las.py 文件源码

def _find_constrained_bicluster(self, data):
        """Find a k x l bicluster."""
        num_rows, num_cols = data.shape

        k = random.randint(1, math.ceil(num_rows / 2))
        l = random.randint(1, math.ceil(num_cols / 2))

        cols = np.random.choice(num_cols, size=l, replace=False)

        old_avg, avg = float('-inf'), 0.0

        while abs(avg - old_avg) > self.tol:
            old_avg = avg

            row_sums = np.sum(data[:, cols], axis=1)
            rows = bn.argpartition(row_sums, num_rows - k)[-k:] # this is usually faster than rows = np.argsort(row_sums)[-k:]

            col_sums = np.sum(data[rows, :], axis=0)
            cols = bn.argpartition(col_sums, num_cols - l)[-l:] # this is usually faster than cols = np.argsort(col_sums)[-l:]

            avg = np.mean(data[np.ix_(rows, cols)])

        return Bicluster(rows, cols)