This is a home-made implementation of a K-means Algorith for Pytorch.
I have a tensor of dimensions [80, 1000] that represents the centroids of the cluster that go changing until they are fixed values.
Also there are the labels of the features that are considered the “centers” in the variable called “indices_”.
I am having some issues when i want to represent the tensor. I am just considering 2 main classes.
I have also tried the scatter but have not very much info on how i could do a propper plot.
Here is the code below:
def k_means_torch(dictionary, model): centroids = torch.randn(len(dictionary), 1000).cuda() dist_centroids = torch.cdist(dictionary,centroids, p=2.0) (values, indices) = torch.min(dist_centroids, dim=1) centroids_new = dictionary[indices] while True: dist_centroids_loop = torch.cdist(dictionary,centroids_new, p=2.0) (values_, indices_) = torch.min(dist_centroids_loop, dim=1) new_centers = dictionary[indices_] torch.allclose(torch.tensor([[1., 2.], [3., 4.]]), torch.tensor([[1., 2.000000001], [3., 4.]])) a = torch.all(torch.lt(torch.abs(torch.add(centroids_new, -new_centers)), 1e-5)) #print(a) if (a == True): break rng = np.random.RandomState(0) centroids_new = (new_centers + centroids_new)/2 #plt.scatter(centroids_new.cpu().numpy(), dictionary.cpu().numpy(), alpha=0.5) #plt.show() return centroids_new
¿Any ideas on how i could plot this? Maybe use another function on the matplotlib?