


from random import shuffle

import tensorflow as tf
from numpy import array







def     _lowercase							(  __lowerCamelCase      :       int		,__lowerCamelCase      :       List[Any]      )	-> Optional[Any]:





     '''simple docstring'''





     UpperCamelCase__				:	List[Any]										=  int(_lowerCAmelCase      )
     assert noofclusters < len(_lowerCAmelCase      )

     # Find out the dimensionality
     UpperCamelCase__				:	str										=  len(vectors[0]      )

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

     # 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__				:	Any										=  tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

          UpperCamelCase__				:	Union[str, Any]										=  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__				:	Optional[int]										=  [
              tf.Variable(vectors[vector_indices[i]]      ) for i in range(_lowerCAmelCase      )
          ]
          ##These nodes will assign the centroid Variables the appropriate
          ##values
          UpperCamelCase__				:	Tuple										=  tf.placeholder('''float64'''		,[dim]      )
          UpperCamelCase__				:	str										=  []
          for centroid in centroids:
               cent_assigns.append(tf.assign(_lowerCAmelCase		,_lowerCAmelCase      )      )

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

          ##Now lets construct the node that will compute the mean
          # The placeholder for the input
          UpperCamelCase__				:	Tuple										=  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__				:	List[str]										=  tf.reduce_mean(_lowerCAmelCase		,0      )

          ##Node for computing Euclidean distances
          # Placeholders for input
          UpperCamelCase__				:	Optional[int]										=  tf.placeholder('''float'''		,[dim]      )
          UpperCamelCase__				:	Any										=  tf.placeholder('''float'''		,[dim]      )
          UpperCamelCase__				:	List[str]										=  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCAmelCase		,_lowerCAmelCase      )		,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__				:	Tuple										=  tf.placeholder('''float'''		,[noofclusters]      )
          UpperCamelCase__				:	str										=  tf.argmin(_lowerCAmelCase		,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__				:	Optional[Any]										=  tf.initialize_all_variables()

          # Initialize all variables
          sess.run(_lowerCAmelCase      )

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

                        # Return centroids and assignments
          UpperCamelCase__				:	Union[str, Any]										=  sess.run(_lowerCAmelCase      )
          UpperCamelCase__				:	Tuple										=  sess.run(_lowerCAmelCase      )
          return centroids, assignments











from typing import Any



def 						A_							(	_lowerCAmelCase	)		->					list[Any]:
 if not input_list:
  return []
 UpperCamelCase      :   List[str]			      =  [input_list.count(_lowerCAmelCase	) for value in input_list]
 UpperCamelCase      :   Dict			      =  max(_lowerCAmelCase	)  # Gets the maximum count in the input list.
 # Gets values of modes
 return sorted({input_list[i] for i, value in enumerate(_lowerCAmelCase	) if value == y}	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
