




from random import shuffle

import tensorflow as tf
from numpy import array


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Optional[int]:
		SCREAMING_SNAKE_CASE_					      =							int(lowerCAmelCase )
		assert noofclusters < len(lowerCAmelCase )

		# Find out the dimensionality
		SCREAMING_SNAKE_CASE_					      =							len(vectors[0] )

		# Will help select random centroids from among the available vectors
		SCREAMING_SNAKE_CASE_					      =							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.

		SCREAMING_SNAKE_CASE_					      =							tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

				SCREAMING_SNAKE_CASE_					      =							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_					      =							[
				    tf.Variable(vectors[vector_indices[i]] ) for i in range(lowerCAmelCase )
				]
				##These nodes will assign the centroid Variables the appropriate
				##values
				SCREAMING_SNAKE_CASE_					      =							tf.placeholder('float64'    ,						[dim] )
				SCREAMING_SNAKE_CASE_					      =							[]
				for centroid in centroids:
						cent_assigns.append(tf.assign(lowerCAmelCase    ,						lowerCAmelCase ) )

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

				##Node for computing Euclidean distances
				# Placeholders for input
				SCREAMING_SNAKE_CASE_					      =							tf.placeholder('float'    ,						[dim] )
				SCREAMING_SNAKE_CASE_					      =							tf.placeholder('float'    ,						[dim] )
				SCREAMING_SNAKE_CASE_					      =							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
				SCREAMING_SNAKE_CASE_					      =							tf.placeholder('float'    ,						[noofclusters] )
				SCREAMING_SNAKE_CASE_					      =							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.
				SCREAMING_SNAKE_CASE_					      =							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.
				SCREAMING_SNAKE_CASE_					      =							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 ) ):
								SCREAMING_SNAKE_CASE_					      =							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_					      =							[
								    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
								SCREAMING_SNAKE_CASE_					      =							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
								SCREAMING_SNAKE_CASE_					      =							[
								    vectors[i]
								    for i in range(len(lowerCAmelCase ) )
								    if sess.run(assignments[i] ) == cluster_n
								]
								# Compute new centroid location
								SCREAMING_SNAKE_CASE_					      =							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
				SCREAMING_SNAKE_CASE_					      =							sess.run(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							sess.run(lowerCAmelCase )
				return centroids, assignments


'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Union[str, Any]    ):
  # "extended trapezoidal rule"
  # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
  UpperCAmelCase    				=						(boundary[1] - boundary[0]) / steps
  UpperCAmelCase    				=						boundary[0]
  UpperCAmelCase    				=						boundary[1]
  UpperCAmelCase    				=						make_points(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						0.0
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  for i in x_i:
    # print(i)
    y += h * f(SCREAMING_SNAKE_CASE    )
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  return y


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : Tuple    ):
  UpperCAmelCase    				=						a + h
  while x < (b - h):
    yield x
    UpperCAmelCase    				=						x + h


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):  # enter your function here
  UpperCAmelCase    				=						(x - 0) * (x - 0)
  return y


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						0.0  # Lower bound of integration
  UpperCAmelCase    				=						1.0  # Upper bound of integration
  UpperCAmelCase    				=						10.0  # define number of steps or resolution
  UpperCAmelCase    				=						[a, b]  # define boundary of integration
  UpperCAmelCase    				=						method_a(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  print(f'''y = {y}'''    )


if __name__ == "__main__":
  main()


