diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py index b683dd013850911f296f06fe13924f322b929dd4..b53ab51773c36fc32e835b1f662a12b73967687b 100644 --- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py +++ b/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']))] diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv2.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv2.py index d2b2b0d8cb1be0dc3e4f86c2a36a87451d452181..0039a4f4754cf3194bd07a33f2e83ab9b9337f86 100644 --- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv2.py +++ b/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