intervalos confianza mc

Alfonso/@alf/@

@@ -0,0 +1,16 @@
+Hola Alfonso.
+
+Al final lo he conseguido hacer hoy.
+
+Adjunto todo:
+
+mEt.mat = matrices de los valores estimados con MLE con 10 filas y 10 columnas que corresponden a los siguientes estados: 21,22,23,24, ... ,29,30
+mLt.mat = matrices lower confidence interval
+mUt.mat = matrices upper confidence interval
+MarkovChain-i.R: código para generar los objetos con R
+
+Más info sobre el paquete: https://cran.r-project.org/web/packages/markovchain/vignettes/an_introduction_to_markovchain_package.pdf
+
+Cualquier cosa, me dices.
+
+Saludos!

Alfonso/markovChain-i.R

@@ -0,0 +1,53 @@
+library("markovchain")
+
+mEt <- array(NA, c(10, 10, ncol(df)))  
+mUt <- array(NA, c(10, 10, ncol(df)))  
+mLt  <- array(NA, c(10, 10, ncol(df)))
+'%!in%' <- function(x,y)!('%in%'(x,y))
+coln=as.character(c(21:30))
+
+f.fillMatrixAurora = function(matrix,coln, with=0){
+  colnmatrix=colnames(matrix)
+  matrix=matrix[match(coln,colnmatrix),match(coln,colnmatrix)]
+  colnames(matrix)=coln
+  rownames(matrix) = coln
+  matrix[is.na(matrix)] <- with
+  return(matrix)
+}
+
+for(i in 1:ncol(df)){
+  print(i)
+  x = as.numeric(unlist(df[,..i]))
+  if(sum(!is.na(x))!=0){
+    y = fillNAgaps(x, firstBack=T)
+    weatherFittedMLE <- markovchainFit(data = y, method = "mle", name = "weatherFittedMLE", confidencelevel = 0.95)
+    mE = weatherFittedMLE$estimate
+    mE = mE@transitionMatrix
+    mU = weatherFittedMLE$upperEndpointMatrix
+    mL = weatherFittedMLE$lowerEndpointMatrix
+    if(ncol(mE)!=length(coln)){
+      print("Filling")
+      mE = f.fillMatrixAurora(mE, coln)
+      mU = f.fillMatrixAurora(mU, coln, with = -999)
+      mL = f.fillMatrixAurora(mL, coln, with = -999)
+    }
+
+    mEt[,,i] = mE
+    mUt[,,i] = mU
+    mLt[,,i] = mL
+  }else{
+    print("TOOODO NA") # se queda con NA
+  }
+
+
+}
+
+
+
+library("R.matlab")
+writeMat("mEt.mat",mEt= mEt)
+writeMat("mUt.mat",mUt= mUt)
+writeMat("mLt.mat",mLt= mLt)
+
+
+

Alfonso/markovChain.R

@@ -0,0 +1,152 @@
+library("markovchain")
+
+weatherStates <- c("sunny", "cloudy", "rain")
+byRow <- TRUE
+weatherMatrix <- matrix(data = c(0.70, 0.2, 0.1,0.3, 0.4, 0.3,
+                                 0.2, 0.45, 0.35), byrow = byRow, nrow = 3,
+                        dimnames = list(weatherStates, weatherStates))
+mcWeather <- new("markovchain", states = weatherStates, byrow = byRow,
+                 transitionMatrix = weatherMatrix, name = "Weather")
+
+weathersOfDays <- rmarkovchain(n = 365, object = mcWeather, t0 = "sunny")
+
+
+
+
+weatherFittedLAPLACE <- markovchainFit(data = weathersOfDays,
+                                          method = "laplace", laplacian = 0.01,
+                                          name = "Weather LAPLACE")
+weatherFittedLAPLACE$estimate
+weatherFittedLAPLACE$confidenceInterval
+
+
+
+
+
+weatherFittedBOOT <- markovchainFit(data = weathersOfDays, method = "bootstrap", nboot = 20)
+weatherFittedBOOT$estimate
+weatherFittedBOOT$confidenceInterval
+
+
+
+weatherFittedMLE <- markovchainFit(data = weathersOfDays, method = "mle", name = "weatherFittedMLE", confidencelevel = 0.95)
+weatherFittedMLE$estimate
+weatherFittedMLE$upperEndpointMatrix
+weatherFittedMLE$lowerEndpointMatrix
+
+
+
+
+
+
+
+################
+library("data.table")
+df <- fread("TsetCleanV7.csv", sep=";")
+
+
+fillNAgaps <- function(x, firstBack=FALSE) {
+  ## NA's in a vector or factor are replaced with last non-NA values
+  ## If firstBack is TRUE, it will fill in leading NA's with the first
+  ## non-NA value. If FALSE, it will not change leading NA's.
+
+  # If it's a factor, store the level labels and convert to integer
+  lvls <- NULL
+  if (is.factor(x)) {
+    lvls <- levels(x)
+    x    <- as.integer(x)
+  }
+
+  goodIdx <- !is.na(x)
+
+  # These are the non-NA values from x only
+  # Add a leading NA or take the first good value, depending on firstBack   
+  if (firstBack)   goodVals <- c(x[goodIdx][1], x[goodIdx])
+  else             goodVals <- c(NA,            x[goodIdx])
+
+  # Fill the indices of the output vector with the indices pulled from
+  # these offsets of goodVals. Add 1 to avoid indexing to zero.
+  fillIdx <- cumsum(goodIdx)+1
+
+  x <- goodVals[fillIdx]
+
+  # If it was originally a factor, convert it back
+  if (!is.null(lvls)) {
+    x <- factor(x, levels=seq_along(lvls), labels=lvls)
+  }
+
+  x
+}
+
+x = as.numeric(unlist(df[,1]))
+y = fillNAgaps(x, firstBack=T)
+
+weatherFittedMLE <- markovchainFit(data = y, method = "mle", name = "weatherFittedMLE", confidencelevel = 0.95)
+weatherFittedMLE$estimate
+weatherFittedMLE$upperEndpointMatrix
+weatherFittedMLE$lowerEndpointMatrix
+
+
+weatherFittedBOOT<- markovchainFit(data = y, method = "bootstrap", nboot = 20)
+weatherFittedBOOT$estimate
+weatherFittedBOOT$confidenceInterval
+
+
+
+
+
+
+
+
+
+mEt <- array(NA, c(10, 10, ncol(df)))  
+mUt <- array(NA, c(10, 10, ncol(df)))  
+mLt  <- array(NA, c(10, 10, ncol(df)))
+'%!in%' <- function(x,y)!('%in%'(x,y))
+coln=as.character(c(21:30))
+
+f.fillMatrixAurora = function(matrix,coln, with=0){
+  colnmatrix=colnames(matrix)
+  matrix=matrix[match(coln,colnmatrix),match(coln,colnmatrix)]
+  colnames(matrix)=coln
+  rownames(matrix) = coln
+  matrix[is.na(matrix)] <- with
+  return(matrix)
+}
+
+for(i in 1:ncol(df)){
+  print(i)
+  x = as.numeric(unlist(df[,..i]))
+  if(sum(!is.na(x))!=0){
+    y = fillNAgaps(x, firstBack=T)
+    weatherFittedMLE <- markovchainFit(data = y, method = "mle", name = "weatherFittedMLE", confidencelevel = 0.95)
+    mE = weatherFittedMLE$estimate
+    mE = mE@transitionMatrix
+    mU = weatherFittedMLE$upperEndpointMatrix
+    mL = weatherFittedMLE$lowerEndpointMatrix
+    if(ncol(mE)!=length(coln)){
+      print("Filling")
+      mE = f.fillMatrixAurora(mE, coln)
+      mU = f.fillMatrixAurora(mU, coln, with = -999)
+      mL = f.fillMatrixAurora(mL, coln, with = -999)
+    }
+
+    mEt[,,i] = mE
+    mUt[,,i] = mU
+    mLt[,,i] = mL
+  }else{
+    print("TOOODO NA") # se queda con NA
+  }
+
+
+}
+
+
+
+library("R.matlab")
+writeMat("mEt.mat",mEt= mEt)
+writeMat("mUt.mat",mUt= mUt)
+writeMat("mLt.mat",mLt= mLt)
+
+
+

Tesis/plantilla-tesis/chapters/chapter02/chap02.tex

@@ -14,7 +14,7 @@ \chapter{Abstract}
 
 \begin{itemize}
 
-\item O1. Indentify and collect datasets relative to smart environments and determine the nature of the data under study
+\item O1. Identify and collect datasets relative to smart environments and determine the nature of the data under study
 \item 02. Find appropriate ways to reduce the volume of such data in order to follow the Big Data paradigm
 \item 03. Determine the models that better help predict and cluster information regarding energy efficiency
 \item 04. Develop an IoT platform oriented towards the proper processing, management and analysis of Big volumes of data 

fernan_jim/Icecream.csv

@@ -0,0 +1,31 @@
+"","cons","income","price","temp"
+"1",0.386,78,0.27,41
+"2",0.374,79,0.282,56
+"3",0.393,81,0.277,63
+"4",0.425,80,0.28,68
+"5",0.406,76,0.272,69
+"6",0.344,78,0.262,65
+"7",0.327,82,0.275,61
+"8",0.288,79,0.267,47
+"9",0.269,76,0.265,32
+"10",0.256,79,0.277,24
+"11",0.286,82,0.282,28
+"12",0.298,85,0.27,26
+"13",0.329,86,0.272,32
+"14",0.318,83,0.287,40
+"15",0.381,84,0.277,55
+"16",0.381,82,0.287,63
+"17",0.47,80,0.28,72
+"18",0.443,78,0.277,72
+"19",0.386,84,0.277,67
+"20",0.342,86,0.277,60
+"21",0.319,85,0.292,44
+"22",0.307,87,0.287,40
+"23",0.284,94,0.277,32
+"24",0.326,92,0.285,27
+"25",0.309,95,0.282,28
+"26",0.359,96,0.265,33
+"27",0.376,94,0.265,41
+"28",0.416,96,0.265,52
+"29",0.437,91,0.268,64
+"30",0.548,90,0.26,71