Cov¶
Covariance.
Parameters¶
-
ddof
Default →
1
Delta Degrees of Freedom.
Attributes¶
- n
Examples¶
from river import stats
x = [-2.1, -1, 4.3]
y = [ 3, 1.1, 0.12]
cov = stats.Cov()
for xi, yi in zip(x, y):
cov.update(xi, yi)
print(cov.get())
0.0
-1.044999
-4.286
This class has a revert
method, and can thus be wrapped by utils.Rolling
:
from river import utils
x = [-2.1, -1, 4.3, 1, -2.1, -1, 4.3]
y = [ 3, 1.1, .12, 1, 3, 1.1, .12]
rcov = utils.Rolling(stats.Cov(), window_size=3)
for xi, yi in zip(x, y):
rcov.update(xi, yi)
print(rcov.get())
0.0
-1.045
-4.286
-1.382
-4.589
-1.415
-4.286
Methods¶
get
Return the current value of the statistic.
revert
update
Update the called instance.
Parameters
- x
- y
- w — defaults to
1.0
update_many
Notes¶
The outcomes of the incremental and parallel updates are consistent with numpy's batch processing when \(\text{ddof} \le 1\).
-
Schubert, E. and Gertz, M., 2018, July. Numerically stable parallel computation of (co-) variance. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management (pp. 1-12). ↩