





'''simple docstring'''

from random import shuffle

import tensorflow as tf
from numpy import array

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       int(__UpperCAmelCase      )
   assert noofclusters < len(__UpperCAmelCase      )

   # 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(__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      							=       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(__UpperCAmelCase      )
      ]
      ##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(__UpperCAmelCase							,	__UpperCAmelCase      )      )

      ##Variables for cluster assignments of individual vectors(initialized
      ##to 0 at first)
      __SCREAMING_SNAKE_CASE      							=       [tf.Variable(0      ) for i in range(len(__UpperCAmelCase      )      )]
      ##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(__UpperCAmelCase							,	__UpperCAmelCase      )      )

      ##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(__UpperCAmelCase							,	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(__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      							=       tf.placeholder("""float"""							,	[noofclusters]      )
      __SCREAMING_SNAKE_CASE      							=       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      							=       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      							=       100
      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      							=       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(__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      							=       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      							=       [
                vectors[i]
                for i in range(len(__UpperCAmelCase      )      )
                if sess.run(assignments[i]      ) == cluster_n
            ]
            # Compute new centroid location
            __SCREAMING_SNAKE_CASE      							=       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      							=       sess.run(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       sess.run(__UpperCAmelCase      )
      return centroids, assignments












'''simple docstring'''

from __future__ import annotations

import bisect

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   if hi < 0:
      __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )

   while lo < hi:
      __SCREAMING_SNAKE_CASE      							=       lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         __SCREAMING_SNAKE_CASE      							=       mid + 1
      else:
         __SCREAMING_SNAKE_CASE      							=       mid

   return lo

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   if hi < 0:
      __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )

   while lo < hi:
      __SCREAMING_SNAKE_CASE      							=       lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         __SCREAMING_SNAKE_CASE      							=       mid + 1
      else:
         __SCREAMING_SNAKE_CASE      							=       mid

   return lo

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					None:

   '''simple docstring'''







   sorted_collection.insert(bisect_left(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					None:

   '''simple docstring'''







   sorted_collection.insert(bisect_right(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       0
   __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      ) - 1

   while left <= right:
      __SCREAMING_SNAKE_CASE      							=       left + (right - left) // 2
      __SCREAMING_SNAKE_CASE      							=       sorted_collection[midpoint]
      if current_item == item:
         return midpoint
      elif item < current_item:
         __SCREAMING_SNAKE_CASE      							=       midpoint - 1
      else:
         __SCREAMING_SNAKE_CASE      							=       midpoint + 1
   return None

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       bisect.bisect_left(__UpperCAmelCase							,	__UpperCAmelCase      )
   if index != len(__UpperCAmelCase      ) and sorted_collection[index] == item:
      return index
   return None

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   if right < left:
      return None

   __SCREAMING_SNAKE_CASE      							=       left + (right - left) // 2

   if sorted_collection[midpoint] == item:
      return midpoint
   elif sorted_collection[midpoint] > item:
      return binary_search_by_recursion(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	midpoint - 1      )
   else:
      return binary_search_by_recursion(__UpperCAmelCase							,	__UpperCAmelCase							,	midpoint + 1							,	__UpperCAmelCase      )


if __name__ == "__main__":
    a        =						input("Enter numbers separated by comma:\n").strip()
    a        =						sorted(int(item) for item in user_input.split(","))
    a        =						int(input("Enter a single number to be found in the list:\n"))
    a        =						binary_search(collection, target)
    if result is None:
        print(F'''{target} was not found in {collection}.''')
    else:
        print(F'''{target} was found at position {result} in {collection}.''')





