[ allennlp.training.metrics.covariance ]
class Covariance(Metric): | def __init__(self) -> None
Metric calculates the unbiased sample covariance between two tensors.
Each element in the two tensors is assumed to be a different observation of the
variable (i.e., the input tensors are implicitly flattened into vectors and the
covariance is calculated between the vectors).
This implementation is mostly modeled after the streaming_covariance function in Tensorflow. See: https://github.com/tensorflow/tensorflow/blob/v1.10.1/tensorflow/contrib/metrics/python/ops/metric_ops.py#L3127
The following is copied from the Tensorflow documentation:
The algorithm used for this online computation is described in
Specifically, the formula used to combine two sample comoments is
C_AB = C_A + C_B + (E[x_A] - E[x_B]) * (E[y_A] - E[y_B]) * n_A * n_B / n_AB
The comoment for a single batch of data is simply
sum((x - E[x]) * (y - E[y])), optionally masked.
| def get_metric(self, reset: bool = False)
- The accumulated covariance.
| @overrides | def reset(self)