logspace¶

logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0, ctx=None)¶

Return numbers spaced evenly on a log scale.

In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below).

Non-scalar start and stop are now supported.

Parameters:

start : int or float: base ** start is the starting value of the sequence.
stop : int or float: base ** stop is the final value of the sequence, unless endpoint is False. In that case, num + 1 values are spaced over the interval in log-space, of which all but the last (a sequence of length num) are returned.
num : integer, optional: Number of samples to generate. Default is 50.
endpoint : boolean, optional: If true, stop is the last sample. Otherwise, it is not included. Default is True.
base : float, optional: The base of the log space. The step size between the elements in ln(samples) / ln(base) (or log_base(samples)) is uniform. Default is 10.0.
dtype : dtype: The type of the output array. If dtype is not given, infer the data type from the other input arguments.
axis : int, optional: The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Now, axis only support axis = 0.
ctx : Context, optional: An optional device context (default is the current default context).

Returns:

samples : ndarray: num samples, equally spaced on a log scale.

See also

arange: Similar to linspace, with the step size specified instead of the number of samples. Note that, when used with a float endpoint, the endpoint may or may not be included.
linspace: Similar to logspace, but with the samples uniformly distributed in linear space, instead of log space.

Notes

Logspace is equivalent to the code

>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
...
>>> power(base, y).astype(dtype)
...

Examples

>>> np.logspace(2.0, 3.0, num=4)
array([ 100.     ,  215.44347,  464.15887, 1000.     ])
>>> np.logspace(2.0, 3.0, num=4, endpoint=False)
array([100.     , 177.82794, 316.22775, 562.3413 ])
>>> np.logspace(2.0, 3.0, num=4, base=2.0)
array([4.       , 5.0396843, 6.349604 , 8.       ])
>>> np.logspace(2.0, 3.0, num=4, base=2.0, dtype=np.int32)
array([4, 5, 6, 8], dtype=int32)
>>> np.logspace(2.0, 3.0, num=4, ctx=npx.gpu(0))
array([ 100.     ,  215.44347,  464.15887, 1000.     ], ctx=gpu(0))