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</style></head><body><div class = "content"><div class = 'SectionBlock containment active'><h2 class = "S1"><span class = "S2">Benjamin Linnik</span></h2>
<h2 class = "S3"><span class = "S2">Recreate Bayesian Linear Regression plots from Christopher M. Bishop - Pattern Recognition and Machine Learning p. 157-158</span></h2>
<h2 class = "S3"><span class = "S2">Part 1</span></h2>
<div class = 'LineNodeBlock contiguous'><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">clear; close </span><span class = "S7">all</span><span class = "S6">; </span><span class = "S8">% clear everything</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">%rng(5); % start seed, make deterministic</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">n = 20; </span><span class = "S8">% number of data points</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">s = 0.1; </span><span class = "S8">% s Used for gaussian basis function width</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">alpha = 100; </span><span class = "S8">% initial spread</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">betaInv = (0.3)^2; </span><span class = "S8">% beta^-1, used as sigma for noise estimation</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">beta = 1/betaInv; </span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">XAxisPrecision = 200 ;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">%[X, t, Xsin, Ysin] = genData(n, L)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">X = rand(n,1); </span><span class = "S8">% data sampling, evenly distributed</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">X = sort(X) </span><span class = "S8">% sort ascending (for convinience)</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsMatrixElement" uid="E15E56E0" data-width="1369" data-testid="output_0" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">X = </span><span class="veVariableValueSummary" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></span></div><div class="valueContainer" data-layout="{"columnWidth":72.5,"totalColumns":1,"totalRows":20,"charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 74.5px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"> 0.0046
0.0844
0.1067
0.1361
0.1455
0.1818
0.2599
0.2638
0.3998
0.4314
</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">noise = randn(n,1); </span><span class = "S8">% sample n normal distributed variables</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">t = sin(2*pi*X)+sqrt(betaInv)*noise </span><span class = "S8">% n targets get generated, with noise added</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsMatrixElement" uid="35F13EEB" data-width="1369" data-testid="output_1" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">t = </span><span class="veVariableValueSummary" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></span></div><div class="valueContainer" data-layout="{"columnWidth":72.5,"totalColumns":1,"totalRows":20,"charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 74.5px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"> -0.3710
0.8442
0.7261
0.6648
0.7991
0.8311
0.4730
0.9105
0.3395
0.1240
</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">Xsin = linspace(0,1,XAxisPrecision); </span><span class = "S8">% used for plotting, evenly distributed X's</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">Ysin = sin(2*pi*Xsin); </span><span class = "S8">% used for plotting of underlying function without noise</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% plot sampled data and underlying function</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">plotCurveBar( Xsin, Ysin, ones(1,XAxisPrecision)*sqrt(betaInv) );</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">on</span><span class = "S9">;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot(X,t,</span><span class = "S7">'ob'</span><span class = "S9">);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">title(</span><span class = "S7">"Data sampled"</span><span class = "S9">);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">ylabel(</span><span class = "S7">"t"</span><span class = "S9">);</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">off</span><span class = "S9">;</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsFigure" uid="6C714E30" data-testid="output_2" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="figureElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><img class="figureImage" 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" style="width: 560px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% get dimension of input</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">%[n,L] = size(X);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">M = 10; </span><span class = "S8">% 10 basis functions</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% take only first column, if matrix was given as input</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">%X = X(:,1)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">xbar = mean(X,1); </span><span class = "S8">% x mean</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">tbar = mean(t,1); </span><span class = "S8">% t mean</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">XM = ones(n,M-1).*X; </span><span class = "S8">% help matrix to construct Phi</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% help matrix to construct Phi, eveny distributed mean values of gaussianbasis functions</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">mu = ones(n,M-1).*linspace(0,1,M-1); </span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">Phi = exp(-((XM-mu).^2)/(2*s^2)); </span><span class = "S8">% part of Phi matrix</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">Phi0 = ones(n,1)*tbar; </span><span class = "S8">% constant part of Phi matrix</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">Phi = [Phi0 Phi] </span><span class = "S8">% complete Phi matrix</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsMatrixElement" uid="4433116D" data-width="1369" data-testid="output_3" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">Phi = </span><span class="veVariableValueSummary" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></span></div><div class="valueContainer" data-layout="{"columnWidth":72.5,"totalColumns":10,"totalRows":20,"charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 727px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"> -0.0417 0.9989 0.4846 0.0493 0.0011 0.0000 0.0000 0.0000 0.0000 0.0000
-0.0417 0.7001 0.9210 0.2540 0.0147 0.0002 0.0000 0.0000 0.0000 0.0000
-0.0417 0.5662 0.9833 0.3579 0.0273 0.0004 0.0000 0.0000 0.0000 0.0000
-0.0417 0.3962 0.9939 0.5226 0.0576 0.0013 0.0000 0.0000 0.0000 0.0000
-0.0417 0.3468 0.9791 0.5795 0.0719 0.0019 0.0000 0.0000 0.0000 0.0000
-0.0417 0.1914 0.8508 0.7928 0.1548 0.0063 0.0001 0.0000 0.0000 0.0000
-0.0417 0.0342 0.4027 0.9951 0.5154 0.0560 0.0013 0.0000 0.0000 0.0000
-0.0417 0.0308 0.3816 0.9905 0.5389 0.0615 0.0015 0.0000 0.0000 0.0000
-0.0417 0.0003 0.0229 0.3257 0.9698 0.6052 0.0792 0.0022 0.0000 0.0000
-0.0417 0.0001 0.0091 0.1929 0.8529 0.7904 0.1535 0.0063 0.0001 0.0000
</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">figure</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">on</span><span class = "S9">;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">phi = [];</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S11">for </span><span class = "S9">index = 1:M-1</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S6">phi</span><span class = "S9"> = [phi exp(-((Xsin'-mu(1,index)).^2)/(2*s^2))];</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> plot(Xsin,phi(:,index)); </span><span class = "S8">% plot basis functions for control</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S12">end</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">titlestr = sprintf(</span><span class = "S7">'Gaussian basis functions, without weights, s = %.1f'</span><span class = "S9">,s);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">title(titlestr);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">phi = [tbar*ones(XAxisPrecision,1) phi];</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">off</span><span class = 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" style="width: 560px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">SNInv = alpha * diag(M)+ beta*(Phi'*Phi); </span><span class = "S8">%S_N^-1</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">SN = inv(SNInv) </span><span class = "S8">%S_N </span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsMatrixElement" uid="8140CCA8" data-width="1369" data-testid="output_5" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">SN = </span><span class="veVariableValueSummary" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></span></div><div class="valueContainer" data-layout="{"columnWidth":72.5,"totalColumns":10,"totalRows":10,"charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 727px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"> 0.2197 -0.0586 0.0291 0.0149 -0.0920 0.1152 -0.0944 -0.1097 0.1731 -0.1964
-0.0586 0.2032 -0.2056 0.2038 -0.2347 0.2426 -0.2092 0.0800 -0.0400 0.0185
0.0291 -0.2056 0.2888 -0.3518 0.4377 -0.4660 0.4003 -0.1680 0.0905 -0.0549
0.0149 0.2038 -0.3518 0.5577 -0.7872 0.8650 -0.7453 0.3075 -0.1583 0.0938
-0.0920 -0.2347 0.4377 -0.7872 1.3238 -1.5185 1.3191 -0.5583 0.2935 -0.1833
0.1152 0.2426 -0.4660 0.8650 -1.5185 1.8400 -1.6376 0.6996 -0.3699 0.2297
-0.0944 -0.2092 0.4003 -0.7453 1.3191 -1.6376 1.5624 -0.7404 0.4326 -0.2875
-0.1097 0.0800 -0.1680 0.3075 -0.5583 0.6996 -0.7404 0.6104 -0.4973 0.3762
0.1731 -0.0400 0.0905 -0.1583 0.2935 -0.3699 0.4326 -0.4973 0.5071 -0.4312
-0.1964 0.0185 -0.0549 0.0938 -0.1833 0.2297 -0.2875 0.3762 -0.4312 0.4353
</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">mN = beta * </span><span class = "S6">SN</span><span class = "S6"> * Phi' *t </span><span class = "S8">%m_N, Bayesian method for weight calclation </span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsMatrixElement" uid="EC9F9B00" data-width="1369" data-testid="output_6" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">mN = </span><span class="veVariableValueSummary" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></span></div><div class="valueContainer" data-layout="{"columnWidth":72.5,"totalColumns":1,"totalRows":10,"charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 74.5px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"> 0.5185
-0.7840
1.1872
-0.0562
0.5508
-0.2112
-0.8347
0.0159
-0.9285
0.5422
</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">%mN = beta * ( SNInv \ Phi' *t ) %m_N same result, just debugging</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">mNphi = (mN'.*ones(XAxisPrecision,M)).*phi; </span><span class = "S8">% weights * basis functions</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">estfctmNphi = []; </span><span class = "S8">% sum of all basis functions</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S11">for </span><span class = "S9">index = 1:XAxisPrecision</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S6">estfctmNphi</span><span class = "S6"> = [estfctmNphi sum(mNphi(index,:))]; </span><span class = "S8">% sum each basis function at given x</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S12">end</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">figure</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot (Xsin, mNphi) </span><span class = "S8">% plot each weighted basis function</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">title(</span><span class = "S7">"Weighted basis functions"</span><span class = "S9">)</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsFigure" uid="47C36D0D" data-testid="output_7" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="figureElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><img class="figureImage" 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" style="width: 560px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">sigmaSquared = []; </span><span class = "S8">% calculate sigma squared</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S11">for </span><span class = "S9">index = 1:n</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S8">%sigmaSquaredAtData = [sigmaSquaredAtData 1/beta + Phi(index,:)*SN*Phi(index,:)'];</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S6">sigmaSquared</span><span class = "S6"> = [sigmaSquared 1/beta + phi(index,:)*</span><span class = "S6">SN</span><span class = "S9">*phi(index,:)'];</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S12">end</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">sigmaSquared </span><span class = "S8">% control and debugging</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsMatrixElement" uid="5264ED78" data-width="1369" data-testid="output_8" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">sigmaSquared = </span><span class="veVariableValueSummary" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></span></div><div class="valueContainer" data-layout="{"columnWidth":72.5,"totalColumns":20,"totalRows":1,"charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 1307px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"> 0.1743 0.1697 0.1648 0.1598 0.1548 0.1498 0.1449 0.1403 0.1361 0.1322 0.1287 0.1256 0.1230 0.1209 0.1191 0.1178 0.1167 0.1159 0.1153 0.1149
</div><div class="horizontalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">sigmaSquaredi = pchip(X',sigmaSquared,Xsin); </span><span class = "S8">% make smoother for plotting</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">figure</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% plot sampled data and underlying function</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plotCurveBar( Xsin, estfctmNphi, sqrt(sigmaSquaredi) ); </span><span class = "S8">% make plot like Bishop</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">on</span><span class = "S9">;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">titlestr = sprintf(</span><span class = "S7">'n = %d, s = %.1f'</span><span class = "S9">,n,s);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot( Xsin, Ysin, </span><span class = "S7">'g-'</span><span class = "S6">, </span><span class = "S7">'linewidth'</span><span class = "S6">,2); </span><span class = "S8">% plot sampling function without noise</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot( X, t, </span><span class = "S7">'bo'</span><span class = "S6">, </span><span class = "S7">'linewidth'</span><span class = "S6">,2); </span><span class = "S8">% plot the data</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">xlim([0 1]) </span><span class = "S8">% set limits for axis</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">ylim([-2 2])</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">xlabel(</span><span class = "S7">"x"</span><span class = "S9">)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">ylabel(</span><span class = "S7">"t"</span><span class = "S9">)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">title(titlestr)</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">off</span><span class = "S9">;</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsFigure" uid="1E64182F" data-testid="output_9" 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" style="width: 560px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">figure </span><span class = "S8">% same plot again, but without limits on axis</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% plot sampled data and underlying function</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">plotCurveBar( Xsin, estfctmNphi, sqrt(sigmaSquaredi) );</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">on</span><span class = "S9">;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot( Xsin, Ysin, </span><span class = "S7">'g-'</span><span class = "S6">, </span><span class = "S7">'linewidth'</span><span class = "S9">,2);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot( X, t, </span><span class = "S7">'bo'</span><span class = "S6">, </span><span class = "S7">'linewidth'</span><span class = "S9">,2);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">xlim([0 1])</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">xlabel(</span><span class = "S7">"x"</span><span class = "S9">)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">ylabel(</span><span class = "S7">"t"</span><span class = "S9">)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">titlestr = sprintf(</span><span class = "S7">'Unzoomed, n = %d, s = %.1f'</span><span class = "S9">,n,s);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">title(titlestr)</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">off</span><span class = "S9">;</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsFigure" uid="6F9E12CC" data-testid="output_10" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="figureElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><img class="figureImage" 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" style="width: 560px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div></div></div><div class = "S0"></div><div class = 'SectionBlock containment'><div class = 'LineNodeBlock contiguous'><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">%% other method to plot</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">cholSN = chol(SN); </span><span class = "S8">% used to calculate sigma of posterior distributions</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">mNphiLower = ((mN'-(ones(1,M)*cholSN)).*ones(XAxisPrecision,M)).*phi; </span><span class = "S8">% m_N * phi - sigma</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">estfctmNphiLower = []; </span><span class = "S8">% sum of all basis functions ; % m_N * phi - sigma</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S11">for </span><span class = "S9">index = 1:XAxisPrecision</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S8">% sum each basis function at given x ; % m_N * phi - sigma</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S6">estfctmNphiLower</span><span class = "S9"> = [estfctmNphiLower sum(mNphiLower(index,:))]; </span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S12">end</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">estfctmNphiLower;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S8">% weights * basis functions; % m_N * phi + sigma</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">mNphiUpper = ((mN'+(ones(1,M)*cholSN)).*ones(XAxisPrecision,M)).*phi;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">estfctmNphiUpper = []; </span><span class = "S8">% sum of all basis functions % m_N * phi + sigma</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S11">for </span><span class = "S9">index = 1:XAxisPrecision</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S8">% sum each basis function at given x % m_N * phi + sigma</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S6">estfctmNphiUpper</span><span class = "S9"> = [estfctmNphiUpper sum(mNphiUpper(index,:))];</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S12">end</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">estfctmNphiUpper;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">figure</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% plot sampled data and underlying function</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">color = [255,228,225]/255; </span><span class = "S8">%pink</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">fill([Xsin,fliplr(Xsin)],[estfctmNphiUpper,fliplr(estfctmNphiLower)],color,</span><span class = "S7">'LineStyle'</span><span class = "S6">,</span><span class = "S7">'none'</span><span class = "S9">);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">on</span><span class = "S9">;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot( Xsin, estfctmNphi, </span><span class = "S7">'r-'</span><span class = "S6">, </span><span class = "S7">'linewidth'</span><span class = "S9">,2);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot( Xsin, Ysin, </span><span class = "S7">'g-'</span><span class = "S6">, </span><span class = "S7">'linewidth'</span><span class = "S9">,2);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot( X, t, </span><span class = "S7">'bo'</span><span class = "S6">, </span><span class = "S7">'linewidth'</span><span class = "S9">,2);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">xlabel(</span><span class = "S7">"x"</span><span class = "S9">)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">ylabel(</span><span class = "S7">"t"</span><span class = "S9">)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">titlestr = sprintf(</span><span class = "S7">'Other method, posterior bases, n = %d, s = %.1f'</span><span class = "S9">,n,s);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">title(titlestr)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">xlim([0 1])</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">ylim([-4 4])</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">off</span><span class = "S9">;</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsFigure" uid="1D06D303" data-testid="output_11" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="figureElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><img class="figureImage" 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" style="width: 560px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div></div></div><div class = "S0"></div><div class = 'SectionBlock containment'><h2 class = "S1"><span class = "S2">Part 2</span></h2><div class = 'LineNodeBlock contiguous'><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">w = randn(n,1); </span><span class = "S8">% sample n normal distributed variables</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">mN' </span><span class = "S8">% for comparison, show average mN</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsMatrixElement" uid="408ADDE0" data-width="1369" data-testid="output_12" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">ans = </span><span class="veVariableValueSummary" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></span></div><div class="valueContainer" data-layout="{"columnWidth":72.5,"totalColumns":10,"totalRows":1,"charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 727px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"> 0.5185 -0.7840 1.1872 -0.0562 0.5508 -0.2112 -0.8347 0.0159 -0.9285 0.5422
</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% repmatrepmat(A,n, m) returns an array containing n x m copies of A</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% in the row and column dimensions</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">mN_array = repmat(mN',5,1) + randn(5,M)*cholSN </span><span class = "S8">% generate n-dimensional matrix</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsMatrixElement" uid="446C1CDC" data-width="1369" data-testid="output_13" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="matrixElement veSpecifier" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="veVariableName variableNameElement" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;">mN_array = </span><span class="veVariableValueSummary" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></span></div><div class="valueContainer" data-layout="{"columnWidth":72.5,"totalColumns":10,"totalRows":5,"charsPerColumn":10}" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"><div class="variableValue" style="width: 727px; white-space: pre; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"> 0.4574 -0.5706 0.6626 0.6380 0.3037 -0.4576 -0.3659 0.0925 -1.0450 0.3415
0.6046 -1.1745 1.9344 -1.1626 2.1912 -1.7838 0.1295 -0.5835 -0.5867 0.4688
0.2953 -0.8695 1.4929 -0.5470 1.1727 -0.7823 -0.4436 -0.0917 -0.6307 0.3919
0.9225 -0.6522 0.9694 0.2846 0.2137 0.1312 -1.1638 -0.4116 -0.4130 0.0724
-0.1198 -0.1637 0.5742 0.7404 -0.3642 0.8857 -2.0180 0.6556 -1.7350 1.5269
</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(64, 64, 64); font-size: 12px;"></div></div></div></div></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% like learned in the lecture, first 'randn'</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% creates a normal distribution with 5 x M values</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% Afterwards scale this distribution to the correct covariance with cholSN</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% Finally move it to the correct mean value by adding the mN vector</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">estfctmNphiArray = []; </span><span class = "S8">% sum of all basis functions</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S11">for </span><span class = "S9">mN_i = 1:5</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S8">% weights * basis functions</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"> mNphi = (mN_array(mN_i,:).*ones(XAxisPrecision,M)).*phi;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> estfctmNphi = []; </span><span class = "S8">% sum of all basis functions</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S11">for </span><span class = "S9">index = 1:XAxisPrecision</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S8">% sum each basis function at given x</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S6">estfctmNphi</span><span class = "S9"> = [estfctmNphi sum(mNphi(index,:))];</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S12">end</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> estfctmNphi</span><span class = "S6">'</span><span class = "S9">;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6"> </span><span class = "S6">estfctmNphiArray</span><span class = "S9"> = [estfctmNphiArray estfctmNphi'];</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S12">end</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">estfctmNphiArray = estfctmNphiArray';</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9"></span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">figure </span><span class = "S8">% same plot again, but without limits on axis</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">% plot sampled data and underlying function</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S8">%plotCurveBar( Xsin, estfctmNphi, sqrt(sigmaSquaredi) );</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">plot( Xsin, estfctmNphi );</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">on</span><span class = "S9">;</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot( Xsin, estfctmNphiArray, </span><span class = "S7">'r-'</span><span class = "S6">, </span><span class = "S7">'linewidth'</span><span class = "S9">,1);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot( Xsin, Ysin, </span><span class = "S7">'g-'</span><span class = "S6">, </span><span class = "S7">'linewidth'</span><span class = "S9">,2);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">plot( X, t, </span><span class = "S7">'bo'</span><span class = "S6">, </span><span class = "S7">'linewidth'</span><span class = "S9">,2);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">xlim([0 1])</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">xlabel(</span><span class = "S7">"x"</span><span class = "S9">)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">ylabel(</span><span class = "S7">"t"</span><span class = "S9">)</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">ylim([-3 3])</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S6">titlestr = sprintf(</span><span class = "S7">Part 2, n = %d, s = %.1f'</span><span class = "S9">,n,s);</span></div></div><div class = 'inlineWrapper'><div class = "S5 lineNode"><span class = "S9">title(titlestr)</span></div></div><div class = 'inlineWrapper outputs'><div class = "S5 lineNode"><span class = "S6">hold </span><span class = "S7">off</span><span class = "S9">;</span></div><div class="outputParagraph" style="white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="inlineElement embeddedOutputsFigure" uid="E9BD66B5" data-testid="output_14" style="width: 1399px; white-space: normal; font-style: normal; color: rgb(64, 64, 64); font-size: 14px;"><div class="figureElement" style="white-space: normal; font-style: normal; color: 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" 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<!--
##### SOURCE BEGIN #####
%% Benjamin Linnik
%% Part 1
%% Recreate Bayesian Linear Regression plots from Christopher M. Bishop - Pattern Recognition and Machine Learning p. 157-158
clear; close all; % clear everything
%rng(5); % start seed, make deterministic
n = 20; % number of data points
s = 0.1; % used for gaussian basis function width
alpha = 100; % initial spread
betaInv = (0.3)^2; % beta^-1, used as sigma for noise estimation
beta = 1/betaInv;
XAxisPrecision = 200 ;
%[X, t, Xsin, Ysin] = genData(n, L)
X = rand(n,1); % data sampling, evenly distributed
X = sort(X) % sort ascending (for convinience)
noise = randn(n,1); % sample n normal distributed variables
t = sin(2*pi*X)+sqrt(betaInv)*noise % n targets get generated, with noise added
Xsin = linspace(0,1,XAxisPrecision); % used for plotting, evenly distributed X's
Ysin = sin(2*pi*Xsin); % used for plotting of underlying function without noise
% plot sampled data and underlying function
plotCurveBar( Xsin, Ysin, ones(1,XAxisPrecision)*sqrt(betaInv) );
hold on;
plot(X,t,'ob');
title("Data sampled");
ylabel("t");
hold off;
% get dimension of input
%[n,L] = size(X);
M = 10; % 10 basis functions
% take only first column, if matrix was given as input
%X = X(:,1)
xbar = mean(X,1); % x mean
tbar = mean(t,1); % t mean
XM = ones(n,M-1).*X; % help matrix to construct Phi
% help matrix to construct Phi, eveny distributed mean values of gaussianbasis functions
mu = ones(n,M-1).*linspace(0,1,M-1);
Phi = exp(-((XM-mu).^2)/(2*s^2)); % part of Phi matrix
Phi0 = ones(n,1)*tbar; % constant part of Phi matrix
Phi = [Phi0 Phi] % complete Phi matrix
figure
hold on;
phi = [];
for index = 1:M-1
phi = [phi exp(-((Xsin'-mu(1,index)).^2)/(2*s^2))];
plot(Xsin,phi(:,index)); % plot basis functions for control
end
titlestr = sprintf('Gaussian basis functions, without weights, s = %.1f',s);
title(titlestr);
phi = [tbar*ones(XAxisPrecision,1) phi];
hold off;
SNInv = alpha * diag(M)+ beta*(Phi'*Phi); %S_N^-1
SN = inv(SNInv) %S_N
mN = beta * SN * Phi' *t %m_N, Bayesian method for weight calclation
%mN = beta * ( SNInv \ Phi' *t ) %m_N same result, just debugging
mNphi = (mN'.*ones(XAxisPrecision,M)).*phi; % weights * basis functions
estfctmNphi = []; % sum of all basis functions
for index = 1:XAxisPrecision
estfctmNphi = [estfctmNphi sum(mNphi(index,:))]; % sum each basis function at given x
end
figure
plot (Xsin, mNphi) % plot each weighted basis function
title("Weighted basis functions")
sigmaSquared = []; % calculate sigma squared
for index = 1:n
%sigmaSquaredAtData = [sigmaSquaredAtData 1/beta + Phi(index,:)*SN*Phi(index,:)'];
sigmaSquared = [sigmaSquared 1/beta + phi(index,:)*SN*phi(index,:)'];
end
sigmaSquared % control and debugging
sigmaSquaredi = pchip(X',sigmaSquared,Xsin); % make smoother for plotting
figure
% plot sampled data and underlying function
plotCurveBar( Xsin, estfctmNphi, sqrt(sigmaSquaredi) ); % make plot like Bishop
hold on;
titlestr = sprintf('n = %d, s = %.1f',n,s);
plot( Xsin, Ysin, 'g-', 'linewidth',2); % plot sampling function without noise
plot( X, t, 'bo', 'linewidth',2); % plot the data
xlim([0 1]) % set limits for axis
ylim([-2 2])
xlabel("x")
ylabel("t")
title(titlestr)
hold off;
figure % same plot again, but without limits on axis
% plot sampled data and underlying function
plotCurveBar( Xsin, estfctmNphi, sqrt(sigmaSquaredi) );
hold on;
plot( Xsin, Ysin, 'g-', 'linewidth',2);
plot( X, t, 'bo', 'linewidth',2);
xlim([0 1])
xlabel("x")
ylabel("t")
titlestr = sprintf('Unzoomed, n = %d, s = %.1f',n,s);
title(titlestr)
hold off;
%%
%% other method to plot
cholSN = chol(SN); % used to calculate sigma of posterior distributions
mNphiLower = ((mN'-(ones(1,M)*cholSN)).*ones(XAxisPrecision,M)).*phi; % m_N * phi - sigma
estfctmNphiLower = []; % sum of all basis functions ; % m_N * phi - sigma
for index = 1:XAxisPrecision
% sum each basis function at given x ; % m_N * phi - sigma
estfctmNphiLower = [estfctmNphiLower sum(mNphiLower(index,:))];
end
estfctmNphiLower;
% weights * basis functions; % m_N * phi + sigma
mNphiUpper = ((mN'+(ones(1,M)*cholSN)).*ones(XAxisPrecision,M)).*phi;
estfctmNphiUpper = []; % sum of all basis functions % m_N * phi + sigma
for index = 1:XAxisPrecision
% sum each basis function at given x % m_N * phi + sigma
estfctmNphiUpper = [estfctmNphiUpper sum(mNphiUpper(index,:))];
end
estfctmNphiUpper;
figure
% plot sampled data and underlying function
color = [255,228,225]/255; %pink
fill([Xsin,fliplr(Xsin)],[estfctmNphiUpper,fliplr(estfctmNphiLower)],color,'LineStyle','none');
hold on;
plot( Xsin, estfctmNphi, 'r-', 'linewidth',2);
plot( Xsin, Ysin, 'g-', 'linewidth',2);
plot( X, t, 'bo', 'linewidth',2);
xlabel("x")
ylabel("t")
titlestr = sprintf('Other method, posterior bases, n = %d, s = %.1f',n,s);
title(titlestr)
xlim([0 1])
ylim([-4 4])
hold off;
%% Part 2
%%
w = randn(n,1); % sample n normal distributed variables
mN' % for comparison, show average mN
% repmatrepmat(A,n, m) returns an array containing n x m copies of A
% in the row and column dimensions
mN_array = repmat(mN',5,1) + randn(5,M)*cholSN % generate n-dimensional matrix
% like learned in the lecture, first 'randn'
% creates a normal distribution with 5 x M values
% Afterwards scale this distribution to the correct covariance with cholSN
% Finally move it to the correct mean value by adding the mN vector
estfctmNphiArray = []; % sum of all basis functions
for mN_i = 1:5
% weights * basis functions
mNphi = (mN_array(mN_i,:).*ones(XAxisPrecision,M)).*phi;
estfctmNphi = []; % sum of all basis functions
for index = 1:XAxisPrecision
% sum each basis function at given x
estfctmNphi = [estfctmNphi sum(mNphi(index,:))];
end
estfctmNphi';
estfctmNphiArray = [estfctmNphiArray estfctmNphi'];
end
estfctmNphiArray = estfctmNphiArray';
figure % same plot again, but without limits on axis
% plot sampled data and underlying function
%plotCurveBar( Xsin, estfctmNphi, sqrt(sigmaSquaredi) );
plot( Xsin, estfctmNphi );
hold on;
plot( Xsin, estfctmNphiArray, 'r-', 'linewidth',1);
plot( Xsin, Ysin, 'g-', 'linewidth',2);
plot( X, t, 'bo', 'linewidth',2);
xlim([0 1])
xlabel("x")
ylabel("t")
ylim([-3 3])
titlestr = sprintf('Part 2, n = %d, s = %.1f',n,s);
title(titlestr)
hold off;
%%
%
##### SOURCE END #####
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