

from random import shuffle

import tensorflow as tf
from numpy import array





def      snake_case_				(__A					:						Tuple ,       __A					:						int					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[Any]    =						int(__A					)
   assert noofclusters < len(__A					)

   # Find out the dimensionality
   __lowerCAmelCase  :      int    =						len(vectors[0]					)

   # Will help select random centroids from among the available vectors
   __lowerCAmelCase  :      List[str]    =						list(range(len(__A					)					)					)
   shuffle(__A					)

   # GRAPH OF COMPUTATION
   # We initialize a new graph and set it as the default during each run
   # of this algorithm. This ensures that as this function is called
   # multiple times, the default graph doesn't keep getting crowded with
   # unused ops and Variables from previous function calls.

   __lowerCAmelCase  :      Dict    =						tf.Graph()

   with graph.as_default():
      # SESSION OF COMPUTATION

      __lowerCAmelCase  :      List[str]    =						tf.Session()

      ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

      ##First lets ensure we have a Variable vector for each centroid,
      ##initialized to one of the vectors from the available data points
      __lowerCAmelCase  :      str    =						[
          tf.Variable(vectors[vector_indices[i]]					) for i in range(__A					)
      ]
      ##These nodes will assign the centroid Variables the appropriate
      ##values
      __lowerCAmelCase  :      List[Any]    =						tf.placeholder("""float64""" ,       [dim]					)
      __lowerCAmelCase  :      List[str]    =						[]
      for centroid in centroids:
         cent_assigns.append(tf.assign(__A ,       __A					)					)

      ##Variables for cluster assignments of individual vectors(initialized
      ##to 0 at first)
      __lowerCAmelCase  :      Any    =						[tf.Variable(0					) for i in range(len(__A					)					)]
      ##These nodes will assign an assignment Variable the appropriate
      ##value
      __lowerCAmelCase  :      str    =						tf.placeholder("""int32"""					)
      __lowerCAmelCase  :      List[Any]    =						[]
      for assignment in assignments:
         cluster_assigns.append(tf.assign(__A ,       __A					)					)

      ##Now lets construct the node that will compute the mean
      # The placeholder for the input
      __lowerCAmelCase  :      Any    =						tf.placeholder("""float""" ,       [None, dim]					)
      # The Node/op takes the input and computes a mean along the 0th
      # dimension, i.e. the list of input vectors
      __lowerCAmelCase  :      Any    =						tf.reduce_mean(__A ,       0					)

      ##Node for computing Euclidean distances
      # Placeholders for input
      __lowerCAmelCase  :      Union[str, Any]    =						tf.placeholder("""float""" ,       [dim]					)
      __lowerCAmelCase  :      Union[str, Any]    =						tf.placeholder("""float""" ,       [dim]					)
      __lowerCAmelCase  :      Union[str, Any]    =						tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__A ,       __A					) ,       2					)					)					)

      ##This node will figure out which cluster to assign a vector to,
      ##based on Euclidean distances of the vector from the centroids.
      # Placeholder for input
      __lowerCAmelCase  :      List[str]    =						tf.placeholder("""float""" ,       [noofclusters]					)
      __lowerCAmelCase  :      List[str]    =						tf.argmin(__A ,       0					)

      ##INITIALIZING STATE VARIABLES

      ##This will help initialization of all Variables defined with respect
      ##to the graph. The Variable-initializer should be defined after
      ##all the Variables have been constructed, so that each of them
      ##will be included in the initialization.
      __lowerCAmelCase  :      Union[str, Any]    =						tf.initialize_all_variables()

      # Initialize all variables
      sess.run(__A					)

      ##CLUSTERING ITERATIONS

      # Now perform the Expectation-Maximization steps of K-Means clustering
      # iterations. To keep things simple, we will only do a set number of
      # iterations, instead of using a Stopping Criterion.
      __lowerCAmelCase  :      Optional[int]    =						1_0_0
      for _ in range(__A					):
         ##EXPECTATION STEP
         ##Based on the centroid locations till last iteration, compute
         ##the _expected_ centroid assignments.
         # Iterate over each vector
         for vector_n in range(len(__A					)					):
            __lowerCAmelCase  :      Tuple    =						vectors[vector_n]
            # Compute Euclidean distance between this vector and each
            # centroid. Remember that this list cannot be named
            #'centroid_distances', since that is the input to the
            # cluster assignment node.
            __lowerCAmelCase  :      List[Any]    =						[
                sess.run(__A ,       feed_dict={va: vect, va: sess.run(__A					)}					)
                for centroid in centroids
            ]
            # Now use the cluster assignment node, with the distances
            # as the input
            __lowerCAmelCase  :      List[Any]    =						sess.run(
                __A ,       feed_dict={centroid_distances: distances}					)
            # Now assign the value to the appropriate state variable
            sess.run(
                cluster_assigns[vector_n] ,       feed_dict={assignment_value: assignment}					)

         ##MAXIMIZATION STEP
         # Based on the expected state computed from the Expectation Step,
         # compute the locations of the centroids so as to maximize the
         # overall objective of minimizing within-cluster Sum-of-Squares
         for cluster_n in range(__A					):
            # Collect all the vectors assigned to this cluster
            __lowerCAmelCase  :      Optional[int]    =						[
                vectors[i]
                for i in range(len(__A					)					)
                if sess.run(assignments[i]					) == cluster_n
            ]
            # Compute new centroid location
            __lowerCAmelCase  :      Dict    =						sess.run(
                __A ,       feed_dict={mean_input: array(__A					)}					)
            # Assign value to appropriate variable
            sess.run(
                cent_assigns[cluster_n] ,       feed_dict={centroid_value: new_location}					)

        # Return centroids and assignments
      __lowerCAmelCase  :      List[Any]    =						sess.run(__A					)
      __lowerCAmelCase  :      int    =						sess.run(__A					)
      return centroids, assignments





from __future__ import annotations





def      snake_case_				(__A					:						list[float] ,       __A					:						list[float]					)      ->     float:
   __lowerCAmelCase  :      Union[str, Any]    =						sorted(numsa + numsa					)
   __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						divmod(len(__A					) ,       2					)
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of first array: """).split()]
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')


