示例#1
0
def linearInterpolation(db):

    for i in range(0, db.shape[1]):
        ts = db[:,i]
        seq = sq.detectSequences(ts) 
        if len(seq)>1: ##In case there are more than one sequence
            seq = sq.joinSequences(seq) ##Join sequences in case they are relatively close
        seq = pointSelector(seq) ##Select points
        for j in range(0, len(seq)):
            s = pd.Series(ts[seq[j][0]:seq[j][1]])
            db[seq[j][0]:seq[j][1],i] = (s.interpolate(method = "linear")).values
    ##In case values are lost at the beggining or the end of the TS,        
    db = (pd.DataFrame(db)).fillna(method='pad').as_matrix()
    db = (pd.DataFrame(db)).fillna(method='bfill').as_matrix()
    return(db) 
示例#2
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def multInterpolation(db):

    for i in range(0, db.shape[1]):
        ts = db[:,i]
        seq = sq.detectSequences(ts) 
        if len(seq)>1: ##In case there are more than one sequence
            seq = sq.joinSequences(seq) ##Join sequences in case they are relatively close
        seq = pointSelector(seq) ##Select points
        for j in range(0, len(seq)):
            s = pd.Series(ts[seq[j][0]:seq[j][1]])
            ##Three types are considered, combining to a simple Multiple Imputation Algorithm.
            s1 = s.interpolate(method = "polynomial", order = int(min(s.notnull().sum()-1, 4)))
            s2 = s.interpolate(method = "spline", order = int(min(s.notnull().sum()-1, 3)))
            s3 = s.interpolate(method = "cubic")
            db[seq[j][0]:seq[j][1],i] = (s1.values + s2.values + s3.values)/3
    ##In case values are lost at the beggining or the end of the TS,        
    db = (pd.DataFrame(db)).fillna(method='pad').as_matrix()
    db = (pd.DataFrame(db)).fillna(method='bfill').as_matrix()
    return(db)