#!/usr/bin/env python
# -*- coding: utf-8 -*-
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#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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import numpy as np
from ... import opcodes
from .core import TensorReduction, TensorReductionMixin
class TensorCountNonzero(TensorReduction, TensorReductionMixin):
_op_type_ = opcodes.COUNT_NONZERO
_func_name = "count_nonzero"
def __init__(self, dtype=None, **kw):
if dtype is None:
dtype = np.dtype(np.intp)
super().__init__(dtype=dtype, **kw)
[docs]
def count_nonzero(a, axis=None):
"""
Counts the number of non-zero values in the tensor ``a``.
The word "non-zero" is in reference to the Python 2.x
built-in method ``__nonzero__()`` (renamed ``__bool__()``
in Python 3.x) of Python objects that tests an object's
"truthfulness". For example, any number is considered
truthful if it is nonzero, whereas any string is considered
truthful if it is not the empty string. Thus, this function
(recursively) counts how many elements in ``a`` (and in
sub-tensors thereof) have their ``__nonzero__()`` or ``__bool__()``
method evaluated to ``True``.
Parameters
----------
a : array_like
The tensor for which to count non-zeros.
axis : int or tuple, optional
Axis or tuple of axes along which to count non-zeros.
Default is None, meaning that non-zeros will be counted
along a flattened version of ``a``.
Returns
-------
count : int or tensor of int
Number of non-zero values in the array along a given axis.
Otherwise, the total number of non-zero values in the tensor
is returned.
See Also
--------
nonzero : Return the coordinates of all the non-zero values.
Examples
--------
>>> import maxframe.tensor as mt
>>> mt.count_nonzero(mt.eye(4)).execute()
4
>>> mt.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]]).execute()
5
>>> mt.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=0).execute()
array([1, 1, 1, 1, 1])
>>> mt.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=1).execute()
array([2, 3])
"""
op = TensorCountNonzero(axis=axis, dtype=np.dtype(int), keepdims=None)
return op(a)