<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def get_row_missing(xc, xd, cdiffs, index, cindices, dindices):

 """ Calculate distance between index instance and all other instances. """

    row = np.empty(0, dtype=np.double)  # initialize empty row
    cinst1 = xc[index]  # continuous-valued features for index instance
    dinst1 = xd[index]  # discrete-valued features for index instance
    # Boolean mask locating missing values for continuous features for index instance
    can = cindices[index]
    # Boolean mask locating missing values for discrete features for index instance
    dan = dindices[index]
    tf = len(cinst1) + len(dinst1)  # total number of features.

    # Progressively compare current instance to all others. Excludes comparison with self indexed instance. (Building the distance matrix triangle).
    for j in range(index):
        dist = 0
        dinst2 = xd[j]  # discrete-valued features for compared instance
        cinst2 = xc[j]  # continuous-valued features for compared instance

        # Manage missing values in discrete features
        # Boolean mask locating missing values for discrete features for compared instance
        dbn = dindices[j]
        # indexes where there is at least one missing value in the feature between an instance pair.
        idx = np.unique(np.append(dan, dbn))
        # Number of features excluded from distance calculation due to one or two missing values within instance pair. Used to normalize distance values for comparison.
        dmc = len(idx)
        d1 = np.delete(dinst1, idx)  # delete unique missing features from index instance
        d2 = np.delete(dinst2, idx)  # delete unique missing features from compared instance

        # Manage missing values in continuous features
        # Boolean mask locating missing values for continuous features for compared instance
        cbn = cindices[j]
        # indexes where there is at least one missing value in the feature between an instance pair.
        idx = np.unique(np.append(can, cbn))
        # Number of features excluded from distance calculation due to one or two missing values within instance pair. Used to normalize distance values for comparison.
        cmc = len(idx)
        c1 = np.delete(cinst1, idx)  # delete unique missing features from index instance
        c2 = np.delete(cinst2, idx)  # delete unique missing features from compared instance
        # delete unique missing features from continuous value difference scores
        cdf = np.delete(cdiffs, idx)

        # Add discrete feature distance contributions (missing values excluded) - Hamming distance
        dist += len(d1[d1 != d2])

        # Add continuous feature distance contributions (missing values excluded) - Manhattan distance (Note that 0-1 continuous value normalization is included ~ subtraction of minimums cancel out)
        dist += np.sum(np.absolute(np.subtract(c1, c2)) / cdf)

        # Normalize distance calculation based on total number of missing values bypassed in either discrete or continuous features.
        tnmc = tf - dmc - cmc  # Total number of unique missing counted
        # Distance normalized by number of features included in distance sum (this seeks to handle missing values neutrally in distance calculation)
        dist = dist/float(tnmc)

        row = np.append(row, dist)

    return row