Beautifying task1.m

ENUME/projectC/functionDataPoints.m

@@ -0,0 +1,16 @@
+function dataPoints = functionDataPoints()
+    dataPoints = (-5:5)';
+    dataPoints(:, 2) = [
+        -6.5743
+        0.9765
+        3.1026
+        1.8572
+        1.3165
+        -0.6144
+        0.1032
+        0.3729
+        2.5327
+        7.3857
+        9.4892
+    ];
+end

ENUME/projectC/task1.m

@@ -0,0 +1,84 @@
+function task1(polynomialDegree)
+    for currentPolynomialDegree = 0 : polynomialDegree
+        dataPoints = functionDataPoints();
+        % obtain LSPSolutions of approximating polynomial
+        [LSPSolutions, error, conditionNumber] = approximate(dataPoints, currentPolynomialDegree);
+        displayInfo(currentPolynomialDegree, error, conditionNumber);
+        plotGraph(currentPolynomialDegree, dataPoints, LSPSolutions)
+    
+    end
+end
+
+function displayInfo(currentPolynomialDegree, error, conditionNumber)
+    disp("Current Polynomial Degree:");
+    disp(currentPolynomialDegree);
+    disp("Error: ");
+    disp(error);
+    disp("Condition number of Gram's Matrix: ");
+    disp(conditionNumber);
+end
+
+function plotGraph(currentPolynomialDegree, dataPoints, LSPSolutions)
+    figure;
+    grid on;
+    hold on;
+    plotDataPoints(currentPolynomialDegree, dataPoints);
+    plotApproximation(dataPoints, LSPSolutions);
+    hold off;
+end
+
+function plotDataPoints(currentPolynomialDegree, dataPoints)
+    title(['Polynomial approximation of the funtion using: ', num2str(currentPolynomialDegree), ' degree']);
+    scatter(dataPoints(:, 1), dataPoints(:, 2), 72, 'x');
+end
+
+function plotApproximation(dataPoints, LSPSolutions)
+    x = dataPoints(1):0.05:dataPoints(end, 1);
+    y = valueApproximationAtx(LSPSolutions, x);
+    plot(x, y);
+end
+
+
+% find the approximating polynomial of the given degree
+function [LSPSolutions, error, conditionNumber] = approximate(dataPoints, polydeg)
+    % initialize A matrix
+    A = zeros(size(dataPoints, 1), polydeg + 1);
+    
+    A = calculateACells(dataPoints, A);
+    LSPSolutions = solveLSP(A, dataPoints);
+
+    % Calculate error of solution
+    error = norm(dataPoints(:, 2) - A * LSPSolutions);
+    % Calculate condition number of Gram's matrix
+    GramsMatrix = A' * A;
+    conditionNumber = cond(GramsMatrix);
+end
+
+function Matrix = calculateACells(dataPoints, Matrix)
+    for row = 1 : size(Matrix, 1)
+        for column = 1 : size(Matrix, 2)            
+            Matrix(row, column) = dataPoints(row, 1) ^ (column - 1);
+        end
+    end
+end
+
+function solutions = solveLSP(Matrix, dataPoints)
+    [Q, eqsys, invqtq] = QRDecomposition(Matrix);
+    eqsys(:, end + 1) = invqtq * Q' * dataPoints(:, 2);
+    solutions = backSubstitution(eqsys);
+end
+
+% evaluate the value of an approximation at the given x
+function arrayOfValues = valueApproximationAtx(LSPSolutions, arrayOfArguments)
+    arrayOfValues = zeros(1, size(arrayOfArguments, 2));
+    arrayOfValues = calculateArrayofValues(arrayOfArguments, arrayOfValues, LSPSolutions);
+
+end
+
+function arrayOfValues = calculateArrayofValues(arrayOfArguments, arrayOfValues, LSPSolutions)
+    for x = 1 : size(arrayOfArguments, 2)
+        for i = 1 : size(LSPSolutions, 1)
+            arrayOfValues(x) = arrayOfValues(x) + LSPSolutions(i) * arrayOfArguments(x) ^ (i - 1);
+        end
+    end
+end

ENUME/projectC/task1lsapprox.m

@@ -1,68 +1,56 @@
-% define function data points
+function task1()
-taskfunc = (-5:5)';
+    for polydeg = 0:3
-taskfunc(:, 2) = [
+        dataPoints = functionDataPoints();
-    -6.5743
+        % obtain factors of approximating polynomial
-    0.9765
+        [factors, error, gramcond] = approximate(dataPoints, polydeg);
-    3.1026
+        
-    1.8572
+        % print error and condition number
-    1.3165
+        disp(['Approximation degree ', num2str(polydeg), ':']);
-    -0.6144
+        disp(['Error: ', num2str(error)]);
-    0.1032
+        disp(['Condition number: ', num2str(gramcond)]);
-    0.3729
+        
-    2.5327
+        % plot data points
-    7.3857
+        figure;
-    9.4892
+        grid on;
-];
+        hold on;
-
+        title(['Polynomial approximation of the function (degree ', ...
-% perform the task
+            num2str(polydeg), ')']);
-for polydeg = 0:3
+        scatter(dataPoints(:, 1), dataPoints(:, 2));
-    % obtain factors of approximating polynomial
+        
-    [factors, error, gramcond] = approximate(taskfunc, polydeg);
+        % plot approximation
-    
+        x = dataPoints(1):0.05:dataPoints(end, 1);
-    % print error and condition number
+        y = evalapprox(factors, x);
-    disp(['Approximation degree ', num2str(polydeg), ':']);
+        plot(x, y);
-    disp(['Error: ', num2str(error)]);
+        
-    disp(['Condition number: ', num2str(gramcond)]);
+        % finish and print graph
-    
+        hold off;
-    % plot data points
+        set(gcf, 'PaperPosition', [0 0 6 4]);
-    figure;
+        set(gcf, 'PaperSize', [6 4]);
-    grid on;
+        %print(['report/approx', num2str(polydeg)], '-dpdf');
-    hold on;
+    end
-    title(['Polynomial approximation of the function (degree ', ...
-        num2str(polydeg), ')']);
-    scatter(taskfunc(:, 1), taskfunc(:, 2));
-    
-    % plot approximation
-    x = taskfunc(1):0.05:taskfunc(end, 1);
-    y = evalapprox(factors, x);
-    plot(x, y);
-    
-    % finish and print graph
-    hold off;
-    set(gcf, 'PaperPosition', [0 0 6 4]);
-    set(gcf, 'PaperSize', [6 4]);
-    %print(['report/approx', num2str(polydeg)], '-dpdf');
 end
 
+
+
 % find the approximating polynomial of the given degree
-function [factors, error, gramcond] = approximate(func, polydeg)
+function [factors, error, gramcond] = approximate(dataPoints, polydeg)
     % define the A matrix used for solving the error minimization problem
-    A = zeros(size(func, 1), polydeg + 1);
+    A = zeros(size(dataPoints, 1), polydeg + 1);
     
     % calculate cells of A using natural basis
     for row = 1:size(A, 1)
         for col = 1:size(A, 2)            
-            A(row, col) = func(row, 1) ^ (col - 1);
+            A(row, col) = dataPoints(row, 1) ^ (col - 1);
         end
     end
     
     % solve least-square problem using QRdash decomposition
     [Q, eqsys, invqtq] = QRDecomposition(A);
-    eqsys(:, end + 1) = invqtq * Q' * func(:, 2);
+    eqsys(:, end + 1) = invqtq * Q' * dataPoints(:, 2);
     factors = backSubstitution(eqsys);
     
     % calculate error and condition number of Gram's matrix
-    error = norm(func(:, 2) - A * factors);
+    error = norm(dataPoints(:, 2) - A * factors);
     gramcond = cond(A' * A);
 end