Evidencia 2: Técnicas predictivas basadas en aprendizaje autónomo
Eduardo Leyva y Sofía Berrón
2023-11-21
Tarea 1: Usar un algoritmo de clasificación que impacte directamente tu vida diaria.
library(openxlsx)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
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## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(cowplot)##
## Attaching package: 'cowplot'
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## stamplibrary(ggpubr)##
## Attaching package: 'ggpubr'
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## get_legendlibrary(cluster)
library(purrr)
library(dplyr)Comentario
Libros <- read.xlsx("/Users/eduardoleyva/Desktop/Evidencia 2/Top_100_Trending_Books.xlsx")
summary(Libros)## Rank book.title book.price rating
## Min. : 1.00 Length:100 Min. : 2.780 Min. :4.10
## 1st Qu.: 25.75 Class :character 1st Qu.: 6.303 1st Qu.:4.60
## Median : 50.50 Mode :character Median :11.480 Median :4.70
## Mean : 50.50 Mean :12.709 Mean :4.69
## 3rd Qu.: 75.25 3rd Qu.:16.990 3rd Qu.:4.80
## Max. :100.00 Max. :48.770 Max. :5.00
## NA's :3
## author year.of.publication genre url
## Length:100 Min. :1947 Length:100 Length:100
## Class :character 1st Qu.:2014 Class :character Class :character
## Mode :character Median :2019 Mode :character Mode :character
## Mean :2014
## 3rd Qu.:2023
## Max. :2024
## Comentario
numeric_columns <- Libros[sapply(Libros, is.numeric)]
Escalar <- scale(numeric_columns, center = TRUE, scale = TRUE)
Escalar <- as.data.frame(Escalar)
Escalar <- Escalar[complete.cases(Escalar), ]
str(Escalar)## 'data.frame': 97 obs. of 4 variables:
## $ Rank : num -1.71 -1.67 -1.64 -1.6 -1.57 ...
## $ book.price : num 0.722 1.039 2.374 1.425 -0.892 ...
## $ rating : num -3.256 -1.047 -1.047 -1.599 0.609 ...
## $ year.of.publication: num 0.611 0.611 0.611 0.611 0.28 ...Comentario
total_within = function(n_clusters, data, iter.max=1000, nstart=50){
cluster_means = kmeans(data,centers = n_clusters,
iter.max = iter.max,
nstart = nstart)
return(cluster_means$tot.withinss)
}
total_withinss <- map_dbl(.x = 1:15,
.f = total_within,
data = Escalar)
total_withinss## [1] 388.01141 286.64929 224.51635 170.23330 135.40300 114.27131 97.98697
## [8] 87.25978 77.25084 68.97324 63.33957 58.50453 53.56437 48.97433
## [15] 45.60682data.frame(n_clusters = 1:15, suma_cuadrados_internos = total_withinss) %>%
ggplot(aes(x = n_clusters, y = suma_cuadrados_internos)) +
geom_line() +
geom_point() +
scale_x_continuous(breaks = 1:15) +
labs(title = "Suma total de cuadrados intra-cluster") +
theme_bw()
Comentario
kmcluster = kmeans(Escalar,centers=4,nstart = 50)
kmcluster## K-means clustering with 4 clusters of sizes 40, 32, 10, 15
##
## Cluster means:
## Rank book.price rating year.of.publication
## 1 -0.4903229 -0.4272087 0.4847771 0.2356170
## 2 1.0534618 0.2557650 -0.1846194 0.2492484
## 3 0.1240888 -0.9026010 0.7746188 -2.6145882
## 4 -1.0352220 1.0399000 -1.4152967 0.4917224
##
## Clustering vector:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 4 4 4 4 1 1 4 1 4 4 1 3 1 1 1 4 4 4 1 1
## 21 22 23 24 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
## 1 1 1 4 1 1 1 1 3 3 1 1 4 1 3 1 1 1 1 1
## 42 43 44 45 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62
## 1 1 3 1 4 1 1 1 4 1 1 1 1 3 1 2 2 1 2 1
## 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82
## 1 4 2 2 1 3 1 2 2 2 2 2 2 3 2 2 2 2 2 2
## 83 84 85 87 88 89 90 91 92 93 94 95 96 97 98 99 100
## 2 2 2 2 2 2 2 3 2 2 2 2 2 3 2 2 2
##
## Within cluster sum of squares by cluster:
## [1] 47.44112 60.03961 20.42951 42.32306
## (between_SS / total_SS = 56.1 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"Comentario
fviz_cluster(kmcluster, Escalar)+
theme_minimal()
Comentario
Escalar <- na.omit(Escalar)
fviz_cluster(kmcluster, Escalar, show.clust.cent = T,
ellipse.type = "euclid", star.plot = T, repel = T) +
labs(title = "Resultados clustering K-means") +
theme_bw()
Comentario
Libros2 <- dist(Libros, method = 'euclidean') #Sacamos la distancia euclidiana de los puntos## Warning in dist(Libros, method = "euclidean"): NAs introduced by coercionLibros3 <- hclust(Libros2, method = 'average') #Hacemos el análisis de clusters
plot(Libros3, cex=0.5, col="red", hang = -1,
main="Dendograma, Distancia Euclídea, Método completo")
rect.hclust(Libros3, k = 4, border = c("blue", "green", "purple", "orange"))
Análisis de Resultados
Tarea 2: Buscar datos del agua en algún estado del país, y entrenar una red neuronal.
library(neuralnet)##
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library(psych)##
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## %+%, alphalibrary(MASS)##
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## selectlibrary(ISLR)
library(fRegression)
library(vcd)## Loading required package: grid##
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## Hitterslibrary(dplyr)
library(mlbench)
library(magrittr)##
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## extractlibrary(neuralnet)
library(keras)
library(caret)## Loading required package: lattice##
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## liftLeer base de datos:
Comentario
Agua <- read.xlsx("/Users/eduardoleyva/Desktop/Evidencia 2/El_Mojarral_20082.xlsx")
str(Agua)## 'data.frame': 4322 obs. of 4 variables:
## $ Fecha : num 39448 39448 39448 39448 39448 ...
## $ Hora : num 0 0.0417 0.0833 0.125 0.1667 ...
## $ Nivel : num 0.393 0.393 0.396 0.399 0.397 ...
## $ Temperatura: num 28.7 28.5 28.4 28.1 27.6 ...colSums(is.na(Agua))## Fecha Hora Nivel Temperatura
## 0 0 0 0Ajustar y Visualizar Redes Neuronales:
Comentario
set.seed(13)
train = createDataPartition(y = Agua$Temperatura, p=0.7,list=FALSE, times = 1)
Agua_train = Agua[train,]
Agua_test = Agua[-train,]
RN <- neuralnet(Temperatura ~ Fecha+Hora+Nivel,
data = Agua_train,
hidden = c(8,6),
linear.output = T,
lifesign = 'full',
threshold = 0.05,
rep=1)## hidden: 8, 6 thresh: 0.05 rep: 1/1 steps: 56 error: 921.67135 time: 0.17 secsComentario
#train/test split en matrices y separando variable a predecir
training <- as.matrix(Agua_train[, c(1, 2, 3)])
trainingtarget <- as.matrix(Agua_train[,4])
test <- as.matrix(Agua_test[, c(1, 2, 3)])
testtarget <- as.matrix(Agua_test[,4])
#Estandarización de variables
m <- colMeans(training) #Obtener medias por columna
s <- apply(training, 2, sd) #Calcular StandDev por columna (por ello el apply lleva el 2, si pusieran 1 sería por renglón)
training <- scale(training, center = m, scale = s)
test <- scale(test, center = m, scale = s)Comentario
Agua_train_S <- as.data.frame(cbind(training,(trainingtarget - mean(trainingtarget))/sd(trainingtarget)))
colnames(Agua_train_S) <- colnames(Agua_train)
RNS <- neuralnet(Temperatura ~ Fecha+Hora+Nivel,
data = Agua_train_S,
hidden = c(8,6),
linear.output = T,
#linear.output se debe poner como T en modelos de regresion y como F en modelos de clasificación
lifesign = 'full',
rep=1,
# threshold=0.02,
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## 285000 min thresh: 0.028718355065831
## 286000 min thresh: 0.028718355065831
## 287000 min thresh: 0.028718355065831
## 288000 min thresh: 0.028718355065831
## 289000 min thresh: 0.028718355065831
## 290000 min thresh: 0.028718355065831
## 291000 min thresh: 0.028718355065831
## 292000 min thresh: 0.028718355065831
## 293000 min thresh: 0.028718355065831
## 294000 min thresh: 0.028718355065831
## 295000 min thresh: 0.028718355065831
## 296000 min thresh: 0.028718355065831
## 297000 min thresh: 0.028718355065831
## 298000 min thresh: 0.028718355065831
## 299000 min thresh: 0.028718355065831
## 3e+05 min thresh: 0.028718355065831
## 301000 min thresh: 0.028718355065831
## 302000 min thresh: 0.028718355065831
## 303000 min thresh: 0.028718355065831
## 304000 min thresh: 0.028718355065831
## 305000 min thresh: 0.028718355065831
## 306000 min thresh: 0.028718355065831
## 307000 min thresh: 0.028718355065831
## 308000 min thresh: 0.028718355065831
## 309000 min thresh: 0.028718355065831
## 310000 min thresh: 0.028718355065831
## 311000 min thresh: 0.028718355065831
## 312000 min thresh: 0.028718355065831
## 313000 min thresh: 0.028718355065831
## 314000 min thresh: 0.028718355065831
## 315000 min thresh: 0.028718355065831
## 316000 min thresh: 0.028718355065831
## 317000 min thresh: 0.028718355065831
## 318000 min thresh: 0.028718355065831
## 319000 min thresh: 0.028718355065831
## 320000 min thresh: 0.028718355065831
## 321000 min thresh: 0.028718355065831
## 322000 min thresh: 0.028718355065831
## 323000 min thresh: 0.028718355065831
## 324000 min thresh: 0.028718355065831
## 325000 min thresh: 0.028718355065831
## 326000 min thresh: 0.028718355065831
## 327000 min thresh: 0.028718355065831
## 328000 min thresh: 0.028718355065831
## 329000 min thresh: 0.028718355065831
## 330000 min thresh: 0.028718355065831
## 331000 min thresh: 0.028718355065831
## 332000 min thresh: 0.028718355065831
## 333000 min thresh: 0.028718355065831
## 334000 min thresh: 0.028718355065831
## 335000 min thresh: 0.028718355065831
## 336000 min thresh: 0.028718355065831
## 337000 min thresh: 0.028718355065831
## 338000 min thresh: 0.028718355065831
## 339000 min thresh: 0.028718355065831
## 340000 min thresh: 0.028718355065831
## 341000 min thresh: 0.028718355065831
## 342000 min thresh: 0.028718355065831
## 343000 min thresh: 0.028718355065831
## 344000 min thresh: 0.028718355065831
## 345000 min thresh: 0.028718355065831
## 346000 min thresh: 0.028718355065831
## 347000 min thresh: 0.028718355065831
## 348000 min thresh: 0.028718355065831
## 349000 min thresh: 0.028718355065831
## 350000 min thresh: 0.028718355065831
## 351000 min thresh: 0.028718355065831
## 352000 min thresh: 0.028718355065831
## 353000 min thresh: 0.028718355065831
## 354000 min thresh: 0.028718355065831
## 355000 min thresh: 0.028718355065831
## 356000 min thresh: 0.028718355065831
## 357000 min thresh: 0.028718355065831
## 358000 min thresh: 0.0286874785545854
## 359000 min thresh: 0.028640661724615
## 360000 min thresh: 0.028640661724615
## 361000 min thresh: 0.028640661724615
## 362000 min thresh: 0.028640661724615
## 363000 min thresh: 0.028640661724615
## 364000 min thresh: 0.028640661724615
## 365000 min thresh: 0.028640661724615
## 366000 min thresh: 0.028640661724615
## 367000 min thresh: 0.028640661724615
## 368000 min thresh: 0.0272449814177336
## 369000 min thresh: 0.0272449814177336
## 370000 min thresh: 0.0272449814177336
## 371000 min thresh: 0.0272449814177336
## 372000 min thresh: 0.0272449814177336
## 373000 min thresh: 0.0272449814177336
## 374000 min thresh: 0.0272285501945671
## 375000 min thresh: 0.0272285501945671
## 376000 min thresh: 0.0256338789811313
## 377000 min thresh: 0.0256338789811313
## 378000 min thresh: 0.0256338789811313
## 379000 min thresh: 0.0256338789811313
## 380000 min thresh: 0.0256338789811313
## 381000 min thresh: 0.0256338789811313
## 382000 min thresh: 0.0256338789811313
## 383000 min thresh: 0.0256338789811313
## 384000 min thresh: 0.0256338789811313
## 385000 min thresh: 0.0254560840199696
## 386000 min thresh: 0.0241917491991012
## 387000 min thresh: 0.0241917491991012
## 388000 min thresh: 0.0241917491991012
## 389000 min thresh: 0.0241917491991012
## 390000 min thresh: 0.0241917491991012
## 391000 min thresh: 0.0241917491991012
## 392000 min thresh: 0.0241917491991012
## 393000 min thresh: 0.0241917491991012
## 394000 min thresh: 0.0241917491991012
## 395000 min thresh: 0.0241917491991012
## 396000 min thresh: 0.0241468104681154
## 397000 min thresh: 0.0235531404948609
## 398000 min thresh: 0.0235531404948609
## 399000 min thresh: 0.0235531404948609
## 4e+05 min thresh: 0.0229995900981518
## 401000 min thresh: 0.0229995900981518
## 402000 min thresh: 0.0229995900981518
## 403000 min thresh: 0.0229995900981518
## 404000 min thresh: 0.0229995900981518
## 405000 min thresh: 0.0229995900981518
## 406000 min thresh: 0.0228457262511518
## 407000 min thresh: 0.0228457262511518
## 408000 min thresh: 0.0213438994876207
## 409000 min thresh: 0.0213438994876207
## 410000 min thresh: 0.0213438994876207
## 411000 min thresh: 0.0213438994876207
## 412000 min thresh: 0.0213438994876207
## 413000 min thresh: 0.0213438994876207
## 414000 min thresh: 0.0213438994876207
## 415000 min thresh: 0.0213438994876207
## 416000 min thresh: 0.0209253026101019
## 417000 min thresh: 0.0199210707896106
## 418000 min thresh: 0.0199210707896106
## 419000 min thresh: 0.0199210707896106
## 420000 min thresh: 0.0199210707896106
## 421000 min thresh: 0.0199210707896106
## 422000 min thresh: 0.0199210707896106
## 423000 min thresh: 0.0197704601100825
## 424000 min thresh: 0.0197704601100825
## 425000 min thresh: 0.0197704601100825
## 426000 min thresh: 0.0197704601100825
## 427000 min thresh: 0.0197017530733182
## 428000 min thresh: 0.0197017530733182
## 429000 min thresh: 0.0190040168388245
## 430000 min thresh: 0.0190040168388245
## 431000 min thresh: 0.0190040168388245
## 432000 min thresh: 0.0188435319571201
## 433000 min thresh: 0.0188435319571201
## 434000 min thresh: 0.0188435319571201
## 435000 min thresh: 0.0183672030075905
## 436000 min thresh: 0.0183672030075905
## 437000 min thresh: 0.0183672030075905
## 438000 min thresh: 0.0183672030075905
## 439000 min thresh: 0.0183672030075905
## 440000 min thresh: 0.0183672030075905
## 441000 min thresh: 0.0177022706600465
## 442000 min thresh: 0.0177022706600465
## 443000 min thresh: 0.0177022706600465
## 444000 min thresh: 0.0177022706600465
## 445000 min thresh: 0.0177022706600465
## 446000 min thresh: 0.0165103106872605
## 447000 min thresh: 0.0165103106872605
## 448000 min thresh: 0.0165103106872605
## 449000 min thresh: 0.0165103106872605
## 450000 min thresh: 0.0165103106872605
## 451000 min thresh: 0.0165103106872605
## 452000 min thresh: 0.0165103106872605
## 453000 min thresh: 0.0165103106872605
## 454000 min thresh: 0.0165103106872605
## 455000 min thresh: 0.0165103106872605
## 456000 min thresh: 0.0165103106872605
## 457000 min thresh: 0.0165103106872605
## 458000 min thresh: 0.0165103106872605
## 459000 min thresh: 0.0165103106872605
## 460000 min thresh: 0.0165103106872605
## 461000 min thresh: 0.0165103106872605
## 462000 min thresh: 0.0165103106872605
## 463000 min thresh: 0.0165103106872605
## 464000 min thresh: 0.0165103106872605
## 465000 min thresh: 0.0165103106872605
## 466000 min thresh: 0.0165103106872605
## 467000 min thresh: 0.0165103106872605
## 468000 min thresh: 0.0165103106872605
## 469000 min thresh: 0.0165103106872605
## 470000 min thresh: 0.0163238931996648
## 471000 min thresh: 0.0163238931996648
## 472000 min thresh: 0.0160968497507938
## 473000 min thresh: 0.0160968497507938
## 474000 min thresh: 0.0160968497507938
## 475000 min thresh: 0.0160968497507938
## 476000 min thresh: 0.0160968497507938
## 477000 min thresh: 0.0160968497507938
## 478000 min thresh: 0.0160968497507938
## 479000 min thresh: 0.0158015925581235
## 480000 min thresh: 0.0158015925581235
## 481000 min thresh: 0.0158015925581235
## 482000 min thresh: 0.0158015925581235
## 483000 min thresh: 0.0158015925581235
## 484000 min thresh: 0.0158015925581235
## 485000 min thresh: 0.0158015925581235
## 486000 min thresh: 0.0158015925581235
## 487000 min thresh: 0.0158015925581235
## 488000 min thresh: 0.0158015925581235
## 489000 min thresh: 0.0158015925581235
## 490000 min thresh: 0.0158015925581235
## 491000 min thresh: 0.0158015925581235
## 492000 min thresh: 0.0158015925581235
## 493000 min thresh: 0.0158015925581235
## 494000 min thresh: 0.0158015925581235
## 495000 min thresh: 0.0158015925581235
## 496000 min thresh: 0.0158015925581235
## 497000 min thresh: 0.0158015925581235
## 498000 min thresh: 0.0158015925581235
## 499000 min thresh: 0.0158015925581235
## 5e+05 min thresh: 0.0158015925581235
## 501000 min thresh: 0.0158015925581235
## 502000 min thresh: 0.0158015925581235
## 503000 min thresh: 0.0158015925581235
## 504000 min thresh: 0.0158015925581235
## 505000 min thresh: 0.0158015925581235
## 506000 min thresh: 0.0158015925581235
## 507000 min thresh: 0.0158015925581235
## 508000 min thresh: 0.0158015925581235
## 509000 min thresh: 0.0158015925581235
## 510000 min thresh: 0.0158015925581235
## 511000 min thresh: 0.0158015925581235
## 512000 min thresh: 0.0158015925581235
## 513000 min thresh: 0.0158015925581235
## 514000 min thresh: 0.0158015925581235
## 515000 min thresh: 0.0158015925581235
## 516000 min thresh: 0.0158015925581235
## 517000 min thresh: 0.0158015925581235
## 518000 min thresh: 0.0158015925581235
## 519000 min thresh: 0.0158015925581235
## 520000 min thresh: 0.0158015925581235
## 521000 min thresh: 0.0158015925581235
## 522000 min thresh: 0.0158015925581235
## 523000 min thresh: 0.0158015925581235
## 524000 min thresh: 0.0158015925581235
## 525000 min thresh: 0.0158015925581235
## 526000 min thresh: 0.0158015925581235
## 527000 min thresh: 0.0158015925581235
## 528000 min thresh: 0.0158015925581235
## 529000 min thresh: 0.0158015925581235
## 530000 min thresh: 0.0158015925581235
## 531000 min thresh: 0.0158015925581235
## 532000 min thresh: 0.0158015925581235
## 533000 min thresh: 0.0158015925581235
## 534000 min thresh: 0.0158015925581235
## 535000 min thresh: 0.0158015925581235
## 536000 min thresh: 0.0158015925581235
## 537000 min thresh: 0.0158015925581235
## 538000 min thresh: 0.0158015925581235
## 539000 min thresh: 0.0158015925581235
## 540000 min thresh: 0.0158015925581235
## 541000 min thresh: 0.0158015925581235
## 542000 min thresh: 0.0158015925581235
## 543000 min thresh: 0.0158015925581235
## 544000 min thresh: 0.0158015925581235
## 545000 min thresh: 0.0158015925581235
## 546000 min thresh: 0.0158015925581235
## 547000 min thresh: 0.0158015925581235
## 548000 min thresh: 0.0158015925581235
## 549000 min thresh: 0.0158015925581235
## 550000 min thresh: 0.0158015925581235
## 551000 min thresh: 0.0158015925581235
## 552000 min thresh: 0.0158015925581235
## 553000 min thresh: 0.0158015925581235
## 554000 min thresh: 0.0158015925581235
## 555000 min thresh: 0.0158015925581235
## 556000 min thresh: 0.0158015925581235
## 557000 min thresh: 0.0158015925581235
## 558000 min thresh: 0.0158015925581235
## 559000 min thresh: 0.0158015925581235
## 560000 min thresh: 0.0158015925581235
## 561000 min thresh: 0.0158015925581235
## 562000 min thresh: 0.0158015925581235
## 563000 min thresh: 0.0158015925581235
## 564000 min thresh: 0.0158015925581235
## 565000 min thresh: 0.0158015925581235
## 566000 min thresh: 0.0158015925581235
## 567000 min thresh: 0.0158015925581235
## 568000 min thresh: 0.0158015925581235
## 569000 min thresh: 0.0158015925581235
## 570000 min thresh: 0.0158015925581235
## 571000 min thresh: 0.0158015925581235
## 572000 min thresh: 0.0158015925581235
## 573000 min thresh: 0.0158015925581235
## 574000 min thresh: 0.0158015925581235
## 575000 min thresh: 0.0158015925581235
## 576000 min thresh: 0.0158015925581235
## 577000 min thresh: 0.0158015925581235
## 578000 min thresh: 0.0158015925581235
## 579000 min thresh: 0.0158015925581235
## 580000 min thresh: 0.0158015925581235
## 581000 min thresh: 0.0158015925581235
## 582000 min thresh: 0.0158015925581235
## 583000 min thresh: 0.0158015925581235
## 584000 min thresh: 0.0158015925581235
## 585000 min thresh: 0.0158015925581235
## 586000 min thresh: 0.0158015925581235
## 587000 min thresh: 0.0158015925581235
## 588000 min thresh: 0.0158015925581235
## 589000 min thresh: 0.0158015925581235
## 590000 min thresh: 0.0158015925581235
## 591000 min thresh: 0.0158015925581235
## 592000 min thresh: 0.0158015925581235
## 593000 min thresh: 0.0158015925581235
## 594000 min thresh: 0.0158015925581235
## 595000 min thresh: 0.0158015925581235
## 596000 min thresh: 0.0158015925581235
## 597000 min thresh: 0.0158015925581235
## 598000 min thresh: 0.0158015925581235
## 599000 min thresh: 0.0158015925581235
## 6e+05 min thresh: 0.0158015925581235
## 601000 min thresh: 0.0158015925581235
## 602000 min thresh: 0.0158015925581235
## 603000 min thresh: 0.0158015925581235
## 604000 min thresh: 0.0158015925581235
## 605000 min thresh: 0.0158015925581235
## 606000 min thresh: 0.0158015925581235
## 607000 min thresh: 0.0158015925581235
## 608000 min thresh: 0.0158015925581235
## 609000 min thresh: 0.0154552140870816
## 610000 min thresh: 0.0154552140870816
## 611000 min thresh: 0.0154552140870816
## 612000 min thresh: 0.0154552140870816
## 613000 min thresh: 0.0154552140870816
## 614000 min thresh: 0.0154552140870816
## 615000 min thresh: 0.0154552140870816
## 616000 min thresh: 0.0154552140870816
## 617000 min thresh: 0.0154552140870816
## 618000 min thresh: 0.0154552140870816
## 619000 min thresh: 0.0154552140870816
## 620000 min thresh: 0.0154552140870816
## 621000 min thresh: 0.0154552140870816
## 622000 min thresh: 0.0151832676823584
## 623000 min thresh: 0.0150551275284734
## 624000 min thresh: 0.0146191325772651
## 625000 min thresh: 0.0133434385107595
## 626000 min thresh: 0.0133434385107595
## 627000 min thresh: 0.0133434385107595
## 628000 min thresh: 0.0133434385107595
## 629000 min thresh: 0.0133434385107595
## 630000 min thresh: 0.0133434385107595
## 631000 min thresh: 0.0133434385107595
## 632000 min thresh: 0.0133434385107595
## 633000 min thresh: 0.0133434385107595
## 634000 min thresh: 0.0133434385107595
## 635000 min thresh: 0.0133434385107595
## 636000 min thresh: 0.0133434385107595
## 637000 min thresh: 0.0133434385107595
## 638000 min thresh: 0.0133434385107595
## 639000 min thresh: 0.0133434385107595
## 640000 min thresh: 0.0133434385107595
## 641000 min thresh: 0.0133434385107595
## 642000 min thresh: 0.0133434385107595
## 643000 min thresh: 0.0133434385107595
## 644000 min thresh: 0.0133434385107595
## 645000 min thresh: 0.0133434385107595
## 646000 min thresh: 0.0133434385107595
## 647000 min thresh: 0.0133434385107595
## 648000 min thresh: 0.0133434385107595
## 649000 min thresh: 0.0131598904887637
## 650000 min thresh: 0.0131598904887637
## 651000 min thresh: 0.0131598904887637
## 652000 min thresh: 0.0131598904887637
## 653000 min thresh: 0.0131598904887637
## 654000 min thresh: 0.0131598904887637
## 655000 min thresh: 0.0131598904887637
## 656000 min thresh: 0.0131598904887637
## 657000 min thresh: 0.0131598904887637
## 658000 min thresh: 0.0131017481909182
## 659000 min thresh: 0.0130772779105495
## 660000 min thresh: 0.0130772779105495
## 661000 min thresh: 0.0130772779105495
## 662000 min thresh: 0.0129969739191129
## 663000 min thresh: 0.0129969739191129
## 664000 min thresh: 0.0125573917270332
## 665000 min thresh: 0.0125573917270332
## 666000 min thresh: 0.0125573917270332
## 667000 min thresh: 0.0125573917270332
## 668000 min thresh: 0.0125573917270332
## 669000 min thresh: 0.0125573917270332
## 670000 min thresh: 0.0125573917270332
## 671000 min thresh: 0.0125573917270332
## 672000 min thresh: 0.0125573917270332
## 673000 min thresh: 0.0125573917270332
## 674000 min thresh: 0.0125573917270332
## 675000 min thresh: 0.0125573917270332
## 676000 min thresh: 0.0125573917270332
## 677000 min thresh: 0.0125573917270332
## 678000 min thresh: 0.0125573917270332
## 679000 min thresh: 0.0125573917270332
## 680000 min thresh: 0.0125573917270332
## 681000 min thresh: 0.0125573917270332
## 682000 min thresh: 0.0125573917270332
## 683000 min thresh: 0.0125573917270332
## 684000 min thresh: 0.0124366742778832
## 685000 min thresh: 0.0124366742778832
## 686000 min thresh: 0.0124366742778832
## 687000 min thresh: 0.0124366742778832
## 688000 min thresh: 0.012078609985964
## 689000 min thresh: 0.012078609985964
## 690000 min thresh: 0.012078609985964
## 691000 min thresh: 0.012078609985964
## 692000 min thresh: 0.012078609985964
## 693000 min thresh: 0.0118578246123828
## 694000 min thresh: 0.0118578246123828
## 695000 min thresh: 0.0118578246123828
## 696000 min thresh: 0.0118578246123828
## 697000 min thresh: 0.0118578246123828
## 698000 min thresh: 0.0118578246123828
## 699000 min thresh: 0.0118578246123828
## 7e+05 min thresh: 0.0118578246123828
## 701000 min thresh: 0.0117662253566247
## 702000 min thresh: 0.0117662253566247
## 703000 min thresh: 0.0117662253566247
## 704000 min thresh: 0.0117662253566247
## 705000 min thresh: 0.0117565974398038
## 706000 min thresh: 0.0116277007661352
## 707000 min thresh: 0.0116277007661352
## 708000 min thresh: 0.0116277007661352
## 709000 min thresh: 0.0116277007661352
## 710000 min thresh: 0.0116277007661352
## 711000 min thresh: 0.0116277007661352
## 712000 min thresh: 0.0112592258345485
## 713000 min thresh: 0.0112592258345485
## 714000 min thresh: 0.0112592258345485
## 715000 min thresh: 0.0112592258345485
## 716000 min thresh: 0.0112592258345485
## 717000 min thresh: 0.0112592258345485
## 718000 min thresh: 0.0112592258345485
## 719000 min thresh: 0.0112592258345485
## 720000 min thresh: 0.0112592258345485
## 721000 min thresh: 0.0112592258345485
## 722000 min thresh: 0.0106879962460397
## 723000 min thresh: 0.0106879962460397
## 724000 min thresh: 0.0106879962460397
## 725000 min thresh: 0.0106879962460397
## 726000 min thresh: 0.0106879962460397
## 727000 min thresh: 0.0106879962460397
## 728000 min thresh: 0.0106879962460397
## 729000 min thresh: 0.0106879962460397
## 730000 min thresh: 0.0106879962460397
## 731000 min thresh: 0.0106879962460397
## 732000 min thresh: 0.0106879962460397
## 733000 min thresh: 0.0106879962460397
## 734000 min thresh: 0.0106879962460397
## 735000 min thresh: 0.0106879962460397
## 736000 min thresh: 0.0106879962460397
## 737000 min thresh: 0.0106879962460397
## 738000 min thresh: 0.0106879962460397
## 739000 min thresh: 0.0106879962460397
## 740000 min thresh: 0.0106879962460397
## 741000 min thresh: 0.0106879962460397
## 742000 min thresh: 0.0106879962460397
## 743000 min thresh: 0.0106879962460397
## 744000 min thresh: 0.0106879962460397
## 745000 min thresh: 0.0106879962460397
## 746000 min thresh: 0.0106438318106519
## 747000 min thresh: 0.0106438318106519
## 748000 min thresh: 0.0106438318106519
## 749000 min thresh: 0.0106438318106519
## 750000 min thresh: 0.0106438318106519
## 751000 min thresh: 0.0106438318106519
## 752000 min thresh: 0.0106438318106519
## 753000 min thresh: 0.0106438318106519
## 754000 min thresh: 0.0106438318106519
## 755000 min thresh: 0.0106438318106519
## 756000 min thresh: 0.0106438318106519
## 757000 min thresh: 0.0106438318106519
## 758000 min thresh: 0.0106438318106519
## 759000 min thresh: 0.0104524272514834
## 760000 min thresh: 0.0104524272514834
## 761000 min thresh: 0.0104524272514834
## 762000 min thresh: 0.0104511861405935
## 763000 min thresh: 0.0104511861405935
## 764000 min thresh: 0.0104511861405935
## 765000 min thresh: 0.0103793680183831
## 766000 min thresh: 0.0103793680183831
## 767000 min thresh: 0.0103793680183831
## 768000 min thresh: 0.0103793680183831
## 769000 min thresh: 0.0102070261739132
## 770000 min thresh: 0.0102070261739132
## 771000 min thresh: 0.0102070261739132
## 772000 min thresh: 0.0100205114943799
## 773000 min thresh: 0.0100205114943799
## 774000 min thresh: 0.0100205114943799
## 775000 min thresh: 0.0100205114943799
## 776000 min thresh: 0.0100205114943799
## 777000 min thresh: 0.0100205114943799
## 778000 min thresh: 0.0100205114943799
## 779000 min thresh: 0.0100205114943799
## 780000 min thresh: 0.0100205114943799
## 781000 min thresh: 0.0100205114943799
## 782000 min thresh: 0.0100205114943799
## 783000 min thresh: 0.0100205114943799
## 784000 min thresh: 0.0100205114943799
## 785000 min thresh: 0.0100205114943799
## 786000 min thresh: 0.0100205114943799
## 787000 min thresh: 0.0100205114943799
## 788000 min thresh: 0.0100205114943799
## 789000 min thresh: 0.0100205114943799
## 790000 min thresh: 0.0100205114943799
## 790492 error: 32.94333 time: 13.24 minsComentario
plot(RNS,col.hidden = 'darkgreen',
col.hidden.synapse = 'darkgreen',
show.weights = T,
information = F,
fill = 'lightblue')Comentario
Agua_test_S <- as.data.frame(test)
colnames(Agua_test_S) <- colnames(Agua_test)[1:3]
RNSPredictions <- predict(RNS,Agua_test_S)
cor(RNSPredictions,(testtarget-mean(trainingtarget))/sd(trainingtarget))## [,1]
## [1,] 0.9872836Comentario
RNSPred <- RNSPredictions*sd(trainingtarget) + mean(trainingtarget)
plot(RNSPred,testtarget)
abline(a=0, b=1)
RSSnn <- (RNSPred - testtarget)^2
sum(RSSnn)/nrow(testtarget)## [1] 0.015704941 - sum(RSSnn)/sum((testtarget - mean(trainingtarget))^2)## [1] 0.9747231Comentario
LRM <- lm(Temperatura ~ Fecha+Hora+Nivel, data=Agua_train)
LRMPred <- predict(LRM, Agua_test[, c(1, 2, 3)])
cor(LRMPred,Agua_test[,4])## [1] 0.7921928plot(LRMPred,Agua_test[,4])
abline(a=0,b=1)
LRMRSS <- (LRMPred - Agua_test[,4])^2
sum(LRMRSS)/nrow(testtarget)## [1] 0.23191711 - sum(LRMRSS)/sum((Agua_test[,4] - mean(Agua_train[, c(1, 2, 3)]))^2)## Warning in mean.default(Agua_train[, c(1, 2, 3)]): argument is not numeric or
## logical: returning NA## [1] NAComentario
par(mfrow=c(1,2))
plot(Agua_test$Temperatura,RNSPred,col='red',main='Real vs predicted NN',pch=19,cex=1)
abline(0,1,lwd=2)
legend('bottomright',legend='NN',pch=18,col='red', bty='n')
plot(Agua_test$Temperatura,LRMPred,col='blue',main='Real vs predicted LM',pch=15, cex=1)
abline(0,1,lwd=2)
legend('bottomright',legend='LM',pch=18,col='blue', bty='n', cex=.95)
Comentario
plot(Agua_test$Temperatura,RNSPred,col='red',main='Real vs predicted NN',pch=19,cex=1)
points(Agua_test$Temperatura,LRMPred,col='blue',pch=15,cex=1)
abline(0,1,lwd=2)
legend('bottomright',legend=c('NN','LM'),pch=c(19,15),col=c('red','blue'))
#### Análisis de Resultados