In this problem, we will investigate the perceptron algorithm with different iteration ordering. Consider applying the perceptron algorithm through the origin based on a small training set containing three points: $[$ x$^(1)=[-1,-1],$ y$^(1)=1$; x$^(2)=[1,0],$ y$^(2)=-1$; x$^(3)=[-1,1\mathrm{.}5],$ y$^(3)=1]$ Given that the algorithm starts with $\backslash$theta $^(0)=0,$ the first point that the algorithm sees is always considered a mistake. The algorithm starts with some data point and then cycles through the data $($in order$)$ until it makes no further mistakes. $/$r$/$n $1.($d$)0/1$ point $($graded$)$ For a fixed iteration order, what are the factors that affect the number of mistakes made by the algorithm between part $($a$)$ and part $($c$)?$Note: Only choose factors that were changed between part $($a$)$ and part $($c$),$ not all factors that can affect the number of mistakes $($Choose all that apply.$)$ Iteration order Maximum margin between positive and negative data points Maximum norm of data points x