mindspore.ops.Cholesky
- class mindspore.ops.Cholesky(upper=False)[source]
Performs the Cholesky decomposition on a single or a batch of symmetric positive-definite matrices.
Warning
This is an experimental API that is subject to change or deletion.
Refer to
mindspore.ops.cholesky()
for more details.- Parameters
upper (bool, optional) – Flag that indicates whether to return a upper or lower triangular matrix. Default:
False
.
- Inputs:
input_x (Tensor) - Tensor of shape \((*, N, N)\), where \(*\) is zero or more batch dimensions consisting of symmetric positive-definite matrices, with float32 or float64 data type.
- Outputs:
Tensor, has the same shape and data type as input_x.
- Supported Platforms:
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> input_x = Tensor(np.array([[1.0, 1.0], [1.0, 2.0]]), mindspore.float32) >>> output = ops.Cholesky()(input_x) >>> print(output) [[1. 0.] [1. 1.]]