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"??? That I don't understand - thank you for any hints/guidance/explanations/ideas/suggestions etc! -- https://mail.python.org/mailman/listinfo/python-list
