def setUp(self, m=100, n=1, nt_sim=50, nt_obs=20, n_theta=3, n_basis=5, sig_n=0.1, seed=42):
n_hier = 3
self.hier_idx = np.array([[0, 0, 0]])
#self.hier_idx = np.array([[1, 1, 1], [2, -1, 2]]) # TODO this fails for multivariate; cant use for univariate now
multi_data_list = []
univ_data_list = []
for si in range(n_hier):
multi_data_dict = generate_data.generate_multi_sim_and_obs(m=m, n=n, nt_sim=nt_sim, nt_obs=nt_obs,
n_theta=n_theta, n_basis=n_basis,
sig_n=sig_n, seed=seed)
univ_data_dict = generate_data.generate_univ_sim_and_obs(m=m, n=n, sig_n=sig_n, seed=seed)
d = SepiaData(t_sim=univ_data_dict['t_sim'], y_sim=univ_data_dict['y_sim'], y_obs=univ_data_dict['y_obs'])
d.transform_xt()
d.standardize_y()
univ_data_list.append(d)
d = SepiaData(t_sim=multi_data_dict['t_sim'], y_sim=multi_data_dict['y_sim'],
y_ind_sim=multi_data_dict['y_ind_sim'], y_obs=multi_data_dict['y_obs'],
y_ind_obs=multi_data_dict['y_ind_obs'])
d.transform_xt()
d.standardize_y()
d.create_K_basis(5)
d.create_D_basis('constant')
multi_data_list.append(d)
self.univ_model_list = [SepiaModel(d) for d in univ_data_list]
self.multi_model_list = [SepiaModel(d) for d in multi_data_list]
def setUp(self,
m=20,
n=1,
nt_sim=30,
nt_obs=20,
n_theta=3,
n_basis=5,
sig_n=0.1,
seed=42):
multi_data_dict = generate_data.generate_multi_sim_and_obs(
m=m,
n=n,
nt_sim=nt_sim,
nt_obs=nt_obs,
n_theta=n_theta,
n_basis=n_basis,
sig_n=sig_n,
seed=seed)
univ_data_dict = generate_data.generate_univ_sim_and_obs(m=m,
n=n,
sig_n=sig_n,
seed=seed)
d = SepiaData(t_sim=univ_data_dict['t_sim'],
y_sim=univ_data_dict['y_sim'])
d.transform_xt()
d.standardize_y()
self.univ_sim_only_model = SepiaModel(d)
d = SepiaData(t_sim=univ_data_dict['t_sim'],
y_sim=univ_data_dict['y_sim'],
y_obs=univ_data_dict['y_obs'])
d.transform_xt()
d.standardize_y()
self.univ_sim_and_obs_model = SepiaModel(d)
d = SepiaData(t_sim=multi_data_dict['t_sim'],
y_sim=multi_data_dict['y_sim'],
y_ind_sim=multi_data_dict['y_ind_sim'])
d.transform_xt()
d.standardize_y()
d.create_K_basis(5)
self.multi_sim_only_model = SepiaModel(d)
t = np.concatenate([
multi_data_dict['t_sim'],
np.random.choice(range(1, 5), (m, 1), replace=True)
],
axis=1)
d = SepiaData(t_sim=t,
y_sim=multi_data_dict['y_sim'],
y_ind_sim=multi_data_dict['y_ind_sim'],
t_cat_ind=[0, 0, 0, 4])
d.transform_xt()
d.standardize_y()
d.create_K_basis(5)
self.multi_sim_only_catind_model = SepiaModel(d)
def setUp(self,
m=100,
n=1,
nt_sim=50,
nt_obs=20,
n_theta=3,
n_basis=5,
sig_n=0.1,
seed=42):
self.data_dict = generate_data.generate_multi_sim_and_obs(
m=m,
n=n,
nt_sim=nt_sim,
nt_obs=nt_obs,
n_theta=n_theta,
n_basis=n_basis,
sig_n=sig_n,
seed=seed)
def setUp(self, m=100, n=1, nt_sim=50, nt_obs=20, n_theta=3, n_basis=5, sig_n=0.1, seed=42):
multi_data_dict = generate_data.generate_multi_sim_and_obs(m=m, n=n, nt_sim=nt_sim, nt_obs=nt_obs,
n_theta=n_theta, n_basis=n_basis,
sig_n=sig_n, seed=seed)
univ_data_dict = generate_data.generate_univ_sim_and_obs(m=m, n=n, sig_n=sig_n, seed=seed)
d = SepiaData(t_sim=univ_data_dict['t_sim'], y_sim=univ_data_dict['y_sim'])
d.transform_xt()
d.standardize_y()
self.univ_sim_only_data = d
d = SepiaData(t_sim=univ_data_dict['t_sim'], y_sim=univ_data_dict['y_sim'], y_obs=univ_data_dict['y_obs'])
d.transform_xt()
d.standardize_y()
self.univ_sim_and_obs_data = d
d = SepiaData(t_sim=multi_data_dict['t_sim'], y_sim=multi_data_dict['y_sim'],
y_ind_sim=multi_data_dict['y_ind_sim'])
d.transform_xt()
d.standardize_y()
d.create_K_basis(5)
self.multi_sim_only_data = d
d = SepiaData(t_sim=multi_data_dict['t_sim'], y_sim=multi_data_dict['y_sim'],
y_ind_sim=multi_data_dict['y_ind_sim'], y_obs=multi_data_dict['y_obs'],
y_ind_obs=multi_data_dict['y_ind_obs'])
d.transform_xt()
d.standardize_y()
d.create_K_basis(5)
self.multi_sim_and_obs_noD_data = d
d = SepiaData(t_sim=multi_data_dict['t_sim'], y_sim=multi_data_dict['y_sim'],
y_ind_sim=multi_data_dict['y_ind_sim'], y_obs=multi_data_dict['y_obs'],
y_ind_obs=multi_data_dict['y_ind_obs'])
d.transform_xt()
d.standardize_y()
d.create_K_basis(5)
d.create_D_basis('linear')
self.multi_sim_and_obs_data = d
from generate_data import generate_multi_sim_and_obs
from sepia.SepiaModelSetup import setup_model
from sepia.SepiaData import SepiaData
#%load_ext autoreload
#%autoreload 2
#%%
seed = 42 # random seed
m = 100 # number of simulated observations
n = 1 # number of observed data
sig_n = 0.01 # observation noise SD
data_dict = generate_multi_sim_and_obs(m=m, n=n, sig_n=sig_n, seed=seed)
#%%
data = SepiaData(t_sim=data_dict['t_sim'],
y_sim=data_dict['y_sim'],
y_ind_sim=data_dict['y_ind_sim'],
y_obs=data_dict['y_obs'],
y_ind_obs=data_dict['y_ind_obs'])
print(data)
plt.plot(data.sim_data.y_ind, data.sim_data.y.T)
plt.plot(data.obs_data.y_ind, data.obs_data.y.T, 'k.', linewidth=3)
plt.title('Synthetic data (obs. in black)')
plt.xlabel('y index')
