




"""simple docstring"""





from random import shuffle

import tensorflow as tf
from numpy import array
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							int(UpperCAmelCase__   )
      assert noofclusters < len(UpperCAmelCase__   )

      # Find out the dimensionality
      UpperCAmelCase_    				=							len(vectors[0]   )

      # Will help select random centroids from among the available vectors
      UpperCAmelCase_    				=							list(range(len(UpperCAmelCase__   )   )   )
      shuffle(UpperCAmelCase__   )

      # 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.

      UpperCAmelCase_    				=							tf.Graph()

      with graph.as_default():
            # SESSION OF COMPUTATION

            UpperCAmelCase_    				=							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
            UpperCAmelCase_    				=							[
                tf.Variable(vectors[vector_indices[i]]   ) for i in range(UpperCAmelCase__   )
            ]
            ##These nodes will assign the centroid Variables the appropriate
            ##values
            UpperCAmelCase_    				=							tf.placeholder("float64"			,    [dim]   )
            UpperCAmelCase_    				=							[]
            for centroid in centroids:
                  cent_assigns.append(tf.assign(UpperCAmelCase__			,    UpperCAmelCase__   )   )

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

            ##Now lets construct the node that will compute the mean
            # The placeholder for the input
            UpperCAmelCase_    				=							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
            UpperCAmelCase_    				=							tf.reduce_mean(UpperCAmelCase__			,    0   )

            ##Node for computing Euclidean distances
            # Placeholders for input
            UpperCAmelCase_    				=							tf.placeholder("float"			,    [dim]   )
            UpperCAmelCase_    				=							tf.placeholder("float"			,    [dim]   )
            UpperCAmelCase_    				=							tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(UpperCAmelCase__			,    UpperCAmelCase__   )			,    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
            UpperCAmelCase_    				=							tf.placeholder("float"			,    [noofclusters]   )
            UpperCAmelCase_    				=							tf.argmin(UpperCAmelCase__			,    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.
            UpperCAmelCase_    				=							tf.initialize_all_variables()

            # Initialize all variables
            sess.run(UpperCAmelCase__   )

            ##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.
            UpperCAmelCase_    				=							100
            for _ in range(UpperCAmelCase__   ):
                  ##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(UpperCAmelCase__   )   ):
                        UpperCAmelCase_    				=							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.
                        UpperCAmelCase_    				=							[
                            sess.run(UpperCAmelCase__			,    feed_dict={va: vect, va: sess.run(UpperCAmelCase__   )}   )
                            for centroid in centroids
                        ]
                        # Now use the cluster assignment node, with the distances
                        # as the input
                        UpperCAmelCase_    				=							sess.run(
                            UpperCAmelCase__			,    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(UpperCAmelCase__   ):
                        # Collect all the vectors assigned to this cluster
                        UpperCAmelCase_    				=							[
                            vectors[i]
                            for i in range(len(UpperCAmelCase__   )   )
                            if sess.run(assignments[i]   ) == cluster_n
                        ]
                        # Compute new centroid location
                        UpperCAmelCase_    				=							sess.run(
                            UpperCAmelCase__			,    feed_dict={mean_input: array(UpperCAmelCase__   )}   )
                        # Assign value to appropriate variable
                        sess.run(
                            cent_assigns[cluster_n]			,    feed_dict={centroid_value: new_location}   )

        # Return centroids and assignments
            UpperCAmelCase_    				=							sess.run(UpperCAmelCase__   )
            UpperCAmelCase_    				=							sess.run(UpperCAmelCase__   )
            return centroids, assignments









"""simple docstring"""





from __future__ import annotations
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_				,					UpperCAmelCase_    				=							input_list[low:mid], input_list[mid : high + 1]
      while left and right:
            result.append((left if left[0] <= right[0] else right).pop(0   )   )
      UpperCAmelCase_    				=							result + left + right
      return input_list
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) <= 1:
            return input_list
      UpperCAmelCase_    				=							list(lowerCAmelCase__   )

      # iteration for two-way merging
      UpperCAmelCase_    				=							2
      while p <= len(lowerCAmelCase__   ):
            # getting low, high and middle value for merge-sort of single list
            for i in range(0			,    len(lowerCAmelCase__   )			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							i
                  UpperCAmelCase_    				=							i + p - 1
                  UpperCAmelCase_    				=							(low + high + 1) // 2
                  UpperCAmelCase_    				=							merge(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
            # final merge of last two parts
            if p * 2 >= len(lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							i
                  UpperCAmelCase_    				=							merge(lowerCAmelCase__			,    0			,    lowerCAmelCase__			,    len(lowerCAmelCase__   ) - 1   )
                  break
            p *= 2

      return input_list


if __name__ == "__main__":
  lowerCamelCase      =		input("""Enter numbers separated by a comma:\n""").strip()
  if user_input == "":
    lowerCamelCase      =		[]
  else:
    lowerCamelCase      =		[int(item.strip()) for item in user_input.split(""",""")]
  print(iter_merge_sort(unsorted))




