Lagt til noe
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Joakim Skogholt
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d08df77283
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005a969653
3 changed files with 232 additions and 2 deletions
71
src/TR.jl
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71
src/TR.jl
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"""
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function TRLooCV
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bpress, bgcv, press, GCV, idminPRESS, lambdaPRESS, idminGCV, lambdaGCV = TRLooCV(X, y, lambdas, regType="L2", regParam1=1, regParam2=1)
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regType: 'bc', 'legendre', 'L2', 'std', 'GL'
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"""
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function TRLooCV(X, y, lambdas, regType="L2", regParam1=1, regParam2=1)
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n, p = size(X);
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mX = mean(X, dims=1);
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X = X .- mX;
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my = mean(y);
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y = y .- my;
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if regType == "bc"
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regMat = [I(p); zeros(regParam1,p)]; for i = 1:regParam1 regMat = diff(regMat, dims = 1); end
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elseif regType == "legendre"
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regMat = [I(p); zeros(regParam1,p)]; for i = 1:regParam1 regMat = diff(regMat, dims = 1); end
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P, _ = plegendre(regParam-1, p);
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regMat[end-regParam1+1:end,:] = sqrt(regParam2) * P;
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elseif regType == "L2"
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regMat = I(p);
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elseif regType == "std"
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regMat = Diagonal(vec(std(X, dims=1)));
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elseif regType == "GL" # Grünwald-Letnikov fractional derivative regulariztion
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# regParam1 is alpha (order of fractional derivative)
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C = ones(p)*1.0;
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for k in 2:p
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C[k] = (1-(regParam1+1)/(k-1)) * C[k-1];
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end
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regMat = zeros(p,p);
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for i in 1:p
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regMat[i:end, i] = C[1:end-i+1];
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end
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end
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X = X / regMat;
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U, s, V = svd(X, full=false)
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denom = broadcast(.+, broadcast(./, lambdas, s'), s')';
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H = broadcast(.+, U.^2 * broadcast(./, s, denom), 1/n);
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resid = broadcast(.-, y, U * broadcast(./, s .* (U'*y), denom));
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rescv = broadcast(./, resid, broadcast(.-, 1, H));
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press = vec(sum(rescv.^2, dims=1));
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GCV = vec(broadcast(./, sum(resid.^2, dims=1), mean(broadcast(.-, 1, H), dims=1).^2));
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idminPRESS = findmin(press)[2][1]; # First index selects coordinates, second selects '1st coordinate'
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idminGCV = findmin(GCV)[2][1]; # First index selects coordinates, second selects '1st coordinate'
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lambdaPRESS = lambdas[idminPRESS];
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lambdaGCV = lambdas[idminGCV];
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bcoeffs = V * broadcast(./, (U' * y), denom);
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bcoeffs = regMat \ bcoeffs;
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if my != 0
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bcoeffs = [my .- mX*bcoeffs; bcoeffs];
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end
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bpress = bcoeffs[:, idminPRESS]
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bgcv = bcoeffs[:, idminGCV]
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return bpress, bgcv, press, GCV, idminPRESS, lambdaPRESS, idminGCV, lambdaGCV
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end
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22
src/Ting.jl
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src/Ting.jl
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module Ting
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# Write your package code here.
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# Dependencies
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using LinearAlgebra
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using Statistics
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using Plots
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using MAT
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using Optimization
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using OptimizationOptimJL
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using OptimizationBBO
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using LaTeXStrings
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using Random
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# From "convenience.jl"
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export predRegression
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export importData
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# From "TR.jl"
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export TRLooCV
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include("convenience.jl")
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include("TR.jl")
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end
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139
src/convenience.jl
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139
src/convenience.jl
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# List of functions
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ypred, rmsep = predRegression(X, beta, y)
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ypred = predRegression(X, beta)
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ypred = predRegression(X::Vector{Float64}, beta)
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X, y, waves = importData("Beer");
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################################################################################
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# predRegression calculates predicted values from a matrix or vector of predictors
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# for a linear regression model by X*beta. If there is a constant term it is assumed
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# to be the first element. If true output is also provided rmsep is returned as well.
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function predRegression(X, beta, y)
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ypred = predRegression(X, beta)
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rmsep = sqrt(mean((y - ypred).^2))
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return ypred, rmsep
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end
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function predRegression(X, beta)
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if length(beta) == size(X,2)
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ypred = X * beta;
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elseif length(beta) == size(X,2) + 1
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ypred = beta[1] .+ X * beta[2:end]
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end
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return ypred
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end
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function predRegression(X::Vector{Float64}, beta)
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if length(beta) == length(X)
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ypred = X' * beta;
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elseif length(beta) == length(X) + 1
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ypred = beta[1] .+ X' * beta[2:end];
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end
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return ypred
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end
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################################################################################
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"""
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importData(datasetName, datasetPath="/home/jovyan/Datasets/")
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Example: X, y, waves = importData("Beer");
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Returns X, Y/G, [waves], +more for some datasets
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Valid values for datasetName: 9tumors, bacteria\\_K4\\_K5\\_K6, Beer, Dough, FTIR\\_AMW\\_tidied, FTIR\\_FTIR\\_FTIR\\_AFLP\\_SakacinP, HPLCforweb,
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leukemia1, MALDI-TOF\\_melk\\_rep\\_6179\\_corr\\_exact, NIR\\_Raman\\_PUFA\\_[NIR/Raman], NMR\\_WineData, onion\\_NMR, OVALung, Raman\\_Adipose\\_fat\\_pig\\_original,
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Ramanmilk, RAMANPorkFat, spectra, Sugar, tumor11
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"""
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function importData(datasetName, datasetPath="/home/jovyan/Datasets/")
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#datasetName = "Beer"
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#datasetPath = "/home/jovyan/Datasets/"
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vars = matread(string(datasetPath,datasetName,".mat"));
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if datasetName == "9tumors"
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X = vars["X"];
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G = vars["G"];
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elseif datasetName == "bacteria_K4_K5_K6"
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# Causes crash for some reason
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elseif datasetName == "Beer"
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X = vars["X"];
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Y = vars["Y"]; # G for some datasets
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waves = 400:2:2250;
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return X, Y, waves
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elseif datasetName == "Dough"
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X = [vars["Xtrain"]; vars["Xtest"]];
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Y = [vars["Ytrain"]; vars["Ytest"]];
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#if response != "all"
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# Y = Y[:,response];
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# Y = dropdims(Y;dims=2);
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#end
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return X, Y
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elseif datasetName == "FTIR_AMW_tidied"
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X = vars["X"];
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Y = vars["Y"];
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elseif datasetName == "FTIR_FTIR_FTIR_AFLP_SakacinP"
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elseif datasetName == "HPLCforweb"
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X = vars["HPLCforweb"]["data"]
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elseif datasetName == "leukemia1"
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X = vars["X"];
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G = vars["G"];
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elseif datasetName == "MALDI-TOF_melk_rep_6179_corr_exact"
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elseif datasetName == "NIR_Raman_PUFA" # ONLY RAMAN FOR NOW!
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X = vars["Ramandata"];
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Y = vars["PUFAdata"];
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waves = vars["Wavelengths_Raman"];
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return X, Y, waves
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elseif datasetName == "NMR_WineData"
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X = vars["X"]
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Y = vars["Y"];
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ppm = vars["ppm"];
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return X, Y, ppm
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elseif datasetName == "onion_NMR"
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X = vars["x"]
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Y = vars["onion"];
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ppm = vars["ppm"];
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return X, Y, ppm
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elseif datasetName == "OVALung"
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X = vars["X"];
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G = vars["y"];
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return X, G
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elseif datasetName == "Raman_Adipose_fat_pig_original"
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X = vars["spectra"]
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Y = vars["fat"]
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baseline = vars["baseline"]
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waves = parse.(Float64,vars["wavelength"])
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return X, Y, waves, baseline
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elseif datasetName == "Ramanmilk"
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# There are replicates that need to be handled
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#X = vars["SpectraR"]
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#Y = vars["CLA"]
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elseif datasetName == "RAMANPorkFat"
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X = vars["X"]["data"]
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Y = vars["Y"]["data"]
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return X, Y
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elseif datasetName == "spectra"
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X = vars["NIR"];
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Y = vars["octane"];
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waves = 900:2:1700;
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return X, Y, waves
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elseif datasetName == "Sugar"
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X = [vars["Xtrain"]; vars["Xtest"]]; Y = [vars["Ytrain"]; vars["Ytest"]];
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waves = vars["wave"];
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return X, Y, waves
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elseif datasetName == "tumor11"
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X = vars["X"];
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G = vars["G"];
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end
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end
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