我有两个大型的多维数组:Y进行三个测量对象,分别处理半百万个对象(例如shape=(500000,3)),并且X具有相同的形状,但包含Y测量值的位置。

首先,我希望包含对象的每一行都适合多项式方程。我知道遍历数组的速度很慢,但是我目前正在做的是:

fit = array([polyfit(X[i],Y[i],deg) for i in xrange(obs.shape[0])])


我的问题是:是否有可能在不显式迭代的情况下拟合两个数组的每一行?

最佳答案

可以不沿第一轴进行迭代。但是,您的第二个轴很短(只有3个),实际上可以拟合不超过2个系数。

In [67]:

import numpy as np
import scipy.optimize as so

In [68]:

def MD_ployError(p, x, y):
    '''if x has the shape of (n,m), y must be (n,m), p must be (n*p, ), where p is degree'''
    #d is no. of degree
    p_rshp=p.reshape((x.shape[0], -1))
    f=y*1.
    for i in range(p_rshp.shape[1]):
        f-=p_rshp[:,i][:,np.newaxis]*(x**i)
    return (f**2).sum()

In [69]:

X=np.random.random((100, 6))
Y=4+2*X+3*X*X
P=(np.zeros((100,3))+[1,1,1]).ravel()

In [70]:

MD_ployError(P, X, Y)

Out[70]:
11012.2067606684

In [71]:

R=so.fmin_slsqp(MD_ployError, P, args=(X, Y))
Iteration limit exceeded    (Exit mode 9) #you can increase iteration limit, but the result is already good enough.
            Current function value: 0.00243784856039
            Iterations: 101
            Function evaluations: 30590
            Gradient evaluations: 101

In [72]:

R.reshape((100, -1))

Out[72]:
array([[ 3.94488512,  2.25402422,  2.74773571],
       [ 4.00474864,  1.97966551,  3.02010015],
       [ 3.99919559,  2.0032741 ,  2.99753804],
..............................................)

09-17 17:49
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