mindspore.numpy.nanmax

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mindspore.numpy.nanmax(a, axis=None, dtype=None, keepdims=False)[source]

Return the maximum of an array or maximum along an axis, ignoring any NaNs.

Note

Numpy arguments out is not supported. For all NaN slices, a very small negative number is returned instead of NaN.

Parameters

  • a (Union[int, float, list, tuple, Tensor]) – Array containing numbers whose maximum is desired. If a is not an array, a conversion is attempted.

  • axis (Union[int, tuple of int, None], optional) – Axis or axes along which the maximum is computed. The default is to compute the maximum of the flattened array.

  • dtype (mindspore.dtype, optional) – Default: None . Overrides the dtype of the output Tensor.

  • keepdims (boolean, optional) – Default: False . 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 a.

Returns

Tensor.

Raises

ValueError – If axes are out of the range of [-a.ndim, a.ndim), or if the axes contain duplicates.

Supported Platforms:

GPU CPU

Examples

>>> import mindspore.numpy as np
>>> a = np.array([[1, 2], [3, np.nan]])
>>> output = np.nanmax(a)
>>> print(output)
3.0
>>> output = np.nanmax(a, axis=0)
>>> print(output)
[3. 2.]