self-organizing-maps

I'm using somoclu to produce an emergent Self-Organising Map of some data. Once I have the BMUs (Best Matching Units) I'm performing a Delaunay Triangulation on the co-ordinates of the BMUs in order to find each BMU's neighbours in the SOM. In the following snippet of Python, is there a more Pythonic version of the a == c and b == d conditional? In other words, how can I compare bmu and point directly without splitting out the separate co-ordinates? points = np.unique(np.array(som.bmus), axis = 0) for idx, bmu in enumerate(som.bmus): a, b = bmu for point_idx, point in enumerate(points): c, d =

I've looked at a lot of theoretical examples of SOMs, but one thing is not really clear to me: is the location of nodes dependent on their weights? For example, will nodes with a larger weight be on one side of the map, while nodes with a smaller weight will be further away on the map? No. In an SOM (aka Kohonen Map) the weight function is applied to your data not the the "Neurons". Weights are used during map construction (training), i.e., calculated at each iteration and for each lattice cell within each iteration. Put another way, for each iteration that comprises map construction, a weight

There is only one question related to this in stackoverflow, and it is more about which one is better. I just dont really understand the difference. I mean they both work with vectors, which are assigned randomly to clusters, they both work with the centroids of the different clusters in order to determine the winning output node. I mean, where exactly lies the difference? In K-means the nodes (centroids) are independent from each other. The winning node gets the chance to adapt each self and only that. In SOM the nodes (centroids) are placed onto a grid and so each node is consider to have

I am looking for hexagonal self-organizing map on Python. ready module. If one exists. way to plot hexagonal cell algorithms to work with hexagonal cells as array or smth else About : A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional) I don't have an answer for point 1, but some hints for point 2 and 3. In your context, you're not modelling a physical 2D space but a conceptual space with tiles that have 6 neighbors. This can be modelled with

How exactly is an U-matrix constructed in order to visualise a self-organizing-map ? More specifically, suppose that I have an output grid of 3x3 nodes (that have already been trained), how do I construct a U-matrix from this? You can e.g. assume that the neurons (and inputs) have dimension 4. I have found several resources on the web, but they are not clear or they are contradictory. For example, the original paper is full of typos. pater A U-matrix is a visual representation of the distances between neurons in the input data dimension space. Namely you calculate the distance between adjacent