MLForge again returns the ensemble covariance, NOT the ensemble covariance divided by n_estimators
@@ -1199,8 +1199,8 @@ class EnsembleForge:
is given, this is a list with results for all entries in vote_expectation_weight.
covariance : ndarray or list of ndarray
- The covariance matrix of the (flattened) predictions, defined as the ensemble covariance divided by the
- number of estimators. If more then one value vote_expectation_weight is given, this is a list with results
+ The covariance matrix of the (flattened) predictions, defined as the ensemble covariance. If more then one
+ value vote_expectation_weight is given, this is a list with results
for all entries in vote_expectation_weight.
weights : ndarray or list of ndarray
@@ -1260,7 +1260,6 @@ class EnsembleForge:
predictions_flat = predictions.reshape((predictions.shape[0], -1))
covariance = np.cov(predictions_flat.T, aweights=these_weights)
- covariance /= self.n_estimators
covariances.append(covariance)
@@ -1316,8 +1315,8 @@ class EnsembleForge:
is given, this is a list with results for all entries in vote_expectation_weight.
covariance : ndarray or list of ndarray
- The covariance matrix of the Fisher information estimate, defined as the ensemble covariance divided by the
- number of estimators. This object has four indices, `cov_(ij)(i'j')`, ordered as i j i' j'. It has shape
+ The covariance matrix of the Fisher information estimate, defined as the ensemble covariance. This object
+ has four indices, `cov_(ij)(i'j')`, ordered as i j i' j'. It has shape
`(n_parameters, n_parameters, n_parameters, n_parameters)`. If more then one value vote_expectation_weight
is given, this is a list with results for all entries in vote_expectation_weight.
@@ -1377,8 +1376,6 @@ class EnsembleForge:
covariance_shape = (predictions.shape[1], predictions.shape[2], predictions.shape[1], predictions.shape[2])
covariance = covariance.reshape(covariance_shape)
- covariance /= self.n_estimators
-
covariances.append(covariance)
# Returns
