Source code for mars.tensor.base.in1d

# 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.

from typing import Union

import numpy as np

from ...typing import TileableType
from .. import asarray

[docs]def in1d( ar1: Union[TileableType, np.ndarray], ar2: Union[TileableType, np.ndarray, list], assume_unique: bool = False, invert: bool = False, ): """ Test whether each element of a 1-D tensor is also present in a second tensor. Returns a boolean tensor the same length as `ar1` that is True where an element of `ar1` is in `ar2` and False otherwise. We recommend using :func:`isin` instead of `in1d` for new code. Parameters ---------- ar1 : (M,) Tensor Input tensor. ar2 : array_like The values against which to test each value of `ar1`. assume_unique : bool, optional If True, the input tensors are both assumed to be unique, which can speed up the calculation. Default is False. invert : bool, optional If True, the values in the returned tensor are inverted (that is, False where an element of `ar1` is in `ar2` and True otherwise). Default is False. ``np.in1d(a, b, invert=True)`` is equivalent to (but is faster than) ``np.invert(in1d(a, b))``. Returns ------- in1d : (M,) Tensor, bool The values `ar1[in1d]` are in `ar2`. See Also -------- isin : Version of this function that preserves the shape of ar1. numpy.lib.arraysetops : Module with a number of other functions for performing set operations on arrays. Notes ----- `in1d` can be considered as an element-wise function version of the python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly equivalent to ``mt.array([item in b for item in a])``. However, this idea fails if `ar2` is a set, or similar (non-sequence) container: As ``ar2`` is converted to a tensor, in those cases ``asarray(ar2)`` is an object tensor rather than the expected tensor of contained values. Examples -------- >>> import mars.tensor as mt >>> test = mt.array([0, 1, 2, 5, 0]) >>> states = [0, 2] >>> mask = mt.in1d(test, states) >>> mask.execute() array([ True, False, True, False, True]) >>> test[mask].execute() array([0, 2, 0]) >>> mask = mt.in1d(test, states, invert=True) >>> mask.execute() array([False, True, False, True, False]) >>> test[mask].execute() array([1, 5]) """ from .isin import isin ar1 = asarray(ar1).ravel() ar2 = asarray(ar2).ravel() return isin(ar1, ar2, assume_unique=assume_unique, invert=invert)