


from random import shuffle

import tensorflow as tf
from numpy import array




def    a__    (		snake_case__					:							Tuple						,   snake_case__					:							str     ):
  _UpperCAmelCase		:							int							=     int(snake_case__     )
  assert noofclusters < len(snake_case__     )

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

  # Will help select random centroids from among the available vectors
  _UpperCAmelCase		:							List[Any]							=     list(range(len(snake_case__     )     )     )
  shuffle(snake_case__     )

  # 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.

  _UpperCAmelCase		:							Optional[int]							=     tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

    _UpperCAmelCase		:							Dict							=     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
    _UpperCAmelCase		:							Union[str, Any]							=     [
        tf.Variable(vectors[vector_indices[i]]     ) for i in range(snake_case__     )
    ]
    ##These nodes will assign the centroid Variables the appropriate
    ##values
    _UpperCAmelCase		:							int							=     tf.placeholder("""float64"""						,   [dim]     )
    _UpperCAmelCase		:							List[str]							=     []
    for centroid in centroids:
      cent_assigns.append(tf.assign(snake_case__						,   snake_case__     )     )

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

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    _UpperCAmelCase		:							str							=     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
    _UpperCAmelCase		:							Optional[int]							=     tf.reduce_mean(snake_case__						,   0     )

    ##Node for computing Euclidean distances
    # Placeholders for input
    _UpperCAmelCase		:							str							=     tf.placeholder("""float"""						,   [dim]     )
    _UpperCAmelCase		:							str							=     tf.placeholder("""float"""						,   [dim]     )
    _UpperCAmelCase		:							str							=     tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(snake_case__						,   snake_case__     )						,   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
    _UpperCAmelCase		:							Optional[int]							=     tf.placeholder("""float"""						,   [noofclusters]     )
    _UpperCAmelCase		:							Dict							=     tf.argmin(snake_case__						,   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.
    _UpperCAmelCase		:							List[Any]							=     tf.initialize_all_variables()

    # Initialize all variables
    sess.run(snake_case__     )

    ##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.
    _UpperCAmelCase		:							Optional[Any]							=     100
    for _ in range(snake_case__     ):
      ##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(snake_case__     )     ):
        _UpperCAmelCase		:							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.
        _UpperCAmelCase		:							List[Any]							=     [
            sess.run(snake_case__						,   feed_dict={va: vect, va: sess.run(snake_case__     )}     )
            for centroid in centroids
        ]
        # Now use the cluster assignment node, with the distances
        # as the input
        _UpperCAmelCase		:							List[Any]							=     sess.run(
            snake_case__						,   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(snake_case__     ):
        # Collect all the vectors assigned to this cluster
        _UpperCAmelCase		:							Tuple							=     [
            vectors[i]
            for i in range(len(snake_case__     )     )
            if sess.run(assignments[i]     ) == cluster_n
        ]
        # Compute new centroid location
        _UpperCAmelCase		:							Union[str, Any]							=     sess.run(
            snake_case__						,   feed_dict={mean_input: array(snake_case__     )}     )
        # Assign value to appropriate variable
        sess.run(
            cent_assigns[cluster_n]						,   feed_dict={centroid_value: new_location}     )

        # Return centroids and assignments
    _UpperCAmelCase		:							Union[str, Any]							=     sess.run(snake_case__     )
    _UpperCAmelCase		:							Union[str, Any]							=     sess.run(snake_case__     )
    return centroids, assignments









SCREAMING_SNAKE_CASE__					:     dict[str, float]       =     {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}




def    a__    (		snake_case__					:							str						,   snake_case__					:							str						,   snake_case__					:							float     ):
  if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
    _UpperCAmelCase		:							Optional[Any]							=     (
        f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
        f'''Valid values are: {", ".join(snake_case__     )}'''
    )
    raise ValueError(snake_case__     )
  return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





