<jupyter_start><jupyter_text>Now lets create the visualization. We need to give the latent space (N x Q) and a dictionary of labels (N x num_labels) to the LatentView instance. There is more options, see the help. Here we generated labels, which are discretized versions of the mixture parameters, which decided how much to take from the different distributions, with different means.<jupyter_code>GPyNotebook.latent.LatentView(m.X.mean, labeldict)
m.kern.plot_ARD()
p = GPy.util.pca.PCA(Y)
X_pca = p.project(Y, 10)
GPyNotebook.latent.LatentView(X_pca, labeldict)
p.plot_fracs(10)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08524590163934426, 'fraction_numerical': 0.006557377049180328, 'mean_word_length': 5.943181818181818, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '653453', 'license': 'BSD-2-Clause', 'n_tokens_mistral': 207, 'n_tokens_neox': 200, 'n_words': 69, 'path': 'ExampleVisualization.ipynb', 'repo_name': 'mzwiessele/GPyNotebook', 'size': '610'}