def runLR(): wrapper = PredictorWrapper() PLIST = [10] for p in PLIST: const.SVM_C = p model = LogisticModel() print(wrapper.evalAModel(model))
def runMF(): wrapper = PredictorWrapper() KLIST = [10 * i for i in range(1, 10)] for k in KLIST: const.N_FEATURE = k model = MFModel() print(wrapper.evalAModel(model))
def runNeu(): wrapper = PredictorWrapper() PLIST = [10 * i for i in range(1, 2)] for p in PLIST: # const.NeuN_H1 = p model = NeuNModel() print(wrapper.evalAModel(model))
def runSVM(): wrapper = PredictorWrapper() PLIST = [i for i in range(1, 2)] for p in PLIST: const.SVM_C = p model = MultiSVM() print(wrapper.evalAModel(model))
def runKGSIM(): wrapper = PredictorWrapper() KLIST = [20 * i for i in range(1, 2)] for k in KLIST: const.KGSIM = k model = KGSIM() print(wrapper.evalAModel(model))
def runSCCA(): wrapper = PredictorWrapper() NCLIST = [10 * i for i in range(1, 2)] for c in NCLIST: const.CCA = c model = RSCCAModel() print(wrapper.evalAModel(model))
def runKNN(): wrapper = PredictorWrapper() KLIST = [10 * i for i in range(1, 10)] for k in KLIST: const.KNN = k model = KNN() print(wrapper.evalAModel(model))
def runGB(): wrapper = PredictorWrapper() PLIST = [60 * i for i in range(1, 2)] for p in PLIST: const.RF = p model = GBModel() print(wrapper.evalAModel(model))
def runRF(): wrapper = PredictorWrapper() PLIST = [10 * i for i in range(1, 10)] for p in PLIST: const.RF = p model = RFModel() print(wrapper.evalAModel(model))
def runLNSM(): from models.models import LNSMModel wrapper = PredictorWrapper() PLIST = [i * 10 for i in range(1, 10)] for p in PLIST: const.KNN = p model = LNSMModel() print(wrapper.evalAModel(model))
def runRandom(): wrapper = PredictorWrapper() model = RandomModel() print(wrapper.evalAModel(model))
def runDCN(): from models.models import CNNModel wrapper = PredictorWrapper() model = CNNModel() print(wrapper.evalAModel(model))