

from random import shuffle

import tensorflow as tf
from numpy import array



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Any									= int(SCREAMING_SNAKE_CASE      )
	assert noofclusters < len(SCREAMING_SNAKE_CASE      )

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

	# Will help select random centroids from among the available vectors
	UpperCAmelCase__:	Dict									= list(range(len(SCREAMING_SNAKE_CASE      )      )      )
	shuffle(SCREAMING_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.

	UpperCAmelCase__:	str									= tf.Graph()

	with graph.as_default():
		# SESSION OF COMPUTATION

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

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

		##Now lets construct the node that will compute the mean
		# The placeholder for the input
		UpperCAmelCase__:	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
		UpperCAmelCase__:	Tuple									= tf.reduce_mean(SCREAMING_SNAKE_CASE	,0      )

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

		# Initialize all variables
		sess.run(SCREAMING_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.
		UpperCAmelCase__:	List[Any]									= 1_0_0
		for _ in range(SCREAMING_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(SCREAMING_SNAKE_CASE      )      ):
				UpperCAmelCase__:	Optional[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__:	str									= [
				    sess.run(SCREAMING_SNAKE_CASE	,feed_dict={va: vect, va: sess.run(SCREAMING_SNAKE_CASE      )}      )
				    for centroid in centroids
				]
				# Now use the cluster assignment node, with the distances
				# as the input
				UpperCAmelCase__:	int									= sess.run(
				    SCREAMING_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(SCREAMING_SNAKE_CASE      ):
				# Collect all the vectors assigned to this cluster
				UpperCAmelCase__:	List[Any]									= [
				    vectors[i]
				    for i in range(len(SCREAMING_SNAKE_CASE      )      )
				    if sess.run(assignments[i]      ) == cluster_n
				]
				# Compute new centroid location
				UpperCAmelCase__:	str									= sess.run(
				    SCREAMING_SNAKE_CASE	,feed_dict={mean_input: array(SCREAMING_SNAKE_CASE      )}      )
				# 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(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	Optional[Any]									= sess.run(SCREAMING_SNAKE_CASE      )
		return centroids, assignments


from __future__ import annotations

from typing import Any

class __UpperCamelCase     :






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= num_of_nodes
						UpperCAmelCase__:	list[list[int]]									= []
						UpperCAmelCase__:	dict[int, int]									= {}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						self.m_edges.append([u_node, v_node, weight]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):

						if self.m_component[u_node] == u_node:
							return u_node
						return self.find_component(self.m_component[u_node]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):

						if self.m_component[u_node] != u_node:
							for k in self.m_component:
								UpperCAmelCase__:	Optional[Any]									= self.find_component(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):

						if component_size[u_node] <= component_size[v_node]:
							UpperCAmelCase__:	Tuple									= v_node
							component_size[v_node] += component_size[u_node]
							self.set_component(lowerCamelCase__						)

						elif component_size[u_node] >= component_size[v_node]:
							UpperCAmelCase__:	Dict									= self.find_component(lowerCamelCase__						)
							component_size[u_node] += component_size[v_node]
							self.set_component(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= []
						UpperCAmelCase__:	int									= 0

						UpperCAmelCase__:	list[Any]									= [-1] * self.m_num_of_nodes

						# A list of components (initialized to all of the nodes)
						for node in range(self.m_num_of_nodes						):
							self.m_component.update({node: node}						)
							component_size.append(1						)

						UpperCAmelCase__:	Union[str, Any]									= self.m_num_of_nodes

						while num_of_components > 1:
							for edge in self.m_edges:
								UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= edge

								UpperCAmelCase__:	str									= self.m_component[u]
								UpperCAmelCase__:	Optional[Any]									= self.m_component[v]

								if u_component != v_component:

									for component in (u_component, v_component):
										if (
										    minimum_weight_edge[component] == -1
										    or minimum_weight_edge[component][2] > w
										):
											UpperCAmelCase__:	List[Any]									= [u, v, w]

							for edge in minimum_weight_edge:
								if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
									UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= edge

									UpperCAmelCase__:	str									= self.m_component[u]
									UpperCAmelCase__:	int									= self.m_component[v]

									if u_component != v_component:
										mst_weight += w
										self.union(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)
										print(F"Added edge [{u} - {v}]\nAdded weight: {w}\n"						)
										num_of_components -= 1

							UpperCAmelCase__:	Any									= [-1] * self.m_num_of_nodes
						print(F"The total weight of the minimal spanning tree is: {mst_weight}"						)



def 			_A	(      ):
	pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()