Le mardi 27 mars 2012 à 09:43 -0700, sachin004 a écrit : > symbolic regression (or symbolic function identification)can be done > by genetic programming (many other methods are available ). symbolic > regression finds the symbolic expression function to the given data > input and outputs and outputs an expression best fitted for the > inputs. the basic difference between symbolic regression and normal > regression is normal regression assumes a model(expression) and > determines the coefficients, where as symbolic regression searches for > the model and fits it. > Currently mathematica, matlab and many more are > supporting(implemented) symbolic regression. > > http://library.wolfram.com/infocenter/Conferences/5392/ > http://sites.google.com/site/gptips4matlab/
That's interesting. However, sympy doesn't deal with data, so I think that this should be an external project depending on sympy and pandas (http://pandas.pydata.org/ ) and/or scikit-learn (http://scikit-learn.org/stable/ ). -- You received this message because you are subscribed to the Google Groups "sympy" group. To post to this group, send email to sympy@googlegroups.com. To unsubscribe from this group, send email to sympy+unsubscr...@googlegroups.com. For more options, visit this group at http://groups.google.com/group/sympy?hl=en.
