upated plot disc, resolved prior error for TP1 cases

linfa/plot_disc.py

@@ -19,6 +19,7 @@
 # lf_model_plus_disc = np.loadtxt(path2)
 # lf_model_plus_disc_plus_noise = np.loadtxt(path3)
 
+<<<<<<< Updated upstream
 # plt.hist(lf_model[:,0], label = 'LF')
 # plt.hist(lf_model_plus_disc[:,0], label = 'LF + disc')
 # plt.hist(lf_model_plus_disc_plus_noise[:,0], label = 'LF + disc + noise')
@@ -76,3 +77,15 @@ def plot_discrepancy(file_path,train_grid_in,train_grid_out,test_grid):
  # Draw histograms of lf+discr
  # Draw histograms of lf
  # Draw histograms of lf+discr+noise
+=======
+plt.hist(lf_model, label = 'LF', alpha =0.5)
+plt.hist(lf_model_plus_disc, label = 'LF + disc', alpha =0.5)
+plt.hist(lf_model_plus_disc_plus_noise, label = 'LF + disc + noise', alpha = 0.5)
+plt.xlabel('Coverage')
+plt.ylabel('Frequency')
+plt.legend()
+plt.show()
+
+# shape: number of no TP-pairs x Batch size (5000)
+print(np.shape(lf_model))
+>>>>>>> Stashed changes

linfa/tests/test_discr_08_prior_TP1.py

@@ -35,7 +35,7 @@ def run_test():
  exp.surr_upd_it = 2000 # int: Number of iterations for the surrogate model update
  exp.calibrate_interval = 1000 #:int: How often the surrogate model is updated
 
- exp.annealing = False # TODO : turn this on eventually
+ exp.annealing = False 
  exp.budget = 216 # int: Total number of true model evaulations
  exp.surr_folder = "./" 
  exp.use_new_surr = True
@@ -61,8 +61,8 @@ def run_test():
  exp.transform = trsf
 
  # Add temperatures and pressures for each evaluation
- variable_inputs = [[350.0, 400.0, 450.0],
- [1.0, 2.0, 3.0, 4.0, 5.0]]
+ variable_inputs = [[350.0],
+ [1.0]]
 
  # Define model
  langmuir_model = PhysChem(variable_inputs)
@@ -108,8 +108,7 @@ def log_density(calib_inputs, model, surrogate, transform):
 
  # Initialize total number of variable inputs
  total_var_inputs = len(model.var_in)
-
- # HAD TO MOVE THIS UP BEFORE EVALUATE DISCREPANCY 
+
  # Evaluate model response - (num_var x num_batch)
  modelOut = langmuir_model.solve_t(transform.forward(calib_inputs)).t()
 
@@ -131,8 +130,6 @@ def log_density(calib_inputs, model, surrogate, transform):
  # Loop on the available observations
  for loopA in range(num_obs):
  l1 = -0.5 * np.prod(langmuir_model.data.shape) * np.log(2.0 * np.pi)
-
- # TODO: generalize to multiple inputs
  l2 = (-0.5 * langmuir_model.data.shape[1] * torch.log(torch.prod(stds))).item()
  l3 = -0.5 * torch.sum(((modelOut + discrepancy.t() - Data[:,loopA].unsqueeze(0)) / stds.t())**2, dim = 1)
 
@@ -198,7 +195,7 @@ def generate_data(use_true_model=False,num_observations=50):
 
  generate_data(use_true_model=True, num_observations=1)
 
- #run_test()
+ run_test()
 
 
 

linfa/tests/test_discr_17_prior_TP1_rep_meas.py

@@ -61,8 +61,8 @@ def run_test():
  exp.transform = trsf
 
  # Add temperatures and pressures for each evaluation
- variable_inputs = [[350.0, 400.0, 450.0],
- [1.0, 2.0, 3.0, 4.0, 5.0]]
+ variable_inputs = [[350.0],
+ [1.0]]
 
  # Define model
  langmuir_model = PhysChem(variable_inputs)