



'''simple docstring'''


from __future__ import annotations







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      float ,  lowercase_    :      float ,  lowercase_    :      float						):
      if (voltage, current, resistance).count(0						) != 1:
            raise ValueError("""One and only one argument must be 0"""						)
      if resistance < 0:
            raise ValueError("""Resistance cannot be negative"""						)
      if voltage == 0:
            return {"voltage": float(current * resistance						)}
      elif current == 0:
            return {"current": voltage / resistance}
      elif resistance == 0:
            return {"resistance": voltage / current}
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()







'''simple docstring'''


from random import shuffle

import tensorflow as tf
from numpy import array







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str] ,  lowercase_    :      Optional[int]						):
						lowercase        =							int(lowercase_						)
						assert noofclusters < len(lowercase_						)

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

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

						# 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(lowercase_						)
												]
												##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(lowercase_ ,  lowercase_						)						)

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

												##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(lowercase_ ,  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(lowercase_ ,  lowercase_						) ,  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(lowercase_ ,  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(lowercase_						)

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

        # Return centroids and assignments
												lowercase        =							sess.run(lowercase_						)
												lowercase        =							sess.run(lowercase_						)
												return centroids, assignments


