



from random import shuffle

import tensorflow as tf
from numpy import array


def 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    int(_A			)
					assert noofclusters < len(_A			)

					# Find out the dimensionality
					_lowerCAmelCase						=    len(vectors[0]			)

					# Will help select random centroids from among the available vectors
					_lowerCAmelCase						=    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						=    tf.Graph()

					with graph.as_default():
										# SESSION OF COMPUTATION

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

										##Variables for cluster assignments of individual vectors(initialized
										##to 0 at first)
										_lowerCAmelCase						=    [tf.Variable(0			) for i in range(len(_A			)			)]
										##These nodes will assign an assignment Variable the appropriate
										##value
										_lowerCAmelCase						=    tf.placeholder("""int32"""			)
										_lowerCAmelCase						=    []
										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						=    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						=    tf.reduce_mean(_A	,						0			)

										##Node for computing Euclidean distances
										# Placeholders for input
										_lowerCAmelCase						=    tf.placeholder("""float"""	,						[dim]			)
										_lowerCAmelCase						=    tf.placeholder("""float"""	,						[dim]			)
										_lowerCAmelCase						=    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						=    tf.placeholder("""float"""	,						[noofclusters]			)
										_lowerCAmelCase						=    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						=    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						=    1_00
										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						=    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						=    [
																				    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						=    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						=    [
																				    vectors[i]
																				    for i in range(len(_A			)			)
																				    if sess.run(assignments[i]			) == cluster_n
																				]
																				# Compute new centroid location
																				_lowerCAmelCase						=    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						=    sess.run(_A			)
										_lowerCAmelCase						=    sess.run(_A			)
										return centroids, assignments






def 			UpperCAmelCase__				(			_A      : dict							):





  '''simple docstring'''







  a__  =set()
  # To detect a back edge, keep track of vertices currently in the recursion stack
  a__  =set()
  return any(
      node not in visited and depth_first_search(_A	,      _A	,      _A	,      _A							)
      for node in graph							)




def 			UpperCAmelCase__				(			_A      : dict	,      _A      : int	,      _A      : set	,      _A      : set							):





  '''simple docstring'''







  visited.add(_A							)
  rec_stk.add(_A							)

  for node in graph[vertex]:
    if node not in visited:
      if depth_first_search(_A	,      _A	,      _A	,      _A							):
        return True
    elif node in rec_stk:
      return True

    # The node needs to be removed from recursion stack before function ends
  rec_stk.remove(_A							)
  return False


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

       testmod()
