mygrad.geomspace#

mygrad.geomspace(start: ArrayLike, stop: ArrayLike, num=50, endpoint=True, dtype=None, axis=0, *, constant: bool | None = None) → Tensor[source]#

Return a Tensor with evenly-spaced values in a geometric progression.

Each output sample is a constant multiple of the previous output.

This docstring was adapted from numpy.geomspace [1]

Parameters:

startArrayLike

The starting value of the output.

stopArrayLike

The ending value of the sequence, inclusive unless endpoint is false.

numint, optional (default=50)

The number of values to generate. Must be non-negative.

endpointbool, optional (default=True)

Whether to include the endpoint in the Tensor. Note that if False, the step size changes to accommodate the sequence excluding the endpoint.

dtypeOptional[DTypeLikeReals]

The data type of the output Tensor, or None to infer from the inputs.

axisint, optional (default=0)

The axis in the result to store the samples - for array-like start/stop.

constantOptional[bool]

If True, this tensor is a constant, and thus does not facilitate back propagation.

Defaults to False for float-type data. Defaults to True for integer-type data.

Integer-type tensors must be constant.

Returns:

Tensor

See also

logspace: Similar to geomspace, but with endpoints specified using log and base.
linspace: Similar to geomspace, but with arithmetic instead of geometric progression.
arange: Similar to linspace, with the step size specified instead of the number of samples.

References

[1]

Retrieved from https://numpy.org/doc/stable/reference/generated/numpy.geomspace.html

Examples

>>> import mygrad as mg
>>> mg.geomspace(1, 1000, num=4)
Tensor([    1.,    10.,   100.,  1000.])
>>> mg.geomspace(1, 1000, num=3, endpoint=False)
Tensor([   1.,   10.,  100.])
>>> mg.geomspace(1, 1000, num=4, endpoint=False)
Tensor([   1.        ,    5.62341325,   31.6227766 ,  177.827941  ])
>>> mg.geomspace(1, 256, num=9)
Tensor([   1.,    2.,    4.,    8.,   16.,   32.,   64.,  128.,  256.])

Note that the above may not produce exact integers:

>>> mg.geomspace(1, 256, num=9, dtype=int)
Tensor([  1,   2,   4,   7,  16,  32,  63, 127, 256])
>>> np.around(mg.geomspace(1, 256, num=9).data).astype(int)
array([  1,   2,   4,   8,  16,  32,  64, 128, 256])

Negative, and decreasing inputs are allowed:

>>> mg.geomspace(1000, 1, num=4)
Tensor([ 1000.,   100.,    10.,     1.])
>>> mg.geomspace(-1000, -1, num=4)
Tensor([-1000.,  -100.,   -10.,    -1.])