On 2015-11-12 15:57, PythonDude wrote:
Hi all,
I've come around a webpage with python-tutorial/description for obtaining
something and I'll solve this:
R = p^T w
where R is a vector and p^T is the transpose of another vector.
...
p is a Nx1 column vector, so p^T turns into a 1xN row vector which can be
multiplied with the
Nx1 weight (column) vector w to give a scalar result. This is equivalent to the
dot
product used in the code. Keep in mind that Python has a reversed definition of
rows and columns and the accurate NumPy version of the previous equation would
be R = w * p.T
...
(source: http://blog.quantopian.com/markowitz-portfolio-optimization-2/ )
I don't understand this: "Keep in mind that Python has a reversed definition of
rows and columns and the accurate NumPy version of the previous equation would
be R = w * p.T"
Not true for numpy, is it? This page:
http://mathesaurus.sourceforge.net/matlab-numpy.html says it python and matlab
looks quite similar...
Anyone could please explain or elaborate on exactly this (quote): "Keep in mind that
Python has a reversed definition of rows and columns"???
He's wrong, simply put. There is no "reversed definition of rows and columns".
He simply instantiated the two vectors as row-vectors instead of column-vectors,
which he could have easily done, so he had to flip the matrix expression.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
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