1. Weizmann 2010 © 1 Introduction to Matlab & Data Analysis Lecture 13: Using Matlab for Numerical Analysis

2. 2 Outline Data Smoothing Data interpolation Correlation coefficients Curve Fitting Optimization Derivatives and integrals

3. 3 Assume we measured the response in time or other input factor, for example: Reaction product as function of substrate Cell growth as function of time Filtering and Smoothing factor response Our measuring device has some random noise One way to subtract the noise from the results is to smooth each data point using its close environment

4. 4 Smoothing – Moving Average span Remark: The Span should be odd span

5. 5 Smoothing – Behavior at the Edges

6. 6 The Smooth Function x = linspace(0, 4 * pi, len_of_vecs); y = sin(x) + (rand(1,len_of_vecs)-0.5)*error_rat; Data: y Generating Function: sin(x) Smoothed data: smooth(x,y)

7. 7 The Smooth Quality Is Affected By The Smooth Function And The Span Like very low pass filter Different method y_smooth = smooth(x,y,11,'rlowess');

8. 8 Data Interpolation Definition “plot” – Performs linear interpolation between the data points Interpolation - A way of estimating values of a function between those given by some set of data points. Interpolation Data points

9. 9 Interpolating Data Using interp1 Function x_full = linspace(0, 2.56 * pi, 32); y_full = sin(x_full); x_missing = x_full; x_missing([14:15,20:23]) = NaN; y_missing = sin(x_missing); x_i = linspace(0, 2.56 * pi, 64); not_nan_i = ~isnan(x_missing); y_i = … interp1(x_missing(not_nan_i),… y_missing(not_nan_i),… x_i); Data points which we want to interpolateDefault: Linear interpolation

10. 10 interp1 Function Can Use Different Interpolation Methods y_i=interp1(x_missing,y_missing,x_i,'cubic');

11. 11 2D Functions Interpolation Also 2D functions can be interpolated Assume we have some data points of a 2D function xx = -2:.5:2; yy = -2:.5:3; [X,Y] = meshgrid(xx,yy); Z = X.*exp(-X.^2-Y.^2); figure; surf(X,Y,Z); hold on; plot3(X,Y,Z+0.01,'ok', 'MarkerFaceColor','r') Surf uses linear interpolation

12. 12 2D Functions Interpolation interp2 function xx_i = -2:.1:2; yy_i = -2:.1:3; [X_i,Y_i] = meshgrid(xx_i,yy_i); Z_i = interp2(xx,yy,Z,X_i,Y_i,'cubic'); figure; surf(X_i,Y_i,Z_i); hold on; plot3(X,Y,Z+0.01,'ok', 'MarkerFaceColor','r') Data pointsPoints to interpolate

13. Weizmann 2010 © 13 Optimization and Curve Fitting

14. 14 Curve Fitting – Assumptions About The Residuals Residual = Response – fitted response: Sum square of residuals Two assumptions: The error exists only in the response data, and not in the predictor data. The errors are random and follow a normal (Gaussian) distribution with zero mean and constant variance. This is what we want to minimize X Y Residual

15. 15 corrcoef Computes the Correlation coefficients Consider the following data: x = sort(repmat(linspace(0,10,11),1,20)); y_p = 10 + 3*x + x.^2 + (rand(size(x))-0.5).*x*10; In many cases we start with computing the correlation between the variables: cor_mat = corrcoef(x, y_p); cor = cor_mat(1,2); figure; plot(x,y_p,'b.'); xlabel('x');ylabel('y'); title(['Correlation: ' num2str(cor)]);

16. 16 Curve fitting Using a GUI Tool (Curve Fitting Tool Box) cftool – A graphical tool for curve fitting Example: Fitting x_full = linspace(0, 2.56 * pi, 32); y_full = sin(x_full); With cubic polynomial

17. 17 polyfit Fits a Curve By a Polynomial of the Variable Find a polynomial fit: poly_y_fit1 = polyfit(x,y_p,1); poly_y_fit1 = 12.6156 -3.3890 y_fit1 = polyval(poly_y_fit1,x); y_fit1 = 12.6156*x-3.3890 poly_y_fit2 = polyfit(x,y_p,2); y_fit2 = polyval(poly_y_fit2,x); poly_y_fit3 = polyfit(x,y_p,3); y_fit3 = polyval(poly_y_fit3,x); X + ( ) We can estimate the fit quality by: mean((y_fit1-y_p).^2)

18. 18 We Can Use polyfit to Fit Exponential Data Using Log Transformation x = sort(repmat(linspace(0,1,11),1,20)); y_exp = exp(5 + 2*x + (rand(size(x))-0.5).*x); poly_exp_y_fit1 = polyfit(x,log(y_exp),1); y_exp_fit1 = exp(polyval(poly_exp_y_fit1,x)) Polyfit on the log of the data: poly_exp_y_fit1 = 1.9562 5.0152

19. Weizmann 2010 © 19 What about fitting a Curve with a linear function of several variables? Can we put constraints on the coefficients values?

20. Weizmann 2010 © 20 For this type of problems (and much more) lets learn the optimization toolbox http://www.mathworks.com/products/optimization/description1.html

21. 21 Optimization Toolbox Can Solve Many Types of Optimization Problems Optimization Toolbox – Extends the capability of the MATLAB numeric computing environment. The toolbox includes routines for many types of optimization including: Unconstrained nonlinear minimization Constrained nonlinear minimization, including goal attainment problems and minimax problems Semi-infinite minimization problems Quadratic and linear programming Nonlinear least-squares and curve fitting Nonlinear system of equation solving Constrained linear least squares Sparse structured large-scale problems ==

22. 22 Optimization Toolbox GUI Can Generate M-files The GUI contains many options. Everything can be done using coding.

23. Weizmann 2010 © 23 Lets learn some of the things the optimization tool box can do

24. 24 Solving Constrained Square Linear Problem lsqlin (Least Square under Linear constraints) Starting point [] – if no constraint

25. 25 Simple Use Of Least Squares Under No Constrains Assume a response that is a linear combination of two variables vars = [ 1 1 -1 1.5 … ] response = [ 0.2 0.4 … ] coeff_lin = lsqlin(vars,response,[],[]); We can also put constraints on the value of the coefficients: coeff_lin = lsqlin(vars,response,[],[],[],[],[-1 -1],[1 1]);

26. 26 coeff_lin = -0.2361 -0.8379 xx = -2:.1:2; yy = -2:.1:2; [X,Y] = meshgrid(xx,yy); Z = coeff_lin(1)*X+ coeff_lin(2)*Y; figure; mesh(X,Y,Z,'FaceAlpha',0.75);colorbar; hold on; plot3(vars(:,1),vars(:,2),response, 'ok', 'MarkerFaceColor','r') Simple Use Of Least Sum of Squares Under No Constraints

27. Weizmann 2010 © 27 What about fitting a Curve with a non linear function?

28. 28 We Can Fit Any Function Using Non-Linear Fitting You Can fit any non linear function using: nlinfit (Non linear fit) lsqnonlin (least squares non-linear fit) lsqcurvefit (least squares curve fit) Example: Hougen-Watson model Write an M-file: function yhat = hougen(beta,x) Run: betafit = nlinfit(reactants,rate,@hougen,beta) 470 300 10 285 80 10 ([x 1 x 2 x 3 ])… 8.55 3.79 (y)… @func: Function handle – A way to pass a function as an argument! 1.00 0.05 (coefficients)… Starting point

29. 29 Optimization Toolbox – Example Fitting a Curve With a Non Linear Function Example for using lsqcurvefit, We will fit the data : Assume we have the following data: xdata = [0.9 1.5 13.8 19.8 24.1 28.2 35.2 60.3 74.6 81.3]; ydata = [455.2 428.6 124.1 67.3 43.2 28.1 13.1 -0.4 -1.3 -1.5]; We want to fit the data with our model: Steps: Write a function which implements the above model: function y_hat = lsqcurvefitExampleFunction(c,xdata) Solve: c0 = [100; -1] % Starting guess [c,resnorm] = lsqcurvefit(@lsqcurvefitExampleFunction,c0,xdata,ydata)

30. Weizmann 2010 © 30 What about solving non linear system of equations?

31. 31 Solving Non Linear System of Equations Using fsolve Assume we want to solve: We can express it as: Solving it: Write the function M-file: function f = fSolveExampleFunc(x) f = [2*x(1) - x(2) - exp(-x(1)); -x(1) + 2*x(2) - exp(-x(2))]; Choose initial guess: x0 = [-5; -5]; Run matlab optimizer: options=optimset('Display','iter'); % Option to display output [x,fval] = fsolve(@fSolveExampleFunc,x0,options ) x = [ 0.5671 0.5671]

32. Weizmann 2010 © 32 Optimization tool box has several features: Summary: Minimization Curve fitting Equations solving

33. Weizmann 2010 © 33 A taste of Symbolic matlab: Derivatives and integrals

34. 34 What Is Symbolic Matlab? “Symbolic Math Toolbox uses symbolic objects to represent symbolic variables, expressions, and matrices.” “Internally, a symbolic object is a data structure that stores a string representation of the symbol.”

35. 35 Defining Symbolic Variables and Functions Define symbolic variables: a_sym = sym('a') b_sym = sym('b') c_sym = sym('c') x_sym = sym('x') Define a symbolic expression f = sym('a*x^2 + b*x + c') Substituting variables: g = subs(f,x_sym,3) g = 9*a+3*b+c

36. 36 We Can Derive And Integrate Symbolic Functions Deriving a function: diff(f,x_sym) diff('sin(x)',x_sym) Integrate a function: int(f,x_sym) Symbolic Matlab can do much more… This is a good place to stop

37. 37 Road Runner issues

38. 38 Summary Matlab is not Excel… If you know what you want to do – You will find the right tool!