Started with PreferenceExpectedImprovement.py

AcquistionFunctions/PreferenceExpectedImprovement.py

@@ -3,20 +3,30 @@ from scipy.stats import norm
 
 
 class PreferenceExpectedImprovement:
-    def __init__(self, nr_samples, upper_bound, lower_bound, nr_dims=2):
+    def __init__(self, nr_samples, nr_dims, lower_bound, upper_bound, seed=None):
 
         self.nr_samples = nr_samples
+        self.nr_dims = nr_dims
+        # check if upper_bound and lower_bound are numpy arrays of shape (nr_dims, 1) or (nr_dims,) or if they are floats
+
         self.upper_bound = upper_bound
         self.lower_bound = lower_bound
 
         self.user_model = None
-        self.proposal_model_mean = np.array()
+        self.proposal_model_mean = np.array((nr_dims, 1))
+        self.proposal_model_covariance = np.diag(np.ones((nr_dims, )) * 5)
+
+        self.rng = np.random.default_rng(seed=seed)
 
     def initialize(self):
         pass
 
     def rejection_sampling(self):
-        pass
+        samples = np.empty((self.nr_samples, self.nr_dims))
+        i = 0
+        while i < self.nr_samples:
+            pass
+
 
     def expected_improvement(self):
         pass
@@ -26,3 +36,8 @@ class PreferenceExpectedImprovement:
 
     def update_proposal_model(self):
         pass
+
+
+if __name__ == '__main__':
+    acquisition = PreferenceExpectedImprovement(10, 2, -1.0, 1.0)
+

DistTesting/Testing.py

@@ -12,7 +12,7 @@ gaussian = gaussian[(gaussian > -1) & (gaussian < 1)]
 uniform = np.random.uniform(-1, 1, len(gaussian))  # Same number of samples as the Gaussian
 
 # Plot the distributions
-plt.figure(figsize=(12,6))
+plt.figure(figsize=(12, 6))
 
 plt.subplot(1, 2, 1)
 plt.hist(gaussian, bins=30, density=True, alpha=0.6, color='g')