## Python, dynamic_programming, k_means_clustering_tensorflow.py_tf

``````import tensorflow as tf
from random import shuffle
from numpy import array

def TFKMeansCluster(vectors, noofclusters):
"""
K-Means Clustering using TensorFlow.
'vectors' should be a n*k 2-D NumPy array, where n is the number
of vectors of dimensionality k.
'noofclusters' should be an integer.
"""

noofclusters = int(noofclusters)
assert noofclusters < len(vectors)

# Find out the dimensionality
dim = len(vectors)

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

# 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.

graph = tf.Graph()

with graph.as_default():

# SESSION OF COMPUTATION

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

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

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

##Node for computing Euclidean distances
# Placeholders for input
v1 = tf.placeholder("float", [dim])
v2 = tf.placeholder("float", [dim])
euclid_dist = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(v1, v2), 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
centroid_distances = tf.placeholder("float", [noofclusters])
cluster_assignment = tf.argmin(centroid_distances, 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.
init_op = tf.initialize_all_variables()

# Initialize all variables
sess.run(init_op)

##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.
noofiterations = 100
for iteration_n in range(noofiterations):

##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(vectors)):
vect = 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.
distances = [
sess.run(euclid_dist, feed_dict={v1: vect, v2: sess.run(centroid)})
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
assignment = sess.run(
cluster_assignment, 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(noofclusters):
# Collect all the vectors assigned to this cluster
assigned_vects = [
vectors[i]
for i in range(len(vectors))
if sess.run(assignments[i]) == cluster_n
]
# Compute new centroid location
new_location = sess.run(
mean_op, feed_dict={mean_input: array(assigned_vects)}
)
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n], feed_dict={centroid_value: new_location}
)

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