bugfix in tell_many_at_point()

The new value of the data at point adopted the value of the mean of the new data samples, instead of the mean over all samples (new and old). Fixed!

Author: AlvaroGI

adaptive/learner/average_learner1D.py

@@ -370,7 +370,6 @@ def tell_many_at_point(self, x, ys):
             )
 
         ys = list(ys)  # cast to list *and* make a copy
-        y_avg = np.mean(ys)
         # If x is a new point:
         if x not in self.data:
             y = ys.pop(0)
@@ -379,21 +378,23 @@ def tell_many_at_point(self, x, ys):
 
         # If x is not a new point or if there were more than 1 sample in ys:
         if len(ys) > 0:
-            self.data[x] = y_avg
             self._data_samples[x].extend(ys)
-            n = len(self._data_samples[x])
+            n = len(ys)+self._number_samples[x]
+                # Same as n=len(self._data_samples[x]) but faster
+            self.data[x] = (np.mean(ys)*len(ys) + self.data[x]*self._number_samples[x])/n
+                # Same as self.data[x]=np.mean(self._data_samples[x]) but faster
             self._number_samples[x] = n
             # `self._update_data(x, y, "new")` included the point
             # in _undersampled_points. We remove it if there are
             # more than min_samples samples, disregarding neighbor_sampling.
             if n > self.min_samples:
                 self._undersampled_points.discard(x)
-            self.error[x] = self._calc_error_in_mean(self._data_samples[x], y_avg, n)
+            self.error[x] = self._calc_error_in_mean(self._data_samples[x], self.data[x], n)
             self._update_distances(x)
             self._update_rescaled_error_in_mean(x, "resampled")
             if self.error[x] <= self.min_error or n >= self.max_samples:
                 self.rescaled_error.pop(x, None)
-            super()._update_scale(x, y_avg)
+            super()._update_scale(x, self.data[x])
             self._update_losses_resampling(x, real=True)
             if self._scale[1] > self._recompute_losses_factor * self._oldscale[1]:
                 for interval in reversed(self.losses):