
"""simple docstring"""







from random import shuffle

import tensorflow as tf
from numpy import array
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Any						      =				int(__a		)
	assert noofclusters < len(__a		)

	# Find out the dimensionality
	__lowerCAmelCase      :						Optional[Any]						      =				len(vectors[0]		)

	# Will help select random centroids from among the available vectors
	__lowerCAmelCase      :						int						      =				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      :						str						      =				tf.Graph()

	with graph.as_default():
		# SESSION OF COMPUTATION

		__lowerCAmelCase      :						Optional[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
		__lowerCAmelCase      :						int						      =				[
		    tf.Variable(vectors[vector_indices[i]]		) for i in range(__a		)
		]
		##These nodes will assign the centroid Variables the appropriate
		##values
		__lowerCAmelCase      :						List[str]						      =				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      :						Union[str, Any]						      =				tf.placeholder('int32'		)
		__lowerCAmelCase      :						List[str]						      =				[]
		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      :						Union[str, Any]						      =				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      :						str						      =				tf.reduce_mean(__a	,  0		)

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

"""simple docstring"""







import qiskit
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Union[str, Any]						      =				qiskit.Aer.get_backend('aer_simulator'		)

	# Create a Quantum Circuit acting on the q register
	__lowerCAmelCase      :						str						      =				qiskit.QuantumCircuit(_UpperCamelCase	,  _UpperCamelCase		)

	# Map the quantum measurement to the classical bits
	circuit.measure([0]	,  [0]		)

	# Execute the circuit on the simulator
	__lowerCAmelCase      :						Optional[int]						      =				qiskit.execute(_UpperCamelCase	,  _UpperCamelCase	,  shots=1000		)

	# Return the histogram data of the results of the experiment.
	return job.result().get_counts(_UpperCamelCase		)


if __name__ == "__main__":
							print(f'Total count for various states are: {single_qubit_measure(1, 1)}')