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scatter.expectativas <- function(Variables, nombres = '', xmin = FALSE, obs = NA, reg = F)
{  
  Datos <-read.csv('Encuestas.csv')
  Datos <- subset(Datos, Datos$Horizonte %in% Variables, drop = TRUE)
  Datos$FechaEnc <- as.Date(sapply(Datos$FechaEncuesta, as.fecha))
  
  if(xmin) Datos <- subset(Datos, Datos$FechaEnc >= xmin, drop = TRUE)

  if(!is.na(obs)) Datos <- subset(Datos, Datos$FechaEnc %in% obs, drop = TRUE)
  

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# Paso 1.
# Descargar este archivo y extraer los contenidos en la carpeta XX
# http://mapserver.inegi.org.mx/MGN/mge2010v5_0a.zip

# Paso 2.
# Fijar el directorio de trabajo la carpeta XX en la que descargaste los archivos

setwd("d:/User Profiles/G13087/Documents/R/Mapas/INEGI/Estados")

# Paso 3.

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# http://mapserver.inegi.org.mx/MGN/mge2010v5_0a.zip

setwd("d:/User Profiles/G13087/Documents/R/Mapas/INEGI/Municipios")

rm(list = ls())
require("rgdal")
require("maptools")
require("ggplot2")
require("plyr")
require("rgeos")

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# Jose Sotelo
# 2015
# Universite de Montreal
import matplotlib.pyplot as plt
import numpy as np

# x = np.linspace(0, 3*np.pi, 500)
# plt.plot(x, np.sin(x**2))
# plt.title('A simple chirp');
# plt.show()

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# coding: utf-8

# In[ ]:

import re
import commands
import numpy as np
import scipy.io.wavfile
import itertools

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import theano
import numpy
from numpy.testing import assert_allclose

from collections import OrderedDict
from fuel.datasets import IndexableDataset
from fuel.streams import DataStream
from fuel.schemes import SequentialScheme
from theano import tensor

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from theano import function
from blocks.bricks import MLP, Identity
from blocks.filter import VariableFilter
from blocks.initialization import Constant
from blocks.roles import INPUT
from blocks.utils import shared_floatx
from theano import function, tensor
from blocks.graph import ComputationGraph, apply_batch_normalization

import numpy

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import theano
import numpy

from collections import OrderedDict
from fuel.datasets import IndexableDataset
from fuel.streams import DataStream
from fuel.schemes import SequentialScheme

from theano import tensor

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from theano import tensor
from theano.ifelse import ifelse
from blocks.utils import shared_like
from blocks.monitoring.aggregation import AggregationScheme, Aggregator

class MeanAndVariance(AggregationScheme):
    """Aggregation scheme which computes the mean.
    Parameters
    ----------
    numerator : :class:`~tensor.TensorVariable`

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library("igraph")

num_layer = 7
num_nodes = (num_layer+1)*num_layer/2

M <-matrix(rep(0,num_nodes*num_nodes),nrow=num_nodes,ncol=num_nodes)
layout <- matrix(rep(0, 2*num_nodes), nrow=num_nodes)

node = 0
for(layer in 1:num_layer){