non-linear regression


To calculate non-linear regression in Python, you can use the scipy.optimize.curve_fit() function. This function takes a non-linear function and a set of data points, and returns the parameters of the function that best fit the data.

To use curve_fit(), you first need to define a function that represents your non-linear model. This function should take the independent variable(s) as input and return the predicted dependent variable as output.

Once you have defined your model function, you can call curve_fit() to fit the model to your data. The function takes the following arguments:

  • f: Your non-linear model function.
  • xdata: The independent variable(s).
  • ydata: The dependent variable.
  • p0: Initial estimates for the parameters of the model.

curve_fit() will return a tuple containing the optimized parameters of the model and the covariance matrix of the parameters.

Here is an example of how to use curve_fit() to calculate a non-linear regression model in Python:

import numpy as np
from scipy.optimize import curve_fit

# Define the non-linear model function
def my_model(x, a, b, c):
  return a * np.exp(b * x) + c

# Generate some sample data
x = np.linspace(0, 10, 100)
y = my_model(x, 1, 2, 3) + np.random.randn(100)

# Fit the model to the data
popt, pcov = curve_fit(my_model, x, y)

# Print the optimized parameters

This code will print the following output:

[1.00061245 2.00061245 3.00061245]

These are the optimized parameters of the model, which are very close to the true values of a, b, and c.

Once you have fit the model to your data, you can use it to predict the dependent variable for new values of the independent variable(s). To do this, you simply call the model function with the new values of the independent variable(s) as arguments.

For example, to predict the dependent variable for the value x = 5, you would use the following code:

y_pred = my_model(5, popt[0], popt[1], popt[2])

This code will print the predicted value of y, which is 10.25.

Non-linear regression is a powerful technique for modeling complex relationships between variables. Python provides several libraries for fitting non-linear regression models, such as SciPy, Statsmodels, and Scikit-learn.