Martin 3

thingiverse

This is a valid Python code snippet that represents a cluster graph of nodes. The size variable is set to 40. The cluster is defined with several cluster indices ranging from 1 to 121. There are 40 data points in this cluster and their attributes can be obtained by index. I'll highlight the key aspects of this code: * **Importing Modules:** The code imports `scipy.stats.qkmeans` for K-means clustering, but no modules related to matplotlib for creating plots. * **Defining a Cluster:** A single data point or vector with an array size of 129 elements represents each cluster index (from 1 to 121). * **Calculating Mean Vector:** For the first time you are calling kmeans algorithm which results in calculating a mean vector for each K = {6,10} cluster numbers which are represented using variable 'a'. * **Size and Centroid of Clusters:** These variables calculate the size and centroids for these 4 different cluster combinations based on cluster indices. Here is some Python code to extract information from this representation: ```python import matplotlib.pyplot as plt def get_centroids_and_size(indices, size): centroids = [] c_size = [] for index in indices: # You can change the value of the centroid calculation here. a = [[0.016189,-0.009590095890410952],[-0.0039391393913914,0.014889884998449894],[1.0049949970499495,-6.996995997005996e-15],0.013999,[[[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],[0,-4.998000000000001e-13],7.003003e-04]],[[6,10],[[11]]]] # Centroid values can be replaced with a function here. centroid = sum([item[index-1] for item in size]) / len(size) # Size can be a simple counter for each point. centroid_size = len(size) centroids.append(centroid) c_size.append(centroid_size) return centroids, c_size centroids_and_size_6_10, _=get_centroids_and_size([3, 18], [[-0.034595479452054796,-0.013997002000010002],[4.003e-03,7.0010030003e-04]])