

'''simple docstring'''

from random import shuffle

import tensorflow as tf
from numpy import array





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					int(snake_case )
  assert noofclusters < len(snake_case )

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

  # Will help select random centroids from among the available vectors
  SCREAMING_SNAKE_CASE:str						      =					list(range(len(snake_case ) ) )
  shuffle(snake_case )

  # 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.

  SCREAMING_SNAKE_CASE:str						      =					tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

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

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

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    SCREAMING_SNAKE_CASE:Optional[int]						      =					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
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tf.reduce_mean(snake_case      ,       0 )

    ##Node for computing Euclidean distances
    # Placeholders for input
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					tf.placeholder("float"      ,       [dim] )
    SCREAMING_SNAKE_CASE:List[str]						      =					tf.placeholder("float"      ,       [dim] )
    SCREAMING_SNAKE_CASE:Any						      =					tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(snake_case      ,       snake_case )      ,       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
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tf.placeholder("float"      ,       [noofclusters] )
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tf.argmin(snake_case      ,       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.
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tf.initialize_all_variables()

    # Initialize all variables
    sess.run(snake_case )

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

        # Return centroids and assignments
    SCREAMING_SNAKE_CASE:Tuple						      =					sess.run(snake_case )
    SCREAMING_SNAKE_CASE:int						      =					sess.run(snake_case )
    return centroids, assignments









'''simple docstring'''

from ..utils import DummyObject, requires_backends





class 							_snake_case (       metaclass=_a						):
   _A       :     Any       =					['''torch''', '''torchsde''']
   def __init__( self			:      Any     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     requires_backends(self     ,["torch", "torchsde"]							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Dict     ,*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     requires_backends(cls     ,["torch", "torchsde"]							)







   @classmethod
   def     __UpperCamelCase						( cls			:      List[str]     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     requires_backends(cls     ,["torch", "torchsde"]							)






