heat.logical

Logical functions for the DNDarrays

Module Contents

all(x: heat.core.dndarray.DNDarray, axis: int | Tuple[int] | None = None, out: heat.core.dndarray.DNDarray | None = None, keepdims: bool = False) → heat.core.dndarray.DNDarray | bool[source]

Test whether all array elements along a given axis evaluate to True. A new boolean or DNDarray is returned unless out is specified, in which case a reference to out is returned.

Parameters:

  • x (DNDarray) – Input array or object that can be converted to an array.

  • axis (None or int or Tuple[int,...], optional) – Axis or axes along which a logical AND reduction is performed. The default (axis=None) is to perform a logical AND over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first axis.

  • out (DNDarray, optional) – Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved.

  • 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 original array.

Examples

>>> x = ht.random.randn(4, 5)
>>> x
DNDarray([[ 0.7199,  1.3718,  1.5008,  0.3435,  1.2884],
          [ 0.1532, -0.0968,  0.3739,  1.7843,  0.5614],
          [ 1.1522,  1.9076,  1.7638,  0.4110, -0.2803],
          [-0.5475, -0.0271,  0.8564, -1.5870,  1.3108]], dtype=ht.float32, device=cpu:0, split=None)
>>> y = x < 0.5
>>> y
DNDarray([[False, False, False,  True, False],
          [ True,  True,  True, False, False],
          [False, False, False,  True,  True],
          [ True,  True, False,  True, False]], dtype=ht.bool, device=cpu:0, split=None)
>>> ht.all(y)
DNDarray([False], dtype=ht.bool, device=cpu:0, split=None)
>>> ht.all(y, axis=0)
DNDarray([False, False, False, False, False], dtype=ht.bool, device=cpu:0, split=None)
>>> ht.all(x, axis=1)
DNDarray([True, True, True, True], dtype=ht.bool, device=cpu:0, split=None)
>>> out = ht.zeros(5)
>>> ht.all(y, axis=0, out=out)
DNDarray([False, False, False, False, False], dtype=ht.float32, device=cpu:0, split=None)
>>> out
DNDarray([False, False, False, False, False], dtype=ht.float32, device=cpu:0, split=None)
allclose(x: heat.core.dndarray.DNDarray, y: heat.core.dndarray.DNDarray, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) → bool[source]

Test whether two tensors are element-wise equal within a tolerance. Returns True if |x-y|<=atol+rtol*|y| for all elements of x and y, False otherwise

Parameters:

  • x (DNDarray) – First array to compare

  • y (DNDarray) – Second array to compare

  • atol (float, optional) – Absolute tolerance.

  • rtol (float, optional) – Relative tolerance (with respect to y).

  • equal_nan (bool, optional) – Whether to compare NaN’s as equal. If True, NaN’s in x will be considered equal to NaN’s in y in the output array.

Examples

>>> x = ht.float32([[2, 2], [2, 2]])
>>> ht.allclose(x, x)
True
>>> y = ht.float32([[2.00005, 2.00005], [2.00005, 2.00005]])
>>> ht.allclose(x, y)
False
>>> ht.allclose(x, y, atol=1e-04)
True
any(x, axis: int | None = None, out: heat.core.dndarray.DNDarray | None = None, keepdims: bool = False) → heat.core.dndarray.DNDarray[source]

Returns a DNDarray containing the result of the test whether any array elements along a given axis evaluate to True. The returning array is one dimensional unless axis is not None.

Parameters:

  • x (DNDarray) – Input tensor

  • axis (int, optional) – Axis along which a logic OR reduction is performed. With axis=None, the logical OR is performed over all dimensions of the array.

  • out (DNDarray, optional) – Alternative output tensor in which to place the result. It must have the same shape as the expected output. The output is a array with datatype=bool.

  • 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 original array.

Examples

>>> x = ht.float32([[0.3, 0, 0.5]])
>>> x.any()
DNDarray([True], dtype=ht.bool, device=cpu:0, split=None)
>>> x.any(axis=0)
DNDarray([ True, False,  True], dtype=ht.bool, device=cpu:0, split=None)
>>> x.any(axis=1)
DNDarray([True], dtype=ht.bool, device=cpu:0, split=None)
>>> y = ht.int32([[0, 0, 1], [0, 0, 0]])
>>> res = ht.zeros(3, dtype=ht.bool)
>>> y.any(axis=0, out=res)
DNDarray([False, False,  True], dtype=ht.bool, device=cpu:0, split=None)
>>> res
DNDarray([False, False,  True], dtype=ht.bool, device=cpu:0, split=None)
isclose(x: heat.core.dndarray.DNDarray, y: heat.core.dndarray.DNDarray, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) → heat.core.dndarray.DNDarray[source]

Returns a boolean DNDarray, with elements True where a and b are equal within the given tolerance. If both x and y are scalars, returns a single boolean value.

Parameters:

  • x (DNDarray) – Input array to compare.

  • y (DNDarray) – Input array to compare.

  • rtol (float) – The relative tolerance parameter.

  • atol (float) – The absolute tolerance parameter.

  • equal_nan (bool) – Whether to compare NaN’s as equal. If True, NaN’s in x will be considered equal to NaN’s in y in the output array.

isfinite(x: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray[source]

Test element-wise for finiteness (not infinity or not Not a Number) and return result as a boolean DNDarray.

Parameters:

x (DNDarray) – Input tensor

Examples

>>> ht.isfinite(ht.array([1, ht.inf, -ht.inf, ht.nan]))
DNDarray([ True, False, False, False], dtype=ht.bool, device=cpu:0, split=None)
isinf(x: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray[source]

Test element-wise for positive or negative infinity and return result as a boolean DNDarray.

Parameters:

x (DNDarray) – Input tensor

Examples

>>> ht.isinf(ht.array([1, ht.inf, -ht.inf, ht.nan]))
DNDarray([False,  True,  True, False], dtype=ht.bool, device=cpu:0, split=None)
isnan(x: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray[source]

Test element-wise for NaN and return result as a boolean DNDarray.

Parameters:

x (DNDarray) – Input tensor

Examples

>>> ht.isnan(ht.array([1, ht.inf, -ht.inf, ht.nan]))
DNDarray([False, False, False,  True], dtype=ht.bool, device=cpu:0, split=None)
isneginf(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) → heat.core.dndarray.DNDarray[source]

Test if each element of x is negative infinite, return result as a boolean DNDarray.

Parameters:

  • x (DNDarray) – Input tensor

  • out (DNDarray, optional) – Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved.

Examples

>>> ht.isnan(ht.array([1, ht.inf, -ht.inf, ht.nan]))
DNDarray([False, False, True, False], dtype=ht.bool, device=cpu:0, split=None)
isposinf(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None)[source]

Test if each element of x is positive infinite, return result as a boolean DNDarray.

Parameters:

  • x (DNDarray) – Input tensor

  • out (DNDarray, optional) – Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved.

Examples

>>> ht.isnan(ht.array([1, ht.inf, -ht.inf, ht.nan]))
DNDarray([False, True, False, False], dtype=ht.bool, device=cpu:0, split=None)
logical_and(x: heat.core.dndarray.DNDarray, y: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray[source]

Compute the truth value of x AND y element-wise. Returns a boolean DNDarray containing the truth value of x AND y element-wise.

Parameters:

  • x (DNDarray) – Input array of same shape

  • y (DNDarray) – Input array of same shape

Examples

>>> ht.logical_and(ht.array([True, False]), ht.array([False, False]))
DNDarray([False, False], dtype=ht.bool, device=cpu:0, split=None)
logical_not(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) → heat.core.dndarray.DNDarray[source]

Computes the element-wise logical NOT of the given input DNDarray.

Parameters:

  • x (DNDarray) – Input array

  • out (DNDarray, optional) – Alternative output array in which to place the result. It must have the same shape as the expected output. The output is a DNDarray with datatype=bool.

Examples

>>> ht.logical_not(ht.array([True, False]))
DNDarray([False,  True], dtype=ht.bool, device=cpu:0, split=None)
logical_or(x: heat.core.dndarray.DNDarray, y: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray[source]

Returns boolean DNDarray containing the element-wise logical NOT of the given input DNDarray.

Parameters:

  • x (DNDarray) – Input array of same shape

  • y (DNDarray) – Input array of same shape

Examples

>>> ht.logical_or(ht.array([True, False]), ht.array([False, False]))
DNDarray([ True, False], dtype=ht.bool, device=cpu:0, split=None)
logical_xor(x: heat.core.dndarray.DNDarray, y: heat.core.dndarray.DNDarray) → heat.core.dndarray.DNDarray[source]

Computes the element-wise logical XOR of the given input DNDarray.

Parameters:

  • x (DNDarray) – Input array of same shape

  • y (DNDarray) – Input array of same shape

Examples

>>> ht.logical_xor(ht.array([True, False, True]), ht.array([True, False, False]))
DNDarray([False, False,  True], dtype=ht.bool, device=cpu:0, split=None)
signbit(x: heat.core.dndarray.DNDarray, out: heat.core.dndarray.DNDarray | None = None) → heat.core.dndarray.DNDarray[source]

Checks if signbit is set element-wise (less than zero).

Parameters:

  • x (DNDarray) – The input array.

  • out (DNDarray, optional) – The output array.

Examples

>>> a = ht.array([2, -1.3, 0])
>>> ht.signbit(a)
DNDarray([False,  True, False], dtype=ht.bool, device=cpu:0, split=None)