





from random import shuffle

import tensorflow as tf
from numpy import array







def        __lowerCamelCase (							__a       :List[Any]   ,					__a       :Dict   )		->    str:






		"""simple docstring"""

		A__  				=       int(__a   )
		assert noofclusters < len(__a   )

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

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

		A__  				=       tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

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

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

				##Node for computing Euclidean distances
				# Placeholders for input
				A__  				=       tf.placeholder("""float"""   ,					[dim]   )
				A__  				=       tf.placeholder("""float"""   ,					[dim]   )
				A__  				=       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
				A__  				=       tf.placeholder("""float"""   ,					[noofclusters]   )
				A__  				=       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.
				A__  				=       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.
				A__  				=       1_0_0
				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   )   ):
								A__  				=       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.
								A__  				=       [
								    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
								A__  				=       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
								A__  				=       [
								    vectors[i]
								    for i in range(len(__a   )   )
								    if sess.run(assignments[i]   ) == cluster_n
								]
								# Compute new centroid location
								A__  				=       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
				A__  				=       sess.run(__a   )
				A__  				=       sess.run(__a   )
				return centroids, assignments













from string import ascii_uppercase

A   : List[str]			=				{str(ord(c) - 5_5): c for c in ascii_uppercase}







def        __lowerCamelCase (							__a       :int   ,					__a       :int   )		->    str:






		"""simple docstring"""

		if isinstance(__a   ,					__a   ):
				raise TypeError("""int() can't convert non-string with explicit base"""   )
		if num < 0:
				raise ValueError("""parameter must be positive int"""   )
		if isinstance(__a   ,					__a   ):
				raise TypeError("""'str' object cannot be interpreted as an integer"""   )
		if isinstance(__a   ,					__a   ):
				raise TypeError("""'float' object cannot be interpreted as an integer"""   )
		if base in (0, 1):
				raise ValueError("""base must be >= 2"""   )
		if base > 3_6:
				raise ValueError("""base must be <= 36"""   )
		A__  				=       """"""
		A__  				=       0
		A__  				=       0
		while div != 1:
				A__				,      A__  				=       divmod(__a   ,					__a   )
				if base >= 1_1 and 9 < mod < 3_6:
						A__  				=       ALPHABET_VALUES[str(__a   )]
				else:
						A__  				=       str(__a   )
				new_value += actual_value
				A__  				=       num // base
				A__  				=       div
				if div == 0:
						return str(new_value[::-1]   )
				elif div == 1:
						new_value += str(__a   )
						return str(new_value[::-1]   )

		return new_value[::-1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					for base in range(2, 3_7):
										for num in range(1_0_0_0):
															assert int(decimal_to_any(num, base), base) == num, (
															    num,
															    base,
															    decimal_to_any(num, base),
															    int(decimal_to_any(num, base), base),
															)



