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

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bang.__find_block_neighbors() B

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"""!

@brief Cluster analysis algorithm: BANG.
@details Implementation based on paper @cite inproceedings::bang::1.

@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 matplotlib
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import matplotlib.gridspec as gridspec;
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import matplotlib.pyplot as plt
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import matplotlib.patches as patches
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

import itertools

from pyclustering.cluster import cluster_visualizer
from pyclustering.cluster.encoder import type_encoding

from pyclustering.utils import data_corners
from pyclustering.utils.color import color as color_list



class bang_visualizer:
    """!
    @brief Visualizer of BANG algorithm's results.
    @details BANG visualizer provides visualization services that are specific for BANG algorithm.

    """


    __maximum_density_alpha = 0.6


    @staticmethod
    def show_blocks(directory):
        """!
        @brief Show BANG-blocks (leafs only) in data space.
        @details BANG-blocks represents grid that was used for clustering process.

        @param[in] directory (bang_directory): Directory that was created by BANG algorithm during clustering process.

        """

        dimension = len(directory.get_data()[0])

        amount_canvases = 1
        if dimension > 1:
            amount_canvases = int(dimension * (dimension - 1) / 2)

        figure = plt.figure();
        grid_spec = gridspec.GridSpec(1, amount_canvases);

        pairs = list(itertools.combinations(range(dimension), 2))
        if len(pairs) == 0: pairs = [(0, 0)]

        for index in range(amount_canvases):
            ax = figure.add_subplot(grid_spec[index]);
            bang_visualizer.__draw_blocks(ax, directory.get_leafs(), pairs[index])
            bang_visualizer.__draw_two_dimension_data(ax, directory.get_data(), pairs[index])

        plt.show()


    @staticmethod
    def show_dendrogram(dendrogram):
        """!
        @brief Display dendrogram of BANG-blocks.

        @param[in] dendrogram (list): List representation of dendrogram of BANG-blocks.

        @see bang.get_dendrogram()

        """
        plt.figure()
        axis = plt.subplot(1, 1, 1)

        current_position = 0
        for index_cluster in range(len(dendrogram)):
            densities = [ block.get_density() for block in dendrogram[index_cluster] ]
            xrange = range(current_position, current_position + len(densities))

            axis.bar(xrange, densities, 1.0, linewidth=0.0, color=color_list.get_color(index_cluster))

            current_position += len(densities)

        axis.set_ylabel("density")
        axis.set_xlabel("block")
        axis.xaxis.set_ticklabels([]);

        plt.xlim([-0.5, current_position - 0.5])
        plt.show()


    @staticmethod
    def show_clusters(data, clusters, noise=[]):
# Bad:
# If array_param is modified inside the function, the next invocation will
# receive the modified object.
def some_function(array_param=[]):
    # ...

# Better: Create an array on each invocation
def some_function(array_param=None):
    array_param = array_param or []
    # ...
        """!
        @brief Display K-Means clustering results.

        @param[in] data (list): Dataset that was used for clustering.
        @param[in] clusters (array_like): Clusters that were allocated by the algorithm.
        @param[in] noise (array_like): Noise that were allocated by the algorithm.

        """
        visualizer = cluster_visualizer()
        visualizer.append_clusters(clusters, data)
        visualizer.append_cluster(noise, data, marker='x')
        visualizer.show()


    @staticmethod
    def __draw_two_dimension_data(ax, data, pair):
        """!
        @brief Display data in two-dimensional canvas.

        @param[in] ax (Axis): Canvas where data should be displayed.
        @param[in] data (list): Data points that should be displayed.
        @param[in] pair (tuple): Pair of dimension indexes.

        """
        ax.set_xlabel("x%d" % pair[0])
        ax.set_ylabel("x%d" % pair[1])

        for point in data:
            if len(data[0]) > 1:
                ax.plot(point[pair[0]], point[pair[1]], color='red', marker='.')
            else:
                ax.plot(point[pair[0]], 0, color='red', marker='.')
                ax.yaxis.set_ticklabels([]);


    @staticmethod
    def __draw_blocks(ax, blocks, pair):
        """!
        @brief Display BANG-blocks on specified figure.

        @param[in] ax (Axis): Axis where bang-blocks should be displayed.
        @param[in] blocks (list): List of blocks that should be displyed.
        @param[in] pair (tuple): Pair of coordinate index that should be displyed.

        """
        ax.grid(False)

        density_scale = blocks[-1].get_density()
        for block in blocks:
            bang_visualizer.__draw_block(ax, pair, block, density_scale)


    @staticmethod
    def __draw_block(ax, pair, block, density_scale):
        """!
        @brief Display BANG-block on the specified ax.

        @param[in] ax (Axis): Axis where block should be displayed.
        @param[in] pair (tuple): Pair of coordinate index that should be displayed.
        @param[in] block (bang_block): BANG-block that should be displayed.
        @param[in] density_scale (double): Max density to display density of the block by appropriate tone.

        """
        max_corner, min_corner = bang_visualizer.__get_rectangle_description(block, pair)

        belong_cluster = block.get_cluster() is not None

        density_scale = bang_visualizer.__maximum_density_alpha * block.get_density() / density_scale

        face_color = matplotlib.colors.to_rgba('blue', alpha=density_scale)
        edge_color = matplotlib.colors.to_rgba('black', alpha=1.0)

        rect = patches.Rectangle(min_corner, max_corner[0] - min_corner[0], max_corner[1] - min_corner[1],
                                 fill=belong_cluster,
                                 facecolor=face_color,
                                 edgecolor=edge_color,
                                 linewidth=0.5)
        ax.add_patch(rect)


    @staticmethod
    def __get_rectangle_description(block, pair):
        """!
        @brief Create rectangle description for block in specific dimension.

        @param[in] pair (tuple): Pair of coordinate index that should be displayed.
        @param[in] block (bang_block): BANG-block that should be displayed

        @return (tuple) Pair of corners that describes rectangle.

        """
        max_corner, min_corner = block.get_spatial_block().get_corners()

        max_corner = [max_corner[pair[0]], max_corner[pair[1]]]
        min_corner = [min_corner[pair[0]], min_corner[pair[1]]]

        if pair == (0, 0):
            max_corner[1], min_corner[1] = 1.0, -1.0

        return max_corner, min_corner


class bang_directory:
    """!
    @brief BANG directory stores BANG-blocks that represents grid in data space.
    @details The directory build BANG-blocks in binary tree manner. Leafs of the tree stored separately to provide
              a direct access to the leafs that should be analysed. Leafs cache data-points.

    """
    def __init__(self, data, levels, density_threshold=0.0):
        """!
        @brief Create BANG directory - basically tree structure with direct access to leafs.

        @param[in] data (list): Input data that is clustered.
        @param[in] levels (uint): Height of the blocks tree.
        @param[in] density_threshold (double): The lowest level of density when contained data by bang-block is
                    considered as a noise and there is no need to split it till the last level.

        """
        self.__data = data
        self.__levels = levels
        self.__density_threshold = density_threshold
        self.__leafs = []
        self.__root = None

        self.__create_directory()


    def get_data(self):
        """!
        @brief Return data that is stored in the directory.

        @return (list) List of points that represents stored data.

        """
        return self.__data


    def get_leafs(self):
        """!
        @brief Return leafs - the smallest blocks.
        @details Some leafs can be bigger than others because splitting is not performed for blocks whose density is
                  less than threshold.

        @return (list) List of blocks that are leafs of BANG directory.

        """
        return self.__leafs


    def __create_directory(self):
        """!
        @brief Create BANG directory as a tree with separate storage for leafs.

        """

        min_corner, max_corner = data_corners(self.__data)
        data_block = spatial_block(max_corner, min_corner)

        cache_require = (self.__levels == 1)
        self.__root = bang_block(self.__data, 0, 0, data_block, cache_require)

        if cache_require:
            self.__leafs.append(self.__root)
        else:
            self.__build_directory_levels()


    def __build_directory_levels(self):
        """!
        @brief Build levels of direction if amount of level is greater than one.

        """
        previous_level_blocks = [ self.__root ]
        for level in range(1, self.__levels):
            previous_level_blocks = self.__build_level(previous_level_blocks, level)

        self.__leafs = sorted(self.__leafs, key=lambda block: block.get_density())


    def __build_level(self, previous_level_blocks, level):
        """!
        @brief Build new level of directory.

        @param[in] previous_level_blocks (list): BANG-blocks on the previous level.
        @param[in] level (uint): Level number that should be built.

        @return (list) New block on the specified level.

        """
        current_level_blocks = []

        split_dimension = level % len(self.__data[0])
        cache_require = (level == self.__levels - 1)

        for block in previous_level_blocks:
            self.__split_block(block, split_dimension, cache_require, current_level_blocks)

        if cache_require:
            self.__leafs += current_level_blocks

        return current_level_blocks


    def __split_block(self, block, split_dimension, cache_require, current_level_blocks):
        """!
        @brief Split specific block in specified dimension.
        @details Split is not performed for block whose density is lower than threshold value, such blocks are putted to
                  leafs.

        @param[in] block (bang_block): BANG-block that should be split.
        @param[in] split_dimension (uint): Dimension at which splitting should be performed.
        @param[in] cache_require (bool): Defines when points in cache should be stored during density calculation.
        @param[in|out] current_level_blocks (list): Block storage at the current level where new blocks should be added.

        """
        if block.get_density() <= self.__density_threshold:
            self.__leafs.append(block)

        else:
            left, right = block.split(split_dimension, cache_require)
            current_level_blocks.append(left)
            current_level_blocks.append(right)



class spatial_block:
    """!
    @brief Geometrical description of BANG block in data space.
    @details Provides services related to spatial function and used by bang_block

    @see bang_block

    """

    def __init__(self, max_corner, min_corner):
        """!
        @brief Creates spatial block in data space.

        @param[in] max_corner (array_like): Maximum corner coordinates of the block.
        @param[in] min_corner (array_like): Minimal corner coordinates of the block.

        """
        self.__max_corner = max_corner
        self.__min_corner = min_corner
        self.__volume = self.__calculate_volume()


    def __str__(self):
        """!
        @brief Returns string block description.

        @return String representation of the block.

        """
        return "(max: %s; min: %s)" % (self.__max_corner, self.__min_corner)


    def __contains__(self, point):
        """!
        @brief Point is considered as contained if it lies in block (belong to it).

        @return (bool) True if point is in block, otherwise False.

        """
        for i in range(len(point)):
            if point[i] < self.__min_corner[i] or point[i] > self.__max_corner[i]:
                return False

        return True


    def get_corners(self):
        """!
        @brief Return spatial description of current block.

        @return (tuple) Pair of maximum and minimum corners (max_corner, min_corner).

        """
        return self.__max_corner, self.__min_corner


    def get_volume(self):
        """!
        @brief Returns volume of current block.
        @details Volume block has uncommon mining here: for 1D is length of a line, for 2D is square of rectangle,
                  for 3D is volume of 3D figure, and for ND is volume of ND figure.

        @return (double) Volume of current block.

        """
        return self.__volume


    def split(self, dimension):
        """!
        @brief Split current block into two spatial blocks in specified dimension.

        @param[in] dimension (uint): Dimension where current block should be split.

        @return (tuple) Pair of new split blocks from current block.

        """
        first_max_corner = self.__max_corner[:]
        second_min_corner = self.__min_corner[:]

        split_border = (self.__max_corner[dimension] + self.__min_corner[dimension]) / 2.0

        first_max_corner[dimension] = split_border
        second_min_corner[dimension] = split_border

        return spatial_block(first_max_corner, self.__min_corner), spatial_block(self.__max_corner, second_min_corner)


    def is_neighbor(self, block):
        """!
        @brief Performs calculation to identify whether specified block is neighbor of current block.

        @param[in] block (spatial_block): Another block that is check whether it is neighbor.

        @return (bool) True is blocks are neighbors, False otherwise.

        """
        if block is not self:
            block_max_corner, _ = block.get_corners()
            dimension = len(block_max_corner)
            neighborhood_score = self.__calculate_neighborhood(block_max_corner)

            if neighborhood_score == dimension:
                return True

        return False


    def __calculate_neighborhood(self, block_max_corner):
        """!
        @brief Calculates neighborhood score that defined whether blocks are neighbors.

        @param[in] block_max_corner (list): Maximum coordinates of other block.

        @return (uint) Neighborhood score.

        """
        dimension = len(block_max_corner)

        length_edges = [self.__max_corner[i] - self.__min_corner[i] for i in range(dimension)]

        neighborhood_score = 0
        for i in range(dimension):
            diff = abs(block_max_corner[i] - self.__max_corner[i])

            if diff <= length_edges[i] + length_edges[i] * 0.0001:
                neighborhood_score += 1

        return neighborhood_score


    def __calculate_volume(self):
        """!
        @brief Calculates volume of current spatial block.

        @return (double) Volume of current spatial block.

        """
        volume = self.__max_corner[0] - self.__min_corner[0]
        for i in range(1, len(self.__max_corner)):
            volume *= self.__max_corner[i] - self.__min_corner[i]

        return volume



class bang_block:
    """!
    @brief BANG-block that represent spatial region in data space.

    """
    def __init__(self, data, region, level, space_block, cache_points=False):
        """!
        @brief Create BANG-block.

        @param[in] data (list): List of points that are processed.
        @param[in] region (uint): Region number - unique value on a level.
        @param[in] level (uint): Level number where block is created.
        @param[in] space_block (spatial_block): Spatial block description in data space.
        @param[in] cache_points (bool): if True then points are stored in memory (used for leaf blocks).

        """
        self.__data = data
        self.__region_number = region
        self.__level = level
        self.__spatial_block = space_block
        self.__cache_points = cache_points

        self.__cluster = None
        self.__points = None
        self.__density = self.__calculate_density()


    def __str__(self):
        """!
        @brief Returns string representation of BANG-block using region number and level where block is located.

        """
        return "(" + str(self.__region_number) + ", " + str(self.__level) + ")"


    def get_region(self):
        """!
        @brief Returns region number of BANG-block.
        @details Region number is unique on among region numbers on a directory level. Pair of region number and level
                  is unique for all directory.

        @return (uint) Region number.

        """
        return self.__region_number


    def get_density(self):
        """!
        @brief Returns density of the BANG-block.

        @return (double) BANG-block density.

        """
        return self.__density


    def get_cluster(self):
        """!
        @brief Return index of cluster to which the BANG-block belongs to.
        @details Index of cluster may have None value if the block was not assigned to any cluster.

        @return (uint) Index of cluster or None if the block does not belong to any cluster.

        """
        return self.__cluster


    def get_spatial_block(self):
        """!
        @brief Return spatial block - BANG-block description in data space.

        @return (spatial_block) Spatial block of the BANG-block.

        """
        return self.__spatial_block


    def get_points(self):
        """!
        @brief Return points that covers by the BANG-block.

        @return (list) List of point indexes that are covered by the block.

        """
        if self.__points is None:
            self.__cache_covered_data()

        return self.__points


    def set_cluster(self, index):
        """!
        @brief Assign cluster to the BANG-block by index.

        @param[in] index (uint): Index cluster that is assigned to BANG-block.

        """
        self.__cluster = index


    def is_neighbor(self, block):
        """!
        @brief Performs calculation to check whether specified block is neighbor to the current.

        @param[in] block (bang_block): Other BANG-block that should be checked for neighborhood.

        @return (bool) True if blocks are neighbors, False if blocks are not neighbors.

        """
        return self.get_spatial_block().is_neighbor(block.get_spatial_block())


    def split(self, split_dimension, cache_points):
        """!
        @brief Split BANG-block into two new blocks in specified dimension.

        @param[in] split_dimension (uint): Dimension where block should be split.
        @param[in] cache_points (bool): If True then covered points are cached. Used for leaf blocks.

        @return (tuple) Pair of BANG-block that were formed from the current.

        """
        left_region_number = self.__region_number
        right_region_number = self.__region_number + 2 ** self.__level

        first_spatial_block, second_spatial_block = self.__spatial_block.split(split_dimension)

        left = bang_block(self.__data, left_region_number, self.__level + 1, first_spatial_block, cache_points)
        right = bang_block(self.__data, right_region_number, self.__level + 1, second_spatial_block, cache_points)

        return left, right


    def __calculate_density(self):
        """!
        @brief Calculates BANG-block density.

        @return (double) BANG-block density.

        """
        return self.__get_amount_points() / self.__spatial_block.get_volume()


    def __get_amount_points(self):
        """!
        @brief Count covered points by the BANG-block and if cache is enable then covered points are stored.

        @return (uint) Amount of covered points.

        """
        amount = 0
        for index in range(len(self.__data)):
            if self.__data[index] in self.__spatial_block:
                self.__cache_point(index)
                amount += 1

        return amount


    def __cache_covered_data(self):
        """!
        @brief Cache covered data.

        """
        self.__cache_points = True
        self.__points = []

        for index_point in range(len(self.__data)):
            if self.__data[index_point] in self.__spatial_block:
                self.__cache_point(index_point)


    def __cache_point(self, index):
        """!
        @brief Store index points.

        @param[in] index (uint): Index point that should be stored.

        """
        if self.__cache_points:
            if self.__points is None:
                self.__points = []

            self.__points.append(index)



class bang:
    """!
    @brief Class implements BANG grid based clustering algorithm.
    @details BANG clustering algorithms uses a multidimensional grid structure to organize the value space surrounding
              the pattern values. The patterns are grouped into blocks and clustered with respect to the blocks by
              a topological neighbor search algorithm @cite inproceedings::bang::1.

    Code example of BANG usage:
    @code
        from pyclustering.cluster.bang import bang, bang_visualizer
        from pyclustering.utils import read_sample
        from pyclustering.samples.definitions import FCPS_SAMPLES

        # Read data three dimensional data.
        data = read_sample(FCPS_SAMPLES.SAMPLE_CHAINLINK)

        # Prepare algorithm's parameters.
        levels = 11

        # Create instance of BANG algorithm.
        bang_instance = bang(data, levels)
        bang_instance.process()

        # Obtain clustering results.
        clusters = bang_instance.get_clusters()
        noise = bang_instance.get_noise()
        directory = bang_instance.get_directory()
        dendrogram = bang_instance.get_dendrogram()

        # Visualize BANG clustering results.
        bang_visualizer.show_blocks(directory)
        bang_visualizer.show_dendrogram(dendrogram)
        bang_visualizer.show_clusters(data, clusters, noise)
    @endcode

    There is visualization of BANG-clustering of three-dimensional data 'chainlink'. BANG-blocks that were formed during
    processing are shown on following figure. The darkest color means highest density, blocks that does not cover points
    are transparent:
    @image html bang_blocks_chainlink.png "Fig. 1. BANG-blocks that cover input data."

    Here is obtained dendrogram that can be used for further analysis to improve clustering results:
    @image html bang_dendrogram_chainlink.png "Fig. 2. BANG dendrogram where the X-axis contains BANG-blocks, the Y-axis contains density."

    BANG clustering result of 'chainlink' data:
    @image html bang_clustering_chainlink.png "Fig. 3. BANG clustering result. Data: 'chainlink'."

    """

    def __init__(self, data, levels, density_threshold=0.0, ccore=False):
        """!
        @brief Create BANG clustering algorithm.

        @param[in] data (list): Input data (list of points) that should be clustered.
        @param[in] levels (uint): Amount of levels in tree (how many times block should be split).
        @param[in] density_threshold (double): If block density is smaller than this value then contained data by this
                    block is considered as a noise.
        @param[in] ccore (bool): Reserved positional argument - not used yet.

        """
        self.__data = data
        self.__levels = levels
        self.__directory = None
        self.__clusters = []
        self.__noise = []
        self.__cluster_blocks = []
        self.__dendrogram = []
        self.__density_threshold = density_threshold
        self.__ccore = ccore

        self.__validate_arguments()


    def process(self):
        """!
        @brief Performs clustering process in line with rules of BANG clustering algorithm.

        @see get_clusters()
        @see get_noise()
        @see get_directory()
        @see get_dendrogram()

        """
        self.__directory = bang_directory(self.__data, self.__levels, self.__density_threshold)
        self.__allocate_clusters()


    def get_clusters(self):
        """!
        @brief Returns allocated clusters.

        @remark Allocated clusters are returned only after data processing (method process()). Otherwise empty list is returned.

        @return (list) List of allocated clusters, each cluster contains indexes of objects in list of data.

        @see process()
        @see get_noise()

        """
        return self.__clusters


    def get_noise(self):
        """!
        @brief Returns allocated noise.

        @remark Allocated noise is returned only after data processing (method process()). Otherwise empty list is returned.

        @return (list) List of indexes that are marked as a noise.

        @see process()
        @see get_clusters()

        """
        return self.__noise


    def get_directory(self):
        """!
        @brief Returns grid directory that describes grid of the processed data.

        @remark Grid directory is returned only after data processing (method process()). Otherwise None value is returned.

        @return (bang_directory) BANG directory that describes grid of process data.

        @see process()

        """
        return self.__directory


    def get_dendrogram(self):
        """!
        @brief Returns dendrogram of clusters.
        @details Dendrogram is created in following way: the density indices of all regions are calculated and sorted
                  in decreasing order for each cluster during clustering process.

        @remark Dendrogram is returned only after data processing (method process()). Otherwise empty list is returned.

        """
        return self.__dendrogram


    def get_cluster_encoding(self):
        """!
        @brief Returns clustering result representation type that indicate how clusters are encoded.

        @return (type_encoding) Clustering result representation.

        @see get_clusters()

        """

        return type_encoding.CLUSTER_INDEX_LIST_SEPARATION


    def __validate_arguments(self):
        """!
        @brief Check input arguments of BANG algorithm and if one of them is not correct then appropriate exception
                is thrown.

        """
        if self.__levels <= 0:
            raise ValueError("Incorrect amount of levels '%d'. Level value should be greater than 0." % self.__levels)

        if len(self.__data) == 0:
            raise ValueError("Empty input data. Data should contain at least one point.")

        if self.__density_threshold < 0:
            raise ValueError("Incorrect density threshold '%f'. Density threshold should not be negative." % self.__density_threshold)


    def __allocate_clusters(self):
        """!
        @brief Performs cluster allocation using leafs of tree in BANG directory (the smallest cells).

        """
        leaf_blocks = self.__directory.get_leafs()
        unhandled_block_indexes = set([i for i in range(len(leaf_blocks)) if leaf_blocks[i].get_density() > self.__density_threshold])
        appropriate_block_indexes = set(unhandled_block_indexes)


        current_block = self.__find_block_center(leaf_blocks, unhandled_block_indexes)
        cluster_index = 0

        while current_block is not None:
            if current_block.get_density() <= self.__density_threshold:
                break

            self.__expand_cluster_block(current_block, cluster_index, leaf_blocks, unhandled_block_indexes)

            current_block = self.__find_block_center(leaf_blocks, unhandled_block_indexes)
            cluster_index += 1

        self.__store_clustering_results(cluster_index, leaf_blocks)


    def __expand_cluster_block(self, block, cluster_index, leaf_blocks, unhandled_block_indexes):
        """!
        @brief Expand cluster from specific block that is considered as a central block.

        @param[in] block (bang_block): Block that is considered as a central block for cluster.
        @param[in] cluster_index (uint): Index of cluster that is assigned to blocks that forms new cluster.
        @param[in] leaf_blocks (list): Leaf BANG-blocks that are considered during cluster formation.
        @param[in] unhandled_block_indexes (set): Set of candidates (BANG block indexes) to become a cluster member. The
                    parameter helps to reduce traversing among BANG-block providing only restricted set of block that
                    should be considered.

        """

        block.set_cluster(cluster_index)
        self.__update_cluster_dendrogram(cluster_index, [block])

        neighbors = self.__find_block_neighbors(block, leaf_blocks, unhandled_block_indexes)
        self.__update_cluster_dendrogram(cluster_index, neighbors)

        for neighbor in neighbors:
            neighbor.set_cluster(cluster_index)
            neighbor_neighbors = self.__find_block_neighbors(neighbor, leaf_blocks, unhandled_block_indexes)
            self.__update_cluster_dendrogram(cluster_index, neighbor_neighbors)

            neighbors += neighbor_neighbors


    def __store_clustering_results(self, amount_clusters, leaf_blocks):
        """!
        @brief Stores clustering results in a convenient way.

        @param[in] amount_clusters (uint): Amount of cluster that was allocated during processing.
        @param[in] leaf_blocks (list): Leaf BANG-blocks (the smallest cells).

        """
        self.__clusters = [[] for _ in range(amount_clusters)]
        for block in leaf_blocks:
            index = block.get_cluster()

            if index is not None:
                self.__clusters[index] += block.get_points()
            else:
                self.__noise += block.get_points()

        self.__clusters = [ list(set(cluster)) for cluster in self.__clusters ]
        self.__noise = list(set(self.__noise))


    def __find_block_center(self, level_blocks, unhandled_block_indexes):
        """!
        @brief Search block that is cluster center for new cluster.

        @return (bang_block) Central block for new cluster, if cluster is not found then None value is returned.

        """
        for i in reversed(range(len(level_blocks))):
            if level_blocks[i].get_density() <= self.__density_threshold:
                return None

            if level_blocks[i].get_cluster() is None:
                unhandled_block_indexes.remove(i)
                return level_blocks[i]

        return None


    def __find_block_neighbors(self, block, level_blocks, unhandled_block_indexes):
        """!
        @brief Search block neighbors that are parts of new clusters (density is greater than threshold and that are
                not cluster members yet), other neighbors are ignored.

        @param[in] block (bang_block): BANG-block for which neighbors should be found (which can be part of cluster).
        @param[in] level_blocks (list): BANG-blocks on specific level.
        @param[in] unhandled_block_indexes (set): Blocks that have not been processed yet.

        @return (list) Block neighbors that can become part of cluster.

        """
        neighbors = []

        handled_block_indexes = []
        for unhandled_index in unhandled_block_indexes:
            if block.is_neighbor(level_blocks[unhandled_index]):
                handled_block_indexes.append(unhandled_index)
                neighbors.append(level_blocks[unhandled_index])

                # Maximum number of neighbors is eight
                if len(neighbors) == 8:
                    break

        for handled_index in handled_block_indexes:
            unhandled_block_indexes.remove(handled_index)

        return neighbors


    def __update_cluster_dendrogram(self, index_cluster, blocks):
        """!
        @brief Append clustered blocks to dendrogram.

        @param[in] index_cluster (uint): Cluster index that was assigned to blocks.
        @param[in] blocks (list): Blocks that were clustered.

        """
        if len(self.__dendrogram) <= index_cluster:
            self.__dendrogram.append([])

        blocks = sorted(blocks, key=lambda block: block.get_density(), reverse=True)
        self.__dendrogram[index_cluster] += blocks

Push — master ( 769c99...57e143 )

bang.__find_block_neighbors() B

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2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

annoviko / pyclustering

Push — master ( 769c99...57e143 )

bang.__find_block_neighbors() B

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

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