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
Secondly, I don't understand how curve_fit knows the number of arguments that
test_func takes.
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()
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