import Classification import NNC import Plotting import pandas as pd #%% Plotting.Plot() #%% print ("1 feature") l1 = Classification.LogReg(2,3) l2 = Classification.SVM(2,3) l3 = Classification.DTC(2,3) l4 = Classification.KNC(2,3) l5 = Classification.RFC(2,3) l6 = Classification.MLP(2,3) l7 = Classification.ABC(2,3) l8 = Classification.GNB(2,3) l9 = Classification.QDA(2,3) l10 = Classification.SGD(2,3) l11= NNC.NNC(2,3) df1 = pd.DataFrame(data = {"LogReg": l1, "SVM": l2, "DTC": l3, "KNC": l4, "RFC": l5,"MLP": l6, "ABC": l7, "GNB": l8, "QDA": l9, "SGD": l10, "NNC":l11}, index = [".9", ".8", ".5", ".25"]) print(df1) #%% print ("9 features") m1 = Classification.LogReg(2,11) m2 = Classification.SVM(2,11) m3 = Classification.DTC(2,11) m4 = Classification.KNC(2,11)
__author__ = 'Prateek' import Classification print("1 feature") Classification.LogReg(2, 3) Classification.SVM(2, 3) Classification.DTC(2, 3) print("9 features") Classification.LogReg(2, 11) Classification.SVM(2, 11) Classification.DTC(2, 11) print("16 features") Classification.LogReg(2, 18) Classification.SVM(2, 18) Classification.DTC(2, 18) print("30 features") Classification.LogReg(2, 32) Classification.SVM(2, 32) Classification.DTC(2, 32)