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import numpy as np from scipy.optimize import curve_fit import matplotlib.pyplot as plt # Exponential function def exponential_func(x, a, b): return a * np.exp(b * x) # Generate sample data np.random.seed(42) x_data = np.linspace(0, 5, 50) # Replace with your desired x values y_true = 2 * np.exp(0.5 * x_data) + np.random.normal(0, 0.5, len(x_data)) # Example exponential data with noise # Fit the exponential function to the data initial_guess = [1, 1] # Initial guess for the parameters (a, b) params, covariance = curve_fit(exponential_func, x_data, y_true, p0=initial_guess) # Extract fitted parameters a_fit, b_fit = params # Generate fitted data using the parameters y_fit = exponential_func(x_data, a_fit, b_fit) # Plot the original data and the fitted curve plt.scatter(x_data, y_true, label='Original Data') plt.plot(x_data, y_fit, color='red', label=f'Exponential Fit: y = {a_fit:.2f} * e^({b_fit:.2f} * x)') plt.legend() plt.xlabel('X') plt.ylabel('Y') plt.title('Exponential Regression Example') plt.show()
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