<jupyter_start><jupyter_text>print preds[:,:2].mean(axis = 0)print np.mean(np.exp(preds[:, 2:4]), axis = 0)print np.mean(0.9*np.tanh(preds[:, -1]), axis = 0)<jupyter_code>standard_net_preds = np.loadtxt('standard_net_preds2_%02d.npy'%boxno)
%%bash
ls *simulated*
mcmc_preds = np.loadtxt('simulated_chain3_10_walkers_500_steps.npy')
mcmc_preds = mcmc_preds.reshape(-1 ,10, 2)[20:, :, :]
mcmc_preds = mcmc_preds.reshape((-1, 2))
mcmc_preds.shape
c = ChainConsumer()

param_names = [r'$\Omega_m$', r'$\sigma_8$', r'$\sigma_{\Omega_m}$', r'$\sigma_{\sigma_8}$',r'$\rho$' ]
c.add_chain(bayes_org_preds, parameters = param_names, name = 'Bayes Correlation NN')
c.add_chain(standard_net_preds, parameters = param_names[:2], name = 'Standard NN')
c.add_chain(mcmc_preds, parameters = param_names[:2], name = 'MCMC')
c.plotter.plot(figsize = 'column', truth = Y, parameters = param_names[:2]);
c.plotter.plot_distributions(chains = 'Bayes Correlation NN', col_wrap = 2);<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.19071644803229063, 'fraction_numerical': 0.03229061553985873, 'mean_word_length': 7.551724137931035, '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': '324662', 'license': 'MIT', 'n_tokens_mistral': 440, 'n_tokens_neox': 420, 'n_words': 62, 'path': 'notebooks/Prediction Visualization.ipynb', 'repo_name': 'mclaughlin6464/uatu', 'size': '991'}