Merge pull request #196 from dpr1005/development

Improved code quality and its documentation

Author: dpuenteramirez

docs/anexos.pdf

@@ -8,9 +8,13 @@
 \usepackage{lmodern} % scalable font
 \usepackage{microtype}
 \usepackage{placeins}
+\usepackage{adjustbox}
 \usepackage{tabularx}
 \usepackage{placeins}
 
+% Moneda
+\usepackage{eurosym}
+
 \RequirePackage{booktabs}
 \RequirePackage[table]{xcolor}
 \RequirePackage{xtab}

docs/anexos.tex

@@ -210,4 +210,22 @@ @misc{Travis-CI-LOG-UBUMLaaS
 
 @misc{PyPI, title={Python Package Index}, url={https://pypi.org/}, note={https://pypi.org/}}
 
-@misc{NearestNeighbors, title={Scikit-Learn Nearest Neighbors}, url={https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html}, note={sklearn.neighbors.NearestNeighbors}}
+@misc{NearestNeighbors, title={Scikit-Learn Nearest Neighbors}, url={https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html}, note={sklearn.neighbors.NearestNeighbors}}
+
+@misc{SalarioJunior, title={Salario medio Programador Junior España}, url={https://es.indeed.com/career/programador-junior/salaries}, note={https://es.indeed.com/career/programador-junior/salaries}}
+
+@misc{SalarioInvestigador, title={Salario medio Investigador España}, url={https://es.indeed.com/career/investigador/salaries}, note={https://es.indeed.com/career/investigador/salaries}}
+
+@misc{ss_cotizacion,
+    author = "Ministerio de Empleo y Seguridad Social",
+  title = {Bases y tipos de cotización 2022},
+  url = {https://www.seg-social.es/wps/portal/wss/internet/Trabajadores/CotizacionRecaudacionTrabajadores/36537}
+}
+
+@misc{softwareLicense, title={What is a Software License?}, url={https://snyk.io/learn/what-is-a-software-license/}, author={Synk}}
+
+@misc{JLA, title={JLA - Find and compare software licenses}, url={https://joinup.ec.europa.eu/collection/eupl/solution/joinup-licensing-assistant/jla-find-and-compare-software-licenses}, author={European Union}}
+
+@misc{CC0, title={Creative Commons - Public Domain}, url={https://wiki.creativecommons.org/wiki/CC0}, note={https://wiki.creativecommons.org/wiki/CC0}}
+
+@misc{CCBYNCND40, title={Creative Commons - Public Domain}, url={https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es}, note={https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es}}

docs/bibliografiaAnexos.bib

@@ -68,8 +68,9 @@ \subsubsection{Bibliotecas Python}
 \begin{table}[p]
 \centering
 \begin{tabular}{lcl}
-	\textbf{Biblioteca} & \textbf{Versión} & \textbf{Descripción}\\
 	\toprule
+	\textbf{Biblioteca} & \textbf{Versión} & \textbf{Descripción}\\
+	\midrule
 	\rowcolor[HTML]{EFEFEF} 
 	\texttt{email-validator} & 1.1.1 & Validar direcciones de correo electrónico.\\
 	\texttt{flask} & 1.1.1 & Web \textit{framework}.\\ \rowcolor[HTML]{EFEFEF}
@@ -100,7 +101,7 @@ \subsubsection{Bibliotecas Python}
 	\texttt{whichcraft} & 0.4.1 & Funcionalidad \textit{shutil.which}. \\
 	\bottomrule
 \end{tabular}
-\caption{Bibliotecas utilizadas y sus versiones.}\label{tab:bibliotecas-python-ubumlaas}
+\caption{Bibliotecas utilizadas por UBUMLaaS y sus versiones.}\label{tab:bibliotecas-python-ubumlaas}
 \end{table}
 
 Las versiones indicadas en la tabla~\ref{tab:bibliotecas-python-ubumlaas} son las que se han utilizado para el desarrollo del proyecto, se pueden actualizar a versiones futuras, siempre y cuando sean compatibles entre sí.
@@ -329,18 +330,19 @@ \subsubsection{Bibliotecas Python}
 \begin{table}[]
 \centering
 \begin{tabular}{lcl}
-	\textbf{Biblioteca} & \textbf{Versión} & \textbf{Descripción}\\ \rowcolor[HTML]{EFEFEF} 
 	\toprule
+	\textbf{Biblioteca} & \textbf{Versión} & \textbf{Descripción}\\ \rowcolor[HTML]{EFEFEF} 
+	\midrule
 	\rowcolor[HTML]{EFEFEF} 
 	\texttt{matplotlib}    & 3.4.3   & Representación gráfica.   \\  
 	\textbf{\texttt{numpy}}    & 1.20.3 &  Computación de \textit{arrays}.  \\ \rowcolor[HTML]{EFEFEF} 
-	\textbf{\texttt{pandas}}    & 1.3.4  & Estructuras de datos    \\  
+	\textbf{\texttt{pandas}}    & 1.3.4  & Estructuras de datos.    \\  
 	\textbf{\texttt{scikit-learn}}    & 0.24.2 & Módulos de minería de datos y ML.   \\ \rowcolor[HTML]{EFEFEF} 
 	\textbf{\texttt{scipy}}    & 1.7.1  & Módulos científicos.    \\ 
 	\texttt{yagmail}    & 0.15.277  & Cliente de GMAIL.    \\ 
 	\bottomrule
 \end{tabular}
-\caption{Bibliotecas utilizadas y sus versiones.}\label{tab:bibliotecas-python-is-ssl}
+\caption{Bibliotecas utilizadas por IS-SSL y sus versiones.}\label{tab:bibliotecas-python-is-ssl}
 \end{table}
 
 Se recomienda el uso de un entorno de desarrollo de \texttt{Conda}, se facilitan ficheros de configuración tanto para \texttt{Conda} como para instalación con \texttt{PIP}.

docs/img/anexos/plan/Licencias.png

@@ -3,34 +3,31 @@
 # @Filename:    CNN.py
 # @Author:      Daniel Puente Ramírez
 # @Time:        19/11/21 07:13
-# @Version:     4.0
+# @Version:     5.0
 
 import numpy as np
 import pandas as pd
 
 from .utils import transform, delete_multiple_element
 
 
-def check_store(store, sample, store_classes):
-    euc = []
-    for s in store:
-        euc.append(np.linalg.norm(s - sample))
-    euc = np.array(euc)
-    euc_nn = np.amin(euc)
-    index_nn = np.ravel(np.where(euc == euc_nn))
-    return store_classes[index_nn[0]]
+class CNN:
 
+    """
+    Hart, P. (1968). The condensed nearest neighbor rule (corresp.). IEEE
+    transactions on information theory, 14(3), 515-516.
 
-class CNN:
+    Parameters
+    ----------
+
+    """
 
     def __init__(self):
+        """A constructor for the class."""
         self.x_attr = None
 
     def filter(self, samples, y):
         """
-        Hart, P. (1968). The condensed nearest neighbor rule (corresp.). IEEE
-            transactions on information theory, 14(3), 515-516.
-
         Implementation of The Condensed Nearest Neighbor Rule
 
         The first sample of each class is placed in *store*. Thus we only have
@@ -48,6 +45,7 @@ def filter(self, samples, y):
         Extracted from:
         The condensed nearest neighbor rule. IEEE Transactions on Information
         Theory ( Volume: 14, Issue: 3, May 1968)
+
         :param samples: DataFrame.
         :param y: DataFrame.
         :return: the input dataset with the remaining samples.
@@ -61,7 +59,7 @@ def filter(self, samples, y):
         handbag = []
 
         for sample_class, sample in zip(samples.target, samples.data):
-            nn_class = check_store(store, sample, store_classes)
+            nn_class = self._check_store(store, sample, store_classes)
 
             if nn_class == sample_class:
                 handbag.append((sample_class, sample))
@@ -75,7 +73,7 @@ def filter(self, samples, y):
             indexes = []
             for index, s2 in enumerate(handbag):
                 sample_class, sample = s2
-                nn_class = check_store(store, sample, store_classes)
+                nn_class = self._check_store(store, sample, store_classes)
                 if nn_class != sample_class:
                     store.append(sample)
                     store_classes.append(sample_class)
@@ -88,3 +86,24 @@ def filter(self, samples, y):
             int))
 
         return samples, y
+
+    @staticmethod
+    def _check_store(store, sample, store_classes):
+        """
+        > The function takes in a sample, a store of samples, and the classes of
+         the store of samples. It then calculates the Euclidean distance
+         between the sample and each sample in the store. It then returns the
+         class of the sample in the store that is closest to the sample
+
+        :param store: the list of samples that have been stored
+        :param sample: the sample we want to classify
+        :param store_classes: the classes of the samples in the store
+        :return: The class of the nearest neighbor.
+        """
+        euc = []
+        for s in store:
+            euc.append(np.linalg.norm(s - sample))
+        euc = np.array(euc)
+        euc_nn = np.amin(euc)
+        index_nn = np.ravel(np.where(euc == euc_nn))
+        return store_classes[index_nn[0]]