abydos.distance._eudex.sim_eudex() - Code Metrics - Inspection of "started new entry in HISTORY for 0.4.0" - chrislit/abydos - Measure and Improve Code Quality continuously with Scrutinizer

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by Chris

created 2019-06-01 01:11 UTC

abydos.distance._eudex.sim_eudex() A

↳ Parent: abydos.distance._eudex

Complexity

Conditions

Size

Total Lines	34
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	2
CRAP Score	1

Importance

Changes

Metric	Value
cc	1
eloc	2
nop	4
dl	0
loc	34
ccs	2
cts	2
cp	1
crap	1
rs	10
c	0
b	0
f	0

# -*- coding: utf-8 -*-

# Copyright 2018 by Christopher C. Little.
# This file is part of Abydos.
#
# Abydos is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Abydos is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Abydos. If not, see <http://www.gnu.org/licenses/>.

"""abydos.distance._eudex.

eudex distance functions
"""

from __future__ import (
    absolute_import,
    division,
    print_function,
    unicode_literals,
)

from types import GeneratorType

from six.moves import range

from ._distance import _Distance
from ..phonetic import eudex

__all__ = ['Eudex', 'dist_eudex', 'eudex_hamming', 'sim_eudex']


class Eudex(_Distance):
    """Distance between the Eudex hashes of two terms.

    Cf. :cite:`Ticki:2016`.
    """

    @staticmethod
    def gen_fibonacci():
        """Yield the next Fibonacci number.

        Based on https://www.python-course.eu/generators.php
        Starts at Fibonacci number 3 (the second 1)

        Yields
        ------
        int
            The next Fibonacci number

        """
        num_a, num_b = 1, 2
        while True:
            yield num_a
            num_a, num_b = num_b, num_a + num_b

    @staticmethod
    def gen_exponential(base=2):
        """Yield the next value in an exponential series of the base.

        Starts at base**0

        Parameters
        ----------
        base : int
            The base to exponentiate

        Yields
        ------
        int
            The next power of `base`

        """
        exp = 0
        while True:
            yield base ** exp
            exp += 1

    def dist_abs(
        self, src, tar, weights='exponential', max_length=8, normalized=False
    ):
        """Calculate the distance between the Eudex hashes of two terms.

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        weights : str, iterable, or generator function
            The weights or weights generator function

                - If set to ``None``, a simple Hamming distance is calculated.
                - If set to ``exponential``, weight decays by powers of 2, as
                  proposed in the eudex specification:
                  https://github.com/ticki/eudex.
                - If set to ``fibonacci``, weight decays through the Fibonacci
                  series, as in the eudex reference implementation.
                - If set to a callable function, this assumes it creates a
                  generator and the generator is used to populate a series of
                  weights.
                - If set to an iterable, the iterable's values should be
                  integers and will be used as the weights.

        max_length : int
            The number of characters to encode as a eudex hash
        normalized : bool
            Normalizes to [0, 1] if True

        Returns
        -------
        int
            The Eudex Hamming distance

        Examples
        --------
        >>> cmp = Eudex()
        >>> cmp.dist_abs('cat', 'hat')
        128
        >>> cmp.dist_abs('Niall', 'Neil')
        2
        >>> cmp.dist_abs('Colin', 'Cuilen')
        10
        >>> cmp.dist_abs('ATCG', 'TAGC')
        403

        >>> cmp.dist_abs('cat', 'hat', weights='fibonacci')
        34
        >>> cmp.dist_abs('Niall', 'Neil', weights='fibonacci')
        2
        >>> cmp.dist_abs('Colin', 'Cuilen', weights='fibonacci')
        7
        >>> cmp.dist_abs('ATCG', 'TAGC', weights='fibonacci')
        117

        >>> cmp.dist_abs('cat', 'hat', weights=None)
        1
        >>> cmp.dist_abs('Niall', 'Neil', weights=None)
        1
        >>> cmp.dist_abs('Colin', 'Cuilen', weights=None)
        2
        >>> cmp.dist_abs('ATCG', 'TAGC', weights=None)
        9

        >>> # Using the OEIS A000142:
        >>> cmp.dist_abs('cat', 'hat', [1, 1, 2, 6, 24, 120, 720, 5040])
        1
        >>> cmp.dist_abs('Niall', 'Neil', [1, 1, 2, 6, 24, 120, 720, 5040])
        720
        >>> cmp.dist_abs('Colin', 'Cuilen',
        ... [1, 1, 2, 6, 24, 120, 720, 5040])
        744
        >>> cmp.dist_abs('ATCG', 'TAGC', [1, 1, 2, 6, 24, 120, 720, 5040])
        6243

        """
        # Calculate the eudex hashes and XOR them
        xored = eudex(src, max_length=max_length) ^ eudex(
            tar, max_length=max_length
        )

        # Simple hamming distance (all bits are equal)
        if not weights:
            binary = bin(xored)
            distance = binary.count('1')
            if normalized:
                return distance / (len(binary) - 2)
            return distance

        # If weights is a function, it should create a generator,
        # which we now use to populate a list
        if callable(weights):
            weights = weights()
        elif weights == 'exponential':
            weights = Eudex.gen_exponential()
        elif weights == 'fibonacci':
            weights = Eudex.gen_fibonacci()
        if isinstance(weights, GeneratorType):
            weights = [next(weights) for _ in range(max_length)][::-1]

        # Sum the weighted hamming distance
        distance = 0
        max_distance = 0
        while (xored or normalized) and weights:
            max_distance += 8 * weights[-1]
            distance += bin(xored & 0xFF).count('1') * weights.pop()
            xored >>= 8

        if normalized:
            distance /= max_distance

        return distance

    def dist(self, src, tar, weights='exponential', max_length=8):
        """Return normalized distance between the Eudex hashes of two terms.

        This is Eudex distance normalized to [0, 1].

        Parameters
        ----------
        src : str
            Source string for comparison
        tar : str
            Target string for comparison
        weights : str, iterable, or generator function
            The weights or weights generator function
        max_length : int
            The number of characters to encode as a eudex hash

        Returns
        -------
        int
            The normalized Eudex Hamming distance

        Examples
        --------
        >>> cmp = Eudex()
        >>> round(cmp.dist('cat', 'hat'), 12)
        0.062745098039
        >>> round(cmp.dist('Niall', 'Neil'), 12)
        0.000980392157
        >>> round(cmp.dist('Colin', 'Cuilen'), 12)
        0.004901960784
        >>> round(cmp.dist('ATCG', 'TAGC'), 12)
        0.197549019608

        """
        return self.dist_abs(src, tar, weights, max_length, True)


def eudex_hamming(
    src, tar, weights='exponential', max_length=8, normalized=False
):
    """Calculate the Hamming distance between the Eudex hashes of two terms.

    This is a wrapper for :py:meth:`Eudex.eudex_hamming`.

    Parameters
    ----------
    src : str
        Source string for comparison
    tar : str
        Target string for comparison
    weights : str, iterable, or generator function
        The weights or weights generator function
    max_length : int
        The number of characters to encode as a eudex hash
    normalized : bool
        Normalizes to [0, 1] if True

    Returns
    -------
    int
        The Eudex Hamming distance

    Examples
    --------
    >>> eudex_hamming('cat', 'hat')
    128
    >>> eudex_hamming('Niall', 'Neil')
    2
    >>> eudex_hamming('Colin', 'Cuilen')
    10
    >>> eudex_hamming('ATCG', 'TAGC')
    403

    >>> eudex_hamming('cat', 'hat', weights='fibonacci')
    34
    >>> eudex_hamming('Niall', 'Neil', weights='fibonacci')
    2
    >>> eudex_hamming('Colin', 'Cuilen', weights='fibonacci')
    7
    >>> eudex_hamming('ATCG', 'TAGC', weights='fibonacci')
    117

    >>> eudex_hamming('cat', 'hat', weights=None)
    1
    >>> eudex_hamming('Niall', 'Neil', weights=None)
    1
    >>> eudex_hamming('Colin', 'Cuilen', weights=None)
    2
    >>> eudex_hamming('ATCG', 'TAGC', weights=None)
    9

    >>> # Using the OEIS A000142:
    >>> eudex_hamming('cat', 'hat', [1, 1, 2, 6, 24, 120, 720, 5040])
    1
    >>> eudex_hamming('Niall', 'Neil', [1, 1, 2, 6, 24, 120, 720, 5040])
    720
    >>> eudex_hamming('Colin', 'Cuilen', [1, 1, 2, 6, 24, 120, 720, 5040])
    744
    >>> eudex_hamming('ATCG', 'TAGC', [1, 1, 2, 6, 24, 120, 720, 5040])
    6243

    """
    return Eudex().dist_abs(src, tar, weights, max_length, normalized)


def dist_eudex(src, tar, weights='exponential', max_length=8):
    """Return normalized Hamming distance between Eudex hashes of two terms.

    This is a wrapper for :py:meth:`Eudex.dist`.

    Parameters
    ----------
    src : str
        Source string for comparison
    tar : str
        Target string for comparison
    weights : str, iterable, or generator function
        The weights or weights generator function
    max_length : int
        The number of characters to encode as a eudex hash

    Returns
    -------
    int
        The normalized Eudex Hamming distance

    Examples
    --------
    >>> round(dist_eudex('cat', 'hat'), 12)
    0.062745098039
    >>> round(dist_eudex('Niall', 'Neil'), 12)
    0.000980392157
    >>> round(dist_eudex('Colin', 'Cuilen'), 12)
    0.004901960784
    >>> round(dist_eudex('ATCG', 'TAGC'), 12)
    0.197549019608

    """
    return Eudex().dist(src, tar, weights, max_length)


def sim_eudex(src, tar, weights='exponential', max_length=8):
    """Return normalized Hamming similarity between Eudex hashes of two terms.

    This is a wrapper for :py:meth:`Eudex.sim`.

    Parameters
    ----------
    src : str
        Source string for comparison
    tar : str
        Target string for comparison
    weights : str, iterable, or generator function
        The weights or weights generator function
    max_length : int
        The number of characters to encode as a eudex hash

    Returns
    -------
    int
        The normalized Eudex Hamming similarity

    Examples
    --------
    >>> round(sim_eudex('cat', 'hat'), 12)
    0.937254901961
    >>> round(sim_eudex('Niall', 'Neil'), 12)
    0.999019607843
    >>> round(sim_eudex('Colin', 'Cuilen'), 12)
    0.995098039216
    >>> round(sim_eudex('ATCG', 'TAGC'), 12)
    0.802450980392

    """
    return Eudex().sim(src, tar, weights, max_length)


if __name__ == '__main__':
    import doctest

    doctest.testmod()


1		# -- coding: utf-8 --
2
3		# Copyright 2018 by Christopher C. Little.
4		# This file is part of Abydos.
5		#
6		# Abydos is free software: you can redistribute it and/or modify
7		# it under the terms of the GNU General Public License as published by
8		# the Free Software Foundation, either version 3 of the License, or
9		# (at your option) any later version.
10		#
11		# Abydos is distributed in the hope that it will be useful,
12		# but WITHOUT ANY WARRANTY; without even the implied warranty of
13		# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14		# GNU General Public License for more details.
15		#
16		# You should have received a copy of the GNU General Public License
17		# along with Abydos. If not, see <http://www.gnu.org/licenses/>.
18
19	1	"""abydos.distance._eudex.
20
21		eudex distance functions
22		"""
23
24	1	from __future__ import (
25		absolute_import,
26		division,
27		print_function,
28		unicode_literals,
29		)
30
31	1	from types import GeneratorType
32
33	1	from six.moves import range
34
35	1	from ._distance import _Distance
36	1	from ..phonetic import eudex
37
38	1	__all__ = ['Eudex', 'dist_eudex', 'eudex_hamming', 'sim_eudex']
39
40
41	1	class Eudex(_Distance):
42		"""Distance between the Eudex hashes of two terms.
43
44		Cf. :cite:`Ticki:2016`.
45		"""
46
47	1	@staticmethod
48		def gen_fibonacci():
49		"""Yield the next Fibonacci number.
50
51		Based on https://www.python-course.eu/generators.php
52		Starts at Fibonacci number 3 (the second 1)
53
54		Yields
55		------
56		int
57		The next Fibonacci number
58
59		"""
60	1	num_a, num_b = 1, 2
61	1	while True:
62	1	yield num_a
63	1	num_a, num_b = num_b, num_a + num_b
64
65	1	@staticmethod
66	1	def gen_exponential(base=2):
67		"""Yield the next value in an exponential series of the base.
68
69		Starts at base**0
70
71		Parameters
72		----------
73		base : int
74		The base to exponentiate
75
76		Yields
77		------
78		int
79		The next power of `base`
80
81		"""
82	1	exp = 0
83	1	while True:
84	1	yield base ** exp
85	1	exp += 1
86
87	1	def dist_abs(
88		self, src, tar, weights='exponential', max_length=8, normalized=False
89		):
90		"""Calculate the distance between the Eudex hashes of two terms.
91
92		Parameters
93		----------
94		src : str
95		Source string for comparison
96		tar : str
97		Target string for comparison
98		weights : str, iterable, or generator function
99		The weights or weights generator function
100
101		- If set to ``None``, a simple Hamming distance is calculated.
102		- If set to ``exponential``, weight decays by powers of 2, as
103		proposed in the eudex specification:
104		https://github.com/ticki/eudex.
105		- If set to ``fibonacci``, weight decays through the Fibonacci
106		series, as in the eudex reference implementation.
107		- If set to a callable function, this assumes it creates a
108		generator and the generator is used to populate a series of
109		weights.
110		- If set to an iterable, the iterable's values should be
111		integers and will be used as the weights.
112
113		max_length : int
114		The number of characters to encode as a eudex hash
115		normalized : bool
116		Normalizes to [0, 1] if True
117
118		Returns
119		-------
120		int
121		The Eudex Hamming distance
122
123		Examples
124		--------
125		>>> cmp = Eudex()
126		>>> cmp.dist_abs('cat', 'hat')
127		128
128		>>> cmp.dist_abs('Niall', 'Neil')
129		2
130		>>> cmp.dist_abs('Colin', 'Cuilen')
131		10
132		>>> cmp.dist_abs('ATCG', 'TAGC')
133		403
134
135		>>> cmp.dist_abs('cat', 'hat', weights='fibonacci')
136		34
137		>>> cmp.dist_abs('Niall', 'Neil', weights='fibonacci')
138		2
139		>>> cmp.dist_abs('Colin', 'Cuilen', weights='fibonacci')
140		7
141		>>> cmp.dist_abs('ATCG', 'TAGC', weights='fibonacci')
142		117
143
144		>>> cmp.dist_abs('cat', 'hat', weights=None)
145		1
146		>>> cmp.dist_abs('Niall', 'Neil', weights=None)
147		1
148		>>> cmp.dist_abs('Colin', 'Cuilen', weights=None)
149		2
150		>>> cmp.dist_abs('ATCG', 'TAGC', weights=None)
151		9
152
153		>>> # Using the OEIS A000142:
154		>>> cmp.dist_abs('cat', 'hat', [1, 1, 2, 6, 24, 120, 720, 5040])
155		1
156		>>> cmp.dist_abs('Niall', 'Neil', [1, 1, 2, 6, 24, 120, 720, 5040])
157		720
158		>>> cmp.dist_abs('Colin', 'Cuilen',
159		... [1, 1, 2, 6, 24, 120, 720, 5040])
160		744
161		>>> cmp.dist_abs('ATCG', 'TAGC', [1, 1, 2, 6, 24, 120, 720, 5040])
162		6243
163
164		"""
165		# Calculate the eudex hashes and XOR them
166	1	xored = eudex(src, max_length=max_length) ^ eudex(
167		tar, max_length=max_length
168		)
169
170		# Simple hamming distance (all bits are equal)
171	1	if not weights:
172	1	binary = bin(xored)
173	1	distance = binary.count('1')
174	1	if normalized:
175	1	return distance / (len(binary) - 2)
176	1	return distance
177
178		# If weights is a function, it should create a generator,
179		# which we now use to populate a list
180	1	if callable(weights):
181	1	weights = weights()
182	1	elif weights == 'exponential':
183	1	weights = Eudex.gen_exponential()
184	1	elif weights == 'fibonacci':
185	1	weights = Eudex.gen_fibonacci()
186	1	if isinstance(weights, GeneratorType):
187	1	weights = [next(weights) for _ in range(max_length)][::-1]
188
189		# Sum the weighted hamming distance
190	1	distance = 0
191	1	max_distance = 0
192	1	while (xored or normalized) and weights:
193	1	max_distance += 8 * weights[-1]
194	1	distance += bin(xored & 0xFF).count('1') * weights.pop()
195	1	xored >>= 8
196
197	1	if normalized:
198	1	distance /= max_distance
199
200	1	return distance
201
202	1	def dist(self, src, tar, weights='exponential', max_length=8):
203		"""Return normalized distance between the Eudex hashes of two terms.
204
205		This is Eudex distance normalized to [0, 1].
206
207		Parameters
208		----------
209		src : str
210		Source string for comparison
211		tar : str
212		Target string for comparison
213		weights : str, iterable, or generator function
214		The weights or weights generator function
215		max_length : int
216		The number of characters to encode as a eudex hash
217
218		Returns
219		-------
220		int
221		The normalized Eudex Hamming distance
222
223		Examples
224		--------
225		>>> cmp = Eudex()
226		>>> round(cmp.dist('cat', 'hat'), 12)
227		0.062745098039
228		>>> round(cmp.dist('Niall', 'Neil'), 12)
229		0.000980392157
230		>>> round(cmp.dist('Colin', 'Cuilen'), 12)
231		0.004901960784
232		>>> round(cmp.dist('ATCG', 'TAGC'), 12)
233		0.197549019608
234
235		"""
236	1	return self.dist_abs(src, tar, weights, max_length, True)
237
238
239	1	def eudex_hamming(
240		src, tar, weights='exponential', max_length=8, normalized=False
241		):
242		"""Calculate the Hamming distance between the Eudex hashes of two terms.
243
244		This is a wrapper for :py:meth:`Eudex.eudex_hamming`.
245
246		Parameters
247		----------
248		src : str
249		Source string for comparison
250		tar : str
251		Target string for comparison
252		weights : str, iterable, or generator function
253		The weights or weights generator function
254		max_length : int
255		The number of characters to encode as a eudex hash
256		normalized : bool
257		Normalizes to [0, 1] if True
258
259		Returns
260		-------
261		int
262		The Eudex Hamming distance
263
264		Examples
265		--------
266		>>> eudex_hamming('cat', 'hat')
267		128
268		>>> eudex_hamming('Niall', 'Neil')
269		2
270		>>> eudex_hamming('Colin', 'Cuilen')
271		10
272		>>> eudex_hamming('ATCG', 'TAGC')
273		403
274
275		>>> eudex_hamming('cat', 'hat', weights='fibonacci')
276		34
277		>>> eudex_hamming('Niall', 'Neil', weights='fibonacci')
278		2
279		>>> eudex_hamming('Colin', 'Cuilen', weights='fibonacci')
280		7
281		>>> eudex_hamming('ATCG', 'TAGC', weights='fibonacci')
282		117
283
284		>>> eudex_hamming('cat', 'hat', weights=None)
285		1
286		>>> eudex_hamming('Niall', 'Neil', weights=None)
287		1
288		>>> eudex_hamming('Colin', 'Cuilen', weights=None)
289		2
290		>>> eudex_hamming('ATCG', 'TAGC', weights=None)
291		9
292
293		>>> # Using the OEIS A000142:
294		>>> eudex_hamming('cat', 'hat', [1, 1, 2, 6, 24, 120, 720, 5040])
295		1
296		>>> eudex_hamming('Niall', 'Neil', [1, 1, 2, 6, 24, 120, 720, 5040])
297		720
298		>>> eudex_hamming('Colin', 'Cuilen', [1, 1, 2, 6, 24, 120, 720, 5040])
299		744
300		>>> eudex_hamming('ATCG', 'TAGC', [1, 1, 2, 6, 24, 120, 720, 5040])
301		6243
302
303		"""
304	1	return Eudex().dist_abs(src, tar, weights, max_length, normalized)
305
306
307	1	def dist_eudex(src, tar, weights='exponential', max_length=8):
308		"""Return normalized Hamming distance between Eudex hashes of two terms.
309
310		This is a wrapper for :py:meth:`Eudex.dist`.
311
312		Parameters
313		----------
314		src : str
315		Source string for comparison
316		tar : str
317		Target string for comparison
318		weights : str, iterable, or generator function
319		The weights or weights generator function
320		max_length : int
321		The number of characters to encode as a eudex hash
322
323		Returns
324		-------
325		int
326		The normalized Eudex Hamming distance
327
328		Examples
329		--------
330		>>> round(dist_eudex('cat', 'hat'), 12)
331		0.062745098039
332		>>> round(dist_eudex('Niall', 'Neil'), 12)
333		0.000980392157
334		>>> round(dist_eudex('Colin', 'Cuilen'), 12)
335		0.004901960784
336		>>> round(dist_eudex('ATCG', 'TAGC'), 12)
337		0.197549019608
338
339		"""
340	1	return Eudex().dist(src, tar, weights, max_length)
341
342
343	1	def sim_eudex(src, tar, weights='exponential', max_length=8):
344		"""Return normalized Hamming similarity between Eudex hashes of two terms.
345
346		This is a wrapper for :py:meth:`Eudex.sim`.
347
348		Parameters
349		----------
350		src : str
351		Source string for comparison
352		tar : str
353		Target string for comparison
354		weights : str, iterable, or generator function
355		The weights or weights generator function
356		max_length : int
357		The number of characters to encode as a eudex hash
358
359		Returns
360		-------
361		int
362		The normalized Eudex Hamming similarity
363
364		Examples
365		--------
366		>>> round(sim_eudex('cat', 'hat'), 12)
367		0.937254901961
368		>>> round(sim_eudex('Niall', 'Neil'), 12)
369		0.999019607843
370		>>> round(sim_eudex('Colin', 'Cuilen'), 12)
371		0.995098039216
372		>>> round(sim_eudex('ATCG', 'TAGC'), 12)
373		0.802450980392
374
375		"""
376	1	return Eudex().sim(src, tar, weights, max_length)
377
378
379		if __name__ == '__main__':
380		import doctest
381
382		doctest.testmod()
383

chrislit / abydos

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abydos.distance._eudex.sim_eudex() A

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