



"""simple docstring"""






from random import shuffle

import tensorflow as tf
from numpy import array


def 			_lowercase				(  __snake_case					,__snake_case	)						->   Tuple:
			__lowerCAmelCase      :  str												=       int(__snake_case	)
			assert noofclusters < len(__snake_case	)

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

			# Will help select random centroids from among the available vectors
			__lowerCAmelCase      :  Optional[int]												=       list(range(len(__snake_case	)	)	)
			shuffle(__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.

			__lowerCAmelCase      :  List[str]												=       tf.Graph()

			with graph.as_default():
						# SESSION OF COMPUTATION

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

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

						##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      :  Optional[int]												=       tf.reduce_mean(__snake_case					,0	)

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

						# Initialize all variables
						sess.run(__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.
						__lowerCAmelCase      :  Any												=       100
						for _ in range(__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(__snake_case	)	):
												__lowerCAmelCase      :  Union[str, Any]												=       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      :  Any												=       [
												    sess.run(__snake_case					,feed_dict={va: vect, va: sess.run(__snake_case	)}	)
												    for centroid in centroids
												]
												# Now use the cluster assignment node, with the distances
												# as the input
												__lowerCAmelCase      :  Union[str, Any]												=       sess.run(
												    __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(__snake_case	):
												# Collect all the vectors assigned to this cluster
												__lowerCAmelCase      :  Optional[Any]												=       [
												    vectors[i]
												    for i in range(len(__snake_case	)	)
												    if sess.run(assignments[i]	) == cluster_n
												]
												# Compute new centroid location
												__lowerCAmelCase      :  Tuple												=       sess.run(
												    __snake_case					,feed_dict={mean_input: array(__snake_case	)}	)
												# Assign value to appropriate variable
												sess.run(
												    cent_assigns[cluster_n]					,feed_dict={centroid_value: new_location}	)

        # Return centroids and assignments
						__lowerCAmelCase      :  Tuple												=       sess.run(__snake_case	)
						__lowerCAmelCase      :  Any												=       sess.run(__snake_case	)
						return centroids, assignments




"""simple docstring"""






import re


def 			_lowercase				(  __snake_case	)						->   str:

			if len(re.findall("[ATCG]"					,__snake_case	)	) != len(__snake_case	):
						raise ValueError("Invalid Strand"	)

			return dna.translate(dna.maketrans("ATCG"					,"TAGC"	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()