It is possible to do so without iterate along the first axis. However, your second axis is rather short (being just 3), you can really fit no more than 2 coefficients.
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],
..............................................)