1. from helpers import visualize_cost_function
2. import numpy as np
3.  
4. def loss_function(sizes, prices, weight0, weight1):
5.     predictions = [weight0 + weight1*sizes[i] for i in range(len(prices))]
6.     return sum([(predictions[i] - prices[i])**2 for i in range(len(prices))])/(2*len(prices))
7.  
8.  
9. def loss_function2(sizes, prices, weight0, weight1):
10.     predictions = [weight0 + weight1*sizes[i] for i in range(len(prices))]
11.     return sum([(np.sin(predictions[i]/prices[i]**2) * (predictions[i]*prices[i]**3)) for i in range(len(prices))])/(2*len(prices))
12.  
13.  
14. #Poniższy kod wyznacza wartości funkcji kosztu w dziedzinie wartości atrybutów w0 i w1
15. w0_values = np.arange(-10, 10, 0.5)
16. w1_values = np.arange(-10, 10, 0.5)
17.  
18. sizes_transformed = ((sizes - np.mean(sizes)) / np.std(sizes)).reshape(-1)
19. prices_transformed = ((prices - np.mean(prices)) / np.std(prices)).reshape(-1)
20.  
21. w0_values = np.arange(-100, 100, 5)
22. w1_values = np.arange(-100, 100, 5)
23.  
24. visualize_cost_function(sizes_transformed, prices_transformed, w0_values, w1_values, loss_function)
25. visualize_cost_function(sizes_transformed, prices_transformed, w0_values, w1_values, loss_function2)