Matlab.md

Table of contents

1. Defining variables
2. Manipulate string
3. Manipulate matrices
4. Math
5. Statistics
6. Algebra
7. Matlab Datatypes
8. Programming

Defining variables

Define a number:

x = 5
x = true % -> 1
x = ~true % -> 0

Define a vector:

v = [1,2,3]
v2 = [ 1 2 3 ] % row vector
v3 = [ 1; 2; 3; ] %column vector
v4 = 1:3 % -> [1 2 3]
v5 = 1:2:10 % start:step:end 
%-> [1 3 5 7 9]

Define a matrix:

A = [ 1 2; 3 4 ]
%-> [1  2;
%    3  4]
B = [ 1 2 3 4; 5 6 7 8; 9 10 11 12; 13 14 15 16]

Define a symbolic variable:

a = 1/3
%-> 0.3333
a = sym(1/3)
%-> 1/3
syms x
y = sym(2x+1)
%-> 2x+1

Show all the variables defined: whos

Clear variables: clear var_name, clear all

Load data: load('file.mat')

Read csv file: csvread('file.csv')

Input, display variable:

x = input('Please enter something')
disp(x)
y = menu('Select', 'Option 1', 'Option 2')
a = "highest number"
b = 9.999
fprintf("The %s is %.4f", a, b)
%-> The highest number is 9.9990

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Manipulate string

Read file: fileread('./file.txt')

Split string or text lines: splitlines(text)

Replace substring: replace(text,{',',';'},' ')

Trim spaces: strip(text)

Split words into string array: split(text)

Join string array in to 1 string: join(text)

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Manipulate matrices

Access element in a matrix:

A(1,2)
%-> 3
A(:,2)
%-> [3;
%    4]
B(2:3,2:3)
%-> [6  7;
%   10 11]
B(1:2:end,:)
%-> [1 2  3  4;
%   9 10 11 12]

Return size of a matrix: size(A)

Return dimension of a matrix that has the highest value: length(A)

Check if element(s) in a matrix: ismember(x,A)

A = [1 2 3; 5 6 0]
ismember(5,A) % -> 1
ismember([1 4 9],A) % -> [1 0 0]
ismember([5 6 0],A) % -> 1

Shift elements in a matrix: circshift(A,[x_shiftLen,y_shiftLen])

A = [1 2 3; 4 5 6; 7 8 9]
cirshift(A,[1,1])
%-> 9  7  8
%   3  1  2
%   6  4  5

Concat 2 matrices:

A = [1 2 3; 4 5 6; 7 8 9]
B = [1 2 3]
[A; B]
%-> 1 2 3
%   4 5 6
%   7 8 9
%   1 2 3

Relational operations, return 0 1:

A > B % gt(A,B)
A < B % lt(A,B)
A >= B % ge(A,B)
A <= B % le(A,B)
A ~= B % ne(A,B)
A == B

Find indexes of non-zero value: find(A)

Sort matrix: sort(A, dimension, direction)

Find the frequency of the values in a vector: tabulate(v)

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Math

Basic expression:

a+b
a-b
a*b
a/b
a^2

Calculate sin, cos, tan:

cos(x)
sin(x)
tan(y)
cos(A)

Calculate e, ln, and log:

exp(1) % e
exp(2) % e^2
log2(8)
log(1) % ln(1)

Calculate differentiation (derivative) (đạo hàm): diff(a,n,dim)

syms x
diff(2x)
%-> 2
diff(2*x^2,2) % diff(diff(2*x^2))
%-> 4

Calculate integration (tích phân):

% definite integral (antiderivative) 
int((2*x-1)^2)
%-> ((2*x - 1)^3)/6

% indefinite integral
int((2*x-1)^2,[1 10])
%-> 1143

Symbolic function:

syms x y
f(x,y) = 2*x + 3*y +2
f(2,3)
%-> 15

Solve equations solve(f):

syms f x y
f = 2*x^2 + 3*x + 2 == 1
solve(f)
%-> -1
%   -1/2
f = 2*x^2*y^2 + 3*x*y + 2 == 1
solve(f)
%-> -1/y
%   -1/(2*y)
solve(f,y)
f1 = 3*x + 2*y == 1
f2 = 2*y + 3*z == 2
f3 = 3*z == 3
[x y z] = solve(f1,f2,f3)

Substitute symbolic variable subs(f,old,new):

f = 3*x + 2*y == 1
subs(f,x,1)
%-> 2*y + 3 == 1

Calculate product: prod(A)

Find greatest common divisor (GCD): gcd(x,y)

Least common multiple (LCM): lcm(x,y)

Check if is prime number: isprime(x), isprime(A)

Return all the permutations: perms([1 2 3])

Set operations:

intersect(x,y)
union(x,y)
setdiff(x,y)

Generate random values:

rand(5,2) % rand(row,col) random between 0 and 1
randn(5,2) % randn(row,col) random negative -1 and 0
randi(10,3,2) % randi(max,row,col) random integer from 1 to max

Discretize values (turn values into discrete parts):

data = [1 3 8 5 9 7]
edges = [0 2 4 6 8]
A = discretize(data,edges)
% --> [1 2 4 3 NaN 4]
% bin 1: 0-1.99; bin 2: 2-3.99; bin 3: 4-5.99; bin 4: 6-8

Find unique numbers unique(A)

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Statistics

A = [1 3 5; 3 6 9; 7 8 2]

Find min: min(A)

min([1,2,3,4]) % 1 (min of the vector)
min(A) % 1 3 2 (min of every column)
min(A,[],1) % min of every column
min(A,[],2) % min of every row
[A, I] = min(A,[],1) % return a min matrice and a matrice of its indexes

Find max: max(A)

Find mean: mean(A)

mean([1 2 3]) % -> 2 (mean of the vector)
mean(A) % -> [3.6667 5.6667 5.3333] (mean of every col)
mean(A,2) % mean of every row

Find sum: sum(A)

Find variance: var(A):

var([1 2 3]) % 1 (variance of the vector)
var(A) % (variance of every col)
var(A,0,2) % (variance of every row)

Find standard division std(A)

Find median median(A)

Find mod mod(A)

Find percentile prctile(A,nth)

prctile(A,25) % 25th percentile of every col
prctile(A,[25,35,45] % percentile of the according col
prctile(A,25,2) % 25th percentile of every row

Signum and Absolute value: sign(x), abs(x)

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Algebra

Add, subtract, multiply, divide vectors and matrices:

A = [1 2; 3 4]
B = [3 4; 5 6]
A+B
A-B
A*B
A\B % = A * inv(B)

Multiply every single element of one to another matrix:

A.*B
%-> [3  8;
%-> 15 24]
A.\B

Calculate products:

dot(x,y)
cross(x,y)
outer(x,y)
inner(x,y)

Transpose a matrix or vector:

A = A'
%-> [1  3;
%-> 2  4]

Generate an identity matrix: eye(3)

Find the inverse matrix: inv(A) A^-1

Find the determinant: det(A)

Return diagonal elements: diag(A)

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Matlab Datatypes

Cell array

Cell array can contains any types of data in each cell.

Create a cell array:

C = {1 2 'text';[1 2; 3 4] {1 2} 3}
C2 = cell(4,2) % create empty cell of 4,2

Visualize cell: cellplot(C)

Display cell: celldisp(C)

Access data in a cell:

C{1,1}
% -> 1
C{2,1}
% ->1 2
%   3 4
C{2,1}(1,2)
% -> 2
C{2,2}{1}
% -> 1

Replace data:

C{2,2} = 9
C{:,1:2} = [] % Remove col 1-2

Tables

A table is like a .csv, has header, values and index. Value can be any types.

Create a table:

LastName = {'Sanchez';'Johnson';'Li';'Diaz'};
Age = [38;43;38;40];
Weight = [176;163;131;133];
BloodPressure = [124 93; 109 77; 125 83; 117 75];
T = table(LastName,Age,Weight,BloodPressure)

 LastName      Age     Weight    BloodPressure
___________    ___     ______    _____________
{'Sanchez'}    38       176       124     93  
{'Johnson'}    43       163       109     77  
{'Li'     }    38       131       125     83  
{'Diaz'   }    40       133       117     75  

Change table index's name (row's name):

T.Properties.RowNames = {'Name1','Name2','Name3','Name4'}

	     LastName      Age     Weight    BloodPressure
	    ___________    ___     ______    _____________
Name1    {'Sanchez'}    38       176       124     93  
Name2    {'Johnson'}    43       163       109     77  
Name3    {'Li'     }    38       131       125     83  
Name4    {'Diaz'   }    40       133       117     75 

Change table column name:

T.Properties.VariableNames = {'Name1','Name2','Name3','Name4'}

Add unit information:

T.Properties.VariableUnits = {'','year','kg',''}

Add description for columns:

T.Properties.VariableDescription{'LastName'} = 'This is lastname'
T.Properties.VariableDescription = {'This is lastname' 'This is age' 'This is Weight' ''This is bloodpressure'}

Accessing data in table:

T.LastName % return the rows of the collumn LastName

Summary a table, will return variable names, description, units, min, max median: summary(T)

Sort rows:

sortrows(T,{'LastName','Age'},{'Ascend','Descend'})

Read table from a file (.csv, .xlsx): T = readtable('file.csv')

Write table to a file:

writetable(T,'table.csv','Delimiter',',')

Select, add, delete rows:

T(2,3) % select 1
T(1:2,:) % select all rows from 1-2
T(:,2:3) % select all cols from 2-3
T.Age % select all rows of column Age
T('IndexName1',:) % select all cols of IndexName1
T([true false true false],:) % select only rows specified as true

Add row:

T2 = {{'John'},11,140,[123 88]}
T(end+1,:) = T2 % add 1 row at the end of the table
T = [T;T2] % add table T2 at the end of table T

Remove row:

T('IndexName1',:) = []

Add column:

T.newColName =  [1 2 3 4] % add new col at the end, must match the table height

T = [T;T2] 
% add columns T2 at the end
% col names must unique and T2's height must match T's height

Remove column:

T.colName = []
T = T(:,1:end-1)

Remove rows/cols with missing data: rmmissing(T,dim,Name,Value)

Sync 2 tables: synchronize(T,T2)

• If 2 cols of 2 tables have the same col name and data, skip and store 1 only.
• If 2 cols of 2 tables have the same col name and different data. add table name to that row (i.e Weight_T1, Weight_T2), store 2 cols.
• If 2 cols of 2 table are different, store 2 cols

Struct

A structure array is a data type that groups related data using data containers called fields. Each field can contain any type of data.

Create a struct: struct(field,value)

student = struct('Name','','Grade',[],'Class','')
student(1).Name = 'John'
student(1).Grade = [10 9 10]
student(1).Class = 'A'
student(2).Name = 'Andy'
student(2).Grade = [10 9 10]
student(2).Class = 'A'

Remove a field: rmfield(s,'fieldName')

Concat 2 structs:

s = [s1;s2]

Store fields in variables:

[v1 v2] = student.Name
v1 % -> 'John'
v2 % -> 'Andy'
A = {student.Name}
% -> { {'John'} {'Andy'} }

Map

A hash map that contain key-value pairs.

Create a map:

mymap = containers.Map({'key1' 'key2' 'key3'},{'value1' 1 [1 2]})

Retrieve keys or values:

mymap('key1') % -> 'value1'
keys(mymap) % -> return cell of all keys
values(mymap) % -> return cell of all values

Remove a key-value pair:

remove(mymap,'key1')

Check if a map contains a key:

isKey(mymap,{'key1' 'key4'}) % -> [1 0]

Concat 2 maps:

mymap2 = containers.Map({'key4' 'key5'},{'value4' 3})
mymap = [mymap;mymap2]

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Programming

If else statement:

a = 1
if a == 1
	disp("it's 1")
elseif a == 2
	disp("it's 2")
else 
	disp("it's not 1 or 2")
end

For loop:

start = 1
step = 2
ends = 10
for i = start:step:ends
	disp(i)
end

for i = [1 3 5 7]
	disp(i)
end

While loop:

i = 5
while i < 5
	i = i + 1
	disp(i)
end

Break, continue loop: continue break

Switch case:

a = 2
switch a
	case 1
		disp("The number is 1")
	case 2
		disp("The number is 2")
	otherwise
		disp("Not 1 or 2")
end
	

Function

Define a function:

% Multiple outputs
function [outArg1,outArg2] = func_name(inArg1,inArg2)
	outArg1 = inArg1;
	outArg2 = inArg2;
end

[a b] = func_name(1,2)

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