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[0]) #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