Could you see what is stack overflow answered below?, I couldn't apply in my code, please if you could to applying on my example I would be thankful. I don't know what is ( words w, u) and what the difference between it and M(w,w).
If I assume that M(w,w) is (wn.res_similarity(s1[0], s2[0], brown_ic)), then what is MN (w, u) really I couldn't understand. thanks. How to normalize a single measure >> >> Let's consider a single arbitrary similarity measure M and take an >> arbitrary word w. >> >> Define m = M(w,w). Then m takes maximum possible value of M. >> >> Let's define MN as a normalized measure M. >> >> For any two words w, u you can compute MN(w, u) = M(w, u) / m. >> >> It's easy to see that if M takes non-negative values, then MN takes >> values in [0, 1]. >> How to normalize a measure combined from many measures >> >> In order to compute your own defined measure F combined of k different >> measures m_1, m_2, ..., m_k first normalize independently each m_i using >> above method and then define: >> >> alpha_1, alpha_2, ..., alpha_k >> >> such that alpha_i denotes the weight of i-th measure. >> >> All alphas must sum up to 1, i.e: >> >> alpha_1 + alpha_2 + ... + alpha_k = 1 >> >> Then to compute your own measure for w, u you do: >> >> F(w, u) = alpha_1 * m_1(w, u) + alpha_2 * m_2(w, u) + ... + alpha_k * m_k(w, >> u) >> >> It's clear that F takes values in [0,1] >> > -- You received this message because you are subscribed to the Google Groups "Python Programming for Autodesk Maya" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/python_inside_maya/344f9795-5444-45c8-9e68-7b7514c5b57b%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
