Modified possible HP for CQB and CQBv2

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py

@@ -311,8 +311,8 @@ def paramsToSet(nIter, randomState):
     """Used for weighted linear early fusion to generate random search sets"""
     paramsSet = []
     for _ in range(nIter):
-        paramsSet.append({"mu": randomState.choice([0.001, 0.002]),
-                          "epsilon": randomState.choice([1e-08, 2e-08]),
+        paramsSet.append({"mu": randomState.uniform(1e-02, 10**(-0.5)),
+                          "epsilon": 10**-randomState.randint(1, 15),
                           "n_max_iterations": None})
     return paramsSet
 
@@ -344,7 +344,7 @@ def genBestParams(detector):
 
 def genParamsFromDetector(detector):
     nIter = len(detector.cv_results_['param_classifier__mu'])
-    return [("mu", np.array([0.001 for _ in range(nIter)])),
+    return [("mu", detector.cv_results_['param_classifier__mu']),
             ("epsilon", np.array(detector.cv_results_['param_classifier__epsilon'])),
             ("n_max_iterations", np.array(detector.cv_results_['param_classifier__n_max_iterations']))]
 

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv2.py

@@ -330,8 +330,8 @@ def paramsToSet(nIter, randomState):
     """Used for weighted linear early fusion to generate random search sets"""
     paramsSet = []
     for _ in range(nIter):
-        paramsSet.append({"mu": randomState.choice([0.001, 0.002]),
-                          "epsilon": randomState.choice([1e-08, 2e-08]),
+        paramsSet.append({"mu": randomState.uniform(1e-02, 10**(-0.5)),
+                          "epsilon": 10**-randomState.randint(1, 15),
                           "n_max_iterations": None})
     return paramsSet