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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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import numpy as np
from ... import opcodes as OperandDef
from ..arithmetic.multiply import TensorTreeMultiply
from ..datasource import tensor as astensor
from .core import TensorCumReduction, TensorCumReductionMixin
class TensorCumprod(TensorCumReduction, TensorCumReductionMixin):
_op_type_ = OperandDef.CUMPROD
_func_name = "cumprod"
def __init__(self, axis=None, **kw):
super().__init__(_axis=axis, **kw)
@staticmethod
def _get_op_types():
return TensorCumprod, TensorTreeMultiply
[docs]def cumprod(a, axis=None, dtype=None, out=None):
"""
Return the cumulative product of elements along a given axis.
Parameters
----------
a : array_like
Input tensor.
axis : int, optional
Axis along which the cumulative product is computed. By default
the input is flattened.
dtype : dtype, optional
Type of the returned tensor, 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 : Tensor, optional
Alternative output tensor 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 : Tensor
A new tensor holding the result is returned unless `out` is
specified, in which case a reference to out is returned.
See Also
--------
numpy.doc.ufuncs : Section "Output arguments"
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.array([1,2,3])
>>> mt.cumprod(a).execute() # intermediate results 1, 1*2
... # total product 1*2*3 = 6
array([1, 2, 6])
>>> a = mt.array([[1, 2, 3], [4, 5, 6]])
>>> mt.cumprod(a, dtype=float).execute() # specify type of output
array([ 1., 2., 6., 24., 120., 720.])
The cumulative product for each column (i.e., over the rows) of `a`:
>>> mt.cumprod(a, axis=0).execute()
array([[ 1, 2, 3],
[ 4, 10, 18]])
The cumulative product for each row (i.e. over the columns) of `a`:
>>> mt.cumprod(a,axis=1).execute()
array([[ 1, 2, 6],
[ 4, 20, 120]])
"""
a = astensor(a)
if dtype is None:
dtype = np.empty((1,), dtype=a.dtype).cumprod().dtype
op = TensorCumprod(axis=axis, dtype=dtype)
return op(a, out=out)