





'''simple docstring'''






from random import shuffle

import tensorflow as tf
from numpy import array







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			__snake_case					:      str							      =     int(_lowerCamelCase )
			assert noofclusters < len(_lowerCamelCase )

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

			# Will help select random centroids from among the available vectors
			__snake_case					:      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.

			__snake_case					:      Dict							      =     tf.Graph()

			with graph.as_default():
						# SESSION OF COMPUTATION

						__snake_case					:      List[Any]							      =     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
						__snake_case					:      List[Any]							      =     [
						    tf.Variable(vectors[vector_indices[i]] ) for i in range(_lowerCamelCase )
						]
						##These nodes will assign the centroid Variables the appropriate
						##values
						__snake_case					:      Dict							      =     tf.placeholder("""float64"""    ,	[dim] )
						__snake_case					:      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)
						__snake_case					:      Dict							      =     [tf.Variable(0 ) for i in range(len(_lowerCamelCase ) )]
						##These nodes will assign an assignment Variable the appropriate
						##value
						__snake_case					:      Optional[Any]							      =     tf.placeholder("""int32""" )
						__snake_case					:      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
						__snake_case					:      Union[str, Any]							      =     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
						__snake_case					:      Optional[int]							      =     tf.reduce_mean(_lowerCamelCase    ,	0 )

						##Node for computing Euclidean distances
						# Placeholders for input
						__snake_case					:      int							      =     tf.placeholder("""float"""    ,	[dim] )
						__snake_case					:      Optional[int]							      =     tf.placeholder("""float"""    ,	[dim] )
						__snake_case					:      int							      =     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
						__snake_case					:      Optional[Any]							      =     tf.placeholder("""float"""    ,	[noofclusters] )
						__snake_case					:      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.
						__snake_case					:      str							      =     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.
						__snake_case					:      int							      =     100
						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 ) ):
												__snake_case					:      Optional[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.
												__snake_case					:      Union[str, Any]							      =     [
												    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
												__snake_case					:      Dict							      =     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
												__snake_case					:      str							      =     [
												    vectors[i]
												    for i in range(len(_lowerCamelCase ) )
												    if sess.run(assignments[i] ) == cluster_n
												]
												# Compute new centroid location
												__snake_case					:      str							      =     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
						__snake_case					:      Tuple							      =     sess.run(_lowerCamelCase )
						__snake_case					:      str							      =     sess.run(_lowerCamelCase )
						return centroids, assignments






from random import randint, random




def    a__    (		snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							bool = False						,   snake_case__					:							bool = False						,   snake_case__					:							int = 5						,   ):
  _UpperCAmelCase		:							Optional[int]							=     [[-1] * number_of_cells]  # Create a highway without any car
  _UpperCAmelCase		:							Optional[Any]							=     0
  _UpperCAmelCase		:							Dict							=     max(snake_case__						,   0     )
  while i < number_of_cells:
    _UpperCAmelCase		:							int							=     (
        randint(0						,   snake_case__     ) if random_speed else initial_speed
    )  # Place the cars
    i += (
        randint(1						,   max_speed * 2     ) if random_frequency else frequency
    )  # Arbitrary number, may need tuning
  return highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							List[Any]							=     0
  _UpperCAmelCase		:							int							=     highway_now[car_index + 1 :]
  for cell in range(len(snake_case__     )     ):  # May need a better name for this
    if cells[cell] != -1:  # If the cell is not empty then
      return distance  # we have the distance we wanted
    distance += 1
  # Here if the car is near the end of the highway
  return distance + get_distance(snake_case__						,   -1     )




def    a__    (		snake_case__					:							list						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     len(snake_case__     )
  # Beforce calculations, the highway is empty
  _UpperCAmelCase		:							Dict							=     [-1] * number_of_cells

  for car_index in range(snake_case__     ):
    if highway_now[car_index] != -1:
      # Add 1 to the current speed of the car and cap the speed
      _UpperCAmelCase		:							Dict							=     min(highway_now[car_index] + 1						,   snake_case__     )
      # Number of empty cell before the next car
      _UpperCAmelCase		:							List[str]							=     get_distance(snake_case__						,   snake_case__     ) - 1
      # We can't have the car causing an accident
      _UpperCAmelCase		:							List[str]							=     min(next_highway[car_index]						,   snake_case__     )
      if random() < probability:
        # Randomly, a driver will slow down
        _UpperCAmelCase		:							Dict							=     max(next_highway[car_index] - 1						,   0     )
  return next_highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Union[str, Any]							=     len(highway[0]     )

  for i in range(snake_case__     ):
    _UpperCAmelCase		:							Tuple							=     update(highway[i]						,   snake_case__						,   snake_case__     )
    _UpperCAmelCase		:							int							=     [-1] * number_of_cells

    for car_index in range(snake_case__     ):
      _UpperCAmelCase		:							List[Any]							=     next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        _UpperCAmelCase		:							Optional[Any]							=     (car_index + speed) % number_of_cells
        # Commit the change of position
        _UpperCAmelCase		:							Optional[Any]							=     speed
    highway.append(snake_case__     )

  return highway


if __name__ == "__main__":
 import doctest

 doctest.testmod()





