无法使用scipy.optimize.leatsq

Question

其他一切都很好，但是当我使用leasesq函数时，pydev编辑器有一个错误，它指出import中的未定义变量：leastsq这里发生了什么？在

代码是麻省理工学院的python成本模型定时.py在url:http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/readings/python-cost-model/timing.py leastsq部分的功能是：

def fit2(A,b):
""" Relative error minimizer """
def f(x):
    assert len(x) == len(A[0])
    resids = []
    for i in range(len(A)):
        sum = 0.0
        for j in range(len(A[0])):
            sum += A[i][j]*x[j]
        relative_error = (sum-b[i])/b[i]
        resids.append(relative_error)
    return resids
ans = scipy.optimize.leastsq(f,[0.0]*len(A[0]))
# print "ans:",ans
if len(A[0])==1:
    x = [ans[0]]
else:
    x = ans[0]
resids = sum([r*r for r in f(x)])
return (x,resids,0,0)

Answer 1

在我看来，您给LSQ函数两个关键字参数，而它需要三个。你给它提供了函数，初始值，而不是实际值，在这个值上进行LSQ？在

Answer 2

与其硬编码残差的计算，不如尝试将残差包装为一个函数，该函数是数据值与要最小化的函数之间的差：

以高斯函数为例：

M = np.array(data) # your data as a Nx2 Matrix of (x, y) data points

initials = [3,2,1] # just some initial guess values

def gaussian(x, p):
    return p[0]*np.exp((-(x-p[1])**2.0)/p[2]**2.0) # definition of the function

def residuals(p, y, x):
    return y - gaussian(x, p) # definition of the residual

cnsts = leastsq(residuals, initials, args=(M[:,1], M[:,0]))[0] # outputs optimized initials