#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2020 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 # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from ... import opcodes as OperandDef from ..array_utils import device, as_same_device from .sum import TensorSum from .core import TensorReduction, TensorReductionMixin class TensorCountNonzero(TensorReduction, TensorReductionMixin): _op_type_ = OperandDef.COUNT_NONZERO def __init__(self, axis=None, dtype=np.intp, keepdims=None, combine_size=None, stage=None, **kw): stage = self._rewrite_stage(stage) super().__init__(_axis=axis, _dtype=dtype, _keepdims=keepdims, _combine_size=combine_size, _stage=stage, **kw) @classmethod def execute_map(cls, ctx, op): (x,), device_id, xp = as_same_device( [ctx[c.key] for c in op.inputs], op.device, ret_extra=True) axis = cls.get_arg_axis(op.axis, op.inputs[0].ndim) keepdims = op.keepdims with device(device_id): nz = xp.count_nonzero(x, axis=axis) if keepdims: slcs = [slice(None)] * op.inputs[0].ndim for ax in op.axis: slcs[ax] = np.newaxis nz = xp.asarray(nz)[tuple(slcs)] ctx[op.outputs[0].key] = nz @classmethod def execute_agg(cls, ctx, op): return TensorSum.execute_agg(ctx, op) @classmethod def execute_one_chunk(cls, ctx, op): a = ctx[op.inputs[0].key] (inp,), device_id, xp = as_same_device( [a], device=op.device, ret_extra=True) with device(device_id): ctx[op.outputs[0].key] = xp.count_nonzero(inp, axis=op.axis) [docs]def count_nonzero(a, axis=None, combine_size=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``. combine_size: int, optional The number of chunks to combine. 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 mars.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(np.int_), keepdims=None, combine_size=combine_size) return op(a)