Attachment 1979290 Details for Bug 2226302 – build.log

build.log

build.log (text/plain), 32.00 KB, created by Fedora Release Engineering on 2023-07-25 19:43:10 UTC

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Created: 2023-07-25 19:43:10 UTC

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>        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)
>                        ]
>                    )
>E                   ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (10,) + inhomogeneous part.
>pyswarms/backend/topology/pyramid.py:81: ValueError
>----------------------------- Captured stderr call -----------------------------
>2023-07-21 13:13:10,046 - pyswarms.single.general_optimizer - INFO - Optimize for 2000 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>
pyswarms.single.general_optimizer:   0%|          |0/2000
pyswarms.single.general_optimizer:   0%|          |0/2000
>------------------------------ Captured log call -------------------------------
>INFO     pyswarms.single.general_optimizer:reporter.py:139 Optimize for 2000 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>__________ TestGeneralOptimizer.test_parallel_evaluation[optimizer1] ___________
>self = <tests.optimizers.test_general_optimizer.TestGeneralOptimizer object at 0x7fb217c4a9f0>
>obj_without_args = <function rosenbrock at 0x7fb2183fc5e0>
>optimizer = <pyswarms.single.general_optimizer.GeneralOptimizerPSO object at 0x7fb215073e30>
>    def test_parallel_evaluation(self, obj_without_args, optimizer):
>        """Test if parallelization breaks the optimization process"""
>        import multiprocessing
>    
>>       optimizer.optimize(
>            obj_without_args, 2000, n_processes=multiprocessing.cpu_count()
>        )
>tests/optimizers/test_general_optimizer.py:77: 
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>pyswarms/single/general_optimizer.py:252: in optimize
>    self.swarm.best_pos, self.swarm.best_cost = self.top.compute_gbest(
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>self = <pyswarms.backend.topology.random.Random object at 0x7fb217c339e0>
>swarm = Swarm(position=array([[-4.88757722,  6.68172228],
>       [-1.72184274,  3.87419572],
>       [ 9.5457183 ,  3.93376239]...98e+05,
>       3.69140480e+05, 5.91885488e+03, 2.62591498e+03, 9.00149012e+03,
>       2.03526321e+04, 5.44202006e+04]))
>k = 2, kwargs = {'c1': 0.3, 'c2': 0.7, 'p': 2, 'r': 1, ...}
>adj_matrix = array([[1, 1, 0, 1, 0, 1, 0, 1, 0, 0],
>       [1, 1, 0, 0, 0, 1, 0, 0, 1, 1],
>       [0, 0, 1, 0, 1, 0, 0, 1, 0, 1],
> ...,
>       [1, 0, 1, 0, 0, 0, 1, 1, 1, 0],
>       [0, 1, 0, 0, 0, 0, 0, 1, 1, 1],
>       [0, 1, 1, 1, 0, 0, 0, 0, 1, 1]])
>    def compute_gbest(self, swarm, k, **kwargs):
>        """Update the global best using a random neighborhood approach
>    
>        This uses random class from :code:`numpy` to give every particle k
>        randomly distributed, non-equal neighbors. The resulting topology
>        is a connected graph. The algorithm to obtain the neighbors was adapted
>        from [TSWJ2013].
>    
>        [TSWJ2013] Qingjian Ni and Jianming Deng, âA New Logistic Dynamic
>        Particle Swarm Optimization Algorithm Based on Random Topology,â
>        The Scientific World Journal, vol. 2013, Article ID 409167, 8 pages, 2013.
>        https://doi.org/10.1155/2013/409167.
>    
>        Parameters
>        ----------
>        swarm : pyswarms.backend.swarms.Swarm
>            a Swarm instance
>        k : int
>            number of neighbors to be considered. Must be a
>            positive integer less than :code:`n_particles-1`
>    
>        Returns
>        -------
>        numpy.ndarray
>            Best position of shape :code:`(n_dimensions, )`
>        float
>            Best cost
>        """
>        try:
>            # Check if the topology is static or dynamic and assign neighbors
>            if (self.static and self.neighbor_idx is None) or not self.static:
>                adj_matrix = self.__compute_neighbors(swarm, k)
>>               self.neighbor_idx = np.array(
>                    [
>                        adj_matrix[i].nonzero()[0]
>                        for i in range(swarm.n_particles)
>                    ]
>                )
>E               ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (10,) + inhomogeneous part.
>pyswarms/backend/topology/random.py:68: ValueError
>----------------------------- Captured stderr call -----------------------------
>2023-07-21 13:13:10,115 - pyswarms.single.general_optimizer - INFO - Optimize for 2000 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>
pyswarms.single.general_optimizer:   0%|          |0/2000
pyswarms.single.general_optimizer:   0%|          |0/2000
>------------------------------ Captured log call -------------------------------
>INFO     pyswarms.single.general_optimizer:reporter.py:139 Optimize for 2000 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>__________ TestGeneralOptimizer.test_general_correct_pos[optimizer0] ___________
>self = <tests.optimizers.test_general_optimizer.TestGeneralOptimizer object at 0x7fb217c4bd10>
>options = {'c1': 0.3, 'c2': 0.7, 'k': 2, 'p': 2, ...}
>optimizer = <pyswarms.single.general_optimizer.GeneralOptimizerPSO object at 0x7fb214e92d80>
>    def test_general_correct_pos(self, options, optimizer):
>        """ Test to check general optimiser returns the correct position corresponding to the best cost """
>>       cost, pos = optimizer.optimize(sphere, iters=5)
>tests/optimizers/test_general_optimizer.py:110: 
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>pyswarms/single/general_optimizer.py:252: in optimize
>    self.swarm.best_pos, self.swarm.best_cost = self.top.compute_gbest(
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>self = <pyswarms.backend.topology.pyramid.Pyramid object at 0x7fb217c33650>
>swarm = Swarm(position=array([[-7.1248994 , -2.11622523],
>       [ 3.3243314 , -1.48069574],
>       [ 9.0088584 ,  1.86083229]...63916, 84.62222649,  0.23524116, 22.28278838,
>       43.63044772, 44.91056116, 38.22385227, 67.12752471, 96.81426336]))
>kwargs = {'c1': 0.3, 'c2': 0.7, 'k': 2, 'p': 2, ...}
>pyramid = <scipy.spatial._qhull.Delaunay object at 0x7fb214f0e210>
>indices = array([ 0,  3,  7, 10, 14, 22, 26, 32, 35, 39, 42], dtype=int32)
>index_pointer = array([3, 6, 1, 3, 0, 4, 2, 4, 1, 9, 6, 0, 1, 4, 6, 3, 7, 1, 2, 9, 8, 5,
>       4, 8, 7, 6, 3, 0, 4, 7, 5, 8, 4, 6, 5, 4, 9, 5, 6, 4, 2, 8],
>      dtype=int32)
>    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)
>                        ]
>                    )
>E                   ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (10,) + inhomogeneous part.
>pyswarms/backend/topology/pyramid.py:81: ValueError
>----------------------------- Captured stderr call -----------------------------
>2023-07-21 13:13:24,840 - pyswarms.single.general_optimizer - INFO - Optimize for 5 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>
pyswarms.single.general_optimizer:   0%|          |0/5
pyswarms.single.general_optimizer:   0%|          |0/5
>------------------------------ Captured log call -------------------------------
>INFO     pyswarms.single.general_optimizer:reporter.py:139 Optimize for 5 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>__________ TestGeneralOptimizer.test_general_correct_pos[optimizer1] ___________
>self = <tests.optimizers.test_general_optimizer.TestGeneralOptimizer object at 0x7fb217c4be60>
>options = {'c1': 0.3, 'c2': 0.7, 'k': 2, 'p': 2, ...}
>optimizer = <pyswarms.single.general_optimizer.GeneralOptimizerPSO object at 0x7fb214f08710>
>    def test_general_correct_pos(self, options, optimizer):
>        """ Test to check general optimiser returns the correct position corresponding to the best cost """
>>       cost, pos = optimizer.optimize(sphere, iters=5)
>tests/optimizers/test_general_optimizer.py:110: 
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>pyswarms/single/general_optimizer.py:252: in optimize
>    self.swarm.best_pos, self.swarm.best_cost = self.top.compute_gbest(
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>self = <pyswarms.backend.topology.random.Random object at 0x7fb217c339e0>
>swarm = Swarm(position=array([[-9.5525505 ,  8.12611623],
>       [-3.97951458,  2.35925133],
>       [ 4.72859165,  8.34408622]...5394,  71.57341199,
>        37.34249238,  63.09024736, 123.39650422,  71.58071449,
>        70.66179799,  96.71670285]))
>k = 2, kwargs = {'c1': 0.3, 'c2': 0.7, 'p': 2, 'r': 1, ...}
>adj_matrix = array([[1, 1, 1, 0, 0, 0, 0, 0, 1, 1],
>       [1, 1, 0, 1, 0, 0, 0, 0, 0, 1],
>       [1, 0, 1, 0, 0, 0, 0, 0, 0, 1],
> ...,
>       [0, 0, 0, 1, 0, 1, 1, 1, 0, 0],
>       [1, 0, 0, 0, 0, 1, 1, 0, 1, 0],
>       [1, 1, 1, 1, 0, 0, 0, 0, 0, 1]])
>    def compute_gbest(self, swarm, k, **kwargs):
>        """Update the global best using a random neighborhood approach
>    
>        This uses random class from :code:`numpy` to give every particle k
>        randomly distributed, non-equal neighbors. The resulting topology
>        is a connected graph. The algorithm to obtain the neighbors was adapted
>        from [TSWJ2013].
>    
>        [TSWJ2013] Qingjian Ni and Jianming Deng, âA New Logistic Dynamic
>        Particle Swarm Optimization Algorithm Based on Random Topology,â
>        The Scientific World Journal, vol. 2013, Article ID 409167, 8 pages, 2013.
>        https://doi.org/10.1155/2013/409167.
>    
>        Parameters
>        ----------
>        swarm : pyswarms.backend.swarms.Swarm
>            a Swarm instance
>        k : int
>            number of neighbors to be considered. Must be a
>            positive integer less than :code:`n_particles-1`
>    
>        Returns
>        -------
>        numpy.ndarray
>            Best position of shape :code:`(n_dimensions, )`
>        float
>            Best cost
>        """
>        try:
>            # Check if the topology is static or dynamic and assign neighbors
>            if (self.static and self.neighbor_idx is None) or not self.static:
>                adj_matrix = self.__compute_neighbors(swarm, k)
>>               self.neighbor_idx = np.array(
>                    [
>                        adj_matrix[i].nonzero()[0]
>                        for i in range(swarm.n_particles)
>                    ]
>                )
>E               ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (10,) + inhomogeneous part.
>pyswarms/backend/topology/random.py:68: ValueError
>----------------------------- Captured stderr call -----------------------------
>2023-07-21 13:13:24,860 - pyswarms.single.general_optimizer - INFO - Optimize for 5 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>
pyswarms.single.general_optimizer:   0%|          |0/5
pyswarms.single.general_optimizer:   0%|          |0/5
>------------------------------ Captured log call -------------------------------
>INFO     pyswarms.single.general_optimizer:reporter.py:139 Optimize for 5 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>____________ TestGeneralOptimizer.test_ftol_iter_effect[optimizer0] ____________
>self = <tests.optimizers.test_general_optimizer.TestGeneralOptimizer object at 0x7fb217c541d0>
>optimizer = <pyswarms.single.general_optimizer.GeneralOptimizerPSO object at 0x7fb214efed50>
>    def test_ftol_iter_effect(self, optimizer):
>        """Test if setting the ftol breaks the optimization process after the set number of iterations"""
>        # Set optimizer tolerance
>        optimizer.ftol = 1e-1
>        optimizer.ftol_iter = 5
>>       optimizer.optimize(sphere, 2000)
>tests/optimizers/test_general_optimizer.py:123: 
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>pyswarms/single/general_optimizer.py:252: in optimize
>    self.swarm.best_pos, self.swarm.best_cost = self.top.compute_gbest(
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>self = <pyswarms.backend.topology.pyramid.Pyramid object at 0x7fb217c33650>
>swarm = Swarm(position=array([[ 1.24947433, -5.91574812],
>       [-0.356437  , -4.75970055],
>       [-0.06733537,  7.50780009]...9627,  46.65515517,
>        77.10656036,   9.24944034,  88.46069656, 138.94994324,
>        14.61918149,  48.35628255]))
>kwargs = {'c1': 0.3, 'c2': 0.7, 'k': 2, 'p': 2, ...}
>pyramid = <scipy.spatial._qhull.Delaunay object at 0x7fb214f55070>
>indices = array([ 0,  4,  9, 13, 19, 23, 28, 32, 38, 42, 46], dtype=int32)
>index_pointer = array([3, 9, 1, 7, 3, 5, 8, 7, 0, 5, 4, 8, 7, 5, 4, 6, 9, 1, 0, 2, 5, 3,
>       6, 2, 4, 8, 3, 1, 9, 7, 3, 4, 6, 9, 8, 2, 1, 0, 2, 7, 5, 1, 6, 7,
>       3, 0], dtype=int32)
>    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)
>                        ]
>                    )
>E                   ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (10,) + inhomogeneous part.
>pyswarms/backend/topology/pyramid.py:81: ValueError
>----------------------------- Captured stderr call -----------------------------
>2023-07-21 13:13:24,903 - pyswarms.single.general_optimizer - INFO - Optimize for 2000 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>
pyswarms.single.general_optimizer:   0%|          |0/2000
pyswarms.single.general_optimizer:   0%|          |0/2000
>------------------------------ Captured log call -------------------------------
>INFO     pyswarms.single.general_optimizer:reporter.py:139 Optimize for 2000 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>____________ TestGeneralOptimizer.test_ftol_iter_effect[optimizer1] ____________
>self = <tests.optimizers.test_general_optimizer.TestGeneralOptimizer object at 0x7fb217c54320>
>optimizer = <pyswarms.single.general_optimizer.GeneralOptimizerPSO object at 0x7fb214eda150>
>    def test_ftol_iter_effect(self, optimizer):
>        """Test if setting the ftol breaks the optimization process after the set number of iterations"""
>        # Set optimizer tolerance
>        optimizer.ftol = 1e-1
>        optimizer.ftol_iter = 5
>>       optimizer.optimize(sphere, 2000)
>tests/optimizers/test_general_optimizer.py:123: 
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>pyswarms/single/general_optimizer.py:252: in optimize
>    self.swarm.best_pos, self.swarm.best_cost = self.top.compute_gbest(
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>self = <pyswarms.backend.topology.random.Random object at 0x7fb217c339e0>
>swarm = Swarm(position=array([[ 9.50072285, -6.0456065 ],
>       [-6.70535112, -0.86183289],
>       [ 8.40665753, -1.29908235]...0574, 117.62446344,
>        79.53355526,  86.82243386,  79.37442788,   6.11656931,
>        66.67489885, 107.27489302]))
>k = 2, kwargs = {'c1': 0.3, 'c2': 0.7, 'p': 2, 'r': 1, ...}
>adj_matrix = array([[1, 0, 0, 1, 0, 0, 0, 1, 0, 0],
>       [0, 1, 0, 1, 0, 0, 1, 0, 0, 1],
>       [0, 0, 1, 0, 1, 1, 1, 1, 0, 0],
> ...,
>       [1, 0, 1, 0, 0, 0, 0, 1, 0, 1],
>       [0, 0, 0, 1, 0, 1, 0, 0, 1, 0],
>       [0, 1, 0, 1, 1, 1, 0, 1, 0, 1]])
>    def compute_gbest(self, swarm, k, **kwargs):
>        """Update the global best using a random neighborhood approach
>    
>        This uses random class from :code:`numpy` to give every particle k
>        randomly distributed, non-equal neighbors. The resulting topology
>        is a connected graph. The algorithm to obtain the neighbors was adapted
>        from [TSWJ2013].
>    
>        [TSWJ2013] Qingjian Ni and Jianming Deng, âA New Logistic Dynamic
>        Particle Swarm Optimization Algorithm Based on Random Topology,â
>        The Scientific World Journal, vol. 2013, Article ID 409167, 8 pages, 2013.
>        https://doi.org/10.1155/2013/409167.
>    
>        Parameters
>        ----------
>        swarm : pyswarms.backend.swarms.Swarm
>            a Swarm instance
>        k : int
>            number of neighbors to be considered. Must be a
>            positive integer less than :code:`n_particles-1`
>    
>        Returns
>        -------
>        numpy.ndarray
>            Best position of shape :code:`(n_dimensions, )`
>        float
>            Best cost
>        """
>        try:
>            # Check if the topology is static or dynamic and assign neighbors
>            if (self.static and self.neighbor_idx is None) or not self.static:
>                adj_matrix = self.__compute_neighbors(swarm, k)
>>               self.neighbor_idx = np.array(
>                    [
>                        adj_matrix[i].nonzero()[0]
>                        for i in range(swarm.n_particles)
>                    ]
>                )
>E               ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (10,) + inhomogeneous part.
>pyswarms/backend/topology/random.py:68: ValueError
>----------------------------- Captured stderr call -----------------------------
>2023-07-21 13:13:24,921 - pyswarms.single.general_optimizer - INFO - Optimize for 2000 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>
pyswarms.single.general_optimizer:   0%|          |0/2000
pyswarms.single.general_optimizer:   0%|          |0/2000
>------------------------------ Captured log call -------------------------------
>INFO     pyswarms.single.general_optimizer:reporter.py:139 Optimize for 2000 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.9, 'k': 2, 'p': 2, 'r': 1}
>=============================== warnings summary ===============================
>tests/optimizers/test_binary.py: 1134 warnings
>tests/optimizers/test_general_optimizer.py: 5979 warnings
>tests/optimizers/test_global_best.py: 2215 warnings
>tests/optimizers/test_local_best.py: 1236 warnings
>tests/optimizers/test_tolerance.py: 293 warnings
>tests/optimizers/test_verbose.py: 303 warnings
>tests/utils/plotters/test_plotters.py: 303 warnings
>tests/utils/search/test_gridsearch.py: 1782 warnings
>tests/utils/search/test_randomsearch.py: 2200 warnings
>  /usr/lib/python3.12/site-packages/tqdm/std.py:465: DeprecationWarning: datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.fromtimestamp(timestamp, datetime.UTC).
>    if rate and total else datetime.utcfromtimestamp(0))
>tests/optimizers/test_binary.py: 6 warnings
>tests/optimizers/test_general_optimizer.py: 30 warnings
>tests/optimizers/test_global_best.py: 6 warnings
>tests/optimizers/test_local_best.py: 6 warnings
>tests/utils/plotters/test_plotters.py: 6 warnings
>  /usr/lib64/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=714) is multi-threaded, use of fork() may lead to deadlocks in the child.
>    self.pid = os.fork()
>tests/utils/plotters/test_plotters.py::test_plot_surface_return_type
>tests/utils/plotters/test_plotters.py::test_parallel_mesh
>  /usr/lib64/python3.12/site-packages/matplotlib/animation.py:884: UserWarning: Animation was deleted without rendering anything. This is most likely not intended. To prevent deletion, assign the Animation to a variable, e.g. `anim`, that exists until you output the Animation using `plt.show()` or `anim.save()`.
>    warnings.warn(
>-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
>=========================== short test summary info ============================
>FAILED tests/backend/topology/test_pyramid.py::TestPyramidTopology::test_neighbor_idx[True]
>FAILED tests/backend/topology/test_pyramid.py::TestPyramidTopology::test_neighbor_idx[False]
>FAILED tests/backend/topology/test_pyramid.py::TestPyramidTopology::test_compute_gbest_return_values[True]
>FAILED tests/backend/topology/test_pyramid.py::TestPyramidTopology::test_compute_gbest_return_values[False]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_neighbor_idx[1-True]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_neighbor_idx[1-False]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_neighbor_idx[2-True]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_neighbor_idx[2-False]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_neighbor_idx[3-True]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_neighbor_idx[3-False]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[1-1-True]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[1-1-False]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[1-2-True]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[1-2-False]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[2-1-True]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[2-1-False]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[2-2-True]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[2-2-False]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[3-1-True]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[3-1-False]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[3-2-True]
>FAILED tests/backend/topology/test_random.py::TestRandomTopology::test_compute_gbest_return_values[3-2-False]
>FAILED tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_ftol_effect[optimizer0]
>FAILED tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_ftol_effect[optimizer1]
>FAILED tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_parallel_evaluation[optimizer0]
>FAILED tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_parallel_evaluation[optimizer1]
>FAILED tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_general_correct_pos[optimizer0]
>FAILED tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_general_correct_pos[optimizer1]
>FAILED tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_ftol_iter_effect[optimizer0]
>FAILED tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_ftol_iter_effect[optimizer1]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_train_history[optimizer_history0-cost_history-expected_shape0]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_train_history[optimizer_history0-mean_pbest_history-expected_shape1]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_train_history[optimizer_history0-mean_neighbor_history-expected_shape2]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_train_history[optimizer_history0-pos_history-expected_shape3]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_train_history[optimizer_history0-velocity_history-expected_shape4]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_train_history[optimizer_history1-cost_history-expected_shape0]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_train_history[optimizer_history1-mean_pbest_history-expected_shape1]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_train_history[optimizer_history1-mean_neighbor_history-expected_shape2]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_train_history[optimizer_history1-pos_history-expected_shape3]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_train_history[optimizer_history1-velocity_history-expected_shape4]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_reset_default_values[optimizer_reset0]
>ERROR tests/optimizers/test_general_optimizer.py::TestGeneralOptimizer::test_reset_default_values[optimizer_reset1]
>= 30 failed, 414 passed, 12 skipped, 15501 warnings, 12 errors in 62.60s (0:01:02) =
>error: Bad exit status from /var/tmp/rpm-tmp.1qrZ1a (%check)
>    Bad exit status from /var/tmp/rpm-tmp.1qrZ1a (%check)
>RPM build errors:
>Child return code was: 1
>EXCEPTION: [Error('Command failed: \n # /usr/bin/systemd-nspawn -q -M aec5107324004616b4925768b175330d -D /var/lib/mock/f39-build-44340745-5276606/root -a -u mockbuild --capability=cap_ipc_lock --bind=/tmp/mock-resolv.a66_jxjs:/etc/resolv.conf --bind=/dev/btrfs-control --bind=/dev/mapper/control --bind=/dev/loop-control --bind=/dev/loop0 --bind=/dev/loop1 --bind=/dev/loop2 --bind=/dev/loop3 --bind=/dev/loop4 --bind=/dev/loop5 --bind=/dev/loop6 --bind=/dev/loop7 --bind=/dev/loop8 --bind=/dev/loop9 --bind=/dev/loop10 --bind=/dev/loop11 --console=pipe --setenv=TERM=vt100 --setenv=SHELL=/bin/bash --setenv=HOME=/builddir --setenv=HOSTNAME=mock --setenv=PATH=/usr/bin:/bin:/usr/sbin:/sbin --setenv=PROMPT_COMMAND=printf "\\033]0;<mock-chroot>\\007" --setenv=PS1=<mock-chroot> \\s-\\v\\$  --setenv=LANG=C.UTF-8 --resolv-conf=off bash --login -c /usr/bin/rpmbuild -bb --noclean --target noarch --nodeps /builddir/build/SPECS/python-pyswarms.spec\n', 1)]
>Traceback (most recent call last):
>  File "/usr/lib/python3.11/site-packages/mockbuild/trace_decorator.py", line 93, in trace
>    result = func(*args, **kw)
>             ^^^^^^^^^^^^^^^^^
>  File "/usr/lib/python3.11/site-packages/mockbuild/util.py", line 597, in do_with_status
>    raise exception.Error("Command failed: \n # %s\n%s" % (command, output), child.returncode)
>mockbuild.exception.Error: Command failed: 
> # /usr/bin/systemd-nspawn -q -M aec5107324004616b4925768b175330d -D /var/lib/mock/f39-build-44340745-5276606/root -a -u mockbuild --capability=cap_ipc_lock --bind=/tmp/mock-resolv.a66_jxjs:/etc/resolv.conf --bind=/dev/btrfs-control --bind=/dev/mapper/control --bind=/dev/loop-control --bind=/dev/loop0 --bind=/dev/loop1 --bind=/dev/loop2 --bind=/dev/loop3 --bind=/dev/loop4 --bind=/dev/loop5 --bind=/dev/loop6 --bind=/dev/loop7 --bind=/dev/loop8 --bind=/dev/loop9 --bind=/dev/loop10 --bind=/dev/loop11 --console=pipe --setenv=TERM=vt100 --setenv=SHELL=/bin/bash --setenv=HOME=/builddir --setenv=HOSTNAME=mock --setenv=PATH=/usr/bin:/bin:/usr/sbin:/sbin --setenv=PROMPT_COMMAND=printf "\033]0;<mock-chroot>\007" --setenv=PS1=<mock-chroot> \s-\v\$  --setenv=LANG=C.UTF-8 --resolv-conf=off bash --login -c /usr/bin/rpmbuild -bb --noclean --target noarch --nodeps /builddir/build/SPECS/python-pyswarms.spec
>

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Attachments on bug 2226302: 1979290 | 1979291 | 1979292