






'''simple docstring'''







from random import shuffle

import tensorflow as tf
from numpy import array





def 		_UpperCAmelCase   (							__A							:   Optional[Any]						,		__A							:   Any      ):
				a_		:  List[str]											=      int(__A      )
				assert noofclusters < len(__A      )

				# Find out the dimensionality
				a_		:  Tuple											=      len(vectors[0]      )

				# Will help select random centroids from among the available vectors
				a_		:  List[Any]											=      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_		:  Dict											=      tf.Graph()

				with graph.as_default():
								# SESSION OF COMPUTATION

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

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

								##Node for computing Euclidean distances
								# Placeholders for input
								a_		:  Tuple											=      tf.placeholder('''float'''						,		[dim]      )
								a_		:  List[Any]											=      tf.placeholder('''float'''						,		[dim]      )
								a_		:  Any											=      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_		:  List[Any]											=      tf.placeholder('''float'''						,		[noofclusters]      )
								a_		:  Union[str, Any]											=      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_		:  str											=      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_		:  Dict											=      1_00
								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_		:  List[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.
																a_		:  Dict											=      [
																    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_		:  Dict											=      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_		:  str											=      [
																    vectors[i]
																    for i in range(len(__A      )      )
																    if sess.run(assignments[i]      ) == cluster_n
																]
																# Compute new centroid location
																a_		:  List[Any]											=      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_		:  Any											=      sess.run(__A      )
								a_		:  Tuple											=      sess.run(__A      )
								return centroids, assignments












'''simple docstring'''







from math import pi, sqrt, tan





def 		_UpperCAmelCase   (							__A							:   float      ):
				if side_length < 0:
								raise ValueError('''surface_area_cube() only accepts non-negative values'''      )
				return 6 * side_length**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if length < 0 or breadth < 0 or height < 0:
								raise ValueError('''surface_area_cuboid() only accepts non-negative values'''      )
				return 2 * ((length * breadth) + (breadth * height) + (length * height))





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''surface_area_sphere() only accepts non-negative values'''      )
				return 4 * pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''surface_area_hemisphere() only accepts non-negative values'''      )
				return 3 * pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius < 0 or height < 0:
								raise ValueError('''surface_area_cone() only accepts non-negative values'''      )
				return pi * radius * (radius + (height**2 + radius**2) ** 0.5)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if radius_a < 0 or radius_a < 0 or height < 0:
								raise ValueError(
								    '''surface_area_conical_frustum() only accepts non-negative values'''      )
				a_		:  Any											=      (height**2 + (radius_a - radius_a) ** 2) ** 0.5
				return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius < 0 or height < 0:
								raise ValueError('''surface_area_cylinder() only accepts non-negative values'''      )
				return 2 * pi * radius * (height + radius)





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if torus_radius < 0 or tube_radius < 0:
								raise ValueError('''surface_area_torus() only accepts non-negative values'''      )
				if torus_radius < tube_radius:
								raise ValueError(
								    '''surface_area_torus() does not support spindle or self intersecting tori'''      )
				return 4 * pow(__A						,		2      ) * torus_radius * tube_radius





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if length < 0 or width < 0:
								raise ValueError('''area_rectangle() only accepts non-negative values'''      )
				return length * width





def 		_UpperCAmelCase   (							__A							:   float      ):
				if side_length < 0:
								raise ValueError('''area_square() only accepts non-negative values'''      )
				return side_length**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if base < 0 or height < 0:
								raise ValueError('''area_triangle() only accepts non-negative values'''      )
				return (base * height) / 2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if sidea < 0 or sidea < 0 or sidea < 0:
								raise ValueError('''area_triangle_three_sides() only accepts non-negative values'''      )
				elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
								raise ValueError('''Given three sides do not form a triangle'''      )
				a_		:  int											=      (sidea + sidea + sidea) / 2
				a_		:  Optional[Any]											=      sqrt(
				    semi_perimeter
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)      )
				return area





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if base < 0 or height < 0:
								raise ValueError('''area_parallelogram() only accepts non-negative values'''      )
				return base * height





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float						,		__A							:   float      ):
				if basea < 0 or basea < 0 or height < 0:
								raise ValueError('''area_trapezium() only accepts non-negative values'''      )
				return 1 / 2 * (basea + basea) * height





def 		_UpperCAmelCase   (							__A							:   float      ):
				if radius < 0:
								raise ValueError('''area_circle() only accepts non-negative values'''      )
				return pi * radius**2





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if radius_x < 0 or radius_y < 0:
								raise ValueError('''area_ellipse() only accepts non-negative values'''      )
				return pi * radius_x * radius_y





def 		_UpperCAmelCase   (							__A							:   float						,		__A							:   float      ):
				if diagonal_a < 0 or diagonal_a < 0:
								raise ValueError('''area_rhombus() only accepts non-negative values'''      )
				return 1 / 2 * diagonal_a * diagonal_a





def 		_UpperCAmelCase   (							__A							:   int						,		__A							:   float      ):
				if not isinstance(__A						,		__A      ) or sides < 3:
								raise ValueError(
								    '''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'''      )
				elif length < 0:
								raise ValueError(
								    '''area_reg_polygon() only accepts non-negative values as \
length of a side'''      )
				return (sides * length**2) / (4 * tan(pi / sides      ))
				return (sides * length**2) / (4 * tan(pi / sides      ))


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

		print('[DEMO] Areas of various geometric shapes: \n')
		print(F"""Rectangle: {area_rectangle(10, 20) = }""")
		print(F"""Square: {area_square(10) = }""")
		print(F"""Triangle: {area_triangle(10, 10) = }""")
		print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
		print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
		print(F"""Rhombus: {area_rhombus(10, 20) = }""")
		print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
		print(F"""Circle: {area_circle(20) = }""")
		print(F"""Ellipse: {area_ellipse(10, 20) = }""")
		print('\nSurface Areas of various geometric shapes: \n')
		print(F"""Cube: {surface_area_cube(20) = }""")
		print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
		print(F"""Sphere: {surface_area_sphere(20) = }""")
		print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
		print(F"""Cone: {surface_area_cone(10, 20) = }""")
		print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
		print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
		print(F"""Torus: {surface_area_torus(20, 10) = }""")
		print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
		print(F"""Square: {area_reg_polygon(4, 10) = }""")
		print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")




