CODEWITHSUNDEEP
python-2.7optimizationscipyleast-squares
htong
htong

Reputation: 53

How to fix "TypeError: only length-1 arrays can be converted to Python scalars" in python regression

I tried to use scipy.optimize package for regression. The model of the function is defined in func with parameters named as coeffs. I want to use the data xdata and ydata to learn the parameters using LS criterion.

I have the following TypeError: only length-1 arrays can be converted to Python scalars

from __future__ import division
import numpy
import scipy
from math import exp
import scipy.optimize as optimization

global m0,t0

t0 = 0.25
m0=1
def func(t, coeffs):
    a = coeffs[0]
    b = coeffs[1]
    m = (a/b + m0 )*exp(b*(t-t0))-a/b
    return m

# fitting test
x0 = numpy.array([5, -5], dtype=float)

def residuals(coeffs, y, t):
    return y - func(t, coeffs)

xdata = numpy.array([0.25,0.5,1])
ydata = numpy.array([1.0,0.803265329856,0.611565080074])

from scipy.optimize import leastsq
x = leastsq(residuals, x0, args=(ydata, xdata))

return parameters are expected around [2,-1].

Upvotes: 0

Views: 88

Answers (1)

Silmathoron
Silmathoron

Reputation: 1981

Do not use from math import exp, replace it by from numpy import exp so that your arrays are correctly handled: the numpy.exp function will return the array expected by scipy, with each element converted to its exponential value.

Upvotes: 2

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