

'''simple docstring'''






from random import shuffle

import tensorflow as tf
from numpy import array


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = int(A__      )
 assert noofclusters < len(A__      )

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

 # Will help select random centroids from among the available vectors
 __lowercase							  = list(range(len(A__      )      )      )
 shuffle(A__      )

 # 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.

 __lowercase							  = tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

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

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

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

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

  # Initialize all variables
  sess.run(A__      )

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

        # Return centroids and assignments
  __lowercase							  = sess.run(A__      )
  __lowercase							  = sess.run(A__      )
  return centroids, assignments




'''simple docstring'''






from __future__ import annotations

from random import random
from typing import Generic, TypeVar

lowerCAmelCase__											=	TypeVar('''KT''')
lowerCAmelCase__											=	TypeVar('''VT''')






class 			lowercase_	(Generic[KT, VT]      ):




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,lowercase__   :     KT | str = "root"					,lowercase__   :     VT | None = None			):
   __lowercase							  = key
   __lowercase							  = value
   __lowercase							  = []






  def __repr__(      self   :     Tuple			):
   return F"Node({self.key}: {self.value})"






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   return len(self.forward			)






class 			lowercase_	(Generic[KT, VT]      ):




  """simple docstring"""




  def __init__(      self   :     int					,lowercase__   :     float = 0.5					,lowercase__   :     int = 1_6			):
   __lowercase							  = Node[KT, VT]()
   __lowercase							  = 0
   __lowercase							  = p
   __lowercase							  = max_level






  def __str__(      self   :     List[str]			):
   __lowercase							  = list(self			)

   if len(lowercase__			) == 0:
    return F"SkipList(level={self.level})"

   __lowercase							  = max((len(str(lowercase__			)			) for item in items)					,default=4			)
   __lowercase							  = max(lowercase__					,4			) + 4

   __lowercase							  = self.head
   __lowercase							  = []

   __lowercase							  = node.forward.copy()
   lines.append(F"[{node.key}]".ljust(lowercase__					,'''-'''			) + '''* ''' * len(lowercase__			)			)
   lines.append(''' ''' * label_size + '''| ''' * len(lowercase__			)			)

   while len(node.forward			) != 0:
    __lowercase							  = node.forward[0]

    lines.append(
        F"[{node.key}]".ljust(lowercase__					,'''-'''			)
        + ''' '''.join(str(n.key			) if n.key == node.key else '''|''' for n in forwards			)			)
    lines.append(''' ''' * label_size + '''| ''' * len(lowercase__			)			)
    __lowercase							  = node.forward

   lines.append('''None'''.ljust(lowercase__			) + '''* ''' * len(lowercase__			)			)
   return F"SkipList(level={self.level})\n" + "\n".join(lowercase__			)






  def __iter__(      self   :     List[str]			):
   __lowercase							  = self.head

   while len(node.forward			) != 0:
    yield node.forward[0].key
    __lowercase							  = node.forward[0]






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = 1
   while random() < self.p and level < self.max_level:
    level += 1

   return level






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     str			):
   __lowercase							  = []

   __lowercase							  = self.head

   for i in reversed(range(self.level			)			):
    # i < node.level - When node level is lesser than `i` decrement `i`.
    # node.forward[i].key < key - Jumping to node with key value higher
    #                             or equal to searched key would result
    #                             in skipping searched key.
    while i < node.level and node.forward[i].key < key:
     __lowercase							  = node.forward[i]
    # Each leftmost node (relative to searched node) will potentially have to
    # be updated.
    update_vector.append(lowercase__			)

   update_vector.reverse()  # Note that we were inserting values in reverse order.

   # len(node.forward) != 0 - If current node doesn't contain any further
   #                          references then searched key is not present.
   # node.forward[0].key == key - Next node key should be equal to search key
   #                              if key is present.
   if len(node.forward			) != 0 and node.forward[0].key == key:
    return node.forward[0], update_vector
   else:
    return None, update_vector






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     KT			):
   __lowercase  ,	__lowercase							  = self._locate_node(lowercase__			)

   if node is not None:
    for i, update_node in enumerate(lowercase__			):
     # Remove or replace all references to removed node.
     if update_node.level > i and update_node.forward[i].key == key:
      if node.level > i:
       __lowercase							  = node.forward[i]
      else:
       __lowercase							  = update_node.forward[:i]






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     KT					,lowercase__   :     VT			):
   __lowercase  ,	__lowercase							  = self._locate_node(lowercase__			)
   if node is not None:
    __lowercase							  = value
   else:
    __lowercase							  = self.random_level()

    if level > self.level:
     # After level increase we have to add additional nodes to head.
     for _ in range(self.level - 1					,lowercase__			):
      update_vector.append(self.head			)
     __lowercase							  = level

    __lowercase							  = Node(lowercase__					,lowercase__			)

    for i, update_node in enumerate(update_vector[:level]			):
     # Change references to pass through new node.
     if update_node.level > i:
      new_node.forward.append(update_node.forward[i]			)

     if update_node.level < i + 1:
      update_node.forward.append(lowercase__			)
     else:
      __lowercase							  = new_node






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     VT			):
   __lowercase  ,	__lowercase							  = self._locate_node(lowercase__			)

   if node is not None:
    return node.value

   return None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.insert('''Key1'''     ,				3      )
 skip_list.insert('''Key2'''     ,				12      )
 skip_list.insert('''Key3'''     ,				41      )
 skip_list.insert('''Key4'''     ,				-19      )

 __lowercase							  = skip_list.head
 __lowercase							  = {}
 while node.level != 0:
  __lowercase							  = node.forward[0]
  __lowercase							  = node.value

 assert len(A__      ) == 4
 assert all_values["Key1"] == 3
 assert all_values["Key2"] == 12
 assert all_values["Key3"] == 41
 assert all_values["Key4"] == -19


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.insert('''Key1'''     ,				10      )
 skip_list.insert('''Key1'''     ,				12      )

 skip_list.insert('''Key5'''     ,				7      )
 skip_list.insert('''Key7'''     ,				10      )
 skip_list.insert('''Key10'''     ,				5      )

 skip_list.insert('''Key7'''     ,				7      )
 skip_list.insert('''Key5'''     ,				5      )
 skip_list.insert('''Key10'''     ,				10      )

 __lowercase							  = skip_list.head
 __lowercase							  = {}
 while node.level != 0:
  __lowercase							  = node.forward[0]
  __lowercase							  = node.value

 if len(A__      ) != 4:
  print()
 assert len(A__      ) == 4
 assert all_values["Key1"] == 12
 assert all_values["Key7"] == 7
 assert all_values["Key5"] == 5
 assert all_values["Key10"] == 10


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 assert skip_list.find('''Some key'''      ) is None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key2'''     ,				20      )
 assert skip_list.find('''Key2'''      ) == 20

 skip_list.insert('''Some Key'''     ,				10      )
 skip_list.insert('''Key2'''     ,				8      )
 skip_list.insert('''V'''     ,				13      )

 assert skip_list.find('''Y'''      ) is None
 assert skip_list.find('''Key2'''      ) == 8
 assert skip_list.find('''Some Key'''      ) == 10
 assert skip_list.find('''V'''      ) == 13


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.delete('''Some key'''      )

 assert len(skip_list.head.forward      ) == 0


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key1'''     ,				12      )
 skip_list.insert('''V'''     ,				13      )
 skip_list.insert('''X'''     ,				14      )
 skip_list.insert('''Key2'''     ,				15      )

 skip_list.delete('''V'''      )
 skip_list.delete('''Key2'''      )

 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''Key2'''      ) is None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key1'''     ,				12      )
 skip_list.insert('''V'''     ,				13      )
 skip_list.insert('''X'''     ,				14      )
 skip_list.insert('''Key2'''     ,				15      )

 skip_list.delete('''V'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) == 14
 assert skip_list.find('''Key1'''      ) == 12
 assert skip_list.find('''Key2'''      ) == 15

 skip_list.delete('''X'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) is None
 assert skip_list.find('''Key1'''      ) == 12
 assert skip_list.find('''Key2'''      ) == 15

 skip_list.delete('''Key1'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) is None
 assert skip_list.find('''Key1'''      ) is None
 assert skip_list.find('''Key2'''      ) == 15

 skip_list.delete('''Key2'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) is None
 assert skip_list.find('''Key1'''      ) is None
 assert skip_list.find('''Key2'''      ) is None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key1'''     ,				12      )
 skip_list.insert('''V'''     ,				13      )
 skip_list.insert('''X'''     ,				142      )
 skip_list.insert('''Key2'''     ,				15      )

 skip_list.delete('''X'''      )

 def traverse_keys(A__      ):
  yield node.key
  for forward_node in node.forward:
   yield from traverse_keys(A__      )

 assert len(set(traverse_keys(skip_list.head      )      )      ) == 4


def 		_A     (    ):






 """simple docstring"""



 def is_sorted(A__      ):
  return all(next_item >= item for item, next_item in zip(A__     ,				lst[1:]      )      )

 __lowercase							  = SkipList()
 for i in range(10      ):
  skip_list.insert(A__     ,				A__      )
 assert is_sorted(list(A__      )      )
 skip_list.delete(5      )
 skip_list.delete(8      )
 skip_list.delete(2      )
 assert is_sorted(list(A__      )      )
 skip_list.insert(-12     ,				-12      )
 skip_list.insert(77     ,				77      )
 assert is_sorted(list(A__      )      )


def 		_A     (    ):






 """simple docstring"""



 for _ in range(100      ):
  # Repeat test 100 times due to the probabilistic nature of skip list
  # random values == random bugs
  test_insert()
  test_insert_overrides_existing_value()

  test_searching_empty_list_returns_none()
  test_search()

  test_deleting_item_from_empty_list_do_nothing()
  test_deleted_items_are_not_founded_by_find_method()
  test_delete_removes_only_given_key()
  test_delete_doesnt_leave_dead_nodes()

  test_iter_always_yields_sorted_values()


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.insert(2     ,				'''2'''      )
 skip_list.insert(4     ,				'''4'''      )
 skip_list.insert(6     ,				'''4'''      )
 skip_list.insert(4     ,				'''5'''      )
 skip_list.insert(8     ,				'''4'''      )
 skip_list.insert(9     ,				'''4'''      )

 skip_list.delete(4      )

 print(A__      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

