

'''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'''

def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					word.split()

  def justify(snake_case      ,       snake_case      ,       snake_case ) -> str:
    SCREAMING_SNAKE_CASE:str						      =					max_width - width
    SCREAMING_SNAKE_CASE:Dict						      =					len(snake_case )
    if len(snake_case ) == 1:
      # if there is only word in line
      # just insert overall_spaces_count for the remainder of line
      return line[0] + " " * overall_spaces_count
    else:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					words_count - 1
      # num_spaces_between_words_list[i] : tells you to insert
      # num_spaces_between_words_list[i] spaces
      # after word on line[i]
      SCREAMING_SNAKE_CASE:List[Any]						      =					spaces_to_insert_between_words * [
          overall_spaces_count // spaces_to_insert_between_words
      ]
      SCREAMING_SNAKE_CASE:List[Any]						      =					(
          overall_spaces_count % spaces_to_insert_between_words
      )
      # distribute spaces via round robin to the left words
      for i in range(snake_case ):
        num_spaces_between_words_list[i] += 1
      SCREAMING_SNAKE_CASE:str						      =					[]
      for i in range(snake_case ):
        # add the word
        aligned_words_list.append(line[i] )
        # add the spaces to insert
        aligned_words_list.append(num_spaces_between_words_list[i] * " " )
      # just add the last word to the sentence
      aligned_words_list.append(line[-1] )
      # join the aligned words list to form a justified line
      return "".join(snake_case )

  SCREAMING_SNAKE_CASE:Any						      =					[]
  SCREAMING_SNAKE_CASE:list[str]						      =					[]
  SCREAMING_SNAKE_CASE:List[str]						      =					0
  for word in words:
    if width + len(snake_case ) + len(snake_case ) <= max_width:
      # keep adding words until we can fill out max_width
      # width = sum of length of all words (without overall_spaces_count)
      # len(word) = length of current word
      # len(line) = number of overall_spaces_count to insert between words
      line.append(snake_case )
      width += len(snake_case )
    else:
      # justify the line and add it to result
      answer.append(justify(snake_case      ,       snake_case      ,       snake_case ) )
      # reset new line and new width
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					[word], len(snake_case )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					max_width - width - len(snake_case )
  answer.append(" ".join(snake_case ) + (remaining_spaces + 1) * " " )
  return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()






