'''simple docstring'''







from random import shuffle

import tensorflow as tf
from numpy import array


def    lowerCamelCase       (			UpperCAmelCase__				:  Dict				,						UpperCAmelCase__				:  str		)			->	Tuple:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Any						    = int(UpperCAmelCase__		)
							assert noofclusters < len(UpperCAmelCase__		)

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

							# Will help select random centroids from among the available vectors
							SCREAMING_SNAKE_CASE__     :int						    = list(range(len(UpperCAmelCase__		)		)		)
							shuffle(UpperCAmelCase__		)

							# 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.

							SCREAMING_SNAKE_CASE__     :str						    = tf.Graph()

							with graph.as_default():
														# SESSION OF COMPUTATION

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

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

														##Now lets construct the node that will compute the mean
														# The placeholder for the input
														SCREAMING_SNAKE_CASE__     :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
														SCREAMING_SNAKE_CASE__     :List[str]						    = tf.reduce_mean(UpperCAmelCase__				,						0		)

														##Node for computing Euclidean distances
														# Placeholders for input
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tf.placeholder('float'				,						[dim]		)
														SCREAMING_SNAKE_CASE__     :int						    = tf.placeholder('float'				,						[dim]		)
														SCREAMING_SNAKE_CASE__     :int						    = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(UpperCAmelCase__				,						UpperCAmelCase__		)				,						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
														SCREAMING_SNAKE_CASE__     :int						    = tf.placeholder('float'				,						[noofclusters]		)
														SCREAMING_SNAKE_CASE__     :List[Any]						    = tf.argmin(UpperCAmelCase__				,						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.
														SCREAMING_SNAKE_CASE__     :Tuple						    = tf.initialize_all_variables()

														# Initialize all variables
														sess.run(UpperCAmelCase__		)

														##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.
														SCREAMING_SNAKE_CASE__     :Tuple						    = 1_0_0
														for _ in range(UpperCAmelCase__		):
																					##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(UpperCAmelCase__		)		):
																												SCREAMING_SNAKE_CASE__     :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.
																												SCREAMING_SNAKE_CASE__     :Optional[Any]						    = [
																												    sess.run(UpperCAmelCase__				,						feed_dict={va: vect, va: sess.run(UpperCAmelCase__		)}		)
																												    for centroid in centroids
																												]
																												# Now use the cluster assignment node, with the distances
																												# as the input
																												SCREAMING_SNAKE_CASE__     :List[str]						    = sess.run(
																												    UpperCAmelCase__				,						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(UpperCAmelCase__		):
																												# Collect all the vectors assigned to this cluster
																												SCREAMING_SNAKE_CASE__     :List[str]						    = [
																												    vectors[i]
																												    for i in range(len(UpperCAmelCase__		)		)
																												    if sess.run(assignments[i]		) == cluster_n
																												]
																												# Compute new centroid location
																												SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = sess.run(
																												    UpperCAmelCase__				,						feed_dict={mean_input: array(UpperCAmelCase__		)}		)
																												# Assign value to appropriate variable
																												sess.run(
																												    cent_assigns[cluster_n]				,						feed_dict={centroid_value: new_location}		)

        # Return centroids and assignments
														SCREAMING_SNAKE_CASE__     :Any						    = sess.run(UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = sess.run(UpperCAmelCase__		)
														return centroids, assignments







'''simple docstring'''







def    lowerCamelCase       (			UpperCAmelCase__				:  int		)			->	int:


							'''simple docstring'''






							assert (
							    isinstance(UpperCAmelCase__				,						UpperCAmelCase__		) and number_of_steps > 0
							), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
							if number_of_steps == 1:
														return 1
							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = 1, 1
							for _ in range(number_of_steps - 1		):
														SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = current + previous, current
							return current


if __name__ == "__main__":
		import doctest

		doctest.testmod()






