# %% estimate parameters using ECoPANN
randn_num = ; steps_n = 4
predictor = ann.RePredict(union, cov_matrix=None, path='union2.1_fwCDM',
randn_num=randn_num, steps_n=steps_n,
params_dict=simulator.params_dict)
predictor.from_chain()
# predictor.from_net()
chain_ann = predictor.chain_ann
predictor.plot_steps()
predictor.plot_contours(fill_contours=False, show_titles=True)
predictor.save_steps()
predictor.save_contours()
predictor.eco.plot_loss()
#%%
chain_mcmc = np.load('data/MCMC_chains/chain_fwCDM_2params.npy')
chain_all = [chain_ann, chain_mcmc]
param_names = ['w', 'omm']
plc.Contours(chain_all).plot(labels=cosmic_params.ParamsProperty(param_names).labels,smooth=5,
fill_contours=False,show_titles=True,line_width=2,layout_adjust=[0.0,0.0],
lims=None,legend=True,legend_labels=['ANN', 'MCMC'])
#%%
plt.show()
params_dict=params_dict,
cov_matrix=None,
init_params=init_params,
epoch=epoch,
num_train=num_train,
local_samples=None,
stepStop_n=stepStop_n)
predictor.train(path='SNe_BAO')
chain_ann = predictor.chain_ann
predictor.plot_steps()
predictor.plot_contours(fill_contours=False, show_titles=True)
predictor.save_steps()
predictor.save_contours()
predictor.eco.plot_loss()
#%%
labels = cosmic_params.ParamsProperty(param_names,
params_dict=params_dict).labels
plc.Contours(chain_ann).plot(bins=100,
smooth=5,
labels=labels,
fill_contours=False,
show_titles=True,
best_values=fid_params,
show_best_value_lines=True)
plt.show()
init_params=init_params,
epoch=epoch,
num_train=num_train,
local_samples=None,
steps_n=steps_n)
predictor.train(path='union2.1_fwCDM')
chain_ann = predictor.chain_ann
predictor.plot_steps()
predictor.plot_contours(fill_contours=False, show_titles=True)
predictor.save_steps()
predictor.save_contours()
# %% MCMC chain
chain_mcmc = np.load('data/MCMC_chains/chain_fwCDM_2params.npy')
chain_all = [chain_ann, chain_mcmc]
plc.Contours(chain_all).plot(
labels=cosmic_params.ParamsProperty(param_names).labels,
smooth=5,
fill_contours=False,
show_titles=True,
line_width=2,
layout_adjust=[0.0, 0.0],
lims=None,
legend=True,
legend_labels=['ANN', 'MCMC'])
plt.show()