flopscope.

flopscope.numpy.cumprod

fnp.cumprod(a, axis=None, dtype=None, out=None)[flopscope source][numpy source]

Return the cumulative product of elements along a given axis.

Adapted from NumPy docs np.cumprod

Areacore
Typecounted
NumPy Refnp.cumprod
Cost
numel(input)\text{numel}(\text{input})
Flopscope Context

Cumulative product of array elements.

Parameters

a:array_like

Input array.

axis:int, optional

Axis along which the cumulative product is computed. By default the input is flattened.

dtype:dtype, optional

Type of the returned array, as well as of the accumulator in which the elements are multiplied. If dtype is not specified, it defaults to the dtype of a, unless a has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead.

out:ndarray, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type of the resulting values will be cast if necessary.

Returns

cumprod:ndarray

A new array holding the result is returned unless out is specified, in which case a reference to out is returned.

See also

Notes

Arithmetic is modular when using integer types, and no error is raised on overflow.

Examples

>>> import flopscope.numpy as fnp
>>> a = flops.array([1,2,3])
>>> flops.cumprod(a) # intermediate results 1, 1*2
... # total product 1*2*3 = 6
array([1, 2, 6])
>>> a = flops.array([[1, 2, 3], [4, 5, 6]])
>>> flops.cumprod(a, dtype=float) # specify type of output
array([   1.,    2.,    6.,   24.,  120.,  720.])

The cumulative product for each column (i.e., over the rows) of a:

>>> flops.cumprod(a, axis=0)
array([[ 1,  2,  3],
       [ 4, 10, 18]])

The cumulative product for each row (i.e. over the columns) of a:

>>> flops.cumprod(a,axis=1)
array([[  1,   2,   6],
       [  4,  20, 120]])