mindspore.ops.ormqr
- mindspore.ops.ormqr(input, tau, other, left=True, transpose=False)[source]
Calculates two matrices multiplication of a product of a general matrix with Householder matrices. Calculates the product of a matrix C(given by other) with dimensions (m, n) and a matrix Q which is represented using Householder reflectors (input, tau). Returns a Tensor.
- Parameters
input (Tensor) – Tensor of shape \((*, mn, k)\), when left is True, mn equals to m, otherwise, mn equals to n. And * is zero or more batch dimensions.
tau (Tensor) – Tensor of shape \((*, min(mn, k))\) where * is zero or more batch dimensions, and its type is the same as input.
other (Tensor) – Tensor of shape \((*, m, n)\) where * is zero or more batch dimensions, and its type is the same as input.
left (bool, optional) – determines the order of multiplication. If True, computes op(Q) * other , otherwise, compute other * op(Q). Default:
True
.transpose (bool, optional) – If True, the matrix Q is conjugate transposed, otherwise, not conjugate transposing matrix Q. Default:
False
.
- Returns
Tensor, with the same type and shape as other.
- Raises
TypeError – If input or tau or other is not Tensor.
TypeError – If dtype of input or tau or other is not one of: float64, float32, complex64, complex128.
ValueError – If the dimension of input or other is less than 2D.
ValueError – If rank(input) - rank(tau) != 1.
ValueError – If tau.shape[:-1] != input.shape[:-2]
ValueError – If other.shape[:-2] != input.shape[:-2]
ValueError – If left == true, other.shape[-2] < tau.shape[-1].
ValueError – If left == true, other.shape[-2] != input.shape[-2].
ValueError – If left == false, other.shape[-1] < tau.shape[-1].
ValueError – If left == false, other.shape[-1] != input.shape[-2].
- Supported Platforms:
GPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> input = Tensor(np.array([[-114.6, 10.9, 1.1], [-0.304, 38.07, 69.38], [-0.45, -0.17, 62]]), ... mindspore.float32) >>> tau = Tensor(np.array([1.55, 1.94, 3.0]), mindspore.float32) >>> other = Tensor(np.array([[-114.6, 10.9, 1.1], ... [-0.304, 38.07, 69.38], ... [-0.45, -0.17, 62]]), mindspore.float32) >>> output = ops.ormqr(input, tau, other) >>> print(output) [[ 63.82713 -13.823125 -116.28614 ] [ -53.659264 -28.157839 -70.42702 ] [ -79.54292 24.00183 -41.34253 ]]