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 | 1 | initial version |
You can use numpy to compute the covariance of two list of numbers:
sage: x = [random() for _ in range(10)]
sage: y = [random() for _ in range(10)]
sage: np.cov(x,y)
array([[ 0.09306006, -0.04053014],
[-0.04053014, 0.10647864]])
The numpy array can be converted back into a SageMath matrix:
sage: matrix(_)
[ 0.09306006427535807 -0.04053014267978353]
[-0.04053014267978353 0.10647864456311205]
More usage examples can be found in the numpy doc.
| | 2 | No.2 Revision |
You can use numpy to compute the covariance of two list of numbers:
sage: x = [random() for _ in range(10)]
sage: y = [random() for _ in range(10)]
sage: import numpy as np
sage: np.cov(x,y)
array([[ 0.09306006, -0.04053014],
[-0.04053014, 0.10647864]])
The numpy array can be converted back into a SageMath matrix:
sage: matrix(_)
[ 0.09306006427535807 -0.04053014267978353]
[-0.04053014267978353 0.10647864456311205]
More usage examples can be found in the numpy doc.
