mars.tensor.log#

mars.tensor.log(x, out=None, where=None, **kwargs)[source]#

Natural logarithm, element-wise.

The natural logarithm log is the inverse of the exponential function, so that log(exp(x)) = x. The natural logarithm is logarithm in base e.

Parameters

  • x (array_like) – Input value.

  • out (Tensor, None, or tuple of tensor and None, optional) – A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated tensor is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

  • where (array_like, optional) – Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.

  • **kwargs –

Returns

y – The natural logarithm of x, element-wise.

Return type

Tensor

See also

log10, log2, log1p

Notes

Logarithm is a multivalued function: for each x there is an infinite number of z such that exp(z) = x. The convention is to return the z whose imaginary part lies in [-pi, pi].

For real-valued input data types, log always returns real output. For each value that cannot be expressed as a real number or infinity, it yields nan and sets the invalid floating point error flag.

For complex-valued input, log is a complex analytical function that has a branch cut [-inf, 0] and is continuous from above on it. log handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.

References

1

M. Abramowitz and I.A. Stegun, “Handbook of Mathematical Functions”, 10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/

2

Wikipedia, “Logarithm”. http://en.wikipedia.org/wiki/Logarithm

Examples

>>> import mars.tensor as mt

>>> mt.log([1, mt.e, mt.e**2, 0]).execute()
array([  0.,   1.,   2., -Inf])