exercise:D10bcb88bf: Difference between revisions

Revision as of 02:08, 2 June 2024

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.

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 <div class="d-none"><math>
-\newcommand{\indexmark}[1]{#1\markboth{#1}{#1}}
+\newcommand{\smallfrac}[2]{\frac{#1}{#2}}
-\newcommand{\red}[1]{\textcolor{red}{#1}}
+\newcommand{\medfrac}[2]{\frac{#1}{#2}}
-\newcommand{\NOTE}[1]{$^{\textcolor{red}\clubsuit}$\marginpar{\setstretch{0.5}$^{\scriptscriptstyle\textcolor{red}\clubsuit}$\textcolor{blue}{\bf\tiny #1}}}
+\newcommand{\textfrac}[2]{\frac{#1}{#2}}
-\newcommand\xoverline[2][0.75]{%
-    \sbox{\myboxA}{$\m@th#2$}%
-    \setbox\myboxB\null% Phantom box
-    \ht\myboxB=\ht\myboxA%
-    \dp\myboxB=\dp\myboxA%
-    \wd\myboxB=#1\wd\myboxA% Scale phantom
-    \sbox\myboxB{$\m@th\overline{\copy\myboxB}$}%  Overlined phantom
-    \setlength\mylenA{\the\wd\myboxA}%   calc width diff
-    \addtolength\mylenA{-\the\wd\myboxB}%
-    \ifdim\wd\myboxB<\wd\myboxA%
-       \rlap{\hskip 0.35\mylenA\usebox\myboxB}{\usebox\myboxA}%
-    \else
-        \hskip -0.5\mylenA\rlap{\usebox\myboxA}{\hskip 0.5\mylenA\usebox\myboxB}%
-    \fi}
-\newcommand{\smallfrac}[2]{\scalebox{1.35}{\ensuremath{\frac{#1}{#2}}}}
-\newcommand{\medfrac}[2]{\scalebox{1.2}{\ensuremath{\frac{#1}{#2}}}}
-\newcommand{\textfrac}[2]{{\textstyle\ensuremath{\frac{#1}{#2}}}}
-\newcommand{\nsum}[1][1.4]{% only for \displaystyle
-    \mathop{%
-        \raisebox
-            {-#1\depthofsumsign+1\depthofsumsign}
 \newcommand{\tr}{\operatorname{tr}}
 \newcommand{\e}{\operatorname{e}}
 \newcommand{\B}{\operatorname{B}}
-\newcommand{\Bbar}{\xoverline[0.75]{\operatorname{B}}}
+\newcommand{\Bbar}{\overline{\operatorname{B}}}
 \newcommand{\pr}{\operatorname{pr}}
 \newcommand{\dd}{\operatorname{d}\hspace{-1pt}}
@@ Line 33: / Line 12: @@
 \newcommand{\V}{\operatorname{V}}
 \newcommand{\Cov}{\operatorname{Cov}}
-\newcommand{\Bigsum}[2]{\ensuremath{\mathop{\textstyle\sum}_{#1}^{#2}}}
+\newcommand{\Bigsum}[2]{\mathop{\textstyle\sum}_{#1}^{#2}}
 \newcommand{\ran}{\operatorname{ran}}
 \newcommand{\card}{\#}
-\newcommand{\Conv}{\mathop{\scalebox{1.1}{\raisebox{-0.08ex}{$\ast$}}}}%
+\renewcommand{\P}{\operatorname{P}}
+\renewcommand{\L}{\operatorname{L}}
+\newcommand{\mathds}{\mathbb}</math></div>
-\usepackage{pgfplots}
+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 [[guide:6588b14666#PROBL-6-1 |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.
-\newcommand{\filledsquare}{\begin{picture}(0,0)(0,0)\put(-4,1.4){$\scriptscriptstyle\text{\ding{110}}$}\end{picture}\hspace{2pt}}
-\newcommand{\mathds}{\mathbb}</math></div>
-\label{PROBL-6-2} 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 [[#PROBL-6-1 |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.