# Source code for pyswarms.backend.topology.pyramid

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

    """
    A Pyramid Network Topology

    This class implements a pyramid topology. In this topology, the particles are connected by N-dimensional simplices.
    """

    # Import standard library
    import logging

    # Import modules
    import numpy as np
    from scipy.spatial import Delaunay

    from .. import operators as ops
    from ..handlers import BoundaryHandler, VelocityHandler
    from ...utils.reporter import Reporter
    from .base import Topology

    class Pyramid(Topology):

        def __init__(self, static=False):
            """Initialize the class

            Parameters
            ----------
            static : bool (Default is :code:`False`)
                a boolean that decides whether the topology
                is static or dynamic
            """
            super(Pyramid, self).__init__(static)
            self.rep = Reporter(logger=logging.getLogger(__name__))

        def compute_gbest(self, swarm, **kwargs):
            """Update the global best using a pyramid neighborhood approach

            This topology uses the :code:`Delaunay` class from :code:`scipy`. To
            prevent precision errors in the Delaunay class, custom
            :code:`qhull_options` were added. Namely, :code:`QJ0.001 Qbb Qc Qx`.
            The meaning of those options is explained in [qhull]. This method is
            used to triangulate N-dimensional space into simplices. The vertices of
            the simplicies consist of swarm particles. This method is adapted from
            the work of Lane et al.[SIS2008]

            [SIS2008] J. Lane, A. Engelbrecht and J. Gain, "Particle swarm optimization with spatially
            meaningful neighbours," 2008 IEEE Swarm Intelligence Symposium, St. Louis, MO, 2008,
            pp. 1-8. doi: 10.1109/SIS.2008.4668281
            [qhull] http://www.qhull.org/html/qh-optq.htm

            Parameters
            ----------
            swarm : pyswarms.backend.swarms.Swarm
                a Swarm instance

            Returns
            -------
            numpy.ndarray
                Best position of shape :code:`(n_dimensions, )`
            float
                Best cost
            """
            try:
                # If there are less than (swarm.dimensions + 1) particles they are all connected
                if swarm.n_particles < swarm.dimensions + 1:
                    self.neighbor_idx = np.tile(
                        np.arange(swarm.n_particles), (swarm.n_particles, 1)
                    )
                    best_pos = swarm.pbest_pos[np.argmin(swarm.pbest_cost)]
                    best_cost = np.min(swarm.pbest_cost)
                else:
                    # Check if the topology is static or dynamic and assign neighbors
                    if (
                        self.static and self.neighbor_idx is None
                    ) or not self.static:
                        pyramid = Delaunay(
                            swarm.position, qhull_options="QJ0.001 Qbb Qc Qx"
                        )
                        indices, index_pointer = pyramid.vertex_neighbor_vertices
                        # Insert all the neighbors for each particle in the idx array
                        self.neighbor_idx = np.array(
                            [
                                index_pointer[indices[i] : indices[i + 1]]
                                for i in range(swarm.n_particles)
                            ], dtype='object'
                        )

                    idx_min = np.array(
                        [
                            swarm.pbest_cost[self.neighbor_idx[i]].argmin()
                            for i in range(len(self.neighbor_idx))
                        ]
                    )
                    best_neighbor = np.array(
                        [
                            self.neighbor_idx[i][idx_min[i]]
                            for i in range(len(self.neighbor_idx))
                        ]
                    ).astype(int)

                    # Obtain best cost and position
                    best_cost = np.min(swarm.pbest_cost[best_neighbor])
                    best_pos = swarm.pbest_pos[best_neighbor]
            except AttributeError:
                self.rep.logger.exception(
                    "Please pass a Swarm class. You passed {}".format(type(swarm))
                )
                raise
            else:
                return (best_pos, best_cost)

        def compute_velocity(
            self,
            swarm,
            clamp=None,
            vh=VelocityHandler(strategy="unmodified"),
            bounds=None,
        ):
            """Compute the velocity matrix

            This method updates the velocity matrix using the best and current
            positions of the swarm. The velocity matrix is computed using the
            cognitive and social terms of the swarm.

            A sample usage can be seen with the following:

            .. code-block :: python

                import pyswarms.backend as P
                from pyswarms.backend.swarm import Swarm
                from pyswarms.backend.handlers import VelocityHandler
                from pyswarms.backend.topology import Pyramid

                my_swarm = P.create_swarm(n_particles, dimensions)
                my_topology = Pyramid(static=False)
                my_vh = VelocityHandler(strategy="zero")

                for i in range(iters):
                    # Inside the for-loop
                    my_swarm.velocity = my_topology.update_velocity(my_swarm, clamp, my_vh,
                    bounds=bounds)

            Parameters
            ----------
            swarm : pyswarms.backend.swarms.Swarm
                a Swarm instance
            clamp : tuple of floats (default is :code:`None`)
                a tuple of size 2 where the first entry is the minimum velocity
                and the second entry is the maximum velocity. It
                sets the limits for velocity clamping.
            vh : pyswarms.backend.handlers.VelocityHandler
                a VelocityHandler instance
            bounds : tuple of :code:`np.ndarray` or list (default is :code:`None`)
                a tuple of size 2 where the first entry is the minimum bound while
                the second entry is the maximum bound. Each array must be of shape
                :code:`(dimensions,)`.

            Returns
            -------
            numpy.ndarray
                Updated velocity matrix
            """
            return ops.compute_velocity(swarm, clamp, vh, bounds=bounds)

        def compute_position(
            self, swarm, bounds=None, bh=BoundaryHandler(strategy="periodic")
        ):
            """Update the position matrix

            This method updates the position matrix given the current position and
            the velocity. If bounded, it waives updating the position.

            Parameters
            ----------
            swarm : pyswarms.backend.swarms.Swarm
                a Swarm instance
            bounds : tuple of :code:`np.ndarray` or list (default is :code:`None`)
                a tuple of size 2 where the first entry is the minimum bound while
                the second entry is the maximum bound. Each array must be of shape
                :code:`(dimensions,)`.
            bh : a BoundaryHandler instance

            Returns
            -------
            numpy.ndarray
                New position-matrix
            """
            return ops.compute_position(swarm, bounds, bh)