





from random import shuffle

import tensorflow as tf
from numpy import array



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	str					=     int(_lowerCAmelCase)
    assert noofclusters < len(_lowerCAmelCase)

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

    # Will help select random centroids from among the available vectors
    UpperCamelCase__:	Dict					=     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.

    UpperCamelCase__:	Optional[Any]					=     tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

        UpperCamelCase__:	Tuple					=     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__:	Optional[Any]					=     [
            tf.Variable(vectors[vector_indices[i]]) for i in range(_lowerCAmelCase)
        ]
        ##These nodes will assign the centroid Variables the appropriate
        ##values
        UpperCamelCase__:	Optional[Any]					=     tf.placeholder("float64"    ,[dim])
        UpperCamelCase__:	Optional[int]					=     []
        for centroid in centroids:
            cent_assigns.append(tf.assign(_lowerCAmelCase    ,_lowerCAmelCase))

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

        ##Node for computing Euclidean distances
        # Placeholders for input
        UpperCamelCase__:	Union[str, Any]					=     tf.placeholder("float"    ,[dim])
        UpperCamelCase__:	Optional[int]					=     tf.placeholder("float"    ,[dim])
        UpperCamelCase__:	Dict					=     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
        UpperCamelCase__:	Optional[Any]					=     tf.placeholder("float"    ,[noofclusters])
        UpperCamelCase__:	Optional[int]					=     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.
        UpperCamelCase__:	Any					=     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.
        UpperCamelCase__:	Any					=     1_00
        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)):
                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[str]					=     [
                    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
                UpperCamelCase__:	Optional[int]					=     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
                UpperCamelCase__:	Tuple					=     [
                    vectors[i]
                    for i in range(len(_lowerCAmelCase))
                    if sess.run(assignments[i]) == cluster_n
                ]
                # Compute new centroid location
                UpperCamelCase__:	int					=     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
        UpperCamelCase__:	Optional[Any]					=     sess.run(_lowerCAmelCase)
        UpperCamelCase__:	Optional[Any]					=     sess.run(_lowerCAmelCase)
        return centroids, assignments






'''simple docstring'''





import functools

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:

  # Validation
  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			) or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for day in days			):
    raise ValueError('The parameter days should be a list of integers'			)

  if len(_lowerCAmelCase			) != 3 or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for cost in costs			):
    raise ValueError('The parameter costs should be a list of three integers'			)

  if len(_lowerCAmelCase			) == 0:
    return 0

  if min(_lowerCAmelCase			) <= 0:
    raise ValueError('All days elements should be greater than 0'			)

  if max(_lowerCAmelCase			) >= 366:
    raise ValueError('All days elements should be less than 366'			)

  __lowerCamelCase			:			int       			=					set(_lowerCAmelCase			)

  @functools.cache
  def dynamic_programming(_lowerCAmelCase			) -> int:
    if index > 365:
      return 0

    if index not in days_set:
      return dynamic_programming(index + 1			)

    return min(
        costs[0] + dynamic_programming(index + 1			)			,costs[1] + dynamic_programming(index + 7			)			,costs[2] + dynamic_programming(index + 30			)			,)

  return dynamic_programming(1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






