distionary: dict
        The distionary object.

Return
----------
result: numpy.array, shape (number of examples,)
        The mapped result.         
"""
result = []
for e in array:
    try:
        result.append(dictionary[e])
    except:
        result.append(0)
# return [dictionary[e] for e in array]
return result

    boundaries = np.unique(
        np.quantile(x, np.arange(0, 1, 1/initial_intervals)[1:])
        )  # Add [1:] so that 0% persentile will not be a threshold
    boundaries=boundaries[~np.isnan(boundaries)]
    if np.Inf in x:
        boundaries=np.append(boundaries,np.Inf)
    if np.NINF in x:
        boundaries=np.append(boundaries,np.NINF)
    boundaries.sort(axis=0)

        intervals, unique_intervals = assign_interval_unique(x, unique_intervals[:, 1])
        pt_value, pt_column, pt_index = pivot_table_np(intervals[:, 1], y)

    df = pd.DataFrame(pt_value, columns=pt_column)
    df['pt_index'] = pt_index
    df['chi2'] = np.append(chi2_array, [np.NaN] * (m - 1))

    merge_index_start=index_adjacent_to_merge[0]
    # print(df.loc[merge_index_start:merge_index_start+m-1, :].sum(axis=0).to_frame())
    df=pd.concat(
        [
            df.loc[:merge_index_start-1,:],
            df.loc[merge_index_start:merge_index_start+m-1, :].sum(axis=0).to_frame().T,
            df.loc[merge_index_start+ m:, :],
        ],
        ignore_index=True
    )
    # print(df)
    df.loc[merge_index_start:merge_index_start  , 'pt_index']=new_interval[0][1]

    pt_value = df[pt_column].to_numpy()
    pt_index = df['pt_index'].to_numpy()
    boundaries_tmp = np.unique(
        np.concatenate((np.array([-float('inf')]),
                        df['pt_index'].to_numpy(), np.array([float('inf')])),
                       axis=0))
    boundaries_tmp.sort()
    unique_intervals=np.array([[boundaries_tmp[i],boundaries_tmp[i+1]] for i in range(len(boundaries_tmp)-1)])