curve_fit多元函数拟合

Curve fitting in Python using the `curve_fit` function from the `scipy.optimize` module allows you to fit a multi-variable function to your data. The `curve_fit` function uses non-linear least squares to minimize the residuals between the predicted and actual data points.

Here is an example of how to perform multi-variable function fitting using `curve_fit`:

import numpy as np
from scipy.optimize import curve_fit

# Define the function you want to fit
def my_func(x, a, b, c):
    return a * x[0] + b * x[1] + c

# Generate some sample data
x_data = np.random.rand(100, 2)  # Two-dimensional input data
y_data = my_func(x_data.T, 2, 3, 4)  # Corresponding output data

# Perform curve fitting
popt, pcov = curve_fit(my_func, x_data.T, y_data)

# Print the optimized parameters
print(popt)

In this example, the `my_func` function is defined with three parameters (`a`, `b`, `c`) that you want to optimize. The `x_data` represents the input data, and `y_data` represents the corresponding output data. The `curve_fit` function returns the optimized parameters (`popt`) and the estimated covariance of the parameters (`pcov`).

You can modify the `my_func` function and provide your own data to fit a multi-variable function to your specific problem.