heat.array_api._statistical_functions

Module Contents

max(x: heat.array_api._array_object.Array, /, *, axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → heat.array_api._array_object.Array[source]

Calculates the maximum value of the input array x.

Parameters:

  • x (Array) – Input array. Must have a numeric data type.

  • axis (Optional[Union[int, Tuple[int, ...]]]) – Axis or axes along which maximum values are computed. By default, the maximum value is computed over the entire array. If a tuple of integers, maximum values are computed over multiple axes. Default: None.

  • keepdims (bool) – If True, the reduced axes (dimensions) are included in the result as singleton dimensions. Otherwise, if False, the reduced axes (dimensions) are be included in the result. Default: False.

mean(x: heat.array_api._array_object.Array, /, *, axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → heat.array_api._array_object.Array[source]

Calculates the arithmetic mean of the input array x.

Parameters:

  • x (Array) – Input array. Must have a floating-point data type.

  • axis (Optional[Union[int, Tuple[int, ...]]]) – Axis or axes along which arithmetic means are computed. By default, the mean is computed over the entire array. If a tuple of integers, arithmetic means are computed over multiple axes. Default: None.

  • keepdims (bool) – If True, the reduced axes (dimensions) are included in the result as singleton dimensions. Otherwise, if False, the reduced axes (dimensions) are be included in the result. Default: False.

min(x: heat.array_api._array_object.Array, /, *, axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → heat.array_api._array_object.Array[source]

Calculates the minimum value of the input array x.

Parameters:

  • x (Array) – Input array. Must have a numeric data type.

  • axis (Optional[Union[int, Tuple[int, ...]]]) – Axis or axes along which minimum values are computed. By default, the minimum value is computed over the entire array. If a tuple of integers, minimum values are computed over multiple axes. Default: None.

  • keepdims (bool) – If True, the reduced axes (dimensions) are included in the result as singleton dimensions. Otherwise, if False, the reduced axes (dimensions) are be included in the result. Default: False.

prod(x: heat.array_api._array_object.Array, /, *, axis: int | Tuple[int, Ellipsis] | None = None, dtype: heat.array_api._typing.Dtype | None = None, keepdims: bool = False) → heat.array_api._array_object.Array[source]

Calculates the product of input array x elements.

Parameters:

  • x (Array) – Input array. Must have a numeric data type.

  • axis (Optional[Union[int, Tuple[int, ...]]]) – Axis or axes along which products are computed. By default, the product is computed over the entire array. If a tuple of integers, products are computed over multiple axes. Default: None.

  • dtype (Optional[Dtype]) – Data type of the returned array.

  • keepdims (bool) – If True, the reduced axes (dimensions) are included in the result as singleton dimensions. Otherwise, if False, the reduced axes (dimensions) are be included in the result. Default: False.

std(x: heat.array_api._array_object.Array, /, *, axis: int | Tuple[int, Ellipsis] | None = None, correction: int | float = 0.0, keepdims: bool = False) → heat.array_api._array_object.Array[source]

Calculates the standard deviation of the input array x.

Parameters:

  • x (Array) – Input array. Must have a floating-point data type.

  • axis (Optional[Union[int, Tuple[int, ...]]]) – Axis or axes along which standard deviations are computed. By default, the standard deviation is computed over the entire array. If a tuple of integers, standard deviations are computed over multiple axes. Default: None.

  • correction (Union[int, float]) – Degrees of freedom adjustment.

  • keepdims (bool) – If True, the reduced axes (dimensions) are included in the result as singleton dimensions. Otherwise, if False, the reduced axes (dimensions) are be included in the result. Default: False.

sum(x: heat.array_api._array_object.Array, /, *, axis: int | Tuple[int, Ellipsis] | None = None, dtype: heat.array_api._typing.Dtype | None = None, keepdims: bool = False) → heat.array_api._array_object.Array[source]

Calculates the sum of the input array x.

Parameters:

  • x (Array) – Input array. Must have a numeric data type.

  • axis (Optional[Union[int, Tuple[int, ...]]]) – Axis or axes along which sums are computed. By default, the sum is computed over the entire array. If a tuple of integers, sums are computed over multiple axes. Default: None.

  • dtype (Optional[Dtype]) – Data type of the returned array.

  • keepdims (bool) – If True, the reduced axes (dimensions) are included in the result as singleton dimensions. Otherwise, if False, the reduced axes (dimensions) are be included in the result. Default: False.

var(x: heat.array_api._array_object.Array, /, *, axis: int | Tuple[int, Ellipsis] | None = None, correction: int | float = 0.0, keepdims: bool = False) → heat.array_api._array_object.Array[source]

Calculates the variance of the input array x.

Parameters:

  • x (Array) – Input array. Must have a floating-point data type.

  • axis (Optional[Union[int, Tuple[int, ...]]]) – Axis or axes along which variances are computed. By default, the variance is computed over the entire array. If a tuple of integers, variances are computed over multiple axes. Default: None.

  • correction (Union[int, float]) – Degrees of freedom adjustment.

  • keepdims (bool) – If True, the reduced axes (dimensions) are included in the result as singleton dimensions. Otherwise, if False, the reduced axes (dimensions) are be included in the result. Default: False.