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

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.])