Remove weight argument in quadpotential add_sample

pymc3/step_methods/hmc/quadpotential.py

@@ -183,8 +183,7 @@ def __init__(
             The number of initial samples that are just discarded and not used to estimate
             the mass matrix.
         early_update : bool
-            Whether to update the mass matrix live during the first half of the first
-            adaptation window.
+            Whether to update the mass matrix live during the first adaptation window.
         store_mass_matrix_trace : bool
             If true, store the mass matrix at each step of the adaptation. Only for debugging
             purposes.
@@ -268,8 +267,8 @@ def update(self, sample, grad, tune):
             return
 
         if self._n_samples > self._discard_window:
-            self._foreground_var.add_sample(sample, weight=1)
-            self._background_var.add_sample(sample, weight=1)
+            self._foreground_var.add_sample(sample)
+            self._background_var.add_sample(sample)
 
         if self._early_update or self._n_samples > self.adaptation_window:
             self._update_from_weightvar(self._foreground_var)
@@ -344,15 +343,13 @@ def __init__(
         if self.mean.shape != (nelem,):
             raise ValueError("Invalid shape for initial mean.")
 
-    def add_sample(self, x, weight):
+    def add_sample(self, x):
         x = np.asarray(x)
-        if weight != 1:
-            raise ValueError("Setting weight != 1 is not supported.")
         self.n_samples += 1
         old_diff = x - self.mean
         self.mean[:] += old_diff / self.n_samples
         new_diff = x - self.mean
-        self.raw_var[:] += weight * old_diff * new_diff
+        self.raw_var[:] += old_diff * new_diff
 
     def current_variance(self, out=None):
         if self.n_samples == 0:
@@ -666,8 +663,8 @@ def update(self, sample, grad, tune):
         # Steps since previous update
         delta = self._n_samples - self._previous_update
 
-        self._foreground_cov.add_sample(sample, weight=1)
-        self._background_cov.add_sample(sample, weight=1)
+        self._foreground_cov.add_sample(sample)
+        self._background_cov.add_sample(sample)
 
         # Update the covariance matrix and recompute the Cholesky factorization
         # every "update_window" steps
@@ -726,13 +723,13 @@ def __init__(
         if self.mean.shape != (nelem,):
             raise ValueError("Invalid shape for initial mean.")
 
-    def add_sample(self, x, weight):
+    def add_sample(self, x):
         x = np.asarray(x)
         self.n_samples += 1
         old_diff = x - self.mean
         self.mean[:] += old_diff / self.n_samples
         new_diff = x - self.mean
-        self.raw_cov[:] += weight * new_diff[:, None] * old_diff[None, :]
+        self.raw_cov[:] += new_diff[:, None] * old_diff[None, :]
 
     def current_covariance(self, out=None):
         if self.n_samples == 0:

pymc3/tests/test_quadpotential.py

@@ -169,7 +169,7 @@ def test_weighted_covariance(ndim=10, seed=5432):
 
     est = quadpotential._WeightedCovariance(ndim)
     for sample in samples:
-        est.add_sample(sample, 1)
+        est.add_sample(sample)
     mu_est = est.current_mean()
     cov_est = est.current_covariance()
 
@@ -184,7 +184,7 @@ def test_weighted_covariance(ndim=10, seed=5432):
         10,
     )
     for sample in samples[10:]:
-        est2.add_sample(sample, 1)
+        est2.add_sample(sample)
     mu_est2 = est2.current_mean()
     cov_est2 = est2.current_covariance()