




"""simple docstring"""
from random import shuffle

import tensorflow as tf
from numpy import array





def 						UpperCAmelCase				(			A__:    Optional[int]							,				A__:    List[str]     )  ->     str:
			__lowerCamelCase					:	Any	       =				int(A__     )
			assert noofclusters < len(A__     )

			# Find out the dimensionality
			__lowerCamelCase					:	Tuple	       =				len(vectors[0]     )

			# Will help select random centroids from among the available vectors
			__lowerCamelCase					:	Tuple	       =				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					:	Union[str, Any]	       =				tf.Graph()

			with graph.as_default():
						# SESSION OF COMPUTATION

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

						##Variables for cluster assignments of individual vectors(initialized
						##to 0 at first)
						__lowerCamelCase					:	Dict	       =				[tf.Variable(0     ) for i in range(len(A__     )     )]
						##These nodes will assign an assignment Variable the appropriate
						##value
						__lowerCamelCase					:	int	       =				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					:	List[str]	       =				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					:	List[Any]	       =				tf.reduce_mean(A__							,				0     )

						##Node for computing Euclidean distances
						# Placeholders for input
						__lowerCamelCase					:	Any	       =				tf.placeholder('float'							,				[dim]     )
						__lowerCamelCase					:	List[Any]	       =				tf.placeholder('float'							,				[dim]     )
						__lowerCamelCase					:	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					:	Tuple	       =				tf.placeholder('float'							,				[noofclusters]     )
						__lowerCamelCase					:	Optional[int]	       =				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					:	Any	       =				100
						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					:	List[str]	       =				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					:	Union[str, 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					:	int	       =				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					:	List[Any]	       =				[
												    vectors[i]
												    for i in range(len(A__     )     )
												    if sess.run(assignments[i]     ) == cluster_n
												]
												# Compute new centroid location
												__lowerCamelCase					:	int	       =				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					:	Tuple	       =				sess.run(A__     )
						__lowerCamelCase					:	List[str]	       =				sess.run(A__     )
						return centroids, assignments









"""simple docstring"""
def 						UpperCAmelCase				(			A__:    Dict     )  ->     Optional[int]:
			stooge(A__							,				0							,				len(A__     ) - 1     )
			return arr





def 						UpperCAmelCase				(			A__:    Dict							,				A__:    List[str]							,				A__:    Optional[int]     )  ->     Any:
			if i >= h:
						return

			# If first element is smaller than the last then swap them
			if arr[i] > arr[h]:
						__lowerCamelCase       ,      __lowerCamelCase					:	Dict	       =				arr[h], arr[i]

			# If there are more than 2 elements in the array
			if h - i + 1 > 2:
						__lowerCamelCase					:	str	       =				(int)((h - i + 1) / 3     )

						# Recursively sort first 2/3 elements
						stooge(A__							,				A__							,				(h - t)     )

						# Recursively sort last 2/3 elements
						stooge(A__							,				i + t							,				(A__)     )

						# Recursively sort first 2/3 elements
						stooge(A__							,				A__							,				(h - t)     )


if __name__ == "__main__":
					a_				:    Tuple										=     input('''Enter numbers separated by a comma:\n''').strip()
					a_				:    Optional[Any]										=     [int(item) for item in user_input.split(''',''')]
					print(stooge_sort(unsorted))



