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))