message. ③ Declare the expected response. The interaction type is determined implicitly from the cardinality of the input and output. The above example is a Request-Stream because one value is sent and a an @MessageMapping method supports is determined from the cardinality of the input (i.e. @Payload argument) and of the output, where cardinality means the following: Cardinali ty Description 1 Either The table below shows all input and output cardinality combinations and the corresponding interaction type(s): Input Cardinality Output Cardinality Interaction Types 0, 1 0 Fire-and-Forget, Request-Response

call_command', 'clearsessions', schedule_type='H') Groups A group example with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s Group example with Parzen-window estimation import numpy from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

call_command', 'clearsessions', schedule_type='H') Groups A group example with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s Group example with Parzen-window estimation import numpy from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

call_command', 'clearsessions', schedule_type='H') Groups A group example with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s Group example with Parzen-window estimation import numpy from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return