vmsR: For analysing VMS data

# # cargamos los datos
# anho_entrenamiento = 2013; year = 2013
#
# dossier.0  <- paste("D:/disco_rocio/disco_duro2/2016/red_neuronal_input/",year, sep = "")
# directorio <- paste("D:/disco_rocio/disco_duro2/2016/red_neuronal_input/",year, sep = "")
# setwd(dossier.0)
# dossier.ant <-dossier.0
# data <- read.csv(paste(dossier.ant,"/",'base_',year,'_final.csv',sep=''))
# data <- data[,-which(colnames(data)=="X")]
#
# colnames(data) <- c("Nombre_Barco","Cod_Barco","Fecha_Matlab","Clase_Emision","Lon","Lat","Zona","Vel_VMS","Rumbo_VMS",
#                     "Puerto_0_Mar_1","Dist_Puerto","Dif_Tiempo","Dist_Emisiones","Vel_Cal","Cambio_Rumbo_Calc",
#                     "Lon_Obs_Ini_Cala","Lat_Obs_Ini_Cala","Cala","Primera_Cala","Dist_Cala_Emis","Cod_Viaje_VMS",
#                     "Cod_Viaje_Cruz","Flota","Pesca_Viaje")
#
# #nnet_out <- calibration_nnet(data = data, directory = getwd(), neurons=4, MSE_max=0.04, nb_loop = 10)
#
# length(unique(data$Nombre_Barco))
#
# nombres2 <- c("MARIA","FRANCHESCA","LUZ","FERIHE","CECILIA","ALEXIA","XIOMARA","PAOLA",
#   "KRISTEL","GERALDINE","KIARA","KAREN","CRISCELY","ANITA","YAJAIRA","ALMENDRA","DIANA","JENIFER","JESYCA", "YVONNE",
#   "MACARENA","ELIZABETH", "AMPARITO","SOFIA","AKEMI", "CINTHIA", "ISABEL", "ERICKA", "VERONICA","ANGELITA","GRECIA",
#   "KARLA", "VERALUCIA", "ESTEFANY", "SANDRA", "SAORI", "BULMA", "KATY", "WENDY", "VANIA", "BERENICE", "BEATRIZ",
#   "BRENDA","DANIELA", "ADRIANA", "FERNANDA", "AMELIA", "CHARITO", "SIRENA", "CAROL", "PATRICIA", "FIORELLA", "XIMENA")
#
# data$Nombre_Barco <- as.character(data$Nombre_Barco)
# for(i in seq_along(unique(data$Nombre_Barco))){
#   data[data$Nombre_Barco == unique(data$Nombre_Barco)[i], "Nombre_Barco"] <- nombres2[i]
# }
#
# data$Cod_Barco <- (data$Cod_Barco*1000)/2
# data_vms <- data
#
# #
# save(data_vms, file = "C:/pablo/D/github/vmsR/data/data_vms.RData")
#
#
# nnet_out <- training_nnet(data = data_vms, directory = getwd(), neurons=4, MSE_max=0.04, loops = 2)
#
# xnx <- nnet(formula = Cala_nnet ~ Vel_Cal + Acel_1 + Acel_2 + hora_transf +
#                      Cambio_Rumbo_Tiempo, data = training_set)

# netsxx = nnet(formula = Cala_nnet ~ Vel_Cal + Acel_1 + Acel_2 + hora_transf +
#               Cambio_Rumbo_Tiempo, data = training_set,size = neurons,
#               linout = FALSE, entropy = FALSE, softmax=TRUE,
#               censored = FALSE, skip = FALSE, rang = 0.7, decay = 0,
#               maxit = 100, Hess = FALSE, trace = FALSE, MaxNWts = 1000,
#               abstol = 1.0e-4, reltol = 1.0e-8)
#
# ,
#               weights, Wts, mask,
#               linout = FALSE, entropy = FALSE, softmax=TRUE,
#               censored = FALSE, skip = FALSE, rang = 0.7, decay = 0,
#               maxit = 100, Hess = FALSE, trace = FALSE, MaxNWts = 1000,
#               abstol = 1.0e-4, reltol = 1.0e-8)

#  predict_sets(data_vms, directory = getwd(), loops = 2)