Description
Exercises
- Write an R function called simple learner that takes as input a data frame df (you may assume that all columns of df are numeric, and the final column provides a class label of 1 or −1) and returns a list having two attributes w and b, where w = c+ − c− and b = w c, where c+ and c− are the respective centers of mass of the positive and negative datapoints, and c = (c+ + c−)/2 is a point on the linear decision surface.
- Write an R function called perceptron learner that takes as input a data frame df (you may assume that all columns of df are numeric, and the final column provides a class label of 1 or −1) and returns a list having two attributes w and b, where w and b are obtained by performing the perceptron learning algorithm using the vectors from df.
- Write an R function called classify that takes as input i) data frame df, and ii) linear decision surface parameters w and b, and returns a vector v that provides the classifications of each of the vectors of df. Thus each component of v is either 1 or -1, and the length of v equals the number of vectors of df. Moreover, we assume that the number of columns of df is equal to the dimension of w.
- Use the data in file exercise-4.csv to build a data frame df, and provide the w and b values that are returned by simple learner(df).
- Repeat Exercise 4, but now provide the w and b values that are returned by perceptron learner(df).
