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

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by Andrei

created 2018-05-28 16:13 UTC

manhattan_distance_numpy() A

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cc	1
dl	0
loc	11
rs	9.4285
c	0
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"""!

@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

"""


import numpy
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

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: 'numpy_usage' 'func' and corresponding additional argument for
                    for specific metric types).

        <b>Keyword Args:</b><br>
            - func (callable): Callable object with two arguments (point #1 and point #2) or (object #1 and object #2) in case of numpy usage.
                                This argument is used only if metric is 'type_metric.USER_DEFINED'.
            - degree (numeric): Only for 'type_metric.MINKOWSKI' - degree of Minkowski equation.
            - numpy_usage (bool): If True then numpy is used for calculation (by default is False).

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

        self.__calculator = self.__create_distance_calculator()


    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.

        """
        return self.__calculator(point1, point2)


    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 enable_numpy_usage(self):
        """!
        @brief Start numpy for distance calculation.
        @details Useful in case matrices to increase performance.

        """
        self.__numpy = True
        self.__calculator = self.__create_distance_calculator()


    def disable_numpy_usage(self):
        """!
        @brief Stop using numpy for distance calculation.
        @details Useful in case of big amount of small data portion when numpy call is longer than calculation itself.

        """
        self.__numpy = False
        self.__calculator = self.__create_distance_calculator()


    def __create_distance_calculator(self):
        if self.__numpy is True:
            return self.__create_distance_calculator_numpy()

        return self.__create_distance_calculator_basic()


    def __create_distance_calculator_basic(self):

        """!
        @brief Creates distance metric calculator.

        @return (callable) Callable object of distance metric calculator.

        """
        if self.__type == type_metric.EUCLIDEAN:
            return euclidean_distance

        elif self.__type == type_metric.EUCLIDEAN_SQUARE:
            return euclidean_distance_square

        elif self.__type == type_metric.MANHATTAN:
            return manhattan_distance

        elif self.__type == type_metric.CHEBYSHEV:
            return chebyshev_distance

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

        elif self.__type == type_metric.USER_DEFINED:
            return self.__func

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


    def __create_distance_calculator_numpy(self):

        """!
        @brief Creates distance metric calculator that uses numpy.

        @return (callable) Callable object of distance metric calculator.

        """
        if self.__type == type_metric.EUCLIDEAN:
            return euclidean_distance_numpy

        elif self.__type == type_metric.EUCLIDEAN_SQUARE:
            return euclidean_distance_square_numpy

        elif self.__type == type_metric.MANHATTAN:
            return manhattan_distance_numpy

        elif self.__type == type_metric.CHEBYSHEV:
            return chebyshev_distance_numpy

        elif self.__type == type_metric.MINKOWSKI:
            return lambda object1, object2: minkowski_distance_numpy(object1, object2, self.__args.get('degree', 2))

        elif self.__type == type_metric.USER_DEFINED:
            return self.__func

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



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 (array_like): The first vector.
    @param[in] point2 (array_like): 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_numpy(object1, object2):
    """!
    @brief Calculate Euclidean distance between two objects using numpy.

    @param[in] object1 (array_like): The first array_like object.
    @param[in] object2 (array_like): The second array_like object.

    @return (double) Euclidean distance between two objects.

    """
    return numpy.sum(numpy.sqrt(numpy.square(object1 - object2)), axis=1).T


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 (array_like): The first vector.
    @param[in] point2 (array_like): 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 euclidean_distance_square_numpy(object1, object2):
    """!
    @brief Calculate square Euclidean distance between two objects using numpy.

    @param[in] object1 (array_like): The first array_like object.
    @param[in] object2 (array_like): The second array_like object.

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

    """
    return numpy.sum(numpy.square(object1 - object2), axis=1).T


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 (array_like): The first vector.
    @param[in] point2 (array_like): 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 manhattan_distance_numpy(object1, object2):
    """!
    @brief Calculate Manhattan distance between two objects using numpy.

    @param[in] object1 (array_like): The first array_like object.
    @param[in] object2 (array_like): The second array_like object.

    @return (double) Manhattan distance between two objects.

    """
    return numpy.sum(numpy.absolute(object1 - object2), axis=1).T


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 (array_like): The first vector.
    @param[in] point2 (array_like): 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 chebyshev_distance_numpy(object1, object2):
    """!
    @brief Calculate Chebyshev distance between two objects using numpy.

    @param[in] object1 (array_like): The first array_like object.
    @param[in] object2 (array_like): The second array_like object.

    @return (double) Chebyshev distance between two objects.

    """
    return numpy.max(numpy.absolute(object1 - object2), axis=1).T


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 (array_like): The first vector.
    @param[in] point2 (array_like): 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)


def minkowski_distance_numpy(object1, object2, degree=2):
    """!
    @brief Calculate Minkowski distance between objects using numpy.

    @param[in] object1 (array_like): The first array_like object.
    @param[in] object2 (array_like): The second array_like object.
    @param[in] degree (numeric): Degree of that is used for Minkowski distance.

    @return (double) Minkowski distance between two object.

    """
    return numpy.sum(numpy.power(numpy.power(object1 - object2, degree), 1/degree), axis=1).T

Push — master ( 57e143...200d41 )

manhattan_distance_numpy() A

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing init.py files

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		import numpy
		0 ignored issues – show Configuration introduced 2018-05-28 13:13 UTC by Report Bug Copy Issue Report The import `numpy` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
28
29		from enum import IntEnum
30
31
32		class type_metric(IntEnum):
33		"""!
34		@brief Enumeration of supported metrics in the module for distance calculation between two points.
35
36		"""
37
38		## Euclidean distance, for more information see function 'euclidean_distance'.
39		EUCLIDEAN = 0
40
41		## Square Euclidean distance, for more information see function 'euclidean_distance_square'.
42		EUCLIDEAN_SQUARE = 1
43
44		## Manhattan distance, for more information see function 'manhattan_distance'.
45		MANHATTAN = 2
46
47		## Chebyshev distance, for more information see function 'chebyshev_distance'.
48		CHEBYSHEV = 3
49
50		## Minkowski distance, for more information see function 'minkowski_distance'.
51		MINKOWSKI = 4
52
53		## User defined function for distance calculation between two points.
54		USER_DEFINED = 1000
55
56
57
58		class distance_metric:
59		"""!
60		@brief Distance metric performs distance calculation between two points in line with encapsulated function, for
61		example, euclidean distance or chebyshev distance, or even user-defined.
62
63		@details
64
65		Example of Euclidean distance metric:
66		@code
67		metric = distance_metric(type_metric.EUCLIDEAN)
68		distance = metric([1.0, 2.5], [-1.2, 3.4])
69		@endcode
70
71		Example of Chebyshev distance metric:
72		@code
73		metric = distance_metric(type_metric.CHEBYSHEV)
74		distance = metric([0.0, 0.0], [2.5, 6.0])
75		@endcode
76
77		In following example additional argument should be specified (generally, 'degree' is a optional argument that is
78		equal to '2' by default) that is specific for Minkowski distance:
79		@code
80		metric = distance_metric(type_metric.MINKOWSKI, degree=4)
81		distance = metric([4.0, 9.2, 1.0], [3.4, 2.5, 6.2])
82		@endcode
83
84		User may define its own function for distance calculation:
85		@code
86		user_function = lambda point1, point2: point1[0] + point2[0] + 2
87		metric = distance_metric(type_metric.USER_DEFINED, func=user_function)
88		distance = metric([2.0, 3.0], [1.0, 3.0])
89		@endcode
90
91		"""
92		def __init__(self, type, **kwargs):
93		"""!
94		@brief Creates distance metric instance for calculation distance between two points.
95
96		@param[in] type (type_metric):
97		@param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'numpy_usage' 'func' and corresponding additional argument for
98		for specific metric types).
99
100		<b>Keyword Args:</b><br>
101		- func (callable): Callable object with two arguments (point #1 and point #2) or (object #1 and object #2) in case of numpy usage.
102		This argument is used only if metric is 'type_metric.USER_DEFINED'.
103		- degree (numeric): Only for 'type_metric.MINKOWSKI' - degree of Minkowski equation.
104		- numpy_usage (bool): If True then numpy is used for calculation (by default is False).
105
106		"""
107		self.__type = type
108		self.__args = kwargs
109		self.__func = self.__args.get('func', None)
110		self.__numpy = self.__args.get('numpy_usage', False)
111
112		self.__calculator = self.__create_distance_calculator()
113
114
115		def __call__(self, point1, point2):
116		"""!
117		@brief Calculates distance between two points.
118
119		@param[in] point1 (list): The first point.
120		@param[in] point2 (list): The second point.
121
122		@return (double) Distance between two points.
123
124		"""
125		return self.__calculator(point1, point2)
126
127
128		def get_type(self):
129		"""!
130		@brief Return type of distance metric that is used.
131
132		@return (type_metric) Type of distance metric.
133
134		"""
135		return self.__type
136
137
138		def get_arguments(self):
139		"""!
140		@brief Return additional arguments that are used by distance metric.
141
142		@return (dict) Additional arguments.
143
144		"""
145		return self.__args
146
147
148		def get_function(self):
149		"""!
150		@brief Return user-defined function for calculation distance metric.
151
152		@return (callable): User-defined distance metric function.
153
154		"""
155		return self.__func
156
157
158		def enable_numpy_usage(self):
159		"""!
160		@brief Start numpy for distance calculation.
161		@details Useful in case matrices to increase performance.
162
163		"""
164		self.__numpy = True
165		self.__calculator = self.__create_distance_calculator()
166
167
168		def disable_numpy_usage(self):
169		"""!
170		@brief Stop using numpy for distance calculation.
171		@details Useful in case of big amount of small data portion when numpy call is longer than calculation itself.
172
173		"""
174		self.__numpy = False
175		self.__calculator = self.__create_distance_calculator()
176
177
178		def __create_distance_calculator(self):
179		if self.__numpy is True:
180		return self.__create_distance_calculator_numpy()
181
182		return self.__create_distance_calculator_basic()
183
184
185	View Code Duplication	def __create_distance_calculator_basic(self):
		0 ignored issues – show Duplication introduced 2018-05-28 13:13 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
186		"""!
187		@brief Creates distance metric calculator.
188
189		@return (callable) Callable object of distance metric calculator.
190
191		"""
192		if self.__type == type_metric.EUCLIDEAN:
193		return euclidean_distance
194
195		elif self.__type == type_metric.EUCLIDEAN_SQUARE:
196		return euclidean_distance_square
197
198		elif self.__type == type_metric.MANHATTAN:
199		return manhattan_distance
200
201		elif self.__type == type_metric.CHEBYSHEV:
202		return chebyshev_distance
203
204		elif self.__type == type_metric.MINKOWSKI:
205		return lambda point1, point2: minkowski_distance(point1, point2, self.__args.get('degree', 2))
206
207		elif self.__type == type_metric.USER_DEFINED:
208		return self.__func
209
210		else:
211		raise ValueError("Unknown type of metric: '%d'", self.__type)
212
213
214	View Code Duplication	def __create_distance_calculator_numpy(self):
		0 ignored issues – show Duplication introduced 2018-05-28 13:13 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
215		"""!
216		@brief Creates distance metric calculator that uses numpy.
217
218		@return (callable) Callable object of distance metric calculator.
219
220		"""
221		if self.__type == type_metric.EUCLIDEAN:
222		return euclidean_distance_numpy
223
224		elif self.__type == type_metric.EUCLIDEAN_SQUARE:
225		return euclidean_distance_square_numpy
226
227		elif self.__type == type_metric.MANHATTAN:
228		return manhattan_distance_numpy
229
230		elif self.__type == type_metric.CHEBYSHEV:
231		return chebyshev_distance_numpy
232
233		elif self.__type == type_metric.MINKOWSKI:
234		return lambda object1, object2: minkowski_distance_numpy(object1, object2, self.__args.get('degree', 2))
235
236		elif self.__type == type_metric.USER_DEFINED:
237		return self.__func
238
239		else:
240		raise ValueError("Unknown type of metric: '%d'", self.__type)
241
242
243
244		def euclidean_distance(point1, point2):
245		"""!
		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...
246		@brief Calculate Euclidean distance between two vectors.
247		@details The Euclidean between vectors (points) a and b is calculated by following formula:
248
249		\f[
250		dist(a, b) = \sqrt{ \sum_{i=0}^{N}(a_{i} - b_{i})^{2} };
251		\f]
252
253		Where N is a length of each vector.
254
255		@param[in] point1 (array_like): The first vector.
256		@param[in] point2 (array_like): The second vector.
257
258		@return (double) Euclidean distance between two vectors.
259
260		@see euclidean_distance_square, manhattan_distance, chebyshev_distance
261
262		"""
263		distance = euclidean_distance_square(point1, point2)
264		return distance ** 0.5
265
266
267		def euclidean_distance_numpy(object1, object2):
268		"""!
269		@brief Calculate Euclidean distance between two objects using numpy.
270
271		@param[in] object1 (array_like): The first array_like object.
272		@param[in] object2 (array_like): The second array_like object.
273
274		@return (double) Euclidean distance between two objects.
275
276		"""
277		return numpy.sum(numpy.sqrt(numpy.square(object1 - object2)), axis=1).T
278
279
280		def euclidean_distance_square(point1, point2):
281		"""!
		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...
282		@brief Calculate square Euclidean distance between two vectors.
283
284		\f[
285		dist(a, b) = \sum_{i=0}^{N}(a_{i} - b_{i})^{2};
286		\f]
287
288		@param[in] point1 (array_like): The first vector.
289		@param[in] point2 (array_like): The second vector.
290
291		@return (double) Square Euclidean distance between two vectors.
292
293		@see euclidean_distance, manhattan_distance, chebyshev_distance
294
295		"""
296		distance = 0.0
297		for i in range(len(point1)):
298		distance += (point1[i] - point2[i]) ** 2.0
299
300		return distance
301
302
303		def euclidean_distance_square_numpy(object1, object2):
304		"""!
305		@brief Calculate square Euclidean distance between two objects using numpy.
306
307		@param[in] object1 (array_like): The first array_like object.
308		@param[in] object2 (array_like): The second array_like object.
309
310		@return (double) Square Euclidean distance between two objects.
311
312		"""
313		return numpy.sum(numpy.square(object1 - object2), axis=1).T
314
315
316		def manhattan_distance(point1, point2):
317		"""!
		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...
318		@brief Calculate Manhattan distance between between two vectors.
319
320		\f[
321		dist(a, b) = \sum_{i=0}^{N}\left \| a_{i} - b_{i} \right \|;
322		\f]
323
324		@param[in] point1 (array_like): The first vector.
325		@param[in] point2 (array_like): The second vector.
326
327		@return (double) Manhattan distance between two vectors.
328
329		@see euclidean_distance_square, euclidean_distance, chebyshev_distance
330
331		"""
332		distance = 0.0
333		dimension = len(point1)
334
335		for i in range(dimension):
336		distance += abs(point1[i] - point2[i])
337
338		return distance
339
340
341		def manhattan_distance_numpy(object1, object2):
342		"""!
343		@brief Calculate Manhattan distance between two objects using numpy.
344
345		@param[in] object1 (array_like): The first array_like object.
346		@param[in] object2 (array_like): The second array_like object.
347
348		@return (double) Manhattan distance between two objects.
349
350		"""
351		return numpy.sum(numpy.absolute(object1 - object2), axis=1).T
352
353
354		def chebyshev_distance(point1, point2):
355		"""!
		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...
356		@brief Calculate Chebyshev distance between between two vectors.
357
358		\f[
359		dist(a, b) = \max_{}i\left (\left \| a_{i} - b_{i} \right \|\right );
360		\f]
361
362		@param[in] point1 (array_like): The first vector.
363		@param[in] point2 (array_like): The second vector.
364
365		@return (double) Chebyshev distance between two vectors.
366
367		@see euclidean_distance_square, euclidean_distance, minkowski_distance
368
369		"""
370		distance = 0.0
371		dimension = len(point1)
372
373		for i in range(dimension):
374		distance = max(distance, abs(point1[i] - point2[i]))
375
376		return distance
377
378
379		def chebyshev_distance_numpy(object1, object2):
380		"""!
381		@brief Calculate Chebyshev distance between two objects using numpy.
382
383		@param[in] object1 (array_like): The first array_like object.
384		@param[in] object2 (array_like): The second array_like object.
385
386		@return (double) Chebyshev distance between two objects.
387
388		"""
389		return numpy.max(numpy.absolute(object1 - object2), axis=1).T
390
391
392		def minkowski_distance(point1, point2, degree=2):
393		"""!
		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...
394		@brief Calculate Minkowski distance between two vectors.
395
396		\f[
397		dist(a, b) = \sqrt[p]{ \sum_{i=0}^{N}\left(a_{i} - b_{i}\right)^{p} };
398		\f]
399
400		@param[in] point1 (array_like): The first vector.
401		@param[in] point2 (array_like): The second vector.
402		@param[in] degree (numeric): Degree of that is used for Minkowski distance.
403
404		@return (double) Minkowski distance between two vectors.
405
406		@see euclidean_distance
407
408		"""
409		distance = 0.0
410		for i in range(len(point1)):
411		distance += (point1[i] - point2[i]) ** degree
412
413		return distance ** (1.0 / degree)
414
415
416		def minkowski_distance_numpy(object1, object2, degree=2):
417		"""!
418		@brief Calculate Minkowski distance between objects using numpy.
419
420		@param[in] object1 (array_like): The first array_like object.
421		@param[in] object2 (array_like): The second array_like object.
422		@param[in] degree (numeric): Degree of that is used for Minkowski distance.
423
424		@return (double) Minkowski distance between two object.
425
426		"""
427		return numpy.sum(numpy.power(numpy.power(object1 - object2, degree), 1/degree), axis=1).T

annoviko / pyclustering

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