

"""simple docstring"""




from random import shuffle

import tensorflow as tf
from numpy import array





def 	lowercase__		(    snake_case_					:Any       ,						snake_case_					:Union[str, Any]  ):
	__UpperCAmelCase							=      int(A__  )
	assert noofclusters < len(A__  )

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

	# Will help select random centroids from among the available vectors
	__UpperCAmelCase							=      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.

	__UpperCAmelCase							=      tf.Graph()

	with graph.as_default():
		# SESSION OF COMPUTATION

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

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

		##Node for computing Euclidean distances
		# Placeholders for input
		__UpperCAmelCase							=      tf.placeholder('''float'''       ,						[dim]  )
		__UpperCAmelCase							=      tf.placeholder('''float'''       ,						[dim]  )
		__UpperCAmelCase							=      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
		__UpperCAmelCase							=      tf.placeholder('''float'''       ,						[noofclusters]  )
		__UpperCAmelCase							=      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.
		__UpperCAmelCase							=      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.
		__UpperCAmelCase							=      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__  )  ):
				__UpperCAmelCase							=      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							=      [
				    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
				__UpperCAmelCase							=      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
				__UpperCAmelCase							=      [
				    vectors[i]
				    for i in range(len(A__  )  )
				    if sess.run(assignments[i]  ) == cluster_n
				]
				# Compute new centroid location
				__UpperCAmelCase							=      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
		__UpperCAmelCase							=      sess.run(A__  )
		__UpperCAmelCase							=      sess.run(A__  )
		return centroids, assignments



'''simple docstring'''






def 		_A     (    A__ = 10     ,				A__ = 22      ):






 """simple docstring"""



 __lowercase							  = range(1     ,				A__      )
 __lowercase							  = range(1     ,				A__      )
 return sum(
     1 for power in powers for base in bases if len(str(base**power      )      ) == power      )


if __name__ == "__main__":
 print(f'{solution(10, 22) = }')

