Actividad3_Proyecto
ANÁLISIS DE DATOS MASIVOS PARA EL NEGOCIO
Para este análisis utilizaremos la base de ventas de la empresa Adventure Works quien por más de 20 años vende bicicletas por todo el mundo.
library(readxl)
base <- read_excel("C:/Users/Jamileth/Documents/MAESTRÍA 2/Actividad3.xlsx",
sheet = "Var Discreta Adq Bicicleta")La base consta de 18.484 observaciones y 19 variables. Como paso inicial verificamos la existencia de datos faltantes.
which(is.na(base))## integer(0)Además, lo corroboramos ya que todas las variables tienen 18.484 observaciones. A simple vista la base contiene variables tanto numéricas como de tipo categóricas. En el caso de la variable Bike Purchase si bien R la reconoce como numérica es una variable dummy que toma el valor de 1 si el cliente compro la bicicleta y 0 caso contrario. Además, las variables Age y BirthDate representan la misma información.
str(base)## tibble [18,484 × 19] (S3: tbl_df/tbl/data.frame)
## $ TotalAmount : num [1:18484] 8139 2994 4118 4631 3400 ...
## $ BikePurchase : num [1:18484] 1 1 1 1 1 1 1 1 1 1 ...
## $ CustomerID : num [1:18484] 11003 14501 21768 25863 28389 ...
## $ Country : chr [1:18484] "Australia" "Southwest" "Canada" "Northwest" ...
## $ CountryRegionCode: chr [1:18484] "AU" "US" "CA" "US" ...
## $ Group : chr [1:18484] "Pacific" "North America" "North America" "North America" ...
## $ PersonID : num [1:18484] 11358 11211 14078 10553 15519 ...
## $ PersonType : chr [1:18484] "IN" "IN" "IN" "IN" ...
## $ DateFirstPurchase: POSIXct[1:18484], format: "2001-07-01" "2001-07-01" ...
## $ BirthDate : POSIXct[1:18484], format: "1968-02-15" "1938-05-13" ...
## $ Age : num [1:18484] 52 82 74 74 56 55 57 48 59 66 ...
## $ MaritalStatus : chr [1:18484] "S" "M" "S" "S" ...
## $ YearlyIncome : chr [1:18484] "50001-75000" "75001-100000" "50001-75000" "25001-50000" ...
## $ Gender : chr [1:18484] "F" "M" "M" "F" ...
## $ TotalChildren : num [1:18484] 0 4 5 5 3 0 4 0 1 2 ...
## $ Education : chr [1:18484] "Bachelors" "Graduate Degree" "Bachelors" "High School" ...
## $ Occupation : chr [1:18484] "Professional" "Management" "Management" "Professional" ...
## $ HomeOwnerFlag : num [1:18484] 0 1 1 1 0 1 1 0 1 0 ...
## $ NumberCarsOwned : num [1:18484] 1 2 3 3 0 1 4 3 4 2 ...Antes de iniciar con cualquier tipo de análisis, es importante primero conocer más a detalle la base, esto lo conseguimos a través de la estadistica descriptiva.
Analizando las variables numéricas se tiene que:en promedio el gasto (TotalAmount) es de 1.588,33, el valor más alto llegó a 13.295,38, sin e,bargo, el 75% de las veces el gasto fue inferior a 2511.28. Los clientes en promedio tienen 58 años, no existen clientes con menos de 40 años y si bien la edad máxima registrada es de 110 años podría tratarse de un error, los clientes tienen de 2 a hijos y hasta 4 autos.
library(dplyr)##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionsummary (select(base, TotalAmount, Age, TotalChildren, NumberCarsOwned))## TotalAmount Age TotalChildren NumberCarsOwned
## Min. : 2.29 Min. : 40.00 Min. :0.000 Min. :0.000
## 1st Qu.: 49.97 1st Qu.: 50.00 1st Qu.:0.000 1st Qu.:1.000
## Median : 270.26 Median : 57.00 Median :2.000 Median :2.000
## Mean : 1588.33 Mean : 58.42 Mean :1.844 Mean :1.503
## 3rd Qu.: 2511.28 3rd Qu.: 66.00 3rd Qu.:3.000 3rd Qu.:2.000
## Max. :13295.38 Max. :110.00 Max. :5.000 Max. :4.000En cuanto a las variables tipo texto se tiene que: 9.132 clientes compraron la bicicleta y 9.352 no, la variable PersonType toma un único valor (IN) por lo cual es indiferente para el análisis, los clientes corresponden a 10 países, existe una similitud en cuanto al número de clientes hombres y mujeres, el 54% de los clientes son casados (10.011) y el 45% solteros. En su mayoría la ocupación de los clientes es “Professional” y en menor medida “Manual”, analizando su nivel de estudio se tiene que 5.356 clientes son “Bachelors” seguido por “Partical College” con 5.064 clientes. Contrastando el ingreso anual con la decisión de compra o no de bicicleta se observa que aquellos clientes con ingreso inferior a 25.000 o con un ingreso entre 75.000 y 100.000 han optado por la no compra de bicibleta.
table (base$BikePurchase)##
## 0 1
## 9352 9132table (base$PersonType)##
## IN
## 18484table (base$Country)##
## Australia Canada Central France Germany
## 3591 1571 8 1810 1780
## Northeast Northwest Southeast Southwest United Kingdom
## 8 3341 12 4450 1913table (base$Gender)##
## F M
## 9133 9351prop.table(table (base$MaritalStatus))##
## M S
## 0.5416035 0.4583965table (base$Occupation)##
## Clerical Management Manual Professional Skilled Manual
## 2928 3075 2384 5520 4577table (base$Education)##
## Bachelors Graduate Degree High School Partial College
## 5356 3189 3294 5064
## Partial High School
## 1581table (base$YearlyIncome)##
## 0-25000 25001-50000 50001-75000 75001-100000
## 2922 5704 5476 2755
## greater than 100000
## 1627table(base$BikePurchase, base$YearlyIncome)##
## 0-25000 25001-50000 50001-75000 75001-100000 greater than 100000
## 0 1736 2770 2608 1454 784
## 1 1186 2934 2868 1301 843A continuación, estimaremos un modelo que nos permita que nos permita determinar (clasificar) si un individuo realizará o no una compra para ello utilizaremos una regresión logística LOGIT y el árbol de decisión.
Primero creamos dos submuestras una de entrenamiento (80%) y una de validación (20%), pero primero fijamos una semilla para que la muestra siempre que se ejecute sea la misma.
library(caTools)
set.seed(12345)
division <- sample.split(base$BikePurchase, SplitRatio = 0.8)
entrenamiento <- subset (base, division==TRUE)
validacion <- subset (base, division==FALSE)Verificamos que esté equilibrado, antes de realizar las estimaciones
prop.table(table(entrenamiento$BikePurchase))##
## 0 1
## 0.5059508 0.4940492prop.table(table(validacion$BikePurchase))##
## 0 1
## 0.5059524 0.4940476Iniciamos con la estimación del árbol de decisión con el fin de identificar la importancia de las variables
library (rpart)
library (rpart.plot)
modeloarbol <- rpart (BikePurchase ~ TotalAmount + as.factor(Group) + as.factor (Country) +
Age + as.factor(MaritalStatus) + as.factor(Gender) + as.factor(YearlyIncome) +
TotalChildren + as.factor(Education) + as.factor(Occupation) +
NumberCarsOwned, data =entrenamiento, method = "class")
summary(modeloarbol)## Call:
## rpart(formula = BikePurchase ~ TotalAmount + as.factor(Group) +
## as.factor(Country) + Age + as.factor(MaritalStatus) + as.factor(Gender) +
## as.factor(YearlyIncome) + TotalChildren + as.factor(Education) +
## as.factor(Occupation) + NumberCarsOwned, data = entrenamiento,
## method = "class")
## n= 14788
##
## CP nsplit rel error xerror xstd
## 1 0.9958938 0 1.000000000 1.000000000 0.0083217430
## 2 0.0100000 1 0.004106214 0.004243088 0.0007612819
##
## Variable importance
## TotalAmount NumberCarsOwned TotalChildren
## 63 12 7
## as.factor(Education) as.factor(Country) Age
## 7 6 6
##
## Node number 1: 14788 observations, complexity param=0.9958938
## predicted class=0 expected loss=0.4940492 P(node) =1
## class counts: 7482 7306
## probabilities: 0.506 0.494
## left son=2 (7452 obs) right son=3 (7336 obs)
## Primary splits:
## TotalAmount < 526.23 to the left, improve=7333.1980, (0 missing)
## NumberCarsOwned < 1.5 to the right, improve= 267.4714, (0 missing)
## TotalChildren < 3.5 to the right, improve= 137.5914, (0 missing)
## Age < 68.5 to the right, improve= 130.6367, (0 missing)
## as.factor(Education) splits as RRLRL, improve= 121.9878, (0 missing)
## Surrogate splits:
## NumberCarsOwned < 1.5 to the right, agree=0.595, adj=0.184, (0 split)
## TotalChildren < 2.5 to the right, agree=0.557, adj=0.107, (0 split)
## as.factor(Education) splits as RRLLL, agree=0.555, adj=0.104, (0 split)
## as.factor(Country) splits as RLLLRLLLLR, agree=0.554, adj=0.100, (0 split)
## Age < 68.5 to the right, agree=0.550, adj=0.093, (0 split)
##
## Node number 2: 7452 observations
## predicted class=0 expected loss=0 P(node) =0.5039221
## class counts: 7452 0
## probabilities: 1.000 0.000
##
## Node number 3: 7336 observations
## predicted class=1 expected loss=0.004089422 P(node) =0.4960779
## class counts: 30 7306
## probabilities: 0.004 0.996Se observa que las variables más importantes a la hora de realizar una compra son: TotalAmount (63%) NumberCarsOwned (12%) TotalChildren (7%) Education (7%) Country (6%) y Age (6%).
Estimamos el modelo solo con las variables importantes y procedemos a realizar la predicción
modeloarbol2 <- rpart (BikePurchase ~ TotalAmount + NumberCarsOwned + TotalChildren + as.factor(Education) + as.factor (Country) + Age, data =entrenamiento, method = "class")
library (caret)## Cargando paquete requerido: ggplot2## Cargando paquete requerido: latticepredictarbol <- predict(modeloarbol2, type = "class")
confusionMatrix(as.factor(entrenamiento$BikePurchase), as.factor(predictarbol))## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 7452 30
## 1 0 7306
##
## Accuracy : 0.998
## 95% CI : (0.9971, 0.9986)
## No Information Rate : 0.5039
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9959
##
## Mcnemar's Test P-Value : 1.192e-07
##
## Sensitivity : 1.0000
## Specificity : 0.9959
## Pos Pred Value : 0.9960
## Neg Pred Value : 1.0000
## Prevalence : 0.5039
## Detection Rate : 0.5039
## Detection Prevalence : 0.5060
## Balanced Accuracy : 0.9980
##
## 'Positive' Class : 0
## Los datos reflejan que el modelo acierta el 99,8% de las veces. Ahora realizamos la predicción con los datos de validación
predicarbolval <- predict (modeloarbol2, newdata = validacion, type="class")
confusionMatrix(as.factor(validacion$BikePurchase), as.factor(predicarbolval))## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1864 6
## 1 0 1826
##
## Accuracy : 0.9984
## 95% CI : (0.9965, 0.9994)
## No Information Rate : 0.5043
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.9968
##
## Mcnemar's Test P-Value : 0.04123
##
## Sensitivity : 1.0000
## Specificity : 0.9967
## Pos Pred Value : 0.9968
## Neg Pred Value : 1.0000
## Prevalence : 0.5043
## Detection Rate : 0.5043
## Detection Prevalence : 0.5060
## Balanced Accuracy : 0.9984
##
## 'Positive' Class : 0
## La exactitud del modelo incrementó 0.04 puntos porcentuales con los datos de validación.
Se dibuja el árbol de decisión y tal como lo mostraba los datos, la variable Total Amount define basicamente si el cliente compra o no la bicicleta, en este caso cuando Total Amount es mayor a 526 el cliente decide comprar.
rpart.plot (modeloarbol2)
Dado que ya conocemos las variables independientes importantes se
procede a realizar un modelo de regresión logística LOGIT.
modelogit<- glm (BikePurchase ~ TotalAmount + NumberCarsOwned + TotalChildren + as.factor(Education) + as.factor (Country) + Age, data =base, family ="binomial")## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(modelogit)##
## Call:
## glm(formula = BikePurchase ~ TotalAmount + NumberCarsOwned +
## TotalChildren + as.factor(Education) + as.factor(Country) +
## Age, family = "binomial", data = base)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.881e+00 8.142e-01 -8.452 <2e-16
## TotalAmount 1.889e-02 5.735e-04 32.947 <2e-16
## NumberCarsOwned -2.532e-01 1.351e-01 -1.875 0.0608
## TotalChildren -4.055e-02 8.890e-02 -0.456 0.6483
## as.factor(Education)Graduate Degree -8.880e-01 3.773e-01 -2.354 0.0186
## as.factor(Education)High School -2.145e-01 3.639e-01 -0.590 0.5555
## as.factor(Education)Partial College -4.695e-01 3.210e-01 -1.463 0.1436
## as.factor(Education)Partial High School 1.624e-02 5.015e-01 0.032 0.9742
## as.factor(Country)Canada -4.170e+00 4.494e-01 -9.280 <2e-16
## as.factor(Country)Central 7.998e-01 6.782e+00 0.118 0.9061
## as.factor(Country)France 2.052e-01 5.668e-01 0.362 0.7174
## as.factor(Country)Germany 7.837e-01 5.577e-01 1.405 0.1599
## as.factor(Country)Northeast -1.039e+01 9.510e+02 -0.011 0.9913
## as.factor(Country)Northwest 6.839e-01 4.311e-01 1.586 0.1127
## as.factor(Country)Southeast -9.749e+00 5.025e+02 -0.019 0.9845
## as.factor(Country)Southwest 9.359e-01 3.953e-01 2.368 0.0179
## as.factor(Country)United Kingdom 5.969e-01 4.889e-01 1.221 0.2221
## Age -3.985e-03 1.257e-02 -0.317 0.7513
##
## (Intercept) ***
## TotalAmount ***
## NumberCarsOwned .
## TotalChildren
## as.factor(Education)Graduate Degree *
## as.factor(Education)High School
## as.factor(Education)Partial College
## as.factor(Education)Partial High School
## as.factor(Country)Canada ***
## as.factor(Country)Central
## as.factor(Country)France
## as.factor(Country)Germany
## as.factor(Country)Northeast
## as.factor(Country)Northwest
## as.factor(Country)Southeast
## as.factor(Country)Southwest *
## as.factor(Country)United Kingdom
## Age
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 25621.65 on 18483 degrees of freedom
## Residual deviance: 715.14 on 18466 degrees of freedom
## AIC: 751.14
##
## Number of Fisher Scoring iterations: 12El modelo logit realizado pone en evidencia que si bien mediante el árbol de decisión se tuvo algunas variables como imporantes, las variables Age y Total Children no son estadísticamente significativas.
ANÁLISIS NO SUPERVISADO
Ahora utilizaremos el análisis no supervisado con el fin de definir tipologías de clientes para ello utilizaremos la metdología de Clusterin mediante kmeans
base1 <- base[,-c(3,4,5,6,7,8,9, 10,12,13,14,16,17)]
library (factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBafviz_nbclust (base1, kmeans)
#fviz_nbclust (base1, kmeans, method ="wss")De acuerdo a la metodología aplicada el número óptimo de clusters es 4, sin embargo, de acuerdo al análisis se trabajará con 3 clusters
kmeans (base1, 3)## K-means clustering with 3 clusters of sizes 3010, 3820, 11654
##
## Cluster means:
## TotalAmount BikePurchase Age TotalChildren HomeOwnerFlag NumberCarsOwned
## 1 5655.3450 1.0000000 57.45249 1.682392 0.7112957 1.507641
## 2 2562.3212 0.9989529 57.64895 1.645288 0.6761780 1.270157
## 3 218.6392 0.1978720 58.92269 1.951433 0.6674103 1.577656
##
## Clustering vector:
## [1] 1 2 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 2 1 1 2 1 1 2 1 1 2 1 1 1 1 2 2
## [37] 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1
## [73] 2 1 1 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 2 1 1 1 1 1 2
## [109] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 2 1 1 1 1 1 1 2 2 1 2 1 3 1 1 1 2
## [145] 1 2 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 1 2 1 1 1 1 1 1 2
## [181] 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 2 1 2 1 1 1 2 1 1
## [217] 1 1 1 2 1 1 1 1 2 1 2 2 2 1 1 3 3 1 2 1 1 1 1 1 2 2 1 1 1 1 1 1 2 1 1 1
## [253] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2
## [289] 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 3 2 1 1 1 1
## [325] 2 2 1 2 2 1 2 1 1 2 2 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 2
## [361] 1 1 1 1 2 1 1 2 2 1 2 1 2 1 1 1 1 2 1 1 1 1 2 1 1 2 1 1 1 2 2 1 2 1 1 1
## [397] 1 2 2 1 2 1 1 1 1 2 1 1 1 1 1 3 1 2 1 1 1 1 2 1 1 2 1 1 2 1 1 1 1 2 1 3
## [433] 3 2 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 2 2 1 1 1 1 1 2 2 1 1 1 1 1 1 1 2 1
## [469] 1 2 1 1 1 2 1 2 1 1 1 2 2 1 1 1 1 2 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1
## [505] 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 2 2 1 1 2 1 1 1 1 3 2 1 2 1 2
## [541] 2 1 1 1 1 1 1 2 2 1 1 1 2 2 1 1 1 1 2 2 1 2 1 1 1 3 2 1 1 2 1 1 1 1 1 1
## [577] 1 1 1 2 3 1 2 2 2 1 2 1 1 1 1 1 1 2 1 1 1 1 3 3 1 1 2 1 1 1 1 1 2 1 1 1
## [613] 2 1 2 2 1 1 1 1 1 1 1 3 2 1 2 2 1 1 2 1 1 2 2 1 1 1 2 2 1 1 1 1 1 2 1 2
## [649] 2 1 1 2 2 2 2 1 2 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 2 2 2 1 1 1 3 2 2 1 1 1
## [685] 1 1 1 1 2 1 1 1 1 1 1 2 2 2 1 2 1 1 3 2 1 1 1 1 2 2 2 1 1 3 2 1 1 1 1 2
## [721] 1 2 2 2 1 1 1 1 2 1 1 1 2 2 2 1 3 2 1 1 1 1 1 1 2 2 1 2 1 1 1 2 1 1 1 1
## [757] 2 1 3 2 1 1 2 1 1 1 2 1 1 1 1 2 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 2 2 1
## [793] 1 1 1 1 1 1 1 2 2 1 2 1 1 2 1 1 1 2 1 2 1 1 1 2 2 1 1 1 1 2 2 1 1 1 2 2
## [829] 1 1 1 1 1 1 1 2 1 1 3 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 2 2 1 2 2 1 2 2 2
## [865] 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 2 1 1 1 2 2 1
## [901] 1 3 2 1 1 1 1 2 2 1 1 1 1 1 1 1 1 3 3 3 2 2 1 1 1 3 1 2 2 1 2 2 2 2 1 2
## [937] 1 1 1 2 1 2 1 1 1 1 2 2 2 2 1 1 1 1 2 2 2 1 1 3 2 2 1 1 2 2 1 1 1 2 1 1
## [973] 1 1 2 1 1 1 1 2 2 1 1 1 2 1 1 2 1 3 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 2 1 1
## [1009] 3 2 2 1 1 1 1 2 3 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 2 1 1 2 1 1 1 1 2 2 2 1
## [1045] 1 1 2 3 1 2 1 1 1 2 2 1 1 1 2 2 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
## [1081] 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 1 2 1
## [1117] 1 1 1 3 3 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 2 1 1 1 2 1
## [1153] 2 1 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 2 1 1 2 1 1 3 2 1 1 1 1 1 3 1 2 1 1
## [1189] 2 1 3 1 1 1 1 1 2 2 1 1 1 2 1 1 1 1 2 1 1 1 2 2 2 1 1 1 1 1 2 2 1 2 1 2
## [1225] 1 1 2 1 1 1 1 1 2 1 2 2 1 2 1 1 3 2 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
## [1261] 1 1 1 1 2 2 1 1 2 1 1 1 1 1 2 2 2 2 1 2 2 1 1 1 2 2 2 2 1 1 1 2 2 2 2 2
## [1297] 1 1 1 1 2 2 1 3 1 1 1 2 1 1 1 1 1 1 1 2 2 1 1 2 2 2 1 1 1 1 1 1 1 1 1 2
## [1333] 2 1 1 1 1 2 1 1 1 2 1 1 1 1 1 2 2 1 2 2 1 2 1 1 1 1 1 2 2 2 2 2 1 1 1 1
## [1369] 1 1 1 2 1 1 1 1 2 2 1 1 1 1 1 1 2 1 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 2 1 1
## [1405] 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## [1441] 2 1 1 1 2 2 1 1 1 1 1 1 1 2 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## [1477] 1 1 2 2 1 1 1 1 1 1 1 1 2 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1513] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1
## [1549] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 2 1 1
## [1585] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1
## [1621] 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## [1657] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
## [1693] 1 1 1 2 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1
## [1729] 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 3 1 1 1 1 1 1 1 1 1 1
## [1765] 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 3 1 1 1 1 1 1 2 2 1 1
## [1801] 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 2 3 1 1 1 1 3 1 1 1 1 1 1 1
## [1837] 1 1 1 3 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1
## [1873] 3 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1909] 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## [1945] 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 3 1 1 1 1
## [1981] 3 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 3 1 2 1 2 1
## [2017] 2 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1
## [2053] 2 1 1 1 1 1 2 1 2 1 1 1 1 1 1 2 1 1 1 2 1 1 2 1 3 1 2 3 1 2 1 1 1 1 1 1
## [2089] 2 2 1 1 1 1 2 1 1 1 1 1 1 2 2 1 1 1 2 2 2 3 1 1 1 2 2 1 1 1 1 1 2 1 2 2
## [2125] 1 1 1 1 3 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 1 1 2 2 1 1
## [2161] 1 1 1 1 3 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1
## [2197] 1 1 2 1 1 2 1 2 1 1 1 1 2 1 2 2 2 2 1 1 2 1 1 2 1 1 1 1 2 2 1 1 2 2 2 2
## [2233] 1 1 2 2 2 2 2 1 2 1 1 2 2 2 2 2 2 1 1 1 2 2 2 2 2 1 2 2 1 1 2 2 1 2 1 1
## [2269] 2 1 1 2 1 1 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1 2 2 2 1 2 1 2 2 2 1 2 1 1 1
## [2305] 1 1 2 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 2 1 2 2 1 2 1 2 2 2 1 1 2 1 2 1 1
## [2341] 2 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 1 2 1 1 1 2 2 1 2 1
## [2377] 1 1 2 2 2 2 2 1 2 1 1 2 1 1 2 1 1 1 1 2 1 2 2 1 1 2 2 1 2 1 2 1 1 1 2 2
## [2413] 2 2 1 1 1 2 2 1 1 2 2 2 1 1 2 2 2 1 2 2 2 1 1 1 2 2 2 1 1 1 1 2 2 1 2 2
## [2449] 1 2 1 1 1 1 1 1 2 1 1 2 1 1 2 2 2 2 2 1 1 1 2 2 2 2 1 2 1 1 1 2 2 1 1 1
## [2485] 2 1 2 2 2 2 2 1 1 1 1 1 2 2 1 2 2 2 2 2 1 2 2 2 2 2 1 1 1 1 1 2 2 2 1 2
## [2521] 2 1 1 1 1 2 2 2 3 1 1 1 1 1 2 2 2 1 1 1 2 1 2 2 2 2 1 2 1 1 1 2 2 2 2 1
## [2557] 2 1 1 1 1 2 2 2 2 1 2 1 1 1 1 2 2 2 2 2 2 1 1 1 2 2 1 2 2 1 2 2 2 1 2 1
## [2593] 2 2 2 2 2 2 2 2 2 2 1 3 2 2 1 2 2 2 2 2 1 1 1 1 1 2 2 2 2 2 3 2 1 1 2 2
## [2629] 2 2 2 1 2 2 2 1 1 1 1 1 2 2 2 1 1 1 2 1 1 1 2 3 2 1 2 1 1 2 1 1 1 1 2 2
## [2665] 1 2 1 1 2 2 2 1 1 1 1 2 2 2 2 1 2 2 1 1 1 1 2 2 2 2 1 2 2 2 1 1 1 2 2 2
## [2701] 2 2 2 2 2 2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 1 2 2 2 1 1 2 2 2 1 1 1 2 1 2
## [2737] 2 2 2 2 2 1 1 1 2 1 2 2 2 1 1 2 2 1 1 1 2 2 2 1 1 1 2 1 2 2 2 1 2 1 1 1
## [2773] 2 1 2 1 1 2 1 1 3 2 1 1 2 2 2 1 1 2 1 1 2 1 1 2 1 2 2 2 1 1 2 2 2 1 1 2
## [2809] 1 1 2 2 1 2 2 1 1 1 2 2 2 2 1 1 1 2 1 1 2 2 2 2 2 1 1 2 1 1 2 2 1 2 1 1
## [2845] 3 1 1 1 1 2 1 1 1 2 1 1 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 1 2 2 1
## [2881] 1 2 2 1 1 2 2 1 2 2 2 3 1 1 2 2 2 2 1 1 1 2 2 1 1 1 2 1 1 1 2 2 1 2 2 1
## [2917] 3 1 2 1 2 3 1 2 1 2 2 2 1 2 2 2 2 1 2 2 2 2 1 1 2 1 1 2 2 3 1 1 1 2 2 2
## [2953] 2 1 2 2 1 1 1 2 2 1 1 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 1 1 1 2 1 1 2
## [2989] 2 2 1 2 1 1 2 2 1 2 2 1 1 1 1 2 2 2 1 2 2 1 2 2 1 1 1 2 2 2 1 2 1 1 2 2
## [3025] 2 1 2 2 1 2 1 1 2 2 1 2 2 1 1 2 2 1 2 1 1 2 2 2 2 2 1 2 2 2 2 1 2 1 1 1
## [3061] 2 2 2 1 1 1 1 1 2 2 2 1 1 1 2 1 2 1 1 2 1 2 2 2 2 2 1 1 1 1 2 2 1 2 2 2
## [3097] 2 1 2 2 2 2 2 1 2 1 1 2 2 2 1 1 1 1 2 2 1 3 2 1 1 2 2 2 2 2 1 1 1 2 2 2
## [3133] 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 1 1 2 1 1 1 2 1 2 2 1 1 2 2 2 2 2 2 2 2 1
## [3169] 2 1 2 2 2 1 2 2 2 1 2 2 2 2 2 1 1 2 1 2 2 2 1 2 2 2 1 2 1 2 2 2 2 2 2 1
## [3205] 1 1 1 1 2 2 2 1 2 2 1 1 1 1 2 2 1 1 1 1 1 2 2 2 2 1 2 1 2 2 2 2 2 1 2 1
## [3241] 1 1 1 1 1 1 2 1 2 1 2 1 2 1 1 2 2 2 2 1 2 1 2 2 2 1 2 2 2 2 2 2 1 2 1 2
## [3277] 1 1 1 2 1 2 2 2 1 1 1 2 1 2 2 3 2 1 2 1 2 2 2 2 2 2 2 2 1 1 1 2 2 1 2 1
## [3313] 2 1 2 2 1 2 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 1 1 2 2 2 3 1 1 2
## [3349] 2 2 2 1 1 1 2 2 2 2 2 1 1 1 2 2 1 2 2 1 2 1 1 1 2 2 2 1 2 1 2 2 1 1 2 2
## [3385] 2 2 1 2 2 2 1 2 2 2 2 2 1 2 2 1 1 1 2 2 1 2 1 2 3 2 2 3 1 1 1 2 1 2 1 2
## [3421] 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 2 1 2 2 2 3 2 1 1 1 1 2 2 1 2 2 1 1 1
## [3457] 2 2 2 2 1 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 2 1 1 1 1 1 2 2 1 2
## [3493] 2 1 1 1 1 2 1 2 1 1 2 1 2 2 1 2 2 2 2 2 1 2 2 2 2 1 3 2 2 1 2 1 2 1 1 1
## [3529] 1 1 2 2 2 2 2 3 2 2 1 1 2 2 1 2 2 2 2 2 2 1 1 1 2 2 2 2 1 2 2 1 1 2 1 2
## [3565] 2 2 1 1 2 2 2 2 2 1 1 1 2 2 1 2 1 2 1 1 2 2 3 1 2 2 1 1 2 2 2 1 1 2 2 2
## [3601] 2 2 2 2 2 2 2 2 2 2 1 2 1 2 1 1 1 1 2 2 2 2 2 2 1 2 2 1 1 2 1 2 2 2 3 1
## [3637] 1 1 1 2 2 2 3 1 1 1 1 1 2 2 2 3 1 1 1 1 2 2 2 2 2 2 1 2 2 2 2 1 1 2 2 2
## [3673] 2 2 3 3 1 2 2 1 1 2 2 1 1 2 1 2 2 2 2 1 2 2 1 1 2 1 1 1 1 1 2 1 2 2 2 2
## [3709] 2 2 1 1 1 2 2 2 2 1 2 1 1 1 2 1 1 1 2 2 2 2 2 1 2 2 1 1 1 2 2 2 1 2 2 1
## [3745] 1 1 2 2 2 2 2 2 2 1 2 1 2 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 3 1 1 2 2 2 2 2
## [3781] 1 1 1 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 1 2 1 2 2 2 2 2 1
## [3817] 1 1 2 1 1 2 2 2 2 1 1 1 2 2 2 1 2 1 2 2 2 2 2 1 1 1 2 2 2 1 1 1 1 2 2 2
## [3853] 1 1 2 2 2 2 2 1 1 2 2 1 2 2 2 2 3 1 1 1 2 2 2 2 2 2 2 2 1 2 3 1 1 2 2 1
## [3889] 2 2 2 2 2 1 1 1 1 2 2 2 1 2 1 2 2 2 1 2 1 1 1 1 2 2 2 2 2 2 2 3 1 2 1 2
## [3925] 2 2 2 1 1 1 2 2 2 2 1 1 1 1 2 2 1 2 2 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 2
## [3961] 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 3 2 1 1 1 2 2 1 1 2 1 1 2 2 2 2 1 2
## [3997] 1 2 2 2 2 2 2 1 1 2 2 2 2 1 1 2 1 1 1 2 1 2 2 2 2 1 1 1 1 1 2 2 2 2 2 2
## [4033] 1 2 1 1 1 1 2 1 1 1 2 1 2 2 1 2 2 2 3 2 2 1 1 2 2 2 1 2 1 2 2 2 1 1 1 2
## [4069] 2 2 2 2 1 2 3 1 1 1 2 1 2 2 2 2 2 3 1 1 1 2 1 1 1 2 2 2 2 2 2 2 2 2 1 1
## [4105] 1 1 1 2 1 1 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 1 2 2 3 2
## [4141] 2 2 2 2 2 2 2 2 1 2 2 1 1 2 2 2 2 2 2 3 1 2 1 1 1 2 2 2 2 2 2 2 2 1 1 2
## [4177] 1 1 1 1 1 2 2 1 2 1 3 2 2 1 2 3 1 2 2 2 2 2 1 1 2 2 1 2 2 2 1 1 2 2 2 2
## [4213] 2 2 2 1 2 1 1 2 2 2 2 2 2 2 2 2 1 2 2 1 1 1 1 2 2 2 1 1 1 1 1 1 3 3 1 1
## [4249] 2 1 2 2 2 2 1 2 2 1 2 1 1 1 1 2 3 1 2 2 2 1 1 2 1 1 2 1 2 2 2 2 2 2 2 2
## [4285] 1 2 3 2 1 1 2 1 2 1 1 1 1 2 2 2 2 3 1 1 2 2 2 1 1 2 3 1 2 2 2 2 2 2 3 2
## [4321] 2 1 1 1 2 2 2 2 1 1 1 1 2 1 2 3 2 2 2 2 2 2 1 2 2 1 2 2 2 1 1 1 1 2 2 2
## [4357] 2 2 1 2 1 1 1 1 1 1 2 2 2 2 2 2 2 1 1 1 2 1 2 1 2 2 2 3 1 2 2 3 2 1 2 2
## [4393] 2 2 2 2 1 1 2 2 3 1 2 2 2 2 3 2 1 1 2 2 2 2 2 2 2 2 3 1 1 2 2 2 3 2 2 2
## [4429] 2 1 3 1 1 3 3 1 1 1 1 1 2 2 2 1 2 2 3 2 2 3 2 1 2 2 2 2 2 1 1 2 2 1 1 1
## [4465] 1 2 2 2 1 2 1 1 2 2 1 2 2 1 1 2 3 1 1 1 2 1 1 2 2 1 1 1 1 1 1 1 2 2 2 2
## [4501] 2 1 1 1 2 2 3 1 1 2 2 3 1 1 2 2 2 2 3 1 1 2 2 2 2 2 2 1 2 2 2 2 2 2 1 1
## [4537] 2 2 2 2 2 2 2 3 2 1 1 2 2 2 2 2 1 1 2 2 2 2 1 1 2 1 2 2 2 1 1 2 1 1 1 1
## [4573] 1 2 1 1 1 2 1 1 2 2 2 2 1 2 2 1 1 2 2 2 2 2 1 1 1 2 1 2 1 2 2 2 2 2 2 2
## [4609] 2 2 2 3 1 1 1 2 2 1 2 2 2 2 2 2 2 2 2 3 2 1 2 1 1 1 1 2 2 2 2 1 3 2 1 1
## [4645] 2 2 2 2 2 2 2 3 1 1 2 1 3 2 2 2 2 2 1 1 2 2 2 2 2 2 1 1 1 2 1 1 2 2 2 2
## [4681] 2 2 2 2 1 2 2 1 2 3 2 2 2 2 3 2 1 2 2 1 2 2 2 2 1 2 2 2 1 2 2 1 2 2 2 1
## [4717] 2 2 2 2 2 2 2 2 3 2 1 1 2 2 2 3 3 2 3 1 2 2 1 1 2 2 2 2 2 2 1 2 2 2 2 2
## [4753] 2 1 2 2 2 2 2 2 2 1 1 2 1 2 1 2 2 2 3 2 1 1 2 1 2 2 2 1 2 2 2 1 1 1 2 2
## [4789] 1 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 1 3 2 2 2 2 2 2 2 3 2 2 1 1 2 1
## [4825] 1 2 2 2 2 2 2 3 3 1 1 1 2 2 1 2 3 2 2 2 2 2 2 1 3 1 2 2 1 1 2 2 2 2 1 1
## [4861] 2 1 1 2 1 1 1 2 2 2 2 2 1 2 2 2 1 2 1 2 2 1 1 2 2 2 2 1 1 2 1 2 1 2 2 2
## [4897] 2 2 3 1 2 1 1 1 2 2 2 1 2 1 3 3 1 1 1 2 2 2 2 3 3 1 2 2 1 2 2 2 2 2 2 1
## [4933] 3 2 1 2 1 2 2 2 2 1 2 2 2 1 2 1 2 1 1 1 1 2 2 2 3 3 1 2 1 1 2 2 2 1 2 1
## [4969] 2 2 1 2 1 2 2 1 3 1 1 2 1 2 2 2 2 1 3 1 2 1 1 1 2 2 2 2 2 2 3 1 1 2 1 1
## [5005] 1 1 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 1 2 2 3 2 1 1 1 3 1 1 2 3 1 2 1 1 2 1
## [5041] 2 1 1 3 1 1 1 2 2 2 2 2 2 2 1 1 1 2 2 2 2 1 2 1 1 3 2 2 1 2 1 1 2 3 3 2
## [5077] 1 1 1 2 1 1 2 2 2 2 2 1 1 3 1 2 2 2 2 3 1 2 1 2 1 2 2 2 1 1 3 1 2 1 1 2
## [5113] 2 2 2 2 2 1 2 2 3 2 1 1 1 1 1 2 1 1 1 2 2 2 2 1 2 2 2 2 3 3 3 2 1 2 2 1
## [5149] 2 2 1 2 2 1 1 1 2 1 3 3 3 2 1 2 2 2 2 1 2 2 3 2 1 2 1 2 1 2 2 2 1 1 3 1
## [5185] 1 1 2 2 3 2 2 1 1 1 2 2 1 2 1 1 1 2 2 2 1 2 2 1 1 2 1 2 2 2 2 2 2 2 2 1
## [5221] 2 1 1 1 1 1 1 2 2 1 2 1 2 1 2 2 2 1 1 2 1 1 1 1 2 2 2 1 2 2 2 1 1 1 1 2
## [5257] 2 2 1 2 3 3 2 1 1 2 1 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 1 1 1 2 1 2 2
## [5293] 2 2 1 1 2 2 2 1 1 1 1 1 2 2 2 2 1 1 1 2 2 1 2 2 2 1 1 1 2 2 2 1 1 3 2 2
## [5329] 2 2 1 1 2 2 2 2 2 1 3 3 3 1 1 2 3 3 2 2 1 2 2 2 2 1 1 2 2 1 1 1 1 1 2 1
## [5365] 1 2 2 2 1 1 1 1 2 2 2 2 2 1 2 2 2 2 2 3 2 1 1 1 1 1 2 1 1 2 1 1 1 2 1 1
## [5401] 1 2 1 1 2 1 2 2 2 3 1 1 2 1 2 1 1 2 1 1 1 1 2 1 1 2 2 1 2 3 2 2 3 3 2 2
## [5437] 3 3 3 2 3 2 3 3 3 2 3 2 3 2 3 2 2 2 3 3 3 2 2 2 2 2 2 3 2 3 2 2 2 3 3 1
## [5473] 3 3 2 3 2 2 2 2 3 3 3 2 3 3 2 2 2 3 3 3 3 2 2 3 3 2 2 2 3 2 3 3 2 1 2 3
## [5509] 3 3 3 3 2 3 2 2 2 2 3 3 3 3 3 2 2 1 3 3 3 3 3 2 2 2 3 2 2 2 2 2 2 2 3 3
## [5545] 2 2 3 3 2 3 3 3 2 3 2 2 2 3 3 2 3 2 2 3 3 3 2 3 2 3 3 3 3 3 2 2 2 2 3 2
## [5581] 3 2 3 2 3 2 3 3 3 2 2 3 2 2 3 2 2 3 3 2 2 3 2 2 2 3 3 2 3 3 3 2 2 2 2 2
## [5617] 2 3 3 2 2 2 3 2 2 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [5653] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [5689] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [5725] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [5761] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [5797] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [5833] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 2 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [5869] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [5905] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [5941] 3 3 3 2 2 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [5977] 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6013] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 2 3
## [6049] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
## [6085] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3
## [6121] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6157] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6193] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6229] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6265] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6301] 3 3 3 3 3 3 1 3 3 3 3 3 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6337] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 1 3 3 3 3 3 2 3 2 2 3
## [6373] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3
## [6409] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 2 2 2
## [6445] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3
## [6481] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6517] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 2 3 3 2 3 3 3 3 3
## [6553] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [6589] 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6625] 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6661] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6697] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6733] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 2 2 3 3 3 3 3 3
## [6769] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3
## [6805] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 2 2 3
## [6841] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3
## [6877] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 2 2 2 3 3 3
## [6913] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 3 3 3 3 2 3
## [6949] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6985] 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3
## [7021] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 2 3 3 3 3
## [7057] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1
## [7093] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7129] 3 3 1 2 2 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7165] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7201] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
## [7237] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
## [7273] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7309] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7345] 3 3 3 3 3 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7381] 3 3 3 3 3 1 2 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7417] 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7453] 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7489] 3 3 3 3 3 3 1 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7525] 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 2 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7561] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7597] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7633] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3
## [7669] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7705] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
## [7741] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 3 3 3 3 3
## [7777] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 2 3 3
## [7813] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
## [7849] 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7885] 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3
## [7921] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 2 3 3 2 2 2 3 3 3 3
## [7957] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [7993] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [8029] 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8065] 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3
## [8101] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 1 1 2 3 3 3 3 3 2
## [8137] 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8173] 3 3 3 3 3 2 3 3 3 3 2 3 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8209] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8245] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 2 3 3 3
## [8281] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2
## [8317] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
## [8353] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 2 2 2 3
## [8389] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 2 3 3 3 3
## [8425] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3
## [8461] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 2 3 3 3 2 3 3 3 2 2 3 3 3 3 3
## [8497] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3
## [8533] 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3
## [8569] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8605] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8641] 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8677] 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8713] 3 3 3 3 3 3 3 3 2 3 3 3 3 2 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8749] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8785] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 2 2 3 3 3 3
## [8821] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3
## [8857] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3
## [8893] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3
## [8929] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8965] 3 3 3 3 3 3 3 3 2 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9001] 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9037] 3 3 3 3 3 3 3 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9073] 3 3 3 3 3 3 3 2 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9109] 3 3 3 3 1 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9145] 3 3 3 3 3 3 3 3 2 1 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9181] 3 3 3 3 3 3 3 3 2 3 2 2 2 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9217] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9253] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9289] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 2 3 3 3 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [9325] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 3 2 2 2 3 3 3 2 3 3 3 3 3 3 3
## [9361] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9397] 3 3 3 3 3 3 3 2 2 1 2 2 3 3 2 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9433] 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9469] 3 3 3 3 3 3 2 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9505] 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [9541] 1 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2
## [9577] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [9613] 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9649] 3 2 2 2 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9685] 3 3 3 3 3 3 3 2 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9721] 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9757] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9793] 3 3 2 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9829] 3 3 3 3 3 3 1 3 2 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9865] 3 3 3 3 3 2 2 2 2 3 3 3 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9901] 3 3 3 3 3 3 1 2 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9937] 3 3 3 3 3 3 3 3 3 2 2 3 2 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [9973] 3 3 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3
## [10009] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 3 2 2 2
## [10045] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10081] 3 3 3 3 2 2 2 3 3 2 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10117] 3 3 2 3 3 3 3 3 2 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10153] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3
## [10189] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 2 2 3 3 3 3 2 3 2 2 3 3 3
## [10225] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 2 3 3 3
## [10261] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 2 3 3 3 3 3
## [10297] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 2 2 3 3 3
## [10333] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 3 3 3 2 3 3 3 3 3 2 3 3 3 3
## [10369] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 2 2 2 2 2
## [10405] 2 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10441] 3 3 1 2 2 3 3 3 2 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10477] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10513] 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10549] 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10585] 3 3 3 3 3 3 3 3 3 2 2 1 3 2 2 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10621] 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [10657] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3
## [10693] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 2 3 3 2 3 3 3 3
## [10729] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10765] 2 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10801] 3 3 3 3 3 2 1 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10837] 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10873] 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 2 2 2 2 3 2 3 2 3 3 3 3 3 3 3 3 3 3
## [10909] 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 2 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [10945] 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 2 3 3 3 3 2 2 2 3 3 2 3 3 3 3 3 3 3
## [10981] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3
## [11017] 3 3 3 3 3 3 3 3 3 3 3 2 1 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11053] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11089] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11125] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11161] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 2 2 2 2 3 2 3 3 3 3 3 3
## [11197] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 2 3 2 3 3 3 3 3 3 3
## [11233] 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 2 3 3 3
## [11269] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 3 2 3 2 3
## [11305] 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 2
## [11341] 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11377] 3 3 3 3 2 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2
## [11413] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2
## [11449] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
## [11485] 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11521] 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11557] 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2
## [11593] 3 3 3 3 3 2 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11629] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11665] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11701] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 2 3 3 3 3 3 3 3
## [11737] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 3 3 2 2 3 3 3
## [11773] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 2 3 2 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3
## [11809] 3 3 3 3 3 3 3 3 3 3 1 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11845] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11881] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11917] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11953] 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11989] 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12025] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12061] 3 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12097] 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 2 3 3 3
## [12133] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12169] 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12205] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12241] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12277] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12313] 3 3 3 3 3 3 2 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12349] 2 3 2 2 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [12385] 2 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [12421] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12457] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12493] 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12529] 3 3 3 3 3 2 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [12565] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 2 2 3 3
## [12601] 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12637] 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 2 2 3 3 3
## [12673] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 2 2
## [12709] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3
## [12745] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 2 2 3 2 3 3 3
## [12781] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12817] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3
## [12853] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 2 3 3 3 3 3 3
## [12889] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 2 2 3 3 3 2 3 3 3
## [12925] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3
## [12961] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12997] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [13033] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 2 3 3 3 3 3 3 3 3 3
## [13069] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3
## [13105] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
## [13141] 2 3 2 2 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [13177] 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
## [13213] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [13249] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [13285] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [13321] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [13357] 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [13393] 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [13429] 3 3 3 3 3 2 3 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [13465] 3 2 2 2 2 2 2 2 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [13501] 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3
## [13537] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3
## [13573] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3
## [13609] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
## [13645] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 2 2 2 3 3
## [13681] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [13717] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 3 3 2 3 3 3 3 3
## [13753] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2
## [13789] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
## [13825] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 2 3 2 2 3 3 3 3 3 3 3
## [13861] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
## [13897] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 2 3 3 3 3
## [13933] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 2 3 3 2 3 3 3 3 3 3 3 3
## [13969] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14005] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14041] 3 2 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14077] 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [14113] 3 2 2 3 2 3 3 3 2 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14149] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14185] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14221] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [14257] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [14293] 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14329] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3
## [14365] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [14401] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14437] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 2 2
## [14473] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14509] 3 3 3 3 3 3 3 2 3 2 2 2 2 2 2 3 3 3 3 3 3 3 3 2 2 2 3 3 2 3 3 3 3 3 3 3
## [14545] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [14581] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 2 2 2
## [14617] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 2 3 3 2 3
## [14653] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 2 3 3 3 3 3 3 3 3 3
## [14689] 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14725] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
## [14761] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 2 2 2 3 3 3 2 3 3
## [14797] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 3 3 3 2 3 3 3
## [14833] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 2 3 3 2 3 3 3 3 3
## [14869] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3
## [14905] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 2 2 3 3 3 3 3 3 3
## [14941] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [14977] 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15013] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 3 3 3
## [15049] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 2 2 2 2 2 3 3 3 2 3 3 3 3 3 3 3
## [15085] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 2 2 3 3 3 3
## [15121] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 2 3 3 3 3 2 3 2 3 3 3 3 3 3
## [15157] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 3 3 3 2 2 3 2 2
## [15193] 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 2 3 3 3 3 3
## [15229] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15265] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15301] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3
## [15337] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15373] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
## [15409] 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15445] 3 3 3 3 3 2 2 2 2 2 2 2 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15481] 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 3 3 2 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15517] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15553] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 3 2 2 3 3 3 3 3
## [15589] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 3 3 3 3 2
## [15625] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2
## [15661] 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 2 3
## [15697] 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
## [15733] 2 3 3 3 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15769] 3 3 3 3 3 2 2 2 2 2 2 2 3 2 3 2 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3
## [15805] 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15841] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 2 2 2 2 3 3 3 2 2 2 2 3 3 3 3 3 3 3
## [15877] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 2 2 2 2 2 2 2 3 3 3 3 3 3
## [15913] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2
## [15949] 2 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2
## [15985] 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3
## [16021] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
## [16057] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 3 3 3 3
## [16093] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 2 2 2 2 2 3 3 3 2 2
## [16129] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 2 2 2 3 3 2
## [16165] 3 3 3 3 3 3 3 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16201] 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16237] 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2
## [16273] 2 2 2 2 2 3 2 2 3 3 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16309] 3 3 2 2 2 2 2 2 2 2 2 2 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16345] 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3
## [16381] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 2 2 2 2 2 3 3 3 3 2 2 3
## [16417] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16453] 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2
## [16489] 2 2 2 2 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16525] 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16561] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16597] 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 3 3 3 3 2 3 2 2 3 3 3 3 3 3 3 3 3 3
## [16633] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [16669] 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16705] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16741] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 3 2 3 2 3 3 3 3 3 3
## [16777] 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16813] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16849] 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2
## [16885] 2 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16921] 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16957] 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [16993] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 3 3 3 2 3 3 2 2 2 3 3 3
## [17029] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 3 3 2 3 3 3 3 3
## [17065] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 3 2 3 3 3 3 3 2 3
## [17101] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 2 3 3 3 2 2 3 2 3 3 3 2 3
## [17137] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 2 2 2 3 3 2 3 3 3 3
## [17173] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 3 2 3 2 2 3 3 3
## [17209] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
## [17245] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 3 3 2 2
## [17281] 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17317] 3 3 3 3 2 3 2 2 2 2 3 3 2 2 3 2 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17353] 3 3 3 3 2 2 2 2 2 3 3 3 3 3 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17389] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3
## [17425] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 2 2 2 2 2 2 3 3 2 3 3 3 3 3 3 3
## [17461] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 2 3 2 2 3 3 2 3 3 3 3 3 2 2
## [17497] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 2 2 3 3 3
## [17533] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2
## [17569] 2 3 2 2 3 3 3 3 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17605] 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 3 3 2 2 3 3 2 3 3 3 3 3 3 3 3 3
## [17641] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17677] 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [17713] 2 2 2 2 3 3 3 2 3 2 2 3 3 2 2 2 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17749] 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17785] 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [17821] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 3 3 3 3 2 3 3 3 3 3 3 3
## [17857] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 2 2 3 3 3
## [17893] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17929] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17965] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18001] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18037] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18073] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18109] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18145] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18181] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18217] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18253] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18289] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18325] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18361] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18397] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18433] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [18469] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##
## Within cluster sum of squares by cluster:
## [1] 5155846329 1639404919 1334478782
## (between_SS / total_SS = 90.3 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"CLUSTER 1 –> clientes con probabilidad de compra alta y un Total Amount medio CLUSTER 2–> clientes que compran bici y tienen un Total Amount alto CLUSTER 3–> clientes que no compran bici y tienen Total Amount bajo
Adicional, el análisis pone en evidencia que la variable que el hecho de que un cliente tenga hijos, posea casa o auto no tienen mayor relevancia en la decisión de compra o no de bicicleta, la variable que realmente define la decisión de compra o no es Total Amount.
PREDICCIÓN VENTAS TOTALES
Para la predicción de las ventas totales de los próximos meses utilizaremos una base de series de tiempo.
library(readxl)
bddventas <- read_excel("C:/Users/Jamileth/Documents/MAESTRÍA 2/Actividad3.xlsx",
sheet = "ST Ventas Totales ")## New names:
## • `Sales` -> `Sales...3`
## • `Sales` -> `Sales...4`
## • `Sales` -> `Sales...5`La damos formato de serie de tiempo.
library(forecast)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zooventas <- ts(bddventas$Ventas, start = c(2011,150), frequency=360)
autoplot(ventas)
Dado que la serie de tiempo evidencia la presencia de estacionalidd se utilizará para la regresión el modelo Holt Winters mismo que permite considerar este efecto. Adicional, el pronóstico de las ventas para los dos siguientes meses se evidencia que tiene un buen ajuste en relación con los datos históricos.
mod1=HoltWinters(ventas, seasonal = "additive")
plot(mod1)
prediccion<-forecast(mod1,60)
plot(prediccion)