dpnp.average

dpnp.average(a, axis=None, weights=None, returned=False, *, keepdims=False)[source]

Compute the weighted average along the specified axis.

For full documentation refer to numpy.average.

Parameters:

a ({dpnp.ndarray, usm_ndarray}) -- Input array.
axis ({None, int, tuple of ints}, optional) -- Axis or axes along which the averages must be computed. If a tuple of unique integers, the averages are computed over multiple axes. If None, the average is computed over the entire array. Default: None.
weights ({array_like}, optional) --
An array of weights associated with the values in a. Each value in a contributes to the average according to its associated weight. The weights array can either be 1-D (in which case its length must be the size of a along the given axis) or of the same shape as a. If weights=None, then all data in a are assumed to have a weight equal to one. The 1-D calculation is:
```
avg = sum(a * weights) / sum(weights)
```
The only constraint on weights is that sum(weights) must not be 0.
returned ({bool}, optional) -- Default is False. If True, the tuple (average, sum_of_weights) is returned, otherwise only the average is returned. If weights=None, sum_of_weights is equivalent to the number of elements over which the average is taken.
keepdims ({None, bool}, optional) -- If True, the reduced axes (dimensions) are included in the result as singleton dimensions, so that the returned array remains compatible with the input array according to Array Broadcasting rules. Otherwise, if False, the reduced axes are not included in the returned array. Default: False.

Returns:

out, [sum_of_weights] -- Return the average along the specified axis. When returned is True, return a tuple with the average as the first element and the sum of the weights as the second element. sum_of_weights is of the same type as out. The result dtype follows a general pattern. If weights is None, the result dtype will be that of a , or default floating point data type for the device where input array a is allocated. Otherwise, if weights is not None and a is non-integral, the result type will be the type of lowest precision capable of representing values of both a and weights. If a happens to be integral, the previous rules still applies but the result dtype will at least be default floating point data type for the device where input array a is allocated.

Return type:

dpnp.ndarray, dpnp.ndarray

See also

dpnp.mean: Compute the arithmetic mean along the specified axis.
dpnp.sum: Sum of array elements over a given axis.

Examples

>>> import dpnp as np
>>> data = np.arange(1, 5)
>>> data
array([1, 2, 3, 4])
>>> np.average(data)
array(2.5)
>>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))
array(4.0)

>>> data = np.arange(6).reshape((3, 2))
>>> data
array([[0, 1],
    [2, 3],
    [4, 5]])
>>> np.average(data, axis=1, weights=[1./4, 3./4])
array([0.75, 2.75, 4.75])
>>> np.average(data, weights=[1./4, 3./4])
TypeError: Axis must be specified when shapes of a and weights differ.

With keepdims=True, the following result has shape (3, 1).

>>> np.average(data, axis=1, keepdims=True)
array([[0.5],
    [2.5],
    [4.5]])

>>> a = np.ones(5, dtype=np.float64)
>>> w = np.ones(5, dtype=np.complex64)
>>> avg = np.average(a, weights=w)
>>> print(avg.dtype)
complex128