cond¶
- paddle.linalg. cond ( x, p=None, name=None ) [source]
-
Computes the condition number of a matrix or batches of matrices with respect to a matrix norm
p.- Parameters
-
x (Tensor) – The input tensor could be tensor of shape
(*, m, n)where*is zero or more batch dimensions forpin(2, -2), or of shape(*, n, n)where every matrix is invertible for any supportedp. And the input data type could befloat32orfloat64.p (float|string, optional) – Order of the norm. Supported values are fro, nuc, 1, -1, 2, -2, inf, -inf. Default value is None, meaning that the order of the norm is 2.
name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name.
- Returns
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computing results of condition number, its data type is the same as input Tensor
x. - Return type
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Tensor
Examples
import paddle x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]]) # compute conditional number when p is None out = paddle.linalg.cond(x) # Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, # 1.41421342) # compute conditional number when order of the norm is 'fro' out_fro = paddle.linalg.cond(x, p='fro') # Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, # 3.16227770) # compute conditional number when order of the norm is 'nuc' out_nuc = paddle.linalg.cond(x, p='nuc') # Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, # 9.24263859) # compute conditional number when order of the norm is 1 out_1 = paddle.linalg.cond(x, p=1) # Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, # 2.) # compute conditional number when order of the norm is -1 out_minus_1 = paddle.linalg.cond(x, p=-1) # Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, # 1.) # compute conditional number when order of the norm is 2 out_2 = paddle.linalg.cond(x, p=2) # Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, # 1.41421342) # compute conditional number when order of the norm is -1 out_minus_2 = paddle.linalg.cond(x, p=-2) # Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, # 0.70710683) # compute conditional number when order of the norm is inf out_inf = paddle.linalg.cond(x, p=float("inf")) # Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, # 2.) # compute conditional number when order of the norm is -inf out_minus_inf = paddle.linalg.cond(x, p=-float("inf")) # Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, # 1.) a = paddle.randn([2, 4, 4]) # Tensor(shape=[2, 4, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True, # [[[-0.06784091, -0.07095790, 1.31792855, -0.58959651], # [ 0.20818676, -0.85640615, -0.89998871, -1.47439921], # [-0.49132481, 0.42250812, -0.77383220, -2.19794774], # [-0.33551720, -1.70003879, -1.09795380, -0.63737559]], # [[ 1.12026262, -0.16119350, -1.21157813, 2.74383283], # [-0.15999718, 0.18798758, -0.69392562, 1.35720372], # [-0.53013402, -2.26304483, 1.40843511, -1.02288902], # [ 0.69533503, 2.05261683, -0.02251151, -1.43127477]]]) a_cond_fro = paddle.linalg.cond(a, p='fro') # Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True, # [8.86691189 , 75.23817444]) b = paddle.randn([2, 3, 4]) # Tensor(shape=[2, 3, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True, # [[[-0.43754861, 1.80796063, -0.78729683, -1.82264030], # [-0.27670753, 0.06620564, 0.29072434, -0.31155765], # [ 0.34123746, -0.05444612, 0.05001324, -1.46877074]], # [[-0.64331555, -1.51103854, -1.26277697, -0.68024760], # [ 2.59375715, -1.06665540, 0.96575671, -0.73330832], # [-0.47064447, -0.23945692, -0.95150250, -1.07125998]]]) b_cond_2 = paddle.linalg.cond(b, p=2) # Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True, # [6.64228773, 3.89068866])