ufunc 的循环不支持浮点类型的参数 0，它没有可调用的 exp 方法

Question

I am having trouble with an optimization that used to work with a previous version of python...

I have a function fp = lambda c, x: (c[0])+ (c[1]*((1- np.exp(-x/c[4]))/(x/c[4])))+ (c[2]*((((1-np.exp(-x/c[4]))/(x/c[4])))- (np.exp(-x/c[4]))))+ (c[3]*((((1-np.exp(-x/c[5]))/(x/c[5])))- (np.exp(-x/c[5]))))

and I need to minimize the error e = lambda c, x, y: (((fp(c,x)-y)**2).sum())

using the initial parameter values p0 = np.array([0.01,0.01,0.01,0.01,0.01,1.00,1.00])

basically p = optimize.fmin(e, p0, args=(x,y))

were x and y are np.arrays (14,) each.

So, this used to work but now it throws this error TypeError: loop of ufunc does not support argument 0 of type float which has no callable exp method

I've done some research and it seems that there is a problem related to np.exp() and some versions of Numpy... Actually this problem appeared when I updated Python and Numpy as well to 3.7 and 1.18.1 repectively.

Any thoughts?

Answer 1

The issue may be caused by the fact the elements of your numpy array are not of type int.

This can be fixed by turning your float array into an int one, at the small cost of losing a bit of accuracy. This fixed the issue for me.

import numpy as np

y_int = np.array(y_float, dtype=int)