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Commit 727b15ac by Stalin Munoz
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algoritmos genéticos para TSP

parent 51b94bd1
Showing with 1189 additions and 8 deletions
curso-search/geneticos/aptitud.svg 0 → 100644
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curso-search/geneticos/ga-intro-01.svg 0 → 100644
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curso-search/geneticos/ga-intro-02.svg 0 → 100644
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curso-search/geneticos/ga-intro-03.svg 0 → 100644
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curso-search/geneticos/ga-intro-04.svg 0 → 100644
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curso-search/geneticos/ga-intro-05.svg 0 → 100644
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curso-search/geneticos/ga-intro-06.svg 0 → 100644
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curso-search/geneticos/ga-intro-07.svg 0 → 100644
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curso-search/geneticos/ga-intro-08.svg 0 → 100644
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curso-search/geneticos/ga-permutacion.aux 0 → 100644
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curso-search/geneticos/genotipo-tsp.svg 0 → 100644
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curso-search/geneticos/genotipo.svg 0 → 100644
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curso-search/geneticos/poblacion.svg 0 → 100644
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curso-search/geneticos/población.svg 0 → 100644
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curso-search/geneticos/seleccion.svg 0 → 100644
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curso-search/geneticos/selecciontorneo.svg 0 → 100644
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This source diff could not be displayed because it is too large. You can view the blob instead.
mdps.log
This is pdfTeX, Version 3.14159265-2.6-1.40.16 (TeX Live 2015/Debian) (preloaded format=pdflatex 2017.8.31) 20 AUG 2018 19:13
This is pdfTeX, Version 3.14159265-2.6-1.40.16 (TeX Live 2015/Debian) (preloaded format=pdflatex 2017.8.31) 6 NOV 2018 12:31
entering extended mode
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Output written on mdps.pdf (54 pages, 2015958 bytes).
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mdps.pdf
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mdps.synctex.gz
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mdps.tex
......@@ -243,7 +243,7 @@ En lo que trataremos asumimos que el agente actua en forma racional.
Para el estado $s$ tenemos que el valor esperado de utilidad es: \\
\begin{center}
$\sum\limits_{s'} {P(S^{t+1}{=}s' \mid S^{t+1}{=}s) V(s')}$
$\sum\limits_{s'} {P(S^{t+1}{=}s' \mid S^{t}{=}s) V(s')}$
\end{center}
\note{
Recordemos que para una loteria podemos calcular su utilidad sumando los productos de las utilidades de las salidas con su probabilidad respectiva.
......@@ -263,7 +263,7 @@ Aquí el valor de utilidad $V(s)$ no será la recompensa instantanea sino la utili
\begin{itemize}
\item La utilidad {\color{blue}\emph{horizonte infinito}} considera la recompensa instantanea y el valor esperado de recompensa para tiempos futuros con su factor de descuento:\\
\begin{center}
$V(s) = R(s) + \gamma \sum\limits_{s'} {P(S^{t+1}{=}s' \mid S^{t+1}{=}s) V(s')}$
$V(s) = R(s) + \gamma \sum\limits_{s'} {P(S^{t+1}{=}s' \mid S^{t}{=}s) V(s')}$
\end{center}
\item Matricialmente:\\
\begin{center}
......
mdps.tex.bak 0 → 100644
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python/.ipynb_checkpoints/markov-Copy1-checkpoint.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cadenas de Markov"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Descripción.\n",
"![Cadena de Markov](\"markov.png\")"
]
},
{
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python/.ipynb_checkpoints/markov-checkpoint.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cadenas de Markov"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Descripción.\n",
"![Cadena de Markov](markov.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matriz de probabilidades de transición"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.4 0.1]\n",
" [ 0.6 0.9]]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# define la matriz de probabilidades de transición\n",
"# utilizando el paquete de cómputo científico de Python numpy\n",
"\n",
"#A = | p(d|d), p(b|d), p(c|d) |\n",
"# | p(b|d), p(b|b), p(c|b) |\n",
"# | p(c|d), p(b|c), p(c|c) |\n",
"\n",
"# por ejemplo la matriz\n",
"#A = | 0.4, 0.1|\n",
"# | 0.6, 0.9|\n",
"# puede definirse en python como\n",
"A = np.matrix([[0.4,0.1],[0.6,0.9]])\n",
"\n",
"print(A)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Encontrar la distribución de probabilidad estacionaria elevando a un exponente grande"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.14285714 0.14285714]\n",
" [ 0.85714286 0.85714286]]\n",
"[[ 0.14285714 0.14285714]\n",
" [ 0.85714286 0.85714286]]\n"
]
}
],
"source": [
"B = np.linalg.matrix_power(A,50)\n",
"print(B)\n",
"B = np.linalg.matrix_power(A,100)\n",
"print(B)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Encontrar la misma distribución calculando los eigenvectores"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"w,v = np.linalg.eig(A)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.3 1. ]\n",
"[[-0.70710678 -0.16439899]\n",
" [ 0.70710678 -0.98639392]]\n"
]
}
],
"source": [
"print(w)\n",
"print(v)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"matrix([[ 0.14285714],\n",
" [ 0.85714286]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v[:,1]/sum(v[:,1])"
]
},
{
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"execution_count": null,
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"outputs": [],
"source": []
}
],
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python/formulas.txt
......@@ -4,5 +4,5 @@ Satisfiable:
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Unsatisfiable:
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Satisfiable:
(__Z__17|__R__NEG__v1__v2)&(__Z__17|(__Z__18|v2__0)&(__Z__17|(__Z__18|~v2__2)&(__Z__17|(~__Z__18|v2__1)&(__Z__17|(~__Z__18|~v2__3)&(~__Z__17|~__R__NEG__v1__v2)&(~__Z__17|(~v2__0|v2__2)&(~__Z__17|(~v2__1|v2__3)&(__Z__19|__R__POS__v1__v2)&(__Z__19|(__Z__20|~v2__0)&(__Z__19|(__Z__20|v2__2)&(__Z__19|(~__Z__20|~v2__1)&(__Z__19|(~__Z__20|v2__3)&(~__Z__19|~__R__POS__v1__v2)&(~__Z__19|(v2__0|~v2__2)&(~__Z__19|(v2__1|~v2__3)&(__R__POS__v1__v2&~__R__NEG__v1__v2)&(__Z__21|__R__NEG__v1__v3)&(__Z__21|v3__0)&(__Z__21|~v3__1)&(~__Z__21|~__R__NEG__v1__v3)&(~__Z__21|(~v3__0|v3__1)&(__Z__22|__R__POS__v1__v3)&(__Z__22|~v3__0)&(__Z__22|v3__1)&(~__Z__22|~__R__POS__v1__v3)&(~__Z__22|(v3__0|~v3__1)&(__R__NEG__v1__v3&~__R__POS__v1__v3)&(__Z__23|__R__NEG__v2__v1)&(__Z__23|v1__0)&(__Z__23|~v1__1)&(~__Z__23|~__R__NEG__v2__v1)&(~__Z__23|(~v1__0|v1__1)&(__Z__24|__R__POS__v2__v1)&(__Z__24|~v1__0)&(__Z__24|v1__1)&(~__Z__24|~__R__POS__v2__v1)&(~__Z__24|(v1__0|~v1__1)&(__R__POS__v2__v1&~__R__NEG__v2__v1)&(__Z__25|__R__NEG__v3__v2)&(__Z__25|(__Z__26|v2__0)&(__Z__25|(__Z__26|~v2__1)&(__Z__25|(~__Z__26|v2__2)&(__Z__25|(~__Z__26|~v2__3)&(~__Z__25|~__R__NEG__v3__v2)&(~__Z__25|(~v2__0|v2__1)&(~__Z__25|(~v2__2|v2__3)&(__Z__27|__R__POS__v3__v2)&(__Z__27|(__Z__28|~v2__0)&(__Z__27|(__Z__28|v2__1)&(__Z__27|(~__Z__28|~v2__2)&(__Z__27|(~__Z__28|v2__3)&(~__Z__27|~__R__POS__v3__v2)&(~__Z__27|(v2__0|~v2__1)&(~__Z__27|(v2__2|~v2__3)&(__R__POS__v3__v2&~__R__NEG__v3__v2)
Satisfiable: LA FORMULA NO ESTA CORRECTA
(__Z__17|__R__NEG__v1__v2)&(__Z__17|(__Z__18|v2__0)&(__Z__17|(__Z__18|~v2__2)&(__Z__17|(~__Z__18|v2__1)&(__Z__17|(~__Z__18|~v2__3)&(~__Z__17|~__R__NEG__v1__v2)&(~__Z__17|(~v2__0|v2__2)&(~__Z__17|(~v2__1|v2__3)&(__Z__19|__R__POS__v1__v2)&(__Z__19|(__Z__20|~v2__0)&(__Z__19|(__Z__20|v2__2)&(__Z__19|(~__Z__20|~v2__1)&(__Z__19|(~__Z__20|v2__3)&(~__Z__19|~__R__POS__v1__v2)&(~__Z__19|(v2__0|~v2__2)&(~__Z__19|(v2__1|~v2__3)&(__R__POS__v1__v2)&(~__R__NEG__v1__v2)&(__Z__21|__R__NEG__v1__v3)&(__Z__21|v3__0)&(__Z__21|~v3__1)&(~__Z__21|~__R__NEG__v1__v3)&(~__Z__21|(~v3__0|v3__1)&(__Z__22|__R__POS__v1__v3)&(__Z__22|~v3__0)&(__Z__22|v3__1)&(~__Z__22|~__R__POS__v1__v3)&(~__Z__22|(v3__0|~v3__1)&(__R__NEG__v1__v3&~__R__POS__v1__v3)&(__Z__23|__R__NEG__v2__v1)&(__Z__23|v1__0)&(__Z__23|~v1__1)&(~__Z__23|~__R__NEG__v2__v1)&(~__Z__23|(~v1__0|v1__1)&(__Z__24|__R__POS__v2__v1)&(__Z__24|~v1__0)&(__Z__24|v1__1)&(~__Z__24|~__R__POS__v2__v1)&(~__Z__24|(v1__0|~v1__1)&(__R__POS__v2__v1)&(~__R__NEG__v2__v1)&(__Z__25|__R__NEG__v3__v2)&(__Z__25|(__Z__26|v2__0)&(__Z__25|(__Z__26|~v2__1)&(__Z__25|(~__Z__26|v2__2)&(__Z__25|(~__Z__26|~v2__3)&(~__Z__25|~__R__NEG__v3__v2)&(~__Z__25|(~v2__0|v2__1)&(~__Z__25|(~v2__2|v2__3)&(__Z__27|__R__POS__v3__v2)&(__Z__27|(__Z__28|~v2__0)&(__Z__27|(__Z__28|v2__1)&(__Z__27|(~__Z__28|~v2__2)&(__Z__27|(~__Z__28|v2__3)&(~__Z__27|~__R__POS__v3__v2)&(~__Z__27|(v2__0|~v2__1)&(~__Z__27|(v2__2|~v2__3)&(__R__POS__v3__v2&~__R__NEG__v3__v2)
python/markov-Copy1.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cadenas de Markov"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Descripción.\n",
"![Cadena de Markov](\"markov.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
python/markov.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cadenas de Markov"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Descripción.\n",
"![Cadena de Markov](markov.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matriz de probabilidades de transición"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.4 0.1]\n",
" [ 0.6 0.9]]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# define la matriz de probabilidades de transición\n",
"# utilizando el paquete de cómputo científico de Python numpy\n",
"\n",
"#A = | p(d|d), p(b|d), p(c|d) |\n",
"# | p(b|d), p(b|b), p(c|b) |\n",
"# | p(c|d), p(b|c), p(c|c) |\n",
"\n",
"# por ejemplo la matriz\n",
"#A = | 0.4, 0.1|\n",
"# | 0.6, 0.9|\n",
"# puede definirse en python como\n",
"A = np.matrix([[0.4,0.1],[0.6,0.9]])\n",
"\n",
"print(A)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Encontrar la distribución de probabilidad estacionaria elevando a un exponente grande"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.14285714 0.14285714]\n",
" [ 0.85714286 0.85714286]]\n",
"[[ 0.14285714 0.14285714]\n",
" [ 0.85714286 0.85714286]]\n"
]
}
],
"source": [
"B = np.linalg.matrix_power(A,50)\n",
"print(B)\n",
"B = np.linalg.matrix_power(A,100)\n",
"print(B)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Encontrar la misma distribución calculando los eigenvectores"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"w,v = np.linalg.eig(A)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.3 1. ]\n",
"[[-0.70710678 -0.16439899]\n",
" [ 0.70710678 -0.98639392]]\n"
]
}
],
"source": [
"print(w)\n",
"print(v)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"matrix([[ 0.14285714],\n",
" [ 0.85714286]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v[:,1]/sum(v[:,1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
python/tsp.py 0 → 100644
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 16 12:09:07 2018
@author: stan
"""
#%%
import simplejson, urllib
orig_coord = 19.3071075,-99.1843678
dest_coord = 20.6665935,-103.3530723
urlstr = "http://maps.googleapis.com/maps/api/distancematrix/json?origins={0}&destinations={1}&mode=driving&language=en-EN&sensor=false".format(str(orig_coord),str(dest_coord))
print(urlstr)
with urllib.request.urlopen(urlstr) as url:
result= simplejson.load(urllib.read(url))
driving_time = result['rows'][0]['elements'][0]['duration']['value']
#%%
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
#plt.figure(num=None, figsize=(8, 6), dpi=300, facecolor='w', edgecolor='k')
m = Basemap(projection='lcc',
lon_0=-121,lat_0=16,\
llcrnrlat=16,urcrnrlat=30,\
llcrnrlon=-121,urcrnrlon=-84,rsphere=6371200.,resolution='h')
# create polar stereographic Basemap instance.
#m = Basemap(projection='stere',lon_0=-120,lat_0=16,\
#llcrnrlat=16,urcrnrlat=29,\
#llcrnrlon=-119,urcrnrlon=-84,\
#rsphere=6371200.,resolution='h') #c, l, i, h, f
#m.fillcontinents(color='white', alpha=0.6, zorder=0)
#m.fillcontinents(color='coral',lake_color='aqua')
#m.bluemarble()
#m.etopo()
m.drawstates()
m.drawcoastlines()
m.drawcountries()
m.fillcontinents(color='lightgray',zorder=0)
#m.drawrivers(color='b', linewidth=.1)
# draw parallels.
#parallels = np.arange(0.,90,1.)
#m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10)
## draw meridians
#meridians = np.arange(180.,360.,1.)
#m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10)
ciudades = {
('Aguascalientes Aguascalientes', -102.305481910559, 21.8475186),
('Mexicali Baja California', -115.4440698, 32.6200699),
('La Paz Baja California', -110.4135535, 24.1017563),
('Tuxtla Gutierrez Chiapas', -93.1418086426027, 16.74521845),
('Chihuahua Chihuahua', -106.384451167241, 28.938764),
('Campeche Campeche', -90.25414928915, 19.59188245),
('Saltillo Tamaulipas', -100.9887188, 25.4466476),
('Colima Colima', -103.654240318428, 19.0986013),
('Durango Durango', -104.919810561772, 23.9112721),
('Guanajuato Guanajuato', -101.242782801753, 21.02405745),
('Chilpancingo Guerrero', -99.5048416, 17.5542149),
('Pachuca Hidalgo', -98.7413535, 20.1165413),
('Guadalajara Jalisco', -103.3469982, 20.676143),
('Toluca Mexico', -99.4890962, 19.2819217),
('Morelia Michoacan', -101.1923818, 19.7027116),
('Cuernavaca Morelos', -99.2342282, 18.9218274),
('Tepic Nayarit', -105.1423419, 21.7206915),
('Monterrey Nuevo Leon', -100.293101629066, 25.63978365),
('Oaxaca Oaxaca', -96.7253575, 17.0604663),
('Puebla Puebla', -98.1984744, 19.0437227),
('Queretaro Queretaro', -100.3879904, 20.5878372),
('Chetumal Quintana Roo', -88.3006444, 18.5132414),
('San Luis Potosi San Luis Potosi', -100.926198413959, 22.3089625),
('Culiacan Sinaloa', -107.3936301, 24.8015732),
('Hermosillo Sonora', -110.9612378, 29.0894152),
('Villa Hermosa Tabasco', -91.5269144, 14.8411997),
('Ciudad Victoria Tamaulipas', -99.1419341, 23.7439573),
('Tlaxcala Tlaxcala', -98.2376413321235, 19.3173725),
('Xalapa Veracruz', -96.9238793, 19.5274085),
('Merida Yucatan', -89.6237402, 20.9670759),
('Zacatecas Zacatecas', -102.668598836424, 22.7293312)}
C,X,Y = zip(*ciudades)
X,Y = m(X,Y)
m.scatter(X, Y,color='red',s=50,marker='o',zorder=1)
plt.show()
#%%
from geopy.geocoders import Nominatim
capitales = ["Aguascalientes Aguascalientes",
"Mexicali Baja California",
"La Paz Baja California",
"Tuxtla Gutierrez Chiapas",
"Chihuahua Chihuahua",
"Campeche Campeche",
"Saltillo Tamaulipas",
"Colima Colima",
"Durango Durango",
"Guanajuato Guanajuato",
"Chilpancingo Guerrero",
"Pachuca Hidalgo",
"Guadalajara Jalisco",
"Toluca Mexico",
"Morelia Michoacan",
"Cuernavaca Morelos",
"Tepic Nayarit",
"Monterrey Nuevo Leon",
"Oaxaca Oaxaca",
"Puebla Puebla",
"Queretaro Queretaro",
"Chetumal Quintana Roo",
"San Luis Potosi San Luis Potosi",
"Culiacan Sinaloa",
"Hermosillo Sonora",
"Villa Hermosa Tabasco",
"Ciudad Victoria Tamaulipas",
"Tlaxcala Tlaxcala",
"Xalapa Veracruz",
"Merida Yucatan",
"Zacatecas Zacatecas"]
geolocator = Nominatim(user_agent='myapplication')
for city in capitales:
location = geolocator.geocode(city)
print((city,location.longitude,location.latitude))
\ No newline at end of file
python/tspmexico.py 0 → 100644
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 16 12:09:07 2018
@author: stan
"""
#%%
import simplejson, urllib
orig_coord = 19.3071075,-99.1843678
dest_coord = 20.6665935,-103.3530723
urlstr = "http://maps.googleapis.com/maps/api/distancematrix/json?origins={0}&destinations={1}&mode=driving&language=en-EN&sensor=false".format(str(orig_coord),str(dest_coord))
print(urlstr)
with urllib.request.urlopen(urlstr) as url:
result= simplejson.load(urllib.read(url))
driving_time = result['rows'][0]['elements'][0]['duration']['value']
#%%
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
#
#fig = plt.figure()
#ax = fig.add_subplot(1,1,1)
#plt.figure(num=None, figsize=(8, 6), dpi=300, facecolor='w', edgecolor='k')
m = Basemap(projection='lcc',
lon_0=-121,lat_0=16,\
llcrnrlat=16,urcrnrlat=30,\
llcrnrlon=-121,urcrnrlon=-84,rsphere=6371200.,resolution='h')
# create polar stereographic Basemap instance.
#m = Basemap(projection='stere',lon_0=-120,lat_0=16,\
#llcrnrlat=16,urcrnrlat=29,\
#llcrnrlon=-119,urcrnrlon=-84,\
#rsphere=6371200.,resolution='h') #c, l, i, h, f
#m.fillcontinents(color='white', alpha=0.6, zorder=0)
#m.fillcontinents(color='coral',lake_color='aqua')
#m.bluemarble()
#m.etopo()
m.drawstates()
m.drawcoastlines()
m.drawcountries()
m.fillcontinents(color='lightgray',zorder=0)
#m.drawrivers(color='b', linewidth=.1)
# draw parallels.
#parallels = np.arange(0.,90,1.)
#m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10)
## draw meridians
#meridians = np.arange(180.,360.,1.)
#m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10)
ciudades = {
('Aguascalientes Aguascalientes', -102.305481910559, 21.8475186),
('Mexicali Baja California', -115.4440698, 32.6200699),
('La Paz Baja California', -110.4135535, 24.1017563),
('Tuxtla Gutierrez Chiapas', -93.1418086426027, 16.74521845),
('Chihuahua Chihuahua', -106.384451167241, 28.938764),
('Campeche Campeche', -90.25414928915, 19.59188245),
('Saltillo Tamaulipas', -100.9887188, 25.4466476),
('Colima Colima', -103.654240318428, 19.0986013),
('Durango Durango', -104.919810561772, 23.9112721),
('Guanajuato Guanajuato', -101.242782801753, 21.02405745),
('Chilpancingo Guerrero', -99.5048416, 17.5542149),
('Pachuca Hidalgo', -98.7413535, 20.1165413),
('Guadalajara Jalisco', -103.3469982, 20.676143),
('Toluca Mexico', -99.4890962, 19.2819217),
('Morelia Michoacan', -101.1923818, 19.7027116),
('Cuernavaca Morelos', -99.2342282, 18.9218274),
('Tepic Nayarit', -105.1423419, 21.7206915),
('Monterrey Nuevo Leon', -100.293101629066, 25.63978365),
('Oaxaca Oaxaca', -96.7253575, 17.0604663),
('Puebla Puebla', -98.1984744, 19.0437227),
('Queretaro Queretaro', -100.3879904, 20.5878372),
('Chetumal Quintana Roo', -88.3006444, 18.5132414),
('San Luis Potosi San Luis Potosi', -100.926198413959, 22.3089625),
('Culiacan Sinaloa', -107.3936301, 24.8015732),
('Hermosillo Sonora', -110.9612378, 29.0894152),
('Villa Hermosa Tabasco', -92.9881407, 17.992608),
('Ciudad Victoria Tamaulipas', -99.1419341, 23.7439573),
('Tlaxcala Tlaxcala', -98.2376413321235, 19.3173725),
('Xalapa Veracruz', -96.9238793, 19.5274085),
('Merida Yucatan', -89.6237402, 20.9670759),
('Zacatecas Zacatecas', -102.668598836424, 22.7293312)}
C,X,Y = zip(*ciudades)
X,Y = m(X,Y)
m.scatter(X, Y,color='red',s=50,marker='o',zorder=1)
lines, = plt.plot(X,Y)
ax = plt.gca()
plt.pause(2)
ax.lines.remove(lines)
plt.draw()
plt.show()
#%%
np.array(list(zip(X,Y)))
#%%
from geopy.geocoders import Nominatim
capitales = ["Aguascalientes Aguascalientes",
"Mexicali Baja California",
"La Paz Baja California",
"Tuxtla Gutierrez Chiapas",
"Chihuahua Chihuahua",
"Campeche Campeche",
"Saltillo Tamaulipas",
"Colima Colima",
"Durango Durango",
"Guanajuato Guanajuato",
"Chilpancingo Guerrero",
"Pachuca Hidalgo",
"Guadalajara Jalisco",
"Toluca Mexico",
"Morelia Michoacan",
"Cuernavaca Morelos",
"Tepic Nayarit",
"Monterrey Nuevo Leon",
"Oaxaca Oaxaca",
"Puebla Puebla",
"Queretaro Queretaro",
"Chetumal Quintana Roo",
"San Luis Potosi San Luis Potosi",
"Culiacan Sinaloa",
"Hermosillo Sonora",
"Villa Hermosa Tabasco",
"Ciudad Victoria Tamaulipas",
"Tlaxcala Tlaxcala",
"Xalapa Veracruz",
"Merida Yucatan",
"Zacatecas Zacatecas"]
geolocator = Nominatim(user_agent='myapplication')
for city in capitales:
location = geolocator.geocode(city)
print((city,location.longitude,location.latitude))
\ No newline at end of file
regexpbayes.txt 0 → 100644
(?m)(.*$)*[^a-zA-Z]*P\(homicida\)\s*=\s*0.0(09[0-9]*|1[0-9]*)[^a-zA-Z]*$[^a-zA-Z]*P\(sangre\)\s*=\s*0.10[0-9]*[^a-zA-Z]*$[^a-zA-Z]*P\(cuchillo\)\s*=\s*0.25[0-9]*[^a-zA-Z]*$[^a-zA-Z]*P\(~cuchillo\)\s*=\s*0.74[0-9]*[^a-zA-Z]*$[^a-zA-Z]*P\(~cuchillo\|homicida\)\s*=\s*0.1[45][0-9]*[^a-zA-Z]*$[^a-zA-Z]*P\(homicida\|cuchillo,sangre\)\s*=\s*0.21[456][0-9]*[^a-zA-Z]*$(.*$)*
respuestabayes.txt 0 → 100644
P(homicida) = 0.01
P(sangre) = 0.10700000000000001
P(cuchillo) = 0.256
P(~cuchillo) = 0.744
P(~cuchillo|homicida) = 0.15000000000000002
P(homicida|cuchillo,sangre) = 0.21553090332805072
respuestabayes1.txt 0 → 100644
P(~cuchillo|homicida) = 0.15000000000000002
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