abydos.distance._euclidean.Euclidean.dist() - Code Metrics - Inspection of "0.3.6" - chrislit/abydos - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Pull Request — master (#149)

by Chris

created 2018-11-17 08:37 UTC

abydos.distance._euclidean.Euclidean.dist() A

↳ Parent: abydos.distance._euclidean

Complexity

Conditions

Size

Total Lines	36
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	2
CRAP Score	1

Importance

Changes

Metric	Value
cc	1
eloc	2
nop	5
dl	0
loc	36
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._euclidean.

Euclidean distance & similarity
"""

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

from ._minkowski import Minkowski

__all__ = ['Euclidean', 'dist_euclidean', 'euclidean', 'sim_euclidean']


class Euclidean(Minkowski):

    """Euclidean distance.

    Euclidean distance is the straigh-line or as-the-crow-flies distance,
    equivalent to Minkowski distance in :math:`L^2`-space.
    """

    def dist_abs(self, src, tar, qval=2, normalized=False, alphabet=None):

        """Return the Euclidean distance between two strings.

        Parameters
        ----------
        src : str
            Source string (or QGrams/Counter objects) for comparison
        tar : str
            Target string (or QGrams/Counter objects) for comparison
        qval : int
            The length of each q-gram; 0 for non-q-gram version
        normalized : bool
            Normalizes to [0, 1] if True
        alphabet : collection or int
            The values or size of the alphabet

        Returns
        -------
        float
            The Euclidean distance

        Examples
        --------
        >>> cmp = Euclidean()
        >>> cmp.dist_abs('cat', 'hat')
        2.0
        >>> round(cmp.dist_abs('Niall', 'Neil'), 12)
        2.645751311065
        >>> cmp.dist_abs('Colin', 'Cuilen')
        3.0
        >>> round(cmp.dist_abs('ATCG', 'TAGC'), 12)
        3.162277660168

        """
        return super(self.__class__, self).dist_abs(

            src, tar, qval, 2, normalized, alphabet
        )

    def dist(self, src, tar, qval=2, alphabet=None):

        """Return the normalized Euclidean distance between two strings.

        The normalized Euclidean distance is a distance
        metric in :math:`L^2`-space, normalized to [0, 1].

        Parameters
        ----------
        src : str
            Source string (or QGrams/Counter objects) for comparison
        tar : str
            Target string (or QGrams/Counter objects) for comparison
        qval : int
            The length of each q-gram; 0 for non-q-gram version
        alphabet : collection or int
            The values or size of the alphabet

        Returns
        -------
        float
            The normalized Euclidean distance

        Examples
        --------
        >>> cmp = Euclidean()
        >>> round(cmp.dist('cat', 'hat'), 12)
        0.57735026919
        >>> round(cmp.dist('Niall', 'Neil'), 12)
        0.683130051064
        >>> round(cmp.dist('Colin', 'Cuilen'), 12)
        0.727606875109
        >>> cmp.dist('ATCG', 'TAGC')
        1.0

        """
        return self.dist_abs(src, tar, qval, True, alphabet)


def euclidean(src, tar, qval=2, normalized=False, alphabet=None):
    """Return the Euclidean distance between two strings.

    This is a wrapper for :py:meth:`Euclidean.dist_abs`.

    Parameters
    ----------
    src : str
        Source string (or QGrams/Counter objects) for comparison
    tar : str
        Target string (or QGrams/Counter objects) for comparison
    qval : int
        The length of each q-gram; 0 for non-q-gram version
    normalized : bool
        Normalizes to [0, 1] if True
    alphabet : collection or int
        The values or size of the alphabet

    Returns
    -------
    float: The Euclidean distance

    Examples
    --------
    >>> euclidean('cat', 'hat')
    2.0
    >>> round(euclidean('Niall', 'Neil'), 12)
    2.645751311065
    >>> euclidean('Colin', 'Cuilen')
    3.0
    >>> round(euclidean('ATCG', 'TAGC'), 12)
    3.162277660168

    """
    return Euclidean().dist_abs(src, tar, qval, normalized, alphabet)


def dist_euclidean(src, tar, qval=2, alphabet=None):
    """Return the normalized Euclidean distance between two strings.

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

    Parameters
    ----------
    src : str
        Source string (or QGrams/Counter objects) for comparison
    tar : str
        Target string (or QGrams/Counter objects) for comparison
    qval : int
        The length of each q-gram; 0 for non-q-gram version
    alphabet : collection or int
        The values or size of the alphabet

    Returns
    -------
    float
        The normalized Euclidean distance

    Examples
    --------
    >>> round(dist_euclidean('cat', 'hat'), 12)
    0.57735026919
    >>> round(dist_euclidean('Niall', 'Neil'), 12)
    0.683130051064
    >>> round(dist_euclidean('Colin', 'Cuilen'), 12)
    0.727606875109
    >>> dist_euclidean('ATCG', 'TAGC')
    1.0

    """
    return Euclidean().dist(src, tar, qval, alphabet)


def sim_euclidean(src, tar, qval=2, alphabet=None):
    """Return the normalized Euclidean similarity of two strings.

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

    Parameters
    ----------
    src : str
        Source string (or QGrams/Counter objects) for comparison
    tar : str
        Target string (or QGrams/Counter objects) for comparison
    qval : int
        The length of each q-gram; 0 for non-q-gram version
    alphabet : collection or int
        The values or size of the alphabet

    Returns
    -------
    float
        The normalized Euclidean similarity

    Examples
    --------
    >>> round(sim_euclidean('cat', 'hat'), 12)
    0.42264973081
    >>> round(sim_euclidean('Niall', 'Neil'), 12)
    0.316869948936
    >>> round(sim_euclidean('Colin', 'Cuilen'), 12)
    0.272393124891
    >>> sim_euclidean('ATCG', 'TAGC')
    0.0

    """
    return Euclidean().sim(src, tar, qval, alphabet)


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._euclidean.
20
21		Euclidean distance & similarity
22		"""
23
24	1	from __future__ import (
25		absolute_import,
26		division,
27		print_function,
28		unicode_literals,
29		)
30
31	1	from ._minkowski import Minkowski
32
33	1	__all__ = ['Euclidean', 'dist_euclidean', 'euclidean', 'sim_euclidean']
34
35
36	1	class Euclidean(Minkowski):
		0 ignored issues – show Unused Code introduced 2018-11-10 01:42 UTC by Report Bug Copy Issue Report The variable `__class__` seems to be unused. Loading history...
37		"""Euclidean distance.
38
39		Euclidean distance is the straigh-line or as-the-crow-flies distance,
40		equivalent to Minkowski distance in :math:`L^2`-space.
41		"""
42
43	1	def dist_abs(self, src, tar, qval=2, normalized=False, alphabet=None):
		0 ignored issues – show best-practice introduced 2018-11-04 08:02 UTC by Report Bug Copy Issue Report Too many arguments (6/5) Loading history... Bug introduced 2018-11-04 08:02 UTC by Report Bug Copy Issue Report Parameters differ from overridden 'dist_abs' method Loading history...
44		"""Return the Euclidean distance between two strings.
45
46		Parameters
47		----------
48		src : str
49		Source string (or QGrams/Counter objects) for comparison
50		tar : str
51		Target string (or QGrams/Counter objects) for comparison
52		qval : int
53		The length of each q-gram; 0 for non-q-gram version
54		normalized : bool
55		Normalizes to [0, 1] if True
56		alphabet : collection or int
57		The values or size of the alphabet
58
59		Returns
60		-------
61		float
62		The Euclidean distance
63
64		Examples
65		--------
66		>>> cmp = Euclidean()
67		>>> cmp.dist_abs('cat', 'hat')
68		2.0
69		>>> round(cmp.dist_abs('Niall', 'Neil'), 12)
70		2.645751311065
71		>>> cmp.dist_abs('Colin', 'Cuilen')
72		3.0
73		>>> round(cmp.dist_abs('ATCG', 'TAGC'), 12)
74		3.162277660168
75
76		"""
77	1	return super(self.__class__, self).dist_abs(
		0 ignored issues – show Bug introduced 2018-11-04 08:02 UTC by Report Bug Copy Issue Report The first argument passed to `super()` should be the super-class name, but `self.__class__` was given. Loading history...
78		src, tar, qval, 2, normalized, alphabet
79		)
80
81	1	def dist(self, src, tar, qval=2, alphabet=None):
		0 ignored issues – show Bug introduced 2018-11-04 08:02 UTC by Report Bug Copy Issue Report Parameters differ from overridden 'dist' method Loading history...
82		"""Return the normalized Euclidean distance between two strings.
83
84		The normalized Euclidean distance is a distance
85		metric in :math:`L^2`-space, normalized to [0, 1].
86
87		Parameters
88		----------
89		src : str
90		Source string (or QGrams/Counter objects) for comparison
91		tar : str
92		Target string (or QGrams/Counter objects) for comparison
93		qval : int
94		The length of each q-gram; 0 for non-q-gram version
95		alphabet : collection or int
96		The values or size of the alphabet
97
98		Returns
99		-------
100		float
101		The normalized Euclidean distance
102
103		Examples
104		--------
105		>>> cmp = Euclidean()
106		>>> round(cmp.dist('cat', 'hat'), 12)
107		0.57735026919
108		>>> round(cmp.dist('Niall', 'Neil'), 12)
109		0.683130051064
110		>>> round(cmp.dist('Colin', 'Cuilen'), 12)
111		0.727606875109
112		>>> cmp.dist('ATCG', 'TAGC')
113		1.0
114
115		"""
116	1	return self.dist_abs(src, tar, qval, True, alphabet)
117
118
119	1	def euclidean(src, tar, qval=2, normalized=False, alphabet=None):
120		"""Return the Euclidean distance between two strings.
121
122		This is a wrapper for :py:meth:`Euclidean.dist_abs`.
123
124		Parameters
125		----------
126		src : str
127		Source string (or QGrams/Counter objects) for comparison
128		tar : str
129		Target string (or QGrams/Counter objects) for comparison
130		qval : int
131		The length of each q-gram; 0 for non-q-gram version
132		normalized : bool
133		Normalizes to [0, 1] if True
134		alphabet : collection or int
135		The values or size of the alphabet
136
137		Returns
138		-------
139		float: The Euclidean distance
140
141		Examples
142		--------
143		>>> euclidean('cat', 'hat')
144		2.0
145		>>> round(euclidean('Niall', 'Neil'), 12)
146		2.645751311065
147		>>> euclidean('Colin', 'Cuilen')
148		3.0
149		>>> round(euclidean('ATCG', 'TAGC'), 12)
150		3.162277660168
151
152		"""
153	1	return Euclidean().dist_abs(src, tar, qval, normalized, alphabet)
154
155
156	1	def dist_euclidean(src, tar, qval=2, alphabet=None):
157		"""Return the normalized Euclidean distance between two strings.
158
159		This is a wrapper for :py:meth:`Euclidean.dist`.
160
161		Parameters
162		----------
163		src : str
164		Source string (or QGrams/Counter objects) for comparison
165		tar : str
166		Target string (or QGrams/Counter objects) for comparison
167		qval : int
168		The length of each q-gram; 0 for non-q-gram version
169		alphabet : collection or int
170		The values or size of the alphabet
171
172		Returns
173		-------
174		float
175		The normalized Euclidean distance
176
177		Examples
178		--------
179		>>> round(dist_euclidean('cat', 'hat'), 12)
180		0.57735026919
181		>>> round(dist_euclidean('Niall', 'Neil'), 12)
182		0.683130051064
183		>>> round(dist_euclidean('Colin', 'Cuilen'), 12)
184		0.727606875109
185		>>> dist_euclidean('ATCG', 'TAGC')
186		1.0
187
188		"""
189	1	return Euclidean().dist(src, tar, qval, alphabet)
190
191
192	1	def sim_euclidean(src, tar, qval=2, alphabet=None):
193		"""Return the normalized Euclidean similarity of two strings.
194
195		This is a wrapper for :py:meth:`Euclidean.sim`.
196
197		Parameters
198		----------
199		src : str
200		Source string (or QGrams/Counter objects) for comparison
201		tar : str
202		Target string (or QGrams/Counter objects) for comparison
203		qval : int
204		The length of each q-gram; 0 for non-q-gram version
205		alphabet : collection or int
206		The values or size of the alphabet
207
208		Returns
209		-------
210		float
211		The normalized Euclidean similarity
212
213		Examples
214		--------
215		>>> round(sim_euclidean('cat', 'hat'), 12)
216		0.42264973081
217		>>> round(sim_euclidean('Niall', 'Neil'), 12)
218		0.316869948936
219		>>> round(sim_euclidean('Colin', 'Cuilen'), 12)
220		0.272393124891
221		>>> sim_euclidean('ATCG', 'TAGC')
222		0.0
223
224		"""
225	1	return Euclidean().sim(src, tar, qval, alphabet)
226
227
228		if __name__ == '__main__':
229		import doctest
230
231		doctest.testmod()
232

chrislit / abydos

Pull Request — master (#149)

abydos.distance._euclidean.Euclidean.dist() A

Complexity

Size

Duplication

Code Coverage

Importance

Duplication Side-by-Side

Filter issues like