

"""simple docstring"""

from random import shuffle

import tensorflow as tf
from numpy import array



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Union[str, Any]:
 lowerCAmelCase__   :				str 			=				int(lowercase__   )
 assert noofclusters < len(lowercase__   )

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

 # Will help select random centroids from among the available vectors
 lowerCAmelCase__   :				int 			=				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.

 lowerCAmelCase__   :				Any 			=				tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

  lowerCAmelCase__   :				Dict 			=				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__   :				Union[str, Any] 			=				[
      tf.Variable(vectors[vector_indices[i]]   ) for i in range(lowercase__   )
  ]
  ##These nodes will assign the centroid Variables the appropriate
  ##values
  lowerCAmelCase__   :				Tuple 			=				tf.placeholder("float64"					,					[dim]   )
  lowerCAmelCase__   :				Tuple 			=				[]
  for centroid in centroids:
   cent_assigns.append(tf.assign(lowercase__					,					lowercase__   )   )

  ##Variables for cluster assignments of individual vectors(initialized
  ##to 0 at first)
  lowerCAmelCase__   :				Optional[int] 			=				[tf.Variable(0   ) for i in range(len(lowercase__   )   )]
  ##These nodes will assign an assignment Variable the appropriate
  ##value
  lowerCAmelCase__   :				Tuple 			=				tf.placeholder("int32"   )
  lowerCAmelCase__   :				int 			=				[]
  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
  lowerCAmelCase__   :				Tuple 			=				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__   :				str 			=				tf.reduce_mean(lowercase__					,					0   )

  ##Node for computing Euclidean distances
  # Placeholders for input
  lowerCAmelCase__   :				Optional[int] 			=				tf.placeholder("float"					,					[dim]   )
  lowerCAmelCase__   :				List[str] 			=				tf.placeholder("float"					,					[dim]   )
  lowerCAmelCase__   :				Dict 			=				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
  lowerCAmelCase__   :				Optional[Any] 			=				tf.placeholder("float"					,					[noofclusters]   )
  lowerCAmelCase__   :				List[str] 			=				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.
  lowerCAmelCase__   :				Tuple 			=				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.
  lowerCAmelCase__   :				Tuple 			=				1_0_0
  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__   )   ):
    lowerCAmelCase__   :				Dict 			=				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__   :				Any 			=				[
        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
    lowerCAmelCase__   :				List[str] 			=				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
    lowerCAmelCase__   :				Any 			=				[
        vectors[i]
        for i in range(len(lowercase__   )   )
        if sess.run(assignments[i]   ) == cluster_n
    ]
    # Compute new centroid location
    lowerCAmelCase__   :				Optional[Any] 			=				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
  lowerCAmelCase__   :				str 			=				sess.run(lowercase__   )
  lowerCAmelCase__   :				Union[str, Any] 			=				sess.run(lowercase__   )
  return centroids, assignments









"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						list:
 lowerCAmelCase__   :				List[str] 			=				len(lowercase__   )
 lowerCAmelCase__   :				Dict 			=				[]
 for i in range(len(lowercase__   ) - pat_len + 1   ):
  lowerCAmelCase__   :				Union[str, Any] 			=				True
  for j in range(lowercase__   ):
   if s[i + j] != pattern[j]:
    lowerCAmelCase__   :				int 			=				False
    break
  if match_found:
   position.append(lowercase__   )
 return position


if __name__ == "__main__":
    assert naive_pattern_search("""ABCDEFG""", """DE""") == [3]
    print(naive_pattern_search("""ABAAABCDBBABCDDEBCABC""", """ABC"""))






