
'''simple docstring'''
from random import shuffle

import tensorflow as tf
from numpy import array







def 		A_(     A						:		Any		,     A						:		List[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








from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any



@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					42
					A__     =					None
					A__     =					None






def 					UpperCamelCase   (      )  ->    Node | None:
		"""simple docstring"""




		lowercase__	     =						Node(1	)
		lowercase__	     =						Node(2	)
		lowercase__	     =						Node(3	)
		lowercase__	     =						Node(4	)
		lowercase__	     =						Node(5	)
		return tree






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [root.data, *preorder(root.left	), *preorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return postorder(root.left	) + postorder(root.right	) + [root.data] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [*inorder(root.left	), root.data, *inorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    int:
		"""simple docstring"""




		return (max(height(root.left	)       ,       height(root.right	)	) + 1) if root else 0






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		if root is None:
				return output

		lowercase__	     =						deque([root]	)

		while process_queue:
				lowercase__	     =						process_queue.popleft()
				output.append(node.data	)

				if node.left:
						process_queue.append(node.left	)
				if node.right:
						process_queue.append(node.right	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if not root:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.left       ,       level - 1	)
						populate_output(root.right       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if root is None:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.right       ,       level - 1	)
						populate_output(root.left       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None] | list[Any]:
		"""simple docstring"""




		if root is None:
				return []

		lowercase__	     =						[]

		lowercase__	     =						0
		lowercase__	     =						height(__magic_name__	)

		for h in range(1       ,       height_tree + 1	):
				if not flag:
						output.append(get_nodes_from_left_to_right(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						1
				else:
						output.append(get_nodes_from_right_to_left(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						0

		return output






def 					UpperCamelCase   (      )  ->    None:  # Main function for testing.
		"""simple docstring"""




		lowercase__	     =						make_tree()
		print(f'''In-order Traversal: {inorder(__magic_name__	)}'''	)
		print(f'''Pre-order Traversal: {preorder(__magic_name__	)}'''	)
		print(f'''Post-order Traversal: {postorder(__magic_name__	)}'''       ,       """\n"""	)

		print(f'''Height of Tree: {height(__magic_name__	)}'''       ,       """\n"""	)

		print("""Complete Level Order Traversal: """	)
		print(level_order(__magic_name__	)       ,       """\n"""	)

		print("""Level-wise order Traversal: """	)

		for level in range(1       ,       height(__magic_name__	) + 1	):
				print(f'''Level {level}:'''       ,       get_nodes_from_left_to_right(__magic_name__       ,       level=__magic_name__	)	)

		print("""\nZigZag order Traversal: """	)
		print(zigzag(__magic_name__	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

