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+#############
+# Chapter 7.1 Principles of Regression
+
+## Linear regression: Straight Line
+
+# R code to fit data points using a straight line
+
+library(pracma)
+
+N <- 50
+x <- runif(N)
+
+a <- 2.5 # true parameter
+b <- 1.3 # true parameter
+y <- a*x + b + 0.2*rnorm(N) # Synthesize training data
+
+X <- cbind(x, rep(1, N))
+theta <- lsfit(X, y)$coefficients
+t <- linspace(0, 1, 200)
+yhat <- theta[2]*t + theta[1]
+plot(x, y, pch=19)
+lines(t, yhat, col='red', lwd=4)
+legend("bottomright", c("Best Fit", "Data"), fill=c("red", "black"))
+
+## Linear regression: Polynomial
+
+# R code to fit data using a quadratic equation
+
+N <- 50
+x <- runif(N)
+a <- -2.5
+b <- 1.3
+c <- 1.2
+y <- a*x**2 + b*x + c + 0.2*rnorm(N)
+X <- cbind(rep(1, N), x, x**2)
+theta <- lsfit(X, y)$coefficients
+t = linspace(0, 1, 200)
+print(theta)
+yhat = theta[1] + theta[2]*t + theta[3]*t**2
+plot(x,y,pch=19)
+lines(t,yhat,col='red',lwd=4)
+
+## Legendre Polynomial
+
+# R code to fit data using a quadratic equation
+
+library(pracma)
+
+N <- 50
+x <- linspace(-1,1,N)
+a <- c(-0.001, 0.01, 0.55, 1.5, 1.2)
+y <- a[1]*legendre(0, x) + a[2]*legendre(1, x)[1,] + 
+  a[3]*legendre(2, x)[1,] + a[4]*legendre(3, x)[1,] + 
+  a[5]*legendre(4, x)[1,] + 0.2*rnorm(N)
+
+X <- cbind(legendre(0, x), legendre(1, x)[1,], 
+           legendre(2, x)[1,], legendre(3, x)[1,],
+           legendre(4, x)[1,]) # good
+
+beta <- mldivide(X, y)
+
+t <- linspace(-1, 1, 50)
+yhat <- beta[1]*legendre(0, x) + beta[2]*legendre(1, x)[1,] + 
+  beta[3]*legendre(2, x)[1,] + beta[4]*legendre(3, x)[1,] + 
+  beta[5]*legendre(4, x)[1,]
+
+plot(x, y, pch=19, col="blue")
+lines(t, yhat, lwd=2, col="orange")
+
+## Auto-regressive model
+
+# R code for auto-regressive model
+
+library(pracma)
+
+N <- 500
+y <- cumsum(0.2*rnorm(N)) + 0.05*rnorm(N)
+
+L <- 100
+c <- c(0, y[0:(400-1)])
+r = rep(0, L)
+X = Toeplitz(c,r)
+beta <- mldivide(X, y[1:400])
+yhat  = X %*% beta
+plot(y[1:400])
+lines(yhat[1:400], col="red")
+
+## Robust regression by linear programming
+
+# R code to demonstrate robust regression TODO
+
+library(pracma)
+
+N <- 50
+x <- linspace(-1,1,N)
+a <- c(-0.001, 0.01, 0.55, 1.5, 1.2)
+y <- a[1]*legendre(0, x) + a[2]*legendre(1, x)[1,] + 
+  a[3]*legendre(2, x)[1,] + a[4]*legendre(3, x)[1,] + 
+  a[5]*legendre(4, x)[1,] + 0.2*rnorm(N)
+
+idx <- c(10, 16, 23, 37, 45)
+y[idx] <- 5
+
+X <- cbind(rep(1,N), x, x**2, x**3, x**4)
+A <- rbind(cbind(X, -1*diag(N)), cbind(-X, -1*diag(N)))
+b <- c(y, -y)
+c <- c(rep(0, 5), rep(1, N))
+res <- linprog(c, A, b, maxiter=1000000)
+beta <- res.x
+t <- linspace(-1, 1, 200)
+
+# yhat <-
+# plot(x, y, pch=19, col="blue")
+# lines(t, yhat, lwd=2, col="orange")
+
+#############
+# Chapter 7.2 Overfitting
+
+## Overfitting example
+
+# R: An overfitting example (TODO)
+
+N <- 20
+x <- sort(1*rnorm(N)*2-1)
+a <- c(-0.001, 0.01, 0.55, 1.5, 1.2)
+y <- a[1]*legendre(0, x) + a[2]*legendre(1, x)[1,] + 
+  a[3]*legendre(2, x)[1,] + a[4]*legendre(3, x)[1,] + 
+  a[5]*legendre(4, x)[1,] + 0.1*rnorm(N)
+
+P <- 20 
+
+beta <- mldivide(X\y)
+
+## Learning curve
+
+# R
+
+#############
+# Chapter 7.3 Bias and Variance
+
+## Mean estimator
+
+# R code to visualize the average predictor (TODO)
+
+#############
+# Chapter 7.4 Regularization
+
+## Ridge regression
+
+# R code to demonstrate a ridge regression example (TODO)
+
+## LASSO regression
+
+# R (TODO)
+ 
+## LASSO vs Ridge
+
+# R code to demonstrate overfitting and LASSO (TODO)