
iPos = sStem.find(sSubs)
sPfx = sStem[:iPos]
sSfx = sStem[iPos+len(sSubs):]
n = sFlex.find(sSubs)
m = len(sFlex) - (len(sSubs)+n)
return chr(n+48) + sPfx + "/" + chr(m+48) + sSfx
return sStem
def changeWordWithAffixCode (sWord, sAffCode):
"apply transformation code <sAffCode> on <sWord> and return the result string"
if sAffCode == "0":
return sWord
if '/' not in sAffCode:
return sAffCode
sPfxCode, sSfxCode = sAffCode.split('/')
sWord = sPfxCode[1:] + sWord[(ord(sPfxCode[0])-48):]
return sWord[:-(ord(sSfxCode[0])-48)] + sSfxCode[1:] if sSfxCode[0] != '0' else sWord + sSfxCode[1:]
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