SOM LARVA en una matriz de 60 x 60 Neuronas

1. Loading raw data and getting it ready

library(readr)
larva <- read_csv("~/GENER/SOMs/Ricardo/1. Raw Data/PSOMML_1Larva.csv")
larva <- larva[,-1:-2]

The dataframe obtained is made of integer values:

str(larva)

Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   262144 obs. of  3 variables:
 $ RED  : int  205 205 206 206 206 206 206 205 205 205 ...
 $ GREEN: int  209 209 210 210 210 210 210 209 209 209 ...
 $ BLUE : int  208 208 209 209 209 209 209 209 208 209 ...

This will be a problem down the line when feeding this data to the algorithm, values have to be changed to numeric:

#larva[,'FILA'] <- as.numeric(as.factor(larva$FILA))
#larva[,'COLUMNA'] <- as.numeric(as.factor(larva$COLUMNA))
larva[,'RED'] <- as.numeric(as.factor(larva$RED))
larva[,'GREEN'] <- as.numeric(as.factor(larva$GREEN))
larva[,'BLUE'] <- as.numeric(as.factor(larva$BLUE))

Whit this taken care of we have a database ready to be fed to the algorithmL

str(larva)

Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   262144 obs. of  3 variables:
 $ RED  : num  162 162 163 163 163 163 163 162 162 162 ...
 $ GREEN: num  169 169 170 170 170 170 170 169 169 169 ...
 $ BLUE : num  171 171 172 172 172 172 172 172 171 172 ...

2 Initial exploration of data

A brief summary of the values of the variable can help with the initial understanding of things:

summary(larva)

      RED            GREEN            BLUE      
 Min.   :  1.0   Min.   :  1.0   Min.   :  1.0  
 1st Qu.:158.0   1st Qu.:163.0   1st Qu.:167.0  
 Median :159.0   Median :165.0   Median :168.0  
 Mean   :157.8   Mean   :162.9   Mean   :165.3  
 3rd Qu.:161.0   3rd Qu.:166.0   3rd Qu.:170.0  
 Max.   :165.0   Max.   :171.0   Max.   :175.0  

3 Self Organizing Map

Loading required package

require(kohonen)

Changing dataframe to a matrix

#I removed the function scale() because it does not need to be scaled.
larva.matrix <- as.matrix(larva)
#TEST
larva.matrixTest <- larva.matrix[1:20000,]

Creating the SOM grid

som_grid60x60 <- somgrid(xdim = 60, ydim = 60, topo = "rectangular")

Training the SOM

Training progress evaluation

plot(som_model60, type = "changes")

Node counts

source('coolBlueHotRed.R')
plot(som_model60, type = "counts", palette.name=coolBlueHotRed)

par(mfrow = c(2,2))
models <- as.data.frame(som_model60$codes)
plot(som_model60,
     type = "property",
     property = models[,1],
     main=names(models)[1],
     palette.name=coolBlueHotRed)
plot(som_model60,
     type = "property",
     property = models[,2],
     main=names(models)[2],
     palette.name=coolBlueHotRed)

plot(som_model60,
     type = "property",
     property = models[,3],
     main=names(models)[3],
     palette.name=coolBlueHotRed)

#source('plotHeatMap.R')
#plotHeatMap(som_model60, data = models, variable=0)

Neighbor Distance

plot(som_model60, type = "dist.neighbours", palette.name = grey.colors)

Code Spread

plot(som_model60, type = "codes", palette.name = coolBlueHotRed)

Clustering

mydata <- som_model60$codes 
wss <- (nrow(mydata)-1)*sum(apply(data.frame(mydata),2,var)) 
for (i in 2:20) {
  wss[i] <- sum(kmeans(data.frame(mydata), centers=i)$withinss)
}

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plot(1:20, wss, type="b", xlab="Number of Clusters",
     ylab="Within groups sum of squares", main="Within cluster sum of squares (WCSS)")

som_cluster <- cutree(hclust(dist(data.frame(som_model60$codes))), 3)

In this graphic we can see that the ideal amount of clusters is not more than 5. Starting with 5 and going down we will choose the smallest number that gives us a smooth distribution in quantity of it’s members:

# 7 Clusters -- the first cluster has considerably less members than the other four ones.
table(cutree(hclust(dist(data.frame(som_model60$codes))), 7))

   1    2    3    4    5    6    7 
  19   41  101   55  701 2556  127 

# 5 Clusters -- the first cluster has considerably less members than the other four ones.
table(cutree(hclust(dist(data.frame(som_model60$codes))), 5))

   1    2    3    4    5 
  60  101  182  701 2556 

#4 clusters -- we have founde the good one.
table(cutree(hclust(dist(data.frame(som_model60$codes))), 4))

   1    2    3    4 
  60  283  701 2556 

pretty_palette <- c("#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')
plot(som_model60, type="mapping", bgcol = pretty_palette[som_cluster], main = "Clusters", keepMargins = TRUE) 
add.cluster.boundaries(som_model60, som_cluster, lwd = 7)