This is a Python forum, but what you are asking is not a Python
question. You might find a better source of answers on a scipy specific
forum.
But here's my attempt at answers:
On 06/19/2018 08:26 AM, sharan.basa...@gmail.com wrote:
Hi All,
I am working out an exercise on curve_fit function available scipy package.
While I understand in general about curve_fit, I am unable to understand the
following:
params, params_covariance = optimize.curve_fit(test_func, x_data, y_data,
p0=[2, 2])
Firstly, I don't understand why test_func is passed as an argument to cur_fit
You are trying to fit a curve to some data, right. The curve_fit
procedure needs to know what curve you are trying to fit. Is it a
linear curve, exponential, polynomial or ...? In this example it's a
sine function with parameters for regulating amplitude and frequency.
But it could be any function with any parameters. To be more precise,
test_function is not a single function y=f(x), but a whole family of
functions y=f(x; a,b) where a and b define a particular function.
Secondly, I don't understand how curve_fit knows the number of arguments that
test_func takes.
Part of the dynamic nature of Python is that a function carries with it
the number of parameters (as just one among many such properties). We
call it "introspection" when we examine such properties of objects. The
curve_fit function usees such an introspection to find that
test_function has two parameters (a and b) defining the family of curves.
Full code is available below for reference:
import numpy as np
# Seed the random number generator for reproducibility
np.random.seed(0)
x_data = np.linspace(-5, 5, num=50)
y_data = 2.9 * np.sin(1.5 * x_data) + np.random.normal(size=50)
# And plot it
import matplotlib.pyplot as plt
plt.figure(figsize=(6, 4))
plt.scatter(x_data, y_data)
from scipy import optimize
def test_func(x, a, b):
return a * np.sin(b * x)
params, params_covariance = optimize.curve_fit(test_func, x_data, y_data,
p0=[2, 2])
print(params)
plt.figure(figsize=(6, 4))
plt.scatter(x_data, y_data, label='Data')
plt.plot(x_data, test_func(x_data, params[0], params[1]),
label='Fitted function')
plt.legend(loc='best')
plt.show()
--
Dr. Gary Herron
Professor of Computer Science
DigiPen Institute of Technology
(425) 895-4418
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