.gitattributes

@@ -1 +0,0 @@
-main.pdf filter=lfs diff=lfs merge=lfs -text

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "ansible.python.interpreterPath": "/bin/python"
+}

Makefile

@@ -1,6 +0,0 @@
-options=-shell-escape -file-line-error
-
-all: main.pdf
-
-%.pdf: %.tex
-	lualatex $(options) $<

content/chapters/1.tex

@@ -1,168 +0,0 @@
-\chapter{Unsupervised Learning}
-
-\begin{definition}[Precision Medicine]
-  Design of treatment for a given patient, based on genomic data.
-\end{definition}
-
-\begin{definition}[Hierarchical clustering]
-\end{definition}
-
-Gene expression time series: look for genes with similar expression footprint.
-
-\paragraph{Representation of data}
-
-\begin{itemize}
-  \item Tables;
-  \item Trees / Graphs;
-  \item Time series...
-\end{itemize}
-
-\begin{figure}
-  \includestandalone{figures/plots/genes_expression_timeseries}
-  \caption{Example of gene expression time series}
-\end{figure}
-
-\section{Distances and Similarities}
-
-\begin{property}[Distance]
-  \begin{description}
-    \item[non-negativity] $d(i, j) \geq 0$
-    \item[isolation] $d(i, i) = 0$
-    \item[symmetry] $d(i, j) = d(j, i)$
-    \item[triangular inequality] $d(i, j) \leq d(i, h) + d(h, j)$   
-  \end{description}
-\end{property}
-
-\begin{definition}[Dissimilarity]
-  Distance without triangular inequality.
-\end{definition}
-
-\begin{definition}[Similarity]
-  Function $s$ from $X \times X$ to $\RR_+$ such that:
-  \begin{enumerate}
-    \item $s$ is symmetric: $(x, y) \in X \times X; s(x, y) = s(y, x)$
-    \item $(x, y) \in X \times X; s(x, x) = s(y, y) > s(x, y)$.
-  \end{enumerate}
-\end{definition}
-
-\begin{exercise}
-
-  Let $d(x, y)$ be the distance, $d(x, y) \in [0, +\infty[$.
-
-  What should be the similarity measure $S(x, y) = f(d(x, y))$ that satisfies the following property:
-  \[
-    (x, y) \in X \times X \: | \: S(x, y) > S(x, y)
-  \]
-  having $S(x, y) \leq M$, $S(x, y) \in ]0, M]$.
-\end{exercise}
-$d(x, y) \geq 0 \: \forall (x, y)$
-\begin{equation}
-  S(x, y) = \frac{M}{d(x, y) + 1}
-  \label{eq:similarity-first}
-\end{equation}
-In \cref{eq:similarity-first}, $S(x, y)$ ranges from 0 to M.
-\begin{eqnarray}
-  \lim_{n \to \infty} \frac{M}{n + 1} = 0 && \lim_{n \to 0}  \frac{M}{n + 1} = M
-\end{eqnarray}
-
-
-\section{Data Representation}
-
-\paragraph{Data matrix}
-
-
-\paragraph{Distance matrix}
-
-\[
-  \begin{bmatrix}
-    0 \\
-    d(2, 1) & 0 \\
-    d(3, 1) & d(3, 2) & 0 \\
-    \vdots & \vdots & \ddots \\
-    d(n, 1) & d(n,2) & \dots & \dots & 0
-  \end{bmatrix}
-\]
-
-
-\begin{table}
-  \centering
-  \begin{tabular}{c|cc}
-    &$s_{1}$ & $s_{2}$ \\
-    \hline
-    $p_{1}$ & 0 & 1 \\
-    $p_{2}$ & 1 & 0 \\
-    $p_{3}$ & 3 & 2 \\
-    \end{tabular}
-  \caption{Example data matrix: 2 symptoms for 3 patients.}
-\end{table}
-
-
-
-\begin{definition}[Minkowski distance]
-  \[
-    L_p (x, y) = \left(\abs{x_1 - y_1}^p + \abs{x_2 - y_2}^p + \ldots + \abs{x_d - y_d}^p\right)^{\sfrac{1}{p}} = \left(\sum_{i=1}^d \left(x_i - y_i\right)^p\right)^{\sfrac{1}{p}}
-  \]
-  where $p$ is a positive integer.
-\end{definition}
-
-\begin{definition}[Manhattan distance]
-  \[
-  L_1(x, y) = \sum_{i=1}^d \abs{x_i - y_i}
-  \]
-\end{definition}
-
-\begin{definition}[Euclidian distance]
-  Let $A$ and $B$ be two points, with $(x_{A}, y_{A})$ and $(x_{B}, y_{B})$ their respective coordinates,
-\end{definition}
-
-If $p=2$, $L_2$ is the Euclidian distance:
-\begin{definition}[Euclidian distance]
-  \[
-    d(x, y) = \sqrt{\abs{x_1 - y_1}^2 + \abs{x_2 - y_2} + \ldots + \abs{x_d - y_d}^2}
-  \]
-\end{definition}
-
-We can add weights
-
-\subsection{K-means}
-
-The cost function is minimized:
-\[
-  Cost(C) \sum_{i=1}^{k}...
-\]
-
-\begin{algorithm}[H]
-  Choose the number of clusters $k$.
-
-  Choose randomly $k$ means.
-
-  For each point, compute the distance between the point and each means.
-  We allocate the point to the cluster represented by the clostest center.
-
-  We set each means to the center of the cluster, and reiterate.
-  \caption{$K$-means algorithm}
-\end{algorithm}
-
-
-\begin{exercise}
-  We have six genes:
-  \begin{table}[H]
-    \centering
-    \begin{tabular}{ccccccc}
-      \toprule
-      & $g_{1}$ & $g_{2}$ & $g_{3}$ & $g_{4}$ & $g_{5}$  & $g_{6}$ \\
-      \midrule
-      $\times 10^{-2}$ & 10 & 12 & 9 & 15 & 17 & 18 \\
-      \bottomrule
-    \end{tabular}
-    \caption{Sample values for six gene expressions.}
-  \end{table}
-
-  With $k=2$ and $m_{1} = 10 \cdot 10^{-2}$ and $m_{2} = 9 \cdot 10^{-2}$ the two initial randomly chosen means, run the $k$-means algorithm.
-\end{exercise}
-
-\begin{figure}
-  \centering
-  \includegraphics[scale=1]{figures/plots/kmeans.pdf}
-  \caption{$k$-means states at each of the 3 steps}
-\end{figure}

content/chapters/include.tex

@@ -11,7 +11,9 @@
 		}
 }
 
-\includechapters{}{2}
+
+\includechapters{part1}{2}
+
 
 % \includechapters{part2}{2}
 

content/genes_expression_timeseries.tex

@@ -1,19 +0,0 @@
-\documentclass[tikz,a4paper]{standalone}
-
-\usepackage{tikz}
-
-\begin{document}
-
-\usetikzlibrary{datavisualization}
-
-\begin{tikzpicture}
-  \datavisualization[visualize as smooth line]
-  data {
-    x, y
-    2, 1,
-    3, 2,
-    4, 1.5
-  };
-\end{tikzpicture}
-
-\end{document}

figures/euclidian_distance.tex

@@ -1,19 +0,0 @@
-
-
-
-\documentclass[tikz]{standalone}
-
-\usepackage{tikz}
-\usepackage{tkz-euclide}
-
-\begin{document}
-
-\begin{tikzpicture}[scale=1]
-  \tkzInit[xmax=5,ymax=5]
-  \tkzDrawX[>=latex]
-  \tkzDraw[>=latex]
-  \tkzDefPoints()
-
-\end{tikzpicture}
-
-\end{document}

figures/plots/.gitattributes

@@ -1,2 +0,0 @@
-genes_expression_timeseries.pdf filter=lfs diff=lfs merge=lfs -text
-kmeans.pdf filter=lfs diff=lfs merge=lfs -text

figures/plots/genes_expression_timeseries.tex

@@ -1,45 +0,0 @@
-\documentclass[tikz]{standalone}
-\usepackage{tikz}
-
-\begin{document}
-
-\usetikzlibrary{datavisualization}
-\begin{tikzpicture}
-
-  \datavisualization data group {genes} = {
-    data[set=gene1] {
-      x, y
-      0, 1,
-      1, 2,
-      2, 1.5
-    }
-    data[set=gene2] {
-      x, y
-      0, 1.5,
-      1, 2.25,
-      2, 1.75
-    }
-    data[set=gene3] {
-      x, y
-      0, 0.25,
-      1, 0.26,
-      2, 0.7
-    }
-    data[set=gene4] {
-      x, y
-      0, 0.5,
-      1, 0.25,
-      2, 1
-    }
-  };
-  \datavisualization [
-  school book axes, all axes={unit length=7.5mm},
-  visualize as smooth line/.list={gene1, gene2, gene3, gene4},
-  style sheet=strong colors,
-  x axis={label=$t$},
-  y axis={label={expression}}]
-data group {genes};
-
-\end{tikzpicture}
-
-\end{document}

figures/plots/kmeans.tex

@@ -1,54 +0,0 @@
-\documentclass[margin=0.5cm]{standalone}
-\usepackage{tikz}
-\usepackage{pyluatex}
-\usepackage{pgf}
-
-\begin{document}
-\begin{python}
-# %%
-import io
-
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans
-
-# %%
-
-
-data = """g1,10
-g2,12
-g3,9
-g4,15
-g5,17
-g6,18"""
-
-points =[int(row.split(",")[1]) for row in data.split("\n")]
-X = np.array([[point] for point in points])
-initial_means = [[10], [9]]
-points
-
-# %%
-kmeans_values = []
-for i in range(1,4): 
-    kmeans = KMeans(n_clusters=2, random_state=42, max_iter=i, init=initial_means, n_init=1)
-    kmeans.fit(X)
-    kmeans_values.append(kmeans.cluster_centers_)
-
-
-# %%
-fig, axs = plt.subplots(len(kmeans_values), 1, sharex=True)
-for i, centroids in enumerate(kmeans_values):
-    ax = axs[i]
-    ax.scatter(centroids, [i]*len(centroids), marker='x')
-    ax.scatter(points, [i]*len(points), s=2, color="black")
-    ax.axis('off')
-
-
-with io.StringIO() as file:
-    fig.savefig(file, format="pgf", bbox_inches="tight", pad_inches=0.1)
-    print(file.getvalue())
-\end{python}
-\begin{tikzpicture}
-    
-\end{tikzpicture}
-\end{document}

main.tex

@@ -1,69 +1,46 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Course on "Data-mining and Machine Learning" - GENIOMHE - M1-S1
+% Course of None
-%
+% 
-% Author: Samuel Ortion <samuel@ortion.fr>
+% Author: Samuel ORTION <samuel@ortion.fr> 
-% Version: 0.1.0
+% Version: 0.0.1
 % Date: 2023
 % Licence: CC-By-SA 4.0+ International 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-\documentclass[twoside=false,fontsize=10pt,fleqn]{scrbook}
+\documentclass[
-\usepackage{mus}
+	a4paper,
-\usepackage{standalone}
+	10pt,
-\titlehead{GENIOMHE}
+	fleqn,
-\title{Data-mining and\newline{}Machine Learning}
+	oneside
-\subtitle{}
+]{talpa}
-\author{Samuel Ortion}
-\date{Fall 2023}
-\teacher{Farida Zerhaoui}
-\cursus{GENIOMHE}
-\university{Université d'Évry val d'Essonne -- Université Paris-Saclay}
-\semester{M1 - S1}
 
-\input{definitions}
+\input{colors.tex}
-\input{preamble}
+\input{meta.tex}
+\input{definitions.tex}
 
 \hypersetup{
-	pdftitle={Course - Data-mining and Machine Learning},
+	pdftitle={
-	pdfauthor={Samuel Ortion},
+		Course - None
+	},
+	pdfauthor={
+		Samuel Ortion
+	},
 	pdfsubject={},
-	pdfkeywords={GENIOMHE, Master, bioinformatics, machine learning, statistics, data},
+	pdfkeywords={},
 	pdfcreator={LaTeX}
 }
 
-\usepackage{ccicons}
+% \addbibressource{bibliography.bib}
-\usepackage[
-    type={CC},
-    modifier={by-sa},
-    version={4.0},
-]{doclicense}
 
-\addbibresource{references.bib}
 \makeindex
-
 \begin{document}
 
-\setkomafont{fullpagetitle}{\fontsize{1.5cm}{3em}\fontseries{b}\selectfont}
+\tableofcontents
-\maketitlefullpage
 
-{
+% \input{content/introduction.tex}
-	\hypersetup{
-		linkcolor=black
-	}
-	\tableofcontents
-}
 
-\doclicenseThis%
+\input{content/chapters/include.tex}
 
-% \input{content/introduction}
+% \input{content/conclusion.tex}
 
-\input{content/chapters/include}
+\end{document}
-
-% \input{content/conclusion}
-
-\nocite{*}
-
-\printbibliography%
-% \printglossary%
-
-\end{document}

notebooks/kmeans1d.ipynb

@@ -1,109 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import io\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.cluster import KMeans"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[10, 12, 9, 15, 17, 18]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "data = \"\"\"g1,10\n",
-    "g2,12\n",
-    "g3,9\n",
-    "g4,15\n",
-    "g5,17\n",
-    "g6,18\"\"\"\n",
-    "\n",
-    "points =[int(row.split(\",\")[1]) for row in data.split(\"\\n\")]\n",
-    "X = np.array([[point] for point in points])\n",
-    "initial_means = [[10], [9]]\n",
-    "points"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kmeans_values = []\n",
-    "for i in range(1,4): \n",
-    "    kmeans = KMeans(n_clusters=2, random_state=42, max_iter=i, init=initial_means, n_init=1)\n",
-    "    kmeans.fit(X)\n",
-    "    kmeans_values.append(kmeans.cluster_centers_)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(len(kmeans_values), 1, sharex=True)\n",
-    "for i, centroids in enumerate(kmeans_values):\n",
-    "    ax = axs[i]\n",
-    "    ax.scatter(centroids, [i]*len(centroids), marker='x')\n",
-    "    ax.scatter(points, [i]*len(points), s=2, color=\"black\")\n",
-    "    ax.axis('off')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "geniomhe-ml",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

preamble.tex

@@ -1,2 +0,0 @@
-\usepackage{mus-learn}
-\usepackage{xfrac}

references.bib

@@ -1,25 +0,0 @@
-
-@book{geron_hands-machine_2019,
-  edition   = {2},
-  title     = {Hands-On Machine Learning with Scikit-Learn, Keras, and {TensorFlow}},
-  abstract  = {Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, … - Selection from Hands-On Machine Learning with Scikit-Learn, Keras, and {TensorFlow}, 2nd Edition [Book]},
-  publisher = {O'{REILLY}},
-  author    = {Géron, Aurélien},
-  date      = {2019},
-  langid    = {english},
-  note      = {{ISBN}: 9781098125974}
-}
-
-@collection{witten_data_2011,
-  location  = {Boston},
-  edition   = {4},
-  title     = {Data Mining - Practical Machine Learning Tools an Techniques},
-  isbn      = {978-0-12-374856-0},
-  series    = {The Morgan Kaufmann Series in Data Management Systems},
-  publisher = {Morgan Kaufmann},
-  editor    = {Witten, Ian H. and Frank, Eibe and Hall, Mark A.},
-  urldate   = {2023-06-16},
-  date      = {2011-01-01},
-  langid    = {english},
-  doi       = {10.1016/B978-0-12-374856-0.00018-3}
-}

requirements.txt

@@ -1,2 +0,0 @@
-scikit-learn
-numpy