Source code for

#!/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 .core import TensorReduction, TensorReductionMixin

class TensorProd(TensorReduction, TensorReductionMixin):
    _op_type_ = OperandDef.PROD
    _func_name = "prod"

    def __init__(self, axis=None, keepdims=None, combine_size=None, stage=None, **kw):
        stage = self._rewrite_stage(stage)

[docs]def prod(a, axis=None, dtype=None, out=None, keepdims=None, combine_size=None): """ Return the product of tensor elements over a given axis. Parameters ---------- a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which a product is performed. The default, axis=None, will calculate the product of all the elements in the input tensor. If axis is negative it counts from the last to the first axis. If axis is a tuple of ints, a product is performed on all of the axes specified in the tuple instead of a single axis or all the axes as before. dtype : dtype, optional The type of the returned tensor, as well as of the accumulator in which the elements are multiplied. The dtype of `a` is used by default unless `a` has an integer dtype of less precision than the default platform integer. In that case, if `a` is signed then the platform integer is used while if `a` is unsigned then an unsigned integer of the same precision as the platform integer is used. out : Tensor, optional Alternative output tensor in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then `keepdims` will not be passed through to the `prod` method of sub-classes of `Tensor`, however any non-default value will be. If the sub-classes `sum` method does not implement `keepdims` any exceptions will be raised. combine_size: int, optional The number of chunks to combine. Returns ------- product_along_axis : Tensor, see `dtype` parameter above. An tensor shaped as `a` but with the specified axis removed. Returns a reference to `out` if specified. See Also -------- : equivalent method Notes ----- Arithmetic is modular when using integer types, and no error is raised on overflow. That means that, on a 32-bit platform: >>> import mars.tensor as mt >>> x = mt.array([536870910, 536870910, 536870910, 536870910]) >>> # random 16 The product of an empty array is the neutral element 1: >>>[]).execute() 1.0 Examples -------- By default, calculate the product of all elements: >>>[1.,2.]).execute() 2.0 Even when the input array is two-dimensional: >>>[[1.,2.],[3.,4.]]).execute() 24.0 But we can also specify the axis over which to multiply: >>>[[1.,2.],[3.,4.]], axis=1).execute() array([ 2., 12.]) If the type of `x` is unsigned, then the output type is the unsigned platform integer: >>> x = mt.array([1, 2, 3], dtype=mt.uint8) >>> == mt.uint True If `x` is of a signed integer type, then the output type is the default platform integer: >>> x = mt.array([1, 2, 3], dtype=mt.int8) >>> == int True """ a = astensor(a) if dtype is None: dtype = np.empty((1,), dtype=a.dtype).prod().dtype op = TensorProd( axis=axis, dtype=dtype, keepdims=keepdims, combine_size=combine_size ) return op(a, out=out)