Coverage for bmm_multitask_learning/coalescent/utils.py: 0%

1import numpy as np 

2from sklearn.linear_model import LinearRegression 

3 

4 

5class grad_optimizer: 

6 def __init__(self, max_steps, coeff, grad_func, ): 

7 self.max_steps = max_steps 

8 self.coeff = coeff 

9 self.grad_func = grad_func 

10 

11 def run(self, x0): 

12 x = x0 

13 for i in range(1, self.max_steps + 1): 

14 grad = self.grad_func(x) 

15 grad = grad / np.linalg.norm(grad) 

16 x_new = x + self.coeff * grad 

17 if np.linalg.norm(x_new - x) < 1e-6: 

18 break 

19 x = x_new 

20 return x 

21 

22 

23class MyModel(LinearRegression): 

24 def __init__(self, weights): 

25 self.weights = weights 

26 

27 def predict(self, X): 

28 return np.dot(X, self.weights.T)