las.py 文件源码

def run(self, data):
        """Compute biclustering.

        Parameters
        ----------
        data : numpy.ndarray
        """
        data = check_array(data, dtype=np.double, copy=True)
        self._validate_parameters()

        data = scale(data)

        if self.transform:
            data = np.sign(data) * np.log(1 + np.abs(data))
            data = scale(data)

        biclusters = []

        for i in range(self.num_biclusters):
            best, avg, score = max((self._find_bicluster(data) for i in range(self.randomized_searches)), key=itemgetter(-1))

            if score < self.score_threshold:
                break

            data[np.ix_(best.rows, best.cols)] -= avg
            biclusters.append(best)

        return Biclustering(biclusters)