Biography matlab code example function

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This is not a Matlab package, but a list of examples in PDF/HTML/Mlx formats. The 2D matrix is contiguous, but can be intermediate dimensions.

  • Summarize a nd dimensional matrix along one or two dimensions group by various other dimensions.
  • m: permute(mn, [3,1,2,4]) + squeeze(num2cell(mn, [1,2])) + celldisp() + ndgrid()
  • ND Array Wide to Long Reshape to Table Dataframe with Variable Values for Each Dimension: mlx | m | pdf | html
    • Given 2D policy function f(a,z), generate table/dataframe with a, z, and f(a,z) columns.
    • There is a ND Array where each dimension is a different attribute, generate 2D dataframe with columns for attribute values and ND Array values stored as a single column.
    • There might be many NaN values in the ND array, drop NaN values in the ND array for 2D dataframe.

      Let us have a detailed look at these functions:

      • The square root function sqrt() 

      This function calculates the principal square root of a number and works for both real and complex numbers. Here's how it works:

      Another common function is `mean()`, which calculates the average value of an array:

      User-defined Functions

      User-defined functions empower you to tailor your code to meet specific needs that aren't covered by built-in functions.

      Convert row names to a column as strings. Stack tables vertically and horizontally.

    • m: array2table() + [tb_a tb_b] + [tb_a; tb_b] + tb.Properties.VariableNames + tb.Properties.RowNames
  • Stack and Join Estimation and Simulation Results: mlx | m | pdf | html
    • Stack different tables together with varying columns via outerjoin, store with parallel parfor.
    • Stack different estiamtion results together into a common table where columns show parameter names and other strings.
    • Simulate a model, column combine simulation parameters with multi-row simulation results.

      This transformation reveals the frequency components present in a signal. Engineers often use this function to model periodic phenomena like sound waves or electrical signals.

      • The Fast Fourier Transform function fft() 

      The fft() function converts data from the time domain to the frequency domain. Here’s an example:

      Using global variables should be done with caution as they can lead to code that is hard to debug.

      Mastering the Erf Function in Matlab: A Quick Guide

      Function Overloading

      What is Function Overloading?

      Function overloading allows you to define multiple functions with the same name but different input parameters.

      The Image Processing Toolbox 

      This MATLAB toolbox helps you work with digital images. Explore upGrad’s Data Analysis Courses to benefit from in-depth modules and live learning sessions.

    • In MATLAB, a function is a block of code that performs a specific task and can be reused with different inputs to produce outputs, defined using the `function` keyword.

    Here's a simple example of a MATLAB function that adds two numbers:

    Understanding Function Types in MATLAB

    Built-in Functions

    MATLAB is renowned for its rich library of built-in functions, which cover a wide range of mathematical operations, data handling tasks, and algorithmic solutions.

    This increases flexibility and enables the same function name to handle different data types.

    Implementation of Function Overloading

    Here's an example of how to implement function overloading:

    This function uses conditional statements to determine the type of input it receives and behaves accordingly.

    Ceil Function in Matlab: Rounding Up Made Easy

    Anonymous Functions

    What are Anonymous Functions?

    Anonymous functions in MATLAB provide a compact way to define simple functions without creating a separate file.

    The Control System Toolbox 

    This MATLAB toolbox serves engineers who design control systems. They promote code reusability and modular programming, making it easier to manage larger projects.

    Creating a Simple User-defined Function

    The structure of a user-defined function in MATLAB is straightforward.

    This further helps reveal patterns and clusters in datasets. Let us discuss the built-in types of functions of MATLAB in detail:

    Core Mathematical Functions

    MATLAB's core mathematical applications and functions serve as important tools for numerical computing.

    biography matlab code example function

    1 Data Structures

    This is a work-in-progress website consisting of Matlab example code for manipulating various data structures, dynamic programming, graphing and associated tasks.

    bookdown site and bookdown pdf.

    Materials gathered from various projects in which matlab is used.

    Take the example of tracking temperature changes throughout the day. The built-in functions of MATLAB compute everything from basic arithmetic to mathematical operations. 

    MATLAB’s functions are not to be confused with MATLAB scripts. The plot() function then draws lines between these points, creating a smooth wave pattern. Container Map works well with large parameter structure.

  • Core model functions with potentially many parameters, possibly override default generation to save time.
  • m: varargin + function [out_put] = func_name(varargin) + cm_defaults = {cm_a, cm_b} + [cm_defaults{1:optional_params_len}] = varargin{:} + cm_c = [cm_a;cm_b]

    • 2.3 Dynamic Functions

    1. Anonymous Function Examples: mlx | m | pdf | html
      • Define a wage equation where individuals working part time earn a fraction of the full time earnings.
      • m: gamrnd() + f_x = @(x) x
    2. Dynamically Generate M File: mlx | m | pdf | html
      • Get current working file file name and path.
      • Generate a m file from strings, add file to path, can call that m file.
      • m: matlab.desktop.editor.getActiveFilename + fileparts() + fullfile() + addpath() + fopen() + fprintf() + fclose()

    3.1 Time Series

    1. Autoregressive Process AR(1): mlx | m | pdf | html
      • The Mean and standard deviation of an AR(1) process.
      • Simulate and graph an AR(1) persistent process.
      • Simulate log income process with parameters estimated from Indian income data.
      • Given a monthly persistent AR(1) process, estimate the persistence of the process when data is aggregated at the annual and quadrennial levels, persistence weakens with aggregation.
      • m: normrnd() + for it_t=1:1:length(ar_shk) + plot(ar_t, ar_y) + polyfit(x, y, 1)
      • stats: fitlm(table, ‘y ~ x1 + x2’)
    2. Moving Average of Neighboring Values: mlx | m | pdf | html
      • Compute moving average of surrounding values with different windows.
      • Visualize moving averages compare actual to smoothed average to fully flat moving average.
      • m: movmean()
      • MEconTools: ff_graph_grid()

    3.2 Cross-sectional Data

    1. Mincer Wage Earnings Equation with Experience, Education and Gamma Shocks: mlx | m | pdf | html
      • Define a wage equation where individuals working part time earn a fraction of the full time earnings.
      • Wage at different education and experience levels.
      • Simluate wage with an array of gamma distribution shocks.
      • m: gamrnd() + f_x = @(x) x + histogram()
      • MEconTools: ff_graph_grid + ff_simu_stats

    4.1 Normal Distribution

    1. Compute CDF for Normal and Bivariate Normal Distributions: mlx | m | pdf | html
      • CDF for normal random variable through simulation and with NORMCDF function.
      • CDF for bivariate normal random variables through simulation and with NORMCDF function, using cholesky deomposition to model correlation from uniform random draws.
      • m: mvncdf + norminv
    2. Cholesky Decomposition Correlated Two Dimensional Normal Shock: mlx | m | pdf | html
      • Draw two correlated normal shocks using the MVNRND function.
      • Draw two correlated normal shocks from uniform random variables using Cholesky Decomposition.
      • m: mvnrnd + corrcoef + norminv
    3. Cholesky Decomposition Correlated Five Dimensional Normal Shock: mlx | m | pdf | html
      • Generate variance-covariance matrix from correlation and standard deviation.
      • Draw five correlated normal shocks using the MVNRND function.
      • Draw five correlated normal shocks from uniform random variables using Cholesky Decomposition.
      • m: mvnrnd + corrcoef + norminv + subplot

    5.1 Linear Estimation

    1. Estimate and Solve for Parameters in Linear System of Equation and OLS Regression: mlx | m | pdf | html
      • Fit a line through the origin with two points of data.
      • Solve/estimate an exactly identified system of linear equations.
      • m: fitlm() + fc_ols_lin = @(y, x) (x’x)^(-1)(x’y);

    5.2 Nonlinear Estimation

    1. Matlab Simple Nonlinear Estimation: mlx | m | pdf | html
      • Nonlinear estimation using fminunc.
      • m: optimset() + fminunc()

    6.1 Figure Components

    1. Image Pick Safe Colors: mlx | m | pdf | html
      • Display safe colors.
      • m: blue = [57 106 177]./255 + fill(x, y, cl_colors{it_color})
    2. Figure Titling and Legend: mlx | m | pdf | html
      • Multi-line titles, add legend lines.
      • Add to legend, select legend to show.
      • m: title({‘Cash-on-Hand’ ‘$\alpha + \beta = \zeta$’},’Interpreter’,’latex’) + legend([g1, g2, g3], {‘near’,’linear’,’spline’}, ‘Location’, ‘best’, ‘NumColumns’, 1, ‘FontSize’, 12, ‘TextColor’, ‘black’);
    3. Graph Many Lines Legend for Subset: mlx | m | pdf | html
      • State-space plots with color spectrum: can not show all states in legend, show subset, add additional line to plot and legend.
      • m: jet() + numel() + fliplr() + jet(numel(chart)), set(chart(m), ‘Color’, clr(m,:))

    6.2 Basic Figure Types

    1. Scatter Plot Examples: mlx | m | pdf | html
      • Scatter multiple lines different colors, shapes and sizes.
      • m: scatter(x, y, size) + Marker + MarkerEdgeColor + MarkerEdgeAlpha + MarkerFaceColor + MarkerFaceAlpha
    2. Scatter Plot Examples: mlx | m | pdf | html
      • Scatter and lines multiple lines different colors, shapes and sizes.
      • X axis, Y axis, and 45 degree line.
      • m: xline(0) + yline(0) + refline([1 0]) + plot(x,y) + HandleVisibility + Color + LineStyle + LineWidth
    3. Three variables Scatter and Lines with Color Spectrum: mlx | m | pdf | html
      • Two dimensional matrix for x and y, a third variable with color spectrum set via loop.
      • m: plot(2d, 2d) + jet + set(chart(m), ‘Color’, clr)

    6.3 Graph Functions

    1. Matlab Plot Polynomials, Budget and Indifference Functions: mlx | m | pdf | html
      • Use fplot to plot a one variable function.
      • Plot budget constraint and indifference curve.
      • m: fplot() + xline() + yline() + title([char(f_x)],’Interpreter’,”none”);

    6.4 Write and Read Plots

    1. Graph Generate EPS Postscript Figures: mlx | m | pdf | html
      • EPS vector graphics, avoid bitmap (jpg, png), use vector graphics.
      • m: figure(‘Renderer’, ‘Painters’)

    7.1 Basic Table Generation

    1. Table Generate Table And Fill Data Row by Row or Cell by Cell: mlx | m | pdf | html
      • Generate an empty table and fill with data row by row.
      • Replace or fill table cell value with tb.col{it_row} = rand() is faster than tb{it_row, st_col} = rand().
      • Convert a random matrix to a table with column and row names defined with arrays.
      • m: table() + array2table() + tb{it_row, st_col} = rand() + tb.col{it_row} = rand() + strcat() + addvars() + matlab.lang.makeValidName() + timeit + tic + toc
    2. Order, Sort and Rename Columns: mlx | m | pdf | html
      • Convert a matrix to table with mean and sd columns.

        The surf() shows how one value (height) changes based on two other values (x and y positions).

        Let us examine this code that demonstrates basic plotting:

        In the first example, the code creates a sine wave visualization.