fitness_model.py 文件源码

def af_fit(self, params):
        # TODO: fix me for continuos prediction
        seasonal_errors = []
        self.pred_vs_true = []
        for s,t in self.fit_test_season_pairs:
            weights = np.exp(self.fitness(params, self.predictor_arrays[s][self.tree.root.season_tips[s],:]))
            pred_af = self.weighted_af(self.seqs[s],weights)
            #seasonal_errors.append(np.mean(np.sum((pred_af-self.af[t])**2, axis=0), axis=0))
            future_diameter = 0.5*np.sum(np.sum(self.af[t]*(1-self.af[t]), axis=0), axis=0)
            seasonal_errors.append(np.sum(np.sum(pred_af*(1-self.af[t]), axis=0), axis=0)-future_diameter)
            good_ind = self.af[s]*(1-self.af[s])>0.05
            self.pred_vs_true.append(np.array(zip(self.af[s][good_ind], self.af[t][good_ind], pred_af[good_ind])))

        mean_error = np.mean(seasonal_errors)
        if any(np.isnan(seasonal_errors)+np.isinf(seasonal_errors)):
            mean_error = 1e10
        self.last_fit = mean_error
        if self.verbose>2: print params, self.last_fit
        return mean_error + regularization*np.sum(params**2)