latools/filtering/classifier_obj.py
def predict(self, data):
        """
        Label new data with cluster identities.

        Parameters
        ----------
        data : dict
            A data dict containing the same analytes used to
            fit the classifier.
        sort_by : str
            The name of an analyte used to sort the resulting
            clusters. If None, defaults to the first analyte
            used in fitting.

        Returns
        -------
        array of clusters the same length as the data.
        """
        size = data[self.analytes[0]].size
        ds, sampled = self.format_data(data)

        # predict clusters
        cs = self.classifier.predict(ds)
        # map clusters to original index
        clusters = self.map_clusters(size, sampled, cs)

        return clusters
def predict(self, data):
        size = data[self.analytes[0]].size
        ds, sampled = self.format_data(data)

        # predict clusters
        cs = self.classifier.predict(ds)
        # map clusters to original index
        clusters = self.map_clusters(size, sampled, cs)

        return clusters
def predict ( self , data ) : size = data [ self . analytes [ 0 ] ] . size ds , sampled = self . format_data ( data ) # predict clusters cs = self . classifier . predict ( ds ) # map clusters to original index clusters = self . map_clusters ( size , sampled , cs ) return clusters
Label new data with cluster identities.

        Parameters
        ----------
        data : dict
            A data dict containing the same analytes used to
            fit the classifier.
        sort_by : str
            The name of an analyte used to sort the resulting
            clusters. If None, defaults to the first analyte
            used in fitting.

        Returns
        -------
        array of clusters the same length as the data.
Label new data with cluster identities .