Source code for mars.tensor.reduction.cumprod

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np

from ... import opcodes as OperandDef
from ..datasource import tensor as astensor
from ..arithmetic.multiply import TensorTreeMultiply
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)

    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)