mindchemistry.e3.nn.SoftOneHotLinspace

class mindchemistry.e3.nn.SoftOneHotLinspace(start, end, number, basis='smooth_finite', cutoff=True, dtype=float32)

Projection on a basis of functions. Returns a set of \(\{y_i(x)\}_{i=1}^N\),

\[y_i(x) = \frac{1}{Z} f_i(x)\]

where \(x\) is the input and \(f_i\) is the ith basis function. \(Z\) is a constant defined (if possible) such that,

\[\langle \sum_{i=1}^N y_i(x)^2 \rangle_x \approx 1\]

Note that bessel basis cannot be normalized.

Parameters

• start (float) – minimum value span by the basis.

• end (float) – maximum value span by the basis.

• number (int) – number of basis functions \(N\).

• basis (str) – {'gaussian', 'cosine', 'smooth_finite', 'fourier', 'bessel'}, the basis family. Default: 'smooth_finite'.

• cutoff (bool) – whether require the \(y_i(x)\) from the outside domain of (start, end) to be vanished. Default: True.

• dtype (mindspore.dtype) – The type of input tensor. Default: mindspore.float32.

Inputs:

• x (Tensor) - The shape of Tensor is \((...)\).

Outputs:

• output (Tensor) - The shape of Tensor is \((..., N)\).

Raises

ValueError – If basis is not in {'gaussian', 'cosine', 'smooth_finite', 'fourier', 'bessel'}.

Supported Platforms:

Ascend

Examples

>>> from mindchemistry.e3.nn import SoftOneHotLinspace
>>> from mindspore import ops, Tensor
>>> soft_one_hot_linspace = SoftOneHotLinspace(-0.5, 1.5, number=4)
>>> x = Tensor(ops.ones((4, 6)))
>>> outputs = soft_one_hot_linspace(x)
>>> print(outputs.shape)
(4, 6, 4)