





"""simple docstring"""





from random import shuffle

import tensorflow as tf
from numpy import array
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:Optional[Any]											=							int(_lowerCAmelCase			)
  assert noofclusters < len(_lowerCAmelCase			)

  # Find out the dimensionality
  lowerCAmelCase__			:Union[str, Any]											=							len(vectors[0]			)

  # Will help select random centroids from among the available vectors
  lowerCAmelCase__			:List[str]											=							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.

  lowerCAmelCase__			:Tuple											=							tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

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

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

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





'''simple docstring'''





from collections.abc import Sequence

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase       )       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Any           = 0.0
  for coeff in reversed(_lowerCAmelCase       ):
    __lowerCamelCase       :       Tuple           = result * x + coeff
  return result


if __name__ == "__main__":
   _UpperCamelCase											=						(0.0, 0.0, 5.0, 9.3, 7.0)
   _UpperCamelCase											=						1_0.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))






