more tests

        structure of w np array is such that each row corresponds to a sample -- e.g., analyze third column for third

        sparse task

        """

        y_np = sparse_np_list[1]

        sparse_np = sparse_np_list[2]

        #debugging

        print(sparse_np[:, 3])

        #print(sparse_np[:, 3])

        frac_train = 0.5

        cutoff = int(math.floor(frac_train * len(sparse_np)))

        sparse_np = sparse_np[:cutoff, :]

        y_np = y_np[:cutoff, :]

        #debugging

        print(sparse_np[:, 3])

        #print(sparse_np[:, 3])

        sparse_df = pd.DataFrame(data = sparse_np)

        y_df = pd.DataFrame(data = y_np)

        #debugging

        print(sparse_df.iloc[:, 3])

        #print(sparse_df.iloc[:, 3])

        total_rows = len(sparse_df.index)

        sparse_flag = False

            if zero_count == total_rows:

                print("good -- one column doesn't have results")

                print(colIndex)

                print("weight column")

                print column

                print("corresponding y column")

                print(y_df.iloc[:, colIndex])

                sparse_flag = True

                assert zero_count == total_rows

                break