Merge pull request #105 from wichmann-lab/fixscaling

Fixes scaling for real
wichmann-lab · Sep 20, 2024 · 7e1f748 · 7e1f748
2 parents e5dea94 + 1a9bd11
commit 7e1f748
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Showing 3 changed files with 16 additions and 13 deletions.
diff --git a/psignifit/_posterior.py b/psignifit/_posterior.py
@@ -242,9 +242,13 @@ def objective(x):
 def marginalize_posterior(parameter_grid: ParameterGrid, posterior_mass: np.ndarray) -> Dict[str, np.ndarray]:
     marginals = dict()
     for i, (param, grid) in enumerate(sorted(parameter_grid.items())):
-        if grid is None:
+        if grid is None or len(grid)==1:
             marginals[param] = None
-        axis = tuple(range(0, i)) + tuple(range(i + 1, len(parameter_grid)))
-        marginals[param] = np.squeeze(posterior_mass.sum(axis))
+        else:
+            axis = tuple(range(0, i)) + tuple(range(i + 1, len(parameter_grid)))
+            # we get first the unnormalized marginal, and then we scale it
+            nmarginal = np.squeeze(posterior_mass.sum(axis))
+            integral = np.trapz(nmarginal, x=grid)
+            marginals[param] = nmarginal / integral
 
     return marginals
diff --git a/psignifit/_priors.py b/psignifit/_priors.py
@@ -155,9 +155,8 @@ def setup_priors(custom_priors, bounds, stimulus_range, width_min, width_alpha,
         priors.update(custom_priors)
     check_priors(priors, stimulus_range, width_min)
 
-    npriors = {}
     for parameter, prior in priors.items():
-        npriors[parameter] = normalize_prior(prior, bounds[parameter])
-    return npriors, priors
+        priors[parameter] = normalize_prior(prior, bounds[parameter])
+    return priors
 
 
diff --git a/psignifit/psignifit.py b/psignifit/psignifit.py
@@ -83,10 +83,10 @@ def psignifit(data: np.ndarray, conf: Optional[Configuration] = None,
         for param, value in conf.fixed_parameters.items():
             bounds[param] = (value, value)
 
-    npriors, priors = setup_priors(custom_priors=conf.priors, bounds=bounds,
-                            stimulus_range=stimulus_range, width_min=width_min, width_alpha=conf.width_alpha,
-                            beta_prior=conf.beta_prior, threshold_perc_correct=conf.thresh_PC)
-    fit_dict, posteriors, grid = _fit_parameters(data, bounds, npriors, sigmoid, conf.steps_moving_bounds,
+    priors = setup_priors(custom_priors=conf.priors, bounds=bounds,
+                          stimulus_range=stimulus_range, width_min=width_min, width_alpha=conf.width_alpha,
+                          beta_prior=conf.beta_prior, threshold_perc_correct=conf.thresh_PC)
+    fit_dict, posteriors, grid = _fit_parameters(data, bounds, priors, sigmoid, conf.steps_moving_bounds,
                                                  conf.max_bound_value, conf.grid_steps)
 
     grid_values = [grid_value for _, grid_value in sorted(grid.items())]
@@ -104,9 +104,9 @@ def psignifit(data: np.ndarray, conf: Optional[Configuration] = None,
     return Result(parameter_estimate=fit_dict,
                   configuration=conf,
                   confidence_intervals=intervals_dict,
-                  parameter_values={k: v.tolist() for k, v in grid.items()},
-                  prior_values={param: priors[param](values).tolist() for param, values in grid.items()},
-                  marginal_posterior_values={k: v.tolist() for k, v in marginals.items()},
+                  parameter_values=grid,
+                  prior_values={param: priors[param](values) for param, values in grid.items()},
+                  marginal_posterior_values=marginals,
                   posterior_mass=posteriors,
                   data=data.tolist())