


'''simple docstring'''



from random import shuffle

import tensorflow as tf
from numpy import array



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							Union[str, Any]				)	->						List[str]:
     __lowerCamelCase  :      str														=						int(UpperCAmelCase_				)
     assert noofclusters < len(UpperCAmelCase_				)

     # Find out the dimensionality
     __lowerCamelCase  :      Any														=						len(vectors[0]				)

     # Will help select random centroids from among the available vectors
     __lowerCamelCase  :      Union[str, Any]														=						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.

     __lowerCamelCase  :      List[Any]														=						tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

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

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

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










'''simple docstring'''



import argparse


A__						:   Optional[Any]				=    """docs/source/_static/js/custom.js"""



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int]				)	->						int:
     with open(UpperCAmelCase_ ,       encoding='utf-8' ,       newline='\n'				) as f:
          __lowerCamelCase  :      Dict														=						f.readlines()
     __lowerCamelCase  :      Tuple														=						0

     # First let's put the right version
     while not lines[index].startswith('const stableVersion ='				):
          index += 1
     __lowerCamelCase  :      Dict														=						F'const stableVersion = "v{version}"\n'

     # Then update the dictionary
     while not lines[index].startswith('const versionMapping = {'				):
          index += 1

     # We go until the end
     while not lines[index].startswith('}'				):
          index += 1
     # We add the new version at the end
     lines[index - 1] += F'    "v{version}": "v{version}",\n'

     with open(UpperCAmelCase_ ,       'w' ,       encoding='utf-8' ,       newline='\n'				) as f:
          f.writelines(UpperCAmelCase_				)


if __name__ == "__main__":
      A__						:   str				=    argparse.ArgumentParser()
      parser.add_argument("""--version""", help="""Release version.""")
      A__						:   Any				=    parser.parse_args()
      update_custom_js(args.version)






