BBy Bot
May 31'24
Exercise
[math]
\newcommand{\smallfrac}[2]{\frac{#1}{#2}}
\newcommand{\medfrac}[2]{\frac{#1}{#2}}
\newcommand{\textfrac}[2]{\frac{#1}{#2}}
\newcommand{\tr}{\operatorname{tr}}
\newcommand{\e}{\operatorname{e}}
\newcommand{\B}{\operatorname{B}}
\newcommand{\Bbar}{\overline{\operatorname{B}}}
\newcommand{\pr}{\operatorname{pr}}
\newcommand{\dd}{\operatorname{d}\hspace{-1pt}}
\newcommand{\E}{\operatorname{E}}
\newcommand{\V}{\operatorname{V}}
\newcommand{\Cov}{\operatorname{Cov}}
\newcommand{\Bigsum}[2]{\mathop{\textstyle\sum}_{#1}^{#2}}
\newcommand{\ran}{\operatorname{ran}}
\newcommand{\card}{\#}
\renewcommand{\P}{\operatorname{P}}
\renewcommand{\L}{\operatorname{L}}
\newcommand{\mathds}{\mathbb}[/math]
Implement the naive separation algorithm, that picks one data point at random and then labels that half of the data set which is closest to the first point as [math]0[/math] and the rest as [math]1[/math]. Test the algorithm on the data set from Problem. When generating the data, mark the data points with [math]0[/math] and [math]1[/math] and after running the separation algorithm, let your code count how many data points got classified correctly.