163eb7df13667e21b0e02a2706e18d1f53eee610,skrebate/iterrelief.py,IterRelief,fit,#IterRelief#,69

Before Change

        // Iterate till max iteration reached or all weights are really tiny
        while ((iteration < self.max_iter) & (any(w >= 0.0001 for w in distance_weights))):
            // Run Core Relief-based algorithm
            core_fit = core.fit(self.X_mat, self._y, distance_weights, self.weight_flag)

After Change

            if iteration == 0:
                no_diff = False
            else:
                for i in range(len(feature_weights)):
                    //previous array of feature_weights
                    prev = weight_history[len(weight_history)-1]
                    diff = abs(prev[i] - feature_weights[i])
                    // first encounter of value that has difference greater than threshold, set no_diff to False, and break out of checking loop
                    if diff >= 0.0001:
                        no_diff = False
                        break;
            if no_diff:
                break;

            mx = max(feature_weights)

            weight_history.append(feature_weights)

            //normalize and update scores
            //negative numbers -> 0, positive numbers become normalized so maximum is 1.
            for i in range(0, len(feature_weights)):
                if feature_weights[i] <= 0:
                    feature_weights[i] = 0
                else:
                    feature_weights[i] = feature_weights[i] / mx

            distance_weights += feature_weights

            iteration += 1

Instances