74c80d4f994c380f1baccd9d6651c233a22abf43,nilmtk/disaggregate/combinatorial_optimisation.py,CombinatorialOptimisation,train,#CombinatorialOptimisation#,40

Before Change

        for i, meter in enumerate(metergroup.submeters().meters):
            print("Training model for submeter "{}"".format(meter))
            for chunk in meter.power_series(**load_kwargs):
                num_total_states = num_states_dict.get(meter)
                if num_total_states is not None:
                    num_on_states = num_total_states - 1
                else:
                    num_on_states = None
                self.train_on_chunk(
                    chunk, meter, max_num_clusters, num_on_states)
                break  // TODO handle multiple chunks per appliance

        // Get centroids
        // If we import sklearn at the top of the file then auto doc fails.
        from sklearn.utils.extmath import cartesian
        centroids = [model["states"] for model in self.model]
        self.state_combinations = cartesian(centroids)
        // self.state_combinations is a 2D array

After Change

            // Check to see if there are any more chunks.
            // TODO handle multiple chunks per appliance.
            try:
                power_series.next()
            except StopIteration:
                pass
            else:
                warn("The current implementation of CombinatorialOptimisation"
                     " can only handle a single chunk.  But there are multiple"
                     " chunks available.  So have only trained on the"
                     " first chunk!")

        // Get centroids
        // If we import sklearn at the top of the file then auto doc fails.
        from sklearn.utils.extmath import cartesian
        centroids = [model["states"] for model in self.model]
        self.state_combinations = cartesian(centroids)
        // self.state_combinations is a 2D array

Instances