jax.numpy.var — JAX documentation (original) (raw)
jax.numpy.var#
jax.numpy.var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=None, correction=None)[source]#
Compute the variance along a given axis.
JAX implementation of numpy.var().
Parameters:
- a (ArrayLike) – input array.
- axis (Axis | None) – optional, int or sequence of ints, default=None. Axis along which the variance is computed. If None, variance is computed along all the axes.
- dtype (DTypeLike | None | None) – The type of the output array. Default=None.
- ddof (int) – int, default=0. Degrees of freedom. The divisor in the variance computation is
N-ddof,Nis number of elements along given axis. - keepdims (bool) – bool, default=False. If true, reduced axes are left in the result with size 1.
- where (ArrayLike | None | None) – optional, boolean array, default=None. The elements to be used in the variance. Array should be broadcast compatible to the input.
- correction (int | float | None | None) – int or float, default=None. Alternative name for
ddof. Both ddof and correction can’t be provided simultaneously. - out (None | None) – Unused by JAX.
Returns:
An array of the variance along the given axis.
Return type:
See also
- jax.numpy.mean(): Compute the mean of array elements over a given axis.
- jax.numpy.std(): Compute the standard deviation of array elements over given axis.
- jax.numpy.nanvar(): Compute the variance along a given axis, ignoring NaNs values.
- jax.numpy.nanstd(): Computed the standard deviation of a given axis, ignoring NaN values.
Examples
By default, jnp.var computes the variance along all axes.
x = jnp.array([[1, 3, 4, 2], ... [5, 2, 6, 3], ... [8, 4, 2, 9]]) with jnp.printoptions(precision=2, suppress=True): ... jnp.var(x) Array(5.74, dtype=float32)
If axis=1, variance is computed along axis 1.
jnp.var(x, axis=1) Array([1.25 , 2.5 , 8.1875], dtype=float32)
To preserve the dimensions of input, you can set keepdims=True.
jnp.var(x, axis=1, keepdims=True) Array([[1.25 ], [2.5 ], [8.1875]], dtype=float32)
If ddof=1:
with jnp.printoptions(precision=2, suppress=True): ... print(jnp.var(x, axis=1, keepdims=True, ddof=1)) [[ 1.67] [ 3.33] [10.92]]
To include specific elements of the array to compute variance, you can usewhere.
where = jnp.array([[1, 0, 1, 0], ... [0, 1, 1, 0], ... [1, 1, 1, 0]], dtype=bool) with jnp.printoptions(precision=2, suppress=True): ... print(jnp.var(x, axis=1, keepdims=True, where=where)) [[2.25] [4. ] [6.22]]