Tested the MHOD algorithm using the generated test case data

neat/locals/overload/mhod/core.py

@@ -125,6 +125,19 @@ def mhod(state_config, otf, window_sizes, bruteforce_step, learning_steps,
     total_time = len(utilization)
     state_vector = build_state_vector(state_config, utilization)
     current_state = get_current_state(state_vector)
+    state['state_history'].append(current_state)
+
+    selected_windows = estimation.select_window(
+        state['variances'],
+        state['acceptable_variances'],
+        window_sizes)
+    p = estimation.select_best_estimates(
+        state['estimate_windows'],
+        selected_windows)
+
+    # These two are saved for testing purposes
+    state['selected_windows'] = selected_windows
+    state['p'] = p
 
     state['request_windows'] = estimation.update_request_windows(
         state['request_windows'],
@@ -144,22 +157,9 @@ def mhod(state_config, otf, window_sizes, bruteforce_step, learning_steps,
         state['previous_state'])
     state['previous_state'] = current_state
 
-    selected_windows = estimation.select_window(
-        state['variances'],
-        state['acceptable_variances'],
-        window_sizes)
-    p = estimation.select_best_estimates(
-        state['estimate_windows'],
-        selected_windows)
-
-    # These two are saved for testing purposes
-    state['selected_windows'] = selected_windows
-    state['p'] = p
-
     log.debug('MHOD utilization:' + str(utilization))
     if len(utilization) >= learning_steps:
         #state_history = utilization_to_states(state_config, utilization)
-        state['state_history'].append(current_state)
         time_in_states = total_time
         time_in_state_n = get_time_in_state_n(state_config, state['state_history'])
         # These two are saved for testing purposes