distance_metric - Code Metrics - Inspection of "Merge branch '0.8.dev'." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 7a46cc...a1c9a1 )

by Andrei

created 2018-04-15 18:36 UTC

distance_metric A

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Complexity

Total Complexity

Size/Duplication

Total Lines	112
Duplicated Lines	0 %

Importance

Changes

Metric	Value
dl	0
loc	112
rs	10
c	0
b	0
f	0
wmc	11

5 Methods

Rating	Name	Size	Complexity
A	get_arguments()	8	1
A	get_function()	8	1
A	__init__()	16	1
C	__call__()	30	7
A	get_type()	8	1

"""!

@brief Module provides various distance metrics - abstraction of the notion of distance in a metric space.

@authors Andrei Novikov ([email protected])
@date 2014-2018
@copyright GNU Public License

@cond GNU_PUBLIC_LICENSE
    PyClustering 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.

    PyClustering 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 this program.  If not, see <http://www.gnu.org/licenses/>.
@endcond

"""


from enum import IntEnum;


class type_metric(IntEnum):
    """!
    @brief Enumeration of supported metrics in the module for distance calculation between two points.

    """

    ## Euclidean distance, for more information see function 'euclidean_distance'.
    EUCLIDEAN = 0;

    ## Square Euclidean distance, for more information see function 'euclidean_distance_square'.
    EUCLIDEAN_SQUARE = 1;

    ## Manhattan distance, for more information see function 'manhattan_distance'.
    MANHATTAN = 2;

    ## Chebyshev distance, for more information see function 'chebyshev_distance'.
    CHEBYSHEV = 3;

    ## Minkowski distance, for more information see function 'minkowski_distance'.
    MINKOWSKI = 4;

    ## User defined function for distance calculation between two points.
    USER_DEFINED = 1000;



class distance_metric:
    """!
    @brief Distance metric performs distance calculation between two points in line with encapsulated function, for
            example, euclidean distance or chebyshev distance, or even user-defined.

    @details

    Example of Euclidean distance metric:
    @code
        metric = distance_metric(type_metric.EUCLIDEAN);
        distance = metric([1.0, 2.5], [-1.2, 3.4]);
    @endcode

    Example of Chebyshev distance metric:
    @code
        metric = distance_metric(type_metric.CHEBYSHEV);
        distance = metric([0.0, 0.0], [2.5, 6.0]);
    @endcode

    In following example additional argument should be specified (generally, 'degree' is a optional argument that is
     equal to '2' by default) that is specific for Minkowski distance:
    @code
        metric = distance_metric(type_metric.MINKOWSKI, degree=4);
        distance = metric([4.0, 9.2, 1.0], [3.4, 2.5, 6.2]);
    @endcode

    User may define its own function for distance calculation:
    @code
        user_function = lambda point1, point2: point1[0] + point2[0] + 2;
        metric = distance_metric(type_metric.USER_DEFINED, func=user_function);
        distance = metric([2.0, 3.0], [1.0, 3.0]);
    @endcode

    """
    def __init__(self, type, **kwargs):
        """!
        @brief Creates distance metric instance for calculation distance between two points.

        @param[in] type (type_metric):
        @param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'func' and corresponding additional argument for
                    for specific metric types).

        Keyword Args:
            func (callable): Callable object with two arguments (point #1 and point #2) that is used only if metric is 'type_metric.USER_DEFINED'.
            degree (numeric): Only for 'type_metric.MINKOWSKI' - degree of Minkowski equation.

        """
        self.__type = type;
        self.__args = kwargs;
        self.__func = self.__args.get('func', None);


    def __call__(self, point1, point2):
        """!
        @brief Calculates distance between two points.

        @param[in] point1 (list): The first point.
        @param[in] point2 (list): The second point.

        @return (double) Distance between two points.

        """
        if self.__type == type_metric.EUCLIDEAN:
            return euclidean_distance(point1, point2);

        elif self.__type == type_metric.EUCLIDEAN_SQUARE:
            return euclidean_distance_square(point1, point2);

        elif self.__type == type_metric.MANHATTAN:
            return manhattan_distance(point1, point2);

        elif self.__type == type_metric.CHEBYSHEV:
            return chebyshev_distance(point1, point2);

        elif self.__type == type_metric.MINKOWSKI:
            return minkowski_distance(point1, point2, self.__args.get('degree', 2));

        elif self.__type == type_metric.USER_DEFINED:
            return self.__func(point1, point2);

        else:
            raise ValueError("Unknown type of metric: '%d'", self.__type);


    def get_type(self):
        """!
        @brief Return type of distance metric that is used.

        @return (type_metric) Type of distance metric.

        """
        return self.__type;


    def get_arguments(self):
        """!
        @brief Return additional arguments that are used by distance metric.

        @return (dict) Additional arguments.

        """
        return self.__args;


    def get_function(self):
        """!
        @brief Return user-defined function for calculation distance metric.

        @return (callable): User-defined distance metric function.

        """
        return self.__func;



def euclidean_distance(point1, point2):
    """!

    @brief Calculate Euclidean distance between two vectors.
    @details The Euclidean between vectors (points) a and b is calculated by following formula:

    \f[
    dist(a, b) = \sqrt{ \sum_{i=0}^{N}(a_{i} - b_{i})^{2} };
    \f]

    Where N is a length of each vector.

    @param[in] point1 (list): The first vector.
    @param[in] point2 (list): The second vector.

    @return (double) Euclidean distance between two vectors.

    @see euclidean_distance_square, manhattan_distance, chebyshev_distance

    """
    distance = euclidean_distance_square(point1, point2);
    return distance ** 0.5;


def euclidean_distance_square(point1, point2):
    """!

    @brief Calculate square Euclidean distance between two vectors.

    \f[
    dist(a, b) = \sum_{i=0}^{N}(a_{i} - b_{i})^{2};
    \f]

    @param[in] point1 (list): The first vector.
    @param[in] point2 (list): The second vector.

    @return (double) Square Euclidean distance between two vectors.

    @see euclidean_distance, manhattan_distance, chebyshev_distance

    """
    distance = 0.0;
    for i in range(len(point1)):
        distance += (point1[i] - point2[i]) ** 2.0;

    return distance;


def manhattan_distance(point1, point2):
    """!

    @brief Calculate Manhattan distance between between two vectors.

    \f[
    dist(a, b) = \sum_{i=0}^{N}\left | a_{i} - b_{i} \right |;
    \f]

    @param[in] point1 (list): The first vector.
    @param[in] point2 (list): The second vector.

    @return (double) Manhattan distance between two vectors.

    @see euclidean_distance_square, euclidean_distance, chebyshev_distance

    """
    distance = 0.0;
    dimension = len(point1);

    for i in range(dimension):
        distance += abs(point1[i] - point2[i]);

    return distance;


def chebyshev_distance(point1, point2):
    """!

    @brief Calculate Chebyshev distance between between two vectors.

    \f[
    dist(a, b) = \max_{}i\left (\left | a_{i} - b_{i} \right |\right );
    \f]

    @param[in] point1 (list): The first vector.
    @param[in] point2 (list): The second vector.

    @return (double) Chebyshev distance between two vectors.

    @see euclidean_distance_square, euclidean_distance, minkowski_distance

    """
    distance = 0.0;
    dimension = len(point1);

    for i in range(dimension):
        distance = max(distance, abs(point1[i] - point2[i]));

    return distance;


def minkowski_distance(point1, point2, degree=2):
    """!

    @brief Calculate Minkowski distance between two vectors.

    \f[
    dist(a, b) = \sqrt[p]{ \sum_{i=0}^{N}\left(a_{i} - b_{i}\right)^{p} };
    \f]

    @param[in] point1 (list): The first vector.
    @param[in] point2 (list): The second vector.
    @param[in] degree (numeric): Degree of that is used for Minkowski distance.

    @return (double) Minkowski distance between two vectors.

    @see euclidean_distance

    """
    distance = 0.0;
    for i in range(len(point1)):
        distance += (point1[i] - point2[i]) ** degree;

    return distance ** (1.0 / degree);

1			"""!
2
3			@brief Module provides various distance metrics - abstraction of the notion of distance in a metric space.
4
5			@authors Andrei Novikov ([email protected])
6			@date 2014-2018
7			@copyright GNU Public License
8
9			@cond GNU_PUBLIC_LICENSE
10			PyClustering is free software: you can redistribute it and/or modify
11			it under the terms of the GNU General Public License as published by
12			the Free Software Foundation, either version 3 of the License, or
13			(at your option) any later version.
14
15			PyClustering is distributed in the hope that it will be useful,
16			but WITHOUT ANY WARRANTY; without even the implied warranty of
17			MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18			GNU General Public License for more details.
19
20			You should have received a copy of the GNU General Public License
21			along with this program. If not, see <http://www.gnu.org/licenses/>.
22			@endcond
23
24			"""
25
26
27			from enum import IntEnum;
28
29
30			class type_metric(IntEnum):
31			"""!
32			@brief Enumeration of supported metrics in the module for distance calculation between two points.
33
34			"""
35
36			## Euclidean distance, for more information see function 'euclidean_distance'.
37			EUCLIDEAN = 0;
38
39			## Square Euclidean distance, for more information see function 'euclidean_distance_square'.
40			EUCLIDEAN_SQUARE = 1;
41
42			## Manhattan distance, for more information see function 'manhattan_distance'.
43			MANHATTAN = 2;
44
45			## Chebyshev distance, for more information see function 'chebyshev_distance'.
46			CHEBYSHEV = 3;
47
48			## Minkowski distance, for more information see function 'minkowski_distance'.
49			MINKOWSKI = 4;
50
51			## User defined function for distance calculation between two points.
52			USER_DEFINED = 1000;
53
54
55
56			class distance_metric:
57			"""!
58			@brief Distance metric performs distance calculation between two points in line with encapsulated function, for
59			example, euclidean distance or chebyshev distance, or even user-defined.
60
61			@details
62
63			Example of Euclidean distance metric:
64			@code
65			metric = distance_metric(type_metric.EUCLIDEAN);
66			distance = metric([1.0, 2.5], [-1.2, 3.4]);
67			@endcode
68
69			Example of Chebyshev distance metric:
70			@code
71			metric = distance_metric(type_metric.CHEBYSHEV);
72			distance = metric([0.0, 0.0], [2.5, 6.0]);
73			@endcode
74
75			In following example additional argument should be specified (generally, 'degree' is a optional argument that is
76			equal to '2' by default) that is specific for Minkowski distance:
77			@code
78			metric = distance_metric(type_metric.MINKOWSKI, degree=4);
79			distance = metric([4.0, 9.2, 1.0], [3.4, 2.5, 6.2]);
80			@endcode
81
82			User may define its own function for distance calculation:
83			@code
84			user_function = lambda point1, point2: point1[0] + point2[0] + 2;
85			metric = distance_metric(type_metric.USER_DEFINED, func=user_function);
86			distance = metric([2.0, 3.0], [1.0, 3.0]);
87			@endcode
88
89			"""
90			def __init__(self, type, **kwargs):
91			"""!
92			@brief Creates distance metric instance for calculation distance between two points.
93
94			@param[in] type (type_metric):
95			@param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'func' and corresponding additional argument for
96			for specific metric types).
97
98			Keyword Args:
99			func (callable): Callable object with two arguments (point #1 and point #2) that is used only if metric is 'type_metric.USER_DEFINED'.
100			degree (numeric): Only for 'type_metric.MINKOWSKI' - degree of Minkowski equation.
101
102			"""
103			self.__type = type;
104			self.__args = kwargs;
105			self.__func = self.__args.get('func', None);
106
107
108			def __call__(self, point1, point2):
109			"""!
110			@brief Calculates distance between two points.
111
112			@param[in] point1 (list): The first point.
113			@param[in] point2 (list): The second point.
114
115			@return (double) Distance between two points.
116
117			"""
118			if self.__type == type_metric.EUCLIDEAN:
119			return euclidean_distance(point1, point2);
120
121			elif self.__type == type_metric.EUCLIDEAN_SQUARE:
122			return euclidean_distance_square(point1, point2);
123
124			elif self.__type == type_metric.MANHATTAN:
125			return manhattan_distance(point1, point2);
126
127			elif self.__type == type_metric.CHEBYSHEV:
128			return chebyshev_distance(point1, point2);
129
130			elif self.__type == type_metric.MINKOWSKI:
131			return minkowski_distance(point1, point2, self.__args.get('degree', 2));
132
133			elif self.__type == type_metric.USER_DEFINED:
134			return self.__func(point1, point2);
135
136			else:
137			raise ValueError("Unknown type of metric: '%d'", self.__type);
138
139
140			def get_type(self):
141			"""!
142			@brief Return type of distance metric that is used.
143
144			@return (type_metric) Type of distance metric.
145
146			"""
147			return self.__type;
148
149
150			def get_arguments(self):
151			"""!
152			@brief Return additional arguments that are used by distance metric.
153
154			@return (dict) Additional arguments.
155
156			"""
157			return self.__args;
158
159
160			def get_function(self):
161			"""!
162			@brief Return user-defined function for calculation distance metric.
163
164			@return (callable): User-defined distance metric function.
165
166			"""
167			return self.__func;
168
169
170
171			def euclidean_distance(point1, point2):
172			"""!
			0 ignored issues – show Bug introduced 2017-09-02 13:52 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\s` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history...
173			@brief Calculate Euclidean distance between two vectors.
174			@details The Euclidean between vectors (points) a and b is calculated by following formula:
175
176			\f[
177			dist(a, b) = \sqrt{ \sum_{i=0}^{N}(a_{i} - b_{i})^{2} };
178			\f]
179
180			Where N is a length of each vector.
181
182			@param[in] point1 (list): The first vector.
183			@param[in] point2 (list): The second vector.
184
185			@return (double) Euclidean distance between two vectors.
186
187			@see euclidean_distance_square, manhattan_distance, chebyshev_distance
188
189			"""
190			distance = euclidean_distance_square(point1, point2);
191			return distance ** 0.5;
192
193
194			def euclidean_distance_square(point1, point2):
195			"""!
			0 ignored issues – show Bug introduced 2018-04-11 13:06 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\s` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history...
196			@brief Calculate square Euclidean distance between two vectors.
197
198			\f[
199			dist(a, b) = \sum_{i=0}^{N}(a_{i} - b_{i})^{2};
200			\f]
201
202			@param[in] point1 (list): The first vector.
203			@param[in] point2 (list): The second vector.
204
205			@return (double) Square Euclidean distance between two vectors.
206
207			@see euclidean_distance, manhattan_distance, chebyshev_distance
208
209			"""
210			distance = 0.0;
211			for i in range(len(point1)):
212			distance += (point1[i] - point2[i]) ** 2.0;
213
214			return distance;
215
216
217			def manhattan_distance(point1, point2):
218			"""!
			0 ignored issues – show Bug introduced 2018-04-11 13:06 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\s` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history... Bug introduced 2018-04-11 13:06 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\l` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history...
219			@brief Calculate Manhattan distance between between two vectors.
220
221			\f[
222			dist(a, b) = \sum_{i=0}^{N}\left \| a_{i} - b_{i} \right \|;
223			\f]
224
225			@param[in] point1 (list): The first vector.
226			@param[in] point2 (list): The second vector.
227
228			@return (double) Manhattan distance between two vectors.
229
230			@see euclidean_distance_square, euclidean_distance, chebyshev_distance
231
232			"""
233			distance = 0.0;
234			dimension = len(point1);
235
236			for i in range(dimension):
237			distance += abs(point1[i] - point2[i]);
238
239			return distance;
240
241
242			def chebyshev_distance(point1, point2):
243			"""!
			0 ignored issues – show Bug introduced 2018-04-11 13:06 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\m` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history... Bug introduced 2018-04-11 13:06 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\l` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history...
244			@brief Calculate Chebyshev distance between between two vectors.
245
246			\f[
247			dist(a, b) = \max_{}i\left (\left \| a_{i} - b_{i} \right \|\right );
248			\f]
249
250			@param[in] point1 (list): The first vector.
251			@param[in] point2 (list): The second vector.
252
253			@return (double) Chebyshev distance between two vectors.
254
255			@see euclidean_distance_square, euclidean_distance, minkowski_distance
256
257			"""
258			distance = 0.0;
259			dimension = len(point1);
260
261			for i in range(dimension):
262			distance = max(distance, abs(point1[i] - point2[i]));
263
264			return distance;
265
266
267			def minkowski_distance(point1, point2, degree=2):
268			"""!
			0 ignored issues – show Bug introduced 2018-04-12 12:41 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\s` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history... Bug introduced 2018-04-12 12:41 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\l` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history...
269			@brief Calculate Minkowski distance between two vectors.
270
271			\f[
272			dist(a, b) = \sqrt[p]{ \sum_{i=0}^{N}\left(a_{i} - b_{i}\right)^{p} };
273			\f]
274
275			@param[in] point1 (list): The first vector.
276			@param[in] point2 (list): The second vector.
277			@param[in] degree (numeric): Degree of that is used for Minkowski distance.
278
279			@return (double) Minkowski distance between two vectors.
280
281			@see euclidean_distance
282
283			"""
284			distance = 0.0;
285			for i in range(len(point1)):
286			distance += (point1[i] - point2[i]) ** degree;
287
288			return distance ** (1.0 / degree);

annoviko / pyclustering

Push — master ( 7a46cc...a1c9a1 )

distance_metric A

Complexity

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5 Methods

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