Started with PreferenceExpectedImprovement.py

AcquistionFunctions/PreferenceExpectedImprovement.py

@@ -0,0 +1,19 @@
+import numpy as np
+from scipy.stats import norm
+
+
+class PreferenceExpectedImprovement:
+    def __init__(self):
+        pass
+
+    def rejection_sampling(self):
+        pass
+
+    def expected_improvement(self):
+        pass
+
+    def update_user_preference_model(self):
+        pass
+
+    def update_proposal_model(self):
+        pass

DistTesting/Testing.py

@@ -0,0 +1,25 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define the Gaussian distribution
+mu, sigma = 0, 5  # High variance
+gaussian = np.random.normal(mu, sigma, 1000000)  # Generate a lot of samples
+
+# Rejection sampling
+gaussian = gaussian[(gaussian > -1) & (gaussian < 1)]
+
+# Generate uniform distribution
+uniform = np.random.uniform(-1, 1, len(gaussian))  # Same number of samples as the Gaussian
+
+# Plot the distributions
+plt.figure(figsize=(12,6))
+
+plt.subplot(1, 2, 1)
+plt.hist(gaussian, bins=30, density=True, alpha=0.6, color='g')
+plt.title('Gaussian Rejection Sampling')
+
+plt.subplot(1, 2, 2)
+plt.hist(uniform, bins=30, density=True, alpha=0.6, color='b')
+plt.title('Uniform Distribution')
+
+plt.show()