

"""simple docstring"""






from random import shuffle

import tensorflow as tf
from numpy import array



def   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		List[str]	):




					'''simple docstring'''






					UpperCAmelCase			:   Tuple      =				int(_lowercase	)
					assert noofclusters < len(_lowercase	)

					# Find out the dimensionality
					UpperCAmelCase			:   Optional[int]      =				len(vectors[0]	)

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

					# 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			:   str      =				tf.Graph()

					with graph.as_default():
										# SESSION OF COMPUTATION

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

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

										##Now lets construct the node that will compute the mean
										# The placeholder for the input
										UpperCAmelCase			:   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
										UpperCAmelCase			:   Optional[Any]      =				tf.reduce_mean(_lowercase							,      0	)

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

										# Initialize all variables
										sess.run(_lowercase	)

										##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			:   List[Any]      =				1_00
										for _ in range(_lowercase	):
															##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(_lowercase	)	):
																				UpperCAmelCase			:   int      =				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			:   List[str]      =				[
																				    sess.run(_lowercase							,      feed_dict={va: vect, va: sess.run(_lowercase	)}	)
																				    for centroid in centroids
																				]
																				# Now use the cluster assignment node, with the distances
																				# as the input
																				UpperCAmelCase			:   Optional[int]      =				sess.run(
																				    _lowercase							,      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(_lowercase	):
																				# Collect all the vectors assigned to this cluster
																				UpperCAmelCase			:   Dict      =				[
																				    vectors[i]
																				    for i in range(len(_lowercase	)	)
																				    if sess.run(assignments[i]	) == cluster_n
																				]
																				# Compute new centroid location
																				UpperCAmelCase			:   Optional[Any]      =				sess.run(
																				    _lowercase							,      feed_dict={mean_input: array(_lowercase	)}	)
																				# 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(_lowercase	)
										UpperCAmelCase			:   List[str]      =				sess.run(_lowercase	)
										return centroids, assignments





"""simple docstring"""






import os



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Union[str, Any]      =				os.path.join(os.path.dirname(_lowercase	)							,      "num.txt"	)
					with open(_lowercase	) as file_hand:
										return str(sum(int(_lowercase	) for line in file_hand	)	)[:10]


if __name__ == "__main__":
	print(solution())


