我有两个大型多维数组:Y
包含 50 万个对象的三个测量值(例如 shape=(500000,3)
)和 X
具有相同的形状,但包含 Y
测量值的位置。
首先,我想为包含一个对象的每一行拟合一个多项式方程。我知道遍历数组非常慢,但我目前正在做的是:
fit = array([polyfit(X[i],Y[i],deg) for i in xrange(obs.shape[0])])
我的问题是:是否有可能在不明确迭代它们的情况下拟合两个数组的每一行?
最佳答案
无需沿第一个轴迭代即可这样做。但是,您的第二个轴很短(只有 3 个),您实际上可以容纳不超过 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],
..............................................)
关于python - Numpy Polyfit 或 X 和 Y 多维数组的任何拟合,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/22508593/