On Thu, Jun 14, 2012 at 5:53 PM, Nathaniel Smith <[email protected]> wrote: > On Thu, Jun 14, 2012 at 9:22 PM, srean <[email protected]> wrote: > No, I'm saying I totally see the advantages. Here's the code I'm talking > about: > > def _loglik(self, params): > alpha, beta = self.used_alpha_beta(params) > if np.any(alpha < 0): > return 1e20 > total = 0 > for group in self._model._groups.itervalues(): > alpha_part = np.dot(group["alpha_matrix"], alpha) > eff_beta_matrix = group["beta_matrix"].copy() > nab = self._model._num_alpha_betas > eff_beta_matrix[:, :nab] *= np.log(alpha_part[:, np.newaxis]) > exponent = np.dot(eff_beta_matrix, beta) > Z = np.exp(exponent).sum() > total += (group["counts"] * exponent).sum() > total += group["counts"].sum() * -np.log(Z) > return total >
You're right, this is totally the kind of code that autodiff can/should be able to help with. I just pushed a first draft at support for np.any, log, exp, inplace operators, and inplace array assignment... so there's a chance that your example might currently run. (You might even see a speedup if Theano graph optimizations work their magic). It's not clear from the code fragment what the various types in play are (see previous rant on static analysis!), but an autodiff PR with a test case would help sort out any remaining problems if you want to follow up on this. - James _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
