Updates for week4

exercises/02450Toolbox_R/Scripts/ex4_1_7.R

+####################
+# Exercise 4.1.7
+####################
+rm(list = ls()) # Clear work space
+
+library(MASS)
+
+# Digits to include in analysis (to include all, n = 1:10)
+n <- c(1) # c(1,5,9)
+n <- sort(n)
+
+# Load the library R.matlab to enable the function readMat,
+# which allows R to read the matlab .mat format.
+library(R.matlab) # install.packages("R.matlab")
+
+# The row of training data that we will look at
+i <- 1
+
+# Read in the data
+data <- readMat(file.path("Data", "zipdata.mat"))
+
+# Check that the structure data contains two matrices, testdata and traindata
+names(data)
+
+ncols <- ncol(data$traindata)
+
+# Extract digits
+X <- data$traindata[, 2:ncols]
+y <- data$traindata[, 1]
+classNames <- c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10")
+classLabels <- classNames[y + 1]
+
+# Remove digits that are not to be inspected
+j <- match(y, n)
+X <- X[!is.na(j), ]
+classLabels <- classLabels[!is.na(j)]
+classNames <- classNames[n + 1]
+y <- y[!is.na(j)]
+for (k in 0:(length(n) - 1))
+{
+  classlab <- n[k + 1]
+  y[y == classlab] <- k
+}
+
+# Compute mean, standard deviations, and covariance matrix
+mu <- colMeans(X)
+s <- apply(X, 2, sd)
+S <- cov(X)
+
+# Generate 10 images with same mean and standard deviation
+Xgen <- mvrnorm(n = 10, mu = mu, Sigma = diag(s))
+
+# Plot images generated using the Normal distribution
+{
+par(mfrow = c(2, 3))
+  for (k in 1:6) {
+    I <- Xgen[k, ]
+    dim(I) <- c(16, 16)
+    image(I[, 16:1], main = "Digits: 1-D Normal", col = gray(32:0 / 32))
+  }
+}
+
+
+# Generate 10 images with same mean and covariance matrix
+Xgen <- mvrnorm(n = 10, mu = mu, Sigma = S)
+
+{
+  par(mfrow = c(2, 3))
+  for (k in 1:6) {
+    I <- Xgen[k, ]
+    dim(I) <- c(16, 16)
+    image(I[, 16:1], main = "Digits: Multivariate Normal", col = gray(32:0 / 32))
+  }
+}