Added Random as dependency

Project.toml

@@ -1,7 +1,7 @@
 name = "MinPakke"
 uuid = "be803360-cecc-4859-8120-03d0223bb960"
 authors = ["Joakim"]
-version = "1.0.0-DEV"
+version = "1.0.1-DEV"
 
 [deps]
 LaTeXStrings = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
@@ -12,6 +12,7 @@ OptimizationBBO = "3e6eede4-6085-4f62-9a71-46d9bc1eb92b"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [compat]
 julia = "1"

src/MinPakke.jl

@@ -8,6 +8,7 @@ using Optimization
 using OptimizationOptimJL
 using OptimizationBBO
 using LaTeXStrings
+using Random
 
 export plegendre
 export SNV
@@ -26,8 +27,12 @@ export createDataSplit
 export createDataSplitBinaryStratified
 export importData
 
+export PCR
+
+
 include("preprocessing.jl")
 include("convenience.jl")
 include("conveniencePlots.jl")
+include("variousRegressionFunctions.jl")
 
 end

src/preprocessing.jl

@@ -37,7 +37,6 @@ Outputs:
     - Q : (d+1) x p matrix with basis
     - R : matrix from QR-factorisation
 """
-
 function plegendre(d, p)
  
 P = ones(p, d+1);

src/variousRegressionFunctions.jl

@@ -0,0 +1,34 @@
+"""
+    function PCR(X, y, kmax, centre=True, standardize=true)
+
+Principal Component Regression (PCR).
+Inputs: Data matrix, response vector, maximum number of components.
+A constant term is included in the modeling.
+Outputs: B (matrix of size (p+1) x kmax), U, s, V
+
+X, y = importData("Beer");
+B, \\_ = PCR(X, y, 10, true, false);
+"""
+function PCR(X, y, kmax, centre=true, standardize=true)
+
+mX   = mean(X, dims=1);
+my   = mean(y, dims=1);
+stdX = std(X, dims=1);
+
+if centre
+    X = X .- mX;
+end
+
+if standardize
+    X = X ./ stdX;
+end
+
+U, s, V = svd(X, full=false);
+
+q  = s[1:kmax].^(-1) .*(U[:,1:kmax]'y);
+B  = cumsum(V[:,1:kmax] .* q', dims=2);
+b0 = my .- mX * B
+B  = [b0; B];
+
+return B, U, s, V
+end