dot(a, b, strict=False, out=None)
This function is the equivalent of numpy.dot
that takes masked values into account. Note that strict
and :None:None:`out` are in different position than in the method version. In order to maintain compatibility with the corresponding method, it is recommended that the optional arguments be treated as keyword only. At some point that may be mandatory.
Works only with 2-D arrays at the moment.
Inputs arrays.
Whether masked data are propagated (True) or set to 0 (False) for the computation. Default is False. Propagating the mask means that if a masked value appears in a row or column, the whole row or column is considered masked.
Output argument. This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for :None:None:`dot(a,b)`. This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible.
Return the dot product of two arrays.
numpy.dot
Equivalent function for ndarrays.
>>> a = np.ma.array([[1, 2, 3], [4, 5, 6]], mask=[[1, 0, 0], [0, 0, 0]])This example is valid syntax, but we were not able to check execution
... b = np.ma.array([[1, 2], [3, 4], [5, 6]], mask=[[1, 0], [0, 0], [0, 0]])
... np.ma.dot(a, b) masked_array( data=[[21, 26], [45, 64]], mask=[[False, False], [False, False]], fill_value=999999)
>>> np.ma.dot(a, b, strict=True) masked_array( data=[[--, --], [--, 64]], mask=[[ True, True], [ True, False]], fill_value=999999)See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.ma.core.MaskedArray.dot
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