Convert some R code to Python code (<400 lines of code)

We need five small R files ported from R to Python. The R code is on github ([login to view URL]). We only need three of the classes (SelfLearning.R, LinearDiscriminantClassifier.R, and ICLinearDiscriminantClassifier.R) and the base classes and helper functions they use (NormalBasedClassifier.R, Classifier.R). In total, without comments, this is less than 400 lines of code to be ported to Python. Any publicly available python libraries, and existing code, may be used as part of the solution (but the mechanism of the solution needs to be the same, e.g. the optimization-based solution in LinearDiscriminantClassifier). Upon completion, the following test code (and its python equivalent) should work equivalently in R and Python: # Generate dataset df <- generate2ClassGaussian(d=2,expected=TRUE) df_lab <- df[sample(nrow(df),10),] df_unlab <- df[sample(nrow(df),500),] df_unlab$Class <- NA df_combined <- rbind(df_lab, df_unlab) # Train Classifiers t_sup <- LinearDiscriminantClassifier(Class~., df_lab) t_self <- SelfLearning(Class~., df_combined, method=LinearDiscriminantClassifier) t_ic <- ICLinearDiscriminantClassifier(Class~., df_combined) # Evaluate performance: Squared Loss & Error Rate mean(loss(t_sup,df)) mean(loss(t_self,df)) mean(loss(t_ic,df)) mean(predict(t_sup,df)!=df$Class) mean(predict(t_self,df)!=df$Class) mean(predict(t_ic,df)!=df$Class)