

from random import shuffle

import tensorflow as tf
from numpy import array
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:
   UpperCAmelCase							=     int(lowerCAmelCase_   )
   assert noofclusters < len(lowerCAmelCase_   )

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

   # Will help select random centroids from among the available vectors
   UpperCAmelCase							=     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							=     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(lowerCAmelCase_   )
      ]
      ##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(lowerCAmelCase_  ,						lowerCAmelCase_   )   )

      ##Variables for cluster assignments of individual vectors(initialized
      ##to 0 at first)
      UpperCAmelCase							=     [tf.Variable(0   ) for i in range(len(lowerCAmelCase_   )   )]
      ##These nodes will assign an assignment Variable the appropriate
      ##value
      UpperCAmelCase							=     tf.placeholder("""int32"""   )
      UpperCAmelCase							=     []
      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							=     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(lowerCAmelCase_  ,						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(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							=     tf.placeholder("""float"""  ,						[noofclusters]   )
      UpperCAmelCase							=     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							=     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							=     1_0_0
      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							=     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(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							=     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							=     [
                vectors[i]
                for i in range(len(lowerCAmelCase_   )   )
                if sess.run(assignments[i]   ) == cluster_n
            ]
            # Compute new centroid location
            UpperCAmelCase							=     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							=     sess.run(lowerCAmelCase_   )
      UpperCAmelCase							=     sess.run(lowerCAmelCase_   )
      return centroids, assignments







import argparse


__a     =			"""docs/source/_static/js/custom.js"""
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:
   with open(lowerCAmelCase_  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      UpperCAmelCase							=     f.readlines()
   UpperCAmelCase							=     0

   # First let's put the right version
   while not lines[index].startswith("""const stableVersion ="""   ):
      index += 1
   UpperCAmelCase							=     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(lowerCAmelCase_  ,						"""w"""  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      f.writelines(lowerCAmelCase_   )


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



