CamDavidsonPilon · February 9, 2020 19:18 · Feb 9, 2020 · Feb 9, 2020
diff --git a/gistfile1.txt → show_problem_eval.py b/gistfile1.txt → show_problem_eval.py
diff --git a/gistfile1.txt b/gistfile1.txt
@@ -0,0 +1,65 @@
+# test previous algorithm
+
+actuals = pd.read_csv("https://gist.github.com/csaid/a57c4ebaa1c7b0671cdc9692638ea4c4/raw/ad1709938834d7bc88b62ff0763733502eb6a329/shower_problem_tau_samples.csv")
+
+
+DELTA = 0.1
+
+def survival_function(t, lambda_=50., rho=1.5):
+    # Assume simple Weibull model
+    return np.exp(-(t/lambda_) ** rho)
+
+
+def w(t1, t2):
+    # equal to Pr(X = t1)
+    return survival_function(t1) / (survival_function(t1) + survival_function(t2))
+
+
+def determine_best_action(current_position, t1, t2):
+    p1 = w(t1, t2) * (1-survival_function(t1 + DELTA) / survival_function(t1))
+    p2 = (1-w(t1, t2)) * (1-survival_function(t2 + DELTA) / survival_function(t2))
+
+    if current_position == 1:
+        if p1 > p2/max(t2, 1):
+            return 1
+        else:
+            return 2
+    else:
+        if p1/max(t1, 1) > p2:
+            return 1
+        else:
+            return 2
+
+
+
+def minimum_time_needed(actual_direction, actual_tau):
+
+    explored = [0.00, 0.00] 
+    time = 0.00
+
+    # choose 1 initially
+    current_position = 1
+    explored[current_position-1] += DELTA
+
+
+    while True:
+        previous_position = current_position
+        choice = determine_best_action(current_position, *explored)
+        if choice == 1:
+            current_position = 1
+        else:
+            current_position = 2
+
+        explored[current_position-1] += DELTA
+
+        if previous_position != current_position:
+            # skip ahead to new region
+            time += explored[current_position-1]
+
+        time += DELTA
+
+        if explored[int(actual_direction)] >= actual_tau:
+            return time
+
+actuals['time_spent'] = actuals.apply(lambda s: minimum_time_needed(s['direction'], s['tau']) , axis=1)
+actuals['time_spent'].mean()
No results found