


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

import tensorflow as tf
from numpy import array

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       str		)					->	Optional[int]:







     """simple docstring"""
     _A				=							int(__UpperCamelCase		)
     assert noofclusters < len(__UpperCamelCase		)

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

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

     _A				=							tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

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

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

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









'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	list:







     """simple docstring"""
     _A				=							int(__UpperCamelCase		)
     if n_element < 1:
          _A				=							ValueError('a should be a positive number'		)
          raise my_error

     _A				=							[1]
     _A		,				_A		,				_A				=							(0, 0, 0)
     _A				=							1
     while index < n_element:
          while hamming_list[i] * 2 <= hamming_list[-1]:
               i += 1
          while hamming_list[j] * 3 <= hamming_list[-1]:
               j += 1
          while hamming_list[k] * 5 <= hamming_list[-1]:
               k += 1
          hamming_list.append(
              min(hamming_list[i] * 2							,    hamming_list[j] * 3							,    hamming_list[k] * 5		)		)
          index += 1
     return hamming_list


if __name__ == "__main__":
    lowerCAmelCase						=					input("""Enter the last number (nth term) of the Hamming Number Series: """)
    print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
    lowerCAmelCase						=					hamming(int(n))
    print("""-----------------------------------------------------""")
    print(F'The list with nth numbers is: {hamming_numbers}')
    print("""-----------------------------------------------------""")





