






"""simple docstring"""






from random import shuffle

import tensorflow as tf
from numpy import array






def   snake_case__     (	__lowerCamelCase       :					Any							,	__lowerCamelCase       :					List[Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	int				=int(__lowerCamelCase			)
			assert noofclusters < len(__lowerCamelCase			)

			# Find out the dimensionality
			lowerCamelCase__	:	List[Any]				=len(vectors[0]			)

			# Will help select random centroids from among the available vectors
			lowerCamelCase__	:	List[str]				=list(range(len(__lowerCamelCase			)			)			)
			shuffle(__lowerCamelCase			)

			# 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__	:	Union[str, Any]				=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(__lowerCamelCase			)
						]
						##These nodes will assign the centroid Variables the appropriate
						##values
						lowerCamelCase__	:	Any				=tf.placeholder('''float64'''							,	[dim]			)
						lowerCamelCase__	:	Tuple				=[]
						for centroid in centroids:
									cent_assigns.append(tf.assign(__lowerCamelCase							,	__lowerCamelCase			)			)

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

						##Now lets construct the node that will compute the mean
						# The placeholder for the input
						lowerCamelCase__	:	Optional[int]				=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__	:	List[str]				=tf.reduce_mean(__lowerCamelCase							,	0			)

						##Node for computing Euclidean distances
						# Placeholders for input
						lowerCamelCase__	:	Optional[int]				=tf.placeholder('''float'''							,	[dim]			)
						lowerCamelCase__	:	Union[str, Any]				=tf.placeholder('''float'''							,	[dim]			)
						lowerCamelCase__	:	Optional[Any]				=tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__lowerCamelCase							,	__lowerCamelCase			)							,	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__	:	Dict				=tf.placeholder('''float'''							,	[noofclusters]			)
						lowerCamelCase__	:	Any				=tf.argmin(__lowerCamelCase							,	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__	:	Optional[Any]				=tf.initialize_all_variables()

						# Initialize all variables
						sess.run(__lowerCamelCase			)

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

        # Return centroids and assignments
						lowerCamelCase__	:	int				=sess.run(__lowerCamelCase			)
						lowerCamelCase__	:	Optional[Any]				=sess.run(__lowerCamelCase			)
						return centroids, assignments









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list[int]			):
			"""simple docstring"""
			if not numbers:
						return 0

			if not isinstance(__lowerCamelCase							,	(list, tuple)			) or not all(
			    isinstance(__lowerCamelCase							,	__lowerCamelCase			) for number in numbers			):
						raise ValueError('''numbers must be an iterable of integers'''			)

			lowerCamelCase__	:	Any				=numbers[0]

			for i in range(1							,	len(__lowerCamelCase			)			):
						# update the maximum and minimum subarray products
						lowerCamelCase__	:	Dict				=numbers[i]
						if number < 0:
									lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=min_till_now, max_till_now
						lowerCamelCase__	:	Optional[int]				=max(__lowerCamelCase							,	max_till_now * number			)
						lowerCamelCase__	:	Dict				=min(__lowerCamelCase							,	min_till_now * number			)

						# update the maximum product found till now
						lowerCamelCase__	:	Tuple				=max(__lowerCamelCase							,	__lowerCamelCase			)

			return max_prod




