On Thursday, 12 November 2015 22:57:21 UTC+1, Robert Kern wrote: > 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".
Great, thank... > 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. Thank you very much Robert - I just had to be sure about it :-) -- https://mail.python.org/mailman/listinfo/python-list
