

'''simple docstring'''




from random import shuffle

import tensorflow as tf
from numpy import array


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : List[str]    ):
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  assert noofclusters < len(SCREAMING_SNAKE_CASE    )

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

  # Will help select random centroids from among the available vectors
  UpperCAmelCase    				=						list(range(len(SCREAMING_SNAKE_CASE    )    )    )
  shuffle(SCREAMING_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    				=						tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

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

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

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    UpperCAmelCase    				=						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    				=						tf.reduce_mean(SCREAMING_SNAKE_CASE							, 0    )

    ##Node for computing Euclidean distances
    # Placeholders for input
    UpperCAmelCase    				=						tf.placeholder('float'							, [dim]    )
    UpperCAmelCase    				=						tf.placeholder('float'							, [dim]    )
    UpperCAmelCase    				=						tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(SCREAMING_SNAKE_CASE							, SCREAMING_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    				=						tf.placeholder('float'							, [noofclusters]    )
    UpperCAmelCase    				=						tf.argmin(SCREAMING_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    				=						tf.initialize_all_variables()

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

        # Return centroids and assignments
    UpperCAmelCase    				=						sess.run(SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						sess.run(SCREAMING_SNAKE_CASE    )
    return centroids, assignments





'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : int    ):
  return not any(
      neighbour == 1 and colored_vertices[i] == color
      for i, neighbour in enumerate(SCREAMING_SNAKE_CASE    )    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[list[int]]							, SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : int    ):

  # Base Case
  if index == len(SCREAMING_SNAKE_CASE    ):
    return True

  # Recursive Step
  for i in range(SCREAMING_SNAKE_CASE    ):
    if valid_coloring(graph[index]							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ):
      # Color current vertex
      UpperCAmelCase    				=						i
      # Validate coloring
      if util_color(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, index + 1    ):
        return True
      # Backtrack
      UpperCAmelCase    				=						-1
  return False



def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[list[int]]							, SCREAMING_SNAKE_CASE   : int    ):
  UpperCAmelCase    				=						[-1] * len(SCREAMING_SNAKE_CASE    )

  if util_color(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, 0    ):
    return colored_vertices

  return []


