libscores.py 文件源码

def mvmean(R, axis=0):
    ''' Moving average to avoid rounding errors. A bit slow, but...
    Computes the mean along the given axis, except if this is a vector, in which case the mean is returned.
    Does NOT flatten.'''
    if len(R.shape)==0: return R
    average = lambda x: reduce(lambda i, j: (0, (j[0]/(j[0]+1.))*i[1]+(1./(j[0]+1))*j[1]), enumerate(x))[1]
    R=np.array(R)    
    if len(R.shape)==1: return average(R)
    if axis==1:
        return np.array(map(average, R))
    else:
        return np.array(map(average, R.transpose()))


# ======= All metrics used for scoring in the challenge ========

### REGRESSION METRICS (work on raw solution and prediction)
# These can be computed on all solutions and predictions (classification included)