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estimate cost of an in-game item ("Strength Upgrades" in Lumberjack Heroes in Fortnite)
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| # A program to estimate the formula for cost of an in-game item; | |
| # Specifically, "Strength Upgrades" in Lumberjack Heroes in Fortnite | |
| # https://chat.openai.com/c/e8e7f768-e9d7-4553-94aa-4e9b5b12d917 | |
| # count,cost | |
| # 1,55.8E45 | |
| # 10,1.13E48 | |
| # 100,11.5E51 | |
| counts = [1, 10, 100] | |
| costs = [55.8E45, 1.13E48, 11.5E51] | |
| import numpy as np | |
| from scipy.optimize import curve_fit | |
| import matplotlib.pyplot as plt | |
| # Exponential function | |
| def exponential_function(x, a, b): | |
| return a * np.exp(b * x) | |
| a = 79.18318938403567 | |
| b = 1.1550242275702656 | |
| # Generate points for the exponential curve | |
| x_curve = np.linspace(1, 100, 100) | |
| y_curve = exponential_function(x_curve, a, b) | |
| # Plotting the original data | |
| plt.scatter(counts, costs, label='Data') | |
| # Plotting the regression curve | |
| plt.plot(x_curve, y_curve, 'r', label='Exponential Regression') | |
| plt.xlabel('Count') | |
| plt.ylabel('Cost') | |
| plt.title('Exponential Regression') | |
| plt.legend() | |
| plt.grid(True) | |
| plt.show() |
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