mars.tensor.isin#
- mars.tensor.isin(element: Union[mars.typing.TileableType, numpy.ndarray], test_elements: Union[mars.typing.TileableType, numpy.ndarray, list], assume_unique: bool = False, invert: bool = False)[源代码]#
Calculates element in test_elements, broadcasting over element only. Returns a boolean array of the same shape as element that is True where an element of element is in test_elements and False otherwise.
- 参数
element (array_like) – Input tensor.
test_elements (array_like) – The values against which to test each value of element. This argument is flattened if it is a tensor or array_like. See notes for behavior with non-array-like parameters.
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, as if calculating element not in test_elements. Default is False.
mt.isin(a, b, invert=True)
is equivalent to (but faster than)mt.invert(mt.isin(a, b))
.
- 返回
isin – Has the same shape as element. The values element[isin] are in test_elements.
- 返回类型
Tensor, bool
参见
in1d
Flattened version of this function.
提示
isin is an element-wise function version of the python keyword in.
isin(a, b)
is roughly equivalent tomt.array([item in b for item in a])
if a and b are 1-D sequences.element and test_elements are converted to tensors if they are not already. If test_elements is a set (or other non-sequence collection) it will be converted to an object tensor with one element, rather than a tensor of the values contained in test_elements. This is a consequence of the tensor constructor’s way of handling non-sequence collections. Converting the set to a list usually gives the desired behavior.
实际案例
>>> import mars.tensor as mt
>>> element = 2*mt.arange(4).reshape((2, 2)) >>> element.execute() array([[0, 2], [4, 6]]) >>> test_elements = [1, 2, 4, 8] >>> mask = mt.isin(element, test_elements) >>> mask.execute() array([[ False, True], [ True, False]]) >>> element[mask].execute() array([2, 4]) >>> mask = mt.isin(element, test_elements, invert=True) >>> mask.execute() array([[ True, False], [ False, True]]) >>> element[mask] array([0, 6])
Because of how array handles sets, the following does not work as expected:
>>> test_set = {1, 2, 4, 8} >>> mt.isin(element, test_set).execute() array([[ False, False], [ False, False]])
Casting the set to a list gives the expected result:
>>> mt.isin(element, list(test_set)).execute() array([[ False, True], [ True, False]])