# mindspore.ops.Cdist

class mindspore.ops.Cdist(p=2.0)[source]

Computes batched the p-norm distance between each pair of the two collections of row vectors.

Parameters

p (float) – P value for the p-norm distance to calculate between each vector pair, P ∈ [0,∞]. Default: 2.0.

Inputs:
• input_x (Tensor) - Input tensor of shape $$(B, P, M)$$. Letter $$B$$ represents 0 or positive int number. When $$B$$ is equal to 0, it means this dimension can be ignored, i.e. shape of the tensor is $$(P, M)$$.

• input_y (Tensor) - Input tensor of shape $$(B, R, M)$$.

Outputs:

Tensor, has the same dtype as input_x, which shape is $$(B, P, R)$$.

Raises
• TypeError – If input_x or input_y is not a Tensor.

• TypeError – If dtype of input_x or input_y is neither float16 nor float32.

• TypeError – If p is not a float.

• ValueError – If p is a negative float.

• ValueError – If dimension of input_x is not the same as input_y.

• ValueError – If dimension of input_x or input_y is neither 2 nor 3.

Supported Platforms:

Ascend

Examples

>>> input_x = Tensor(np.array([[[1.0, 1.0], [2.0, 2.0]]]).astype(np.float32))
>>> input_y = Tensor(np.array([[[3.0, 3.0], [3.0, 3.0]]]).astype(np.float32))
>>> op = ops.Cdist(p=2.0)
>>> output = op(input_x, input_y)
>>> print(output)
[[[2.8284273 2.8284273]
[1.4142137 1.4142137]]]