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Introduction to SQL REGEXP

A regular expression in standard query language (SQL) is a special rule that is used to define or describe a search pattern or characters that a particular expression can hold. For example, a phone number can only have 10 digits, so in order to check if a string of numbers is a phone number or not, we can create a regular expression for it. It is an in-built specification supported in almost all SQL databases.

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Regular expressions are very helpful as they let us place multiple lines of code or information in just 1 line. It is particularly helpful in SQL databases when we want to perform validation tasks like if the information provided is a valid PIN code, Contact No, email address, etc. Regular expressions also help in pattern matching or searching the database.

List of Operators Used for REGEXP in SQL

Here is the list of some of the most frequently used operators or metacharacters for making regular expressions in SQL.

Operator

Operator Name

(.) Any character – Dot Quantifier Matches any single character in the character set of the database.

(*) Asterisk or Star Quantifier Matches zero or more occurrences of the subexpression/ strings preceding to it.

(+) Plus Quantifier Matches one or more occurrences of the subexpression/ strings preceding to it.

(?) Question mark Quantifier Matches zero or one occurrence of the subexpression/ strings preceding to it.

[ABC] / [abc] Matching Character List Matches any character mentioned in the list.

[^ABC] / [^abc] Non-Matching Character List Matches any character except the ones mentioned in the list.

[0-9] Digit List Matches any digit from 0 to 9.

{a} Exact count Interval Matches exact ‘a’ occurrences of subexpression or string preceding to it.

{a,} At Least one count Interval Matches at least ‘a’ occurrences of subexpression or string preceding to it.

{a,b} Between count Interval Matches at least ‘a’ occurrences of subexpression or string preceding to it but not more than ‘b’ occurrences.

(^) Caret Quantifier Matches an expression only if it occurs at the beginning of a line.

($) Dollar or End Quantifier Matches an expression only if it occurs at the end of a line.

Vertical Bar Quantifier It is used to isolate different alternatives in a regular expression.

[[:class:]] Class Quantifier Matches the character class , for example , matches [[:digit:]] to digits, [[:space:]] to space, [[:alnum:]_] to alpha numerics, etc.

In the table above, we have tried to incorporate the most basic and frequently used meta-characters or quantifiers used for creating complex regular expressions. On the basis of this table, you can create other quantifiers such as +? Or *? for non-greedy versions of plus (+) quantifier and asterisk (+) quantifier, i.e matching just zero or one occurrences.

Examples of SQL REGEXP

Here are a few examples to illustrate the use and functions of different quantifiers in regular expressions.

In order to do so, let us first create a ‘customer_details’ table which contains customer id, his or her name, contact details, and the city where they live. We can use the following SQL statements to perform the task.

CREATE TABLE customer_details ( customer_id integer, customer_name character varying(255), city character varying(255), contact_no character varying(255), email_address character varying(255) );

Having created the customer_details table, let us now feed some information into the table columns using insert statements as shown below.

select * from customer_details

Output:

Example #1

SQL query to Illustrate the use of Dot (.) quantifier.

SELECT * FROM customer_details WHERE customer_name ~ 'K.';

Output:

Any Character Dot (.) quantifier matches with any string containing that character. In this example, customer_names with capital ‘K’ in them will be matched.

Example #2

SQL query to Illustrate use of Star (*) quantifier.

SELECT * FROM customer_details WHERE customer_name ~ 'Ked*';

Output:

Example #3

SQL query to Illustrate the use of Plus(+) quantifier.

SELECT * FROM customer_details WHERE email_address ~ 'gmail+';

Output:

Example #4

SQL query to Illustrate the use of Question Mark (?) quantifier.

SELECT * FROM customer_details WHERE city ~ 'Los?';

Output:

Example #5

SQL query to Illustrate the use of [… ] Character List quantifier.

SELECT * FROM customer_details WHERE customer_name ~ '[CA]';

Output:

[…] quantifier matches with strings that contain any character mentioned in the list. In this example, customer_names containing Capital A and C will be selected.

Example #6

[…] quantifier matches with strings that contain any character mentioned in the list. In this example, customer_names containing Capital A and C will be selected.

SQL query to Illustrate the use of [^…] non-matching character list quantifier.

SELECT * FROM customer_details WHERE customer_name ~ '^[^CA]';

Output:

[^…] quantifier matches a string that does not contain any of the characters mentioned in the list. In this case, customer_names which do not start with A or C will be selected.

Example #7

[^…] quantifier matches a string that does not contain any of the characters mentioned in the list. In this case, customer_names which do not start with A or C will be selected.

SQL query to Illustrate the use of Digit [0-9] quantifier.

SELECT * FROM customer_details WHERE contact_no ~ '[8-9]';

Output:

[0-9] quantifier matches for digits. In this example, [8-9] regexp matches with contact_nos which contain digits 8 or 9.

Example #8 SELECT * FROM customer_details WHERE contact_no ~ '^[8]';

Output:

SELECT * FROM customer_details WHERE city ~ '^New';

Output:

‘^’ quantifier matches an expression if and only if a string or line begins with it. For example, contact_no starting with 8 and cities starting with ‘New’.

Example #9 SELECT * FROM customer_details

Output:

The vertical bar is used to create one or more versions of the matching subexpression. For example, in this case, matching those contact numbers which starts with 8 or 9.

Example #10

SQL query to Illustrate the use of Dollar or End ($) quantifier.

SELECT * FROM customer_details WHERE contact_no ~ '8$';

Output:

$ matches an expression when it occurs at the end of a string. In this case, we are trying to find those contact numbers which end in digit 8.

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6 Commonly Used Different Kinds Of Sql Constraints

Introduction to SQL Constraints

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In SQL, we have many different kinds of constraints. Let us look at the following few constraints in this article.

NOT NULL

CHECK

UNIQUE

PRIMARY KEY

FOREIGN KEY

DEFAULT

Different Kinds of SQL Constraints

Below given are different kinds:

1. NOT NULL Constraint

This constraint is used when you do not want any value in that particular column to be a Null value. This means that we cannot insert a Null value for that column while inserting a new row in the table. Therefore, every field in this column always has a non-Null value. A null value means that a particular field has been left empty, and values such as zero or blank space do not come under Null values.

Let us look at an example to create a table called Employee, having 5 columns, where empid, name, and mobile columns do not accept NULL values.

CREATE TABLE Employee ( empid INT NOT NULL, name VARCHAR(20) NOT NULL, dob DATE, mobile VARCHAR(10) NOT NULL, address VARCHAR(20) ); 2. CHECK Constraint

This constraint limits the values that can be entered in that particular column of the table. To understand this better, let us take the example of passing marks in an exam. The range of values for these marks can only be from 35 to 100. To ensure that only values in this range are entered, we can create a CHECK constraint.

CREATE TABLE Employee ( empid INT NOT NULL, name VARCHAR(20) NOT NULL, dob DATE, mobile VARCHAR(10) NOT NULL, address VARCHAR(20) ); 3. UNIQUE Constraint

This constraint is applied to ensure that the particular column accepts only unique values, and repetitive values are not allowed with such a constraint on the column. We can create multiple UNIQUE constraints on various columns in a table. A UNIQUE constraint allows NULL values to be entered.

Let us look at an example of enforcing the UNIQUE constraint. In this example, we are creating a column called mobile in the table Employee, which is to be unique and cannot accept the same mobile number twice.

CREATE TABLE Employee ( empid INT NOT NULL, name VARCHAR(20) NOT NULL, dob DATE, mobile VARCHAR(10) NOT NULL UNIQUE, address VARCHAR(20) ); 4. PRIMARY KEY Constraint

This constraint is used to identify a particular column or a group of columns that can uniquely identify a row in the table. With the PRIMARY KEY constraint in place, any row cannot have a duplicate value. We cannot have NULL as the value for such a column. Even though both a PRIMARY KEY constraint and a UNIQUE constraint impose that the values are unique, we use a UNIQUE constraint when we do not want to declare the column as Primary Key but still want the values in that column to be unique. We can have only a single PRIMARY KEY column or a group of columns in a table, but we can declare many individual columns UNIQUE.

CREATE TABLE Employee ( empid INT NOT NULL PRIMARY KEY, name VARCHAR(20) NOT NULL, dob DATE, mobile VARCHAR(10) NOT NULL UNIQUE, address VARCHAR(20) ); 5. FOREIGN KEY Constraint

This constraint helps the data in one table to establish a relationship with the data in another table in the database. Foreign Key can be a single column or a set of columns. For example, let us consider two tables, Employee and Departments. Suppose we a column called depicted in Employee and a departed in Departments. Then, we can reference the departed of Employee to the departed of Departments if the columns match. In this case, the column in Employee becomes a foreign key reference to the column in the Department table, which is a primary key.

CREATE TABLE Employee ( empid INT NOT NULL PRIMARY KEY, name VARCHAR(20) NOT NULL, dob DATE, mobile VARCHAR(10) NOT NULL UNIQUE, address VARCHAR(20), depicted INT FOREIGN KEY REFERENCES Department(depicted) ); 6. DEFAULT Constraint

This constraint is used to specify the default value for a particular column in the table. This way, if there is no value inserted for that column explicitly, the database engine can always refer to the default value specified and insert that in the column. If we have inserted a column with the constraint NOT NULL and the constraint DEFAULT, we do not need to define a default value explicitly. Even without giving a default value, the particular row will be inserted in the table.

Let us look at an example where we have entered the place in the address as default to have the value ‘India’.

CREATE TABLE Employee ( empid INT NOT NULL PRIMARY KEY, name VARCHAR(20) NOT NULL, dob DATE, mobile VARCHAR(10) NOT NULL UNIQUE, address VARCHAR(20) DEFAULT ‘India’, depicted INT FOREIGN KEY REFERENCES Department(depicted), ); Conclusion

SQL constraints help the developer by specifying restrictions and rules for the data that is to be inserted in the table. Constraints can be applied at the column level, just to the particular column or at the table level, where the constraints are applied to the complete table. These constraints restrict the kind of information that can be inserted into the table. This guarantees the correctness and consistency of the data in the table. In case of any violations of the rules specified by the constraints, the action is terminated.

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A Quick Glance Of Sql All With Query Examples

Introduction to SQL ALL

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Syntax and Parameters

The basic syntax for using ALL operator with SELECT statement is as follows :

SELECT ALL column_name FROM table_name WHERE condition(s);

The basic syntax for using ALL operator in WHERE clause is as follows :

SELECT column_name1, column_name2 FROM table_name WHERE column_name comparison_operator ALL (SELECT column_name FROM table_name WHERE condition_expression ); Parameters

The parameters used in the above-mentioned syntaxes is as follows :

table_name: Name of the database table from which the said columns will be fetched.

column_name: column which has to be used as a part of WHERE condition for comparison.

(SELECT column_name FROM table_name WHERE condition_expression ): The values obtained from the result set of this subquery will be compared with the column_name.

The syntax for using ALL operator with a HAVING clause is similar to the WHERE clause. The only difference is HAVING is generally used with GROUP BY clauses.

Examples of SQL ALL

In order to illustrate the functionality of ALL operator in SQL, what could be better than trying a few examples on a dummy table. Ergo, let us create two database tables called “employee” and “departments” respectively.

We can use the following CREATE table statements to create these tables.

CREATE TABLE employee ( employee_id integer, employee_name character varying(255), department_id character varying(255), salary numeric, highest_qualification character varying(255) )

CREATE TABLE departments ( department_id character varying(255), department_name character varying(255), location character varying(255), manager_id integer )

We have successfully created both the tables, namely “employee” and “departments”. Now with the help of the given INSERT queries given below, let us insert a few records in both the tables to work with.

(i) INSERT statement for inserting records in the employee table.

INSERT INTO public.employee( employee_id, employee_name, department_id, salary, highest_qualification) VALUES (101,'Roy Bernard','D01',5000,'B.Sc'), (102,'Gina Messenger','D01',6200,'M.Sc'), (105,'Jim Perkins','D03',5000,'B.A'), (106,'Erica Silverman','D03',7000,'MBA'), (107,'Priyanka M','D01',5000,'B.Tech');

(ii) INSERT statement for inserting records in departments table.

INSERT INTO public.departments( department_id, department_name, location, manager_id) VALUES ('D01','Research','Singapore',102), ('D02','Human Resource','Santa Monica',104), ('D003','Sales','New York',106);

Now we are all set to try a few examples based on these tables.

Example #1 – ALL operator with SELECT statement

Show the list of all the employees depicting their employee_id and names.

Code:

SELECT ALL employee_id, employee_name FROM employee WHERE department_id = 'D003';

Output:

Example #2 – ALL operator with WHERE clause

Find the employee_ids and salaries of employees who earn less than or equal to all the employees in the ‘D003’ department.

SELECT employee_id, salary FROM employee WHERE salary <= ALL(SELECT salary FROM employee WHERE department_id = 'D003');

Output:

Example #3

Find the employee_id, salary, and highest qualification of employees who earn equal to all the managers in the company’s New York office.

SELECT employee_id, salary, highest_qualification FROM employee WHERE employee_id = ALL(SELECT manager_id FROM departments WHERE location = 'New York');

Output:

Example #4 – ALL operator with HAVING clause

Prepare a summary table consisting of total employees and average salaries grouped together by highest qualification, provided that salaries of these employees is more than the average salary of all the departments.

Code:

SELECT highest_qualification, count(employee_id) as "Total_employees", ROUND(AVG(salary),2) as "Average_salary" FROM employee GROUP BY highest_qualification FROM departments as d JOIN employee as e ON d.department_id = e.department_id );

Output:

Example #5 – ALL operator with the UPDATE statement

Suppose the company has decided to raise the salaries of employees who have been earning a minimum salary until now to $5100. Write an update query to perform this task.

Code:

UPDATE employee SET salary = 5100 WHERE salary <= ALL(SELECT MIN(salary) FROM employee );

Output:

The query returned successfully. Let us check using the following SELECT query if the desired changes have been made.

SELECT * FROM employee;

Output:

It can be observed from the image that the salaries of employees who have been earning $5000 have been updated to $5100.

ALL operator with the DELETE statement

DELETE FROM employee WHERE employee_id = ALL( SELECT manager_id FROM departments WHERE department_id = 'D01');

Output:

Use the following SELECT query to check if the desired rows have been deleted.

SELECT * FROM employee;

Output:

The query returned successfully and has deleted the details of department D01’s manager.

Conclusion

ALL is a comparison operator that returns TRUE if all the values in the result set obtained from a subquery meet the specified condition. The operator can be used along with a SELECT statement, WHERE, and HAVING clause.

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Guide To Uses Of Pl/Sql Exit With Examples

Introduction to PL/SQL exit

Pl/SQL exit statement is used for terminating the execution, especially while working with loops and nested loops. In case, if you have a requirement where you need to halt or stop the execution of loop then you can specify the same y making the use of EXIT statement in PL/ SQL program in the LOOP body. There is one more way in which we can use EXIT statement which is along with WHEN statement. The EXIT WHEN statement allows you to specify the condition when you have to exit from that block of code. In this article, we will have a look at the syntax of EXIT and EXIT WHEN, usage of both of them in particular scenarios, and the implementation of these statements along with the help of certain examples.

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Use of EXIT

When working with loops, at the time of recursive execution of loop body, if a certain condition evaluates to true and the flow encounters the EXIT statement then the control is transferred to the statement that is present just below the place where the loop is ending.

When we have nested loops where there is a presence of a loop inside the loop then the use of EXIT statement will make the termination of execution of the innermost loop and the flow of control will be transferred to the end of the block of the innermost loop. The execution will begin from the line after the last line of the innermost loop.

Syntax:

The syntax of the EXIT statement is as shown below in Pl/ SQL –

EXIT;

The use of the above syntax of EXIT is mostly done along with the conditional statements. If the evaluation of the condition inside this statement becomes true then or when it is false in case of ELSE statement, we can make the use of EXIT function inside IF body or ELSE body according to our necessity and requirement.

Example 1

Let us study how we can implement the simple EXIT statement along with the use of conditional statements in PL/ SQL program along with the help of an example –

DECLARE numberCounter number(2) := 5; BEGIN WHILE numberCounter < 15 LOOP numberCounter := numberCounter + 1; EXIT; END IF; END LOOP; END;

The output of the above PL/ SQL program is as shown below –

In the above example when the value of the number counter becomes more than 10 which is 11 then the condition specified inside the while loop in the if the statement becomes true and execution encounters the EXIT statement which results in the flow of execution being transferred to the statement outside the WHILE loop which stops printing the numbers after 10 in the output.

When the condition mentioned after WHEN statement evaluates to false then the EXIT statement does not terminate the execution and behaves like a NULL statement in that scenario. When the condition becomes true the execution of the loop terminates and the control is transferred to the statement after the END LOOP statement.

Syntax:

The syntax of EXIT WHEN statement in PL/ SQL programming is as shown below:

EXIT WHEN condition to be evaluated

The condition to be evaluated should result in a Boolean value which is either true or false. The use of EXIT THEN statement is helpful as it helps in writing the code for exiting even without using the conditional statements like IF else or IF THEN in PL/ SQL.

Considerations of EXIT WHEN statement –

The EXIT WHEN statement can be used in PL/ SQL considering two main aspects which are listed below –

The value of the condition which is evaluated should be changed by the statements which are present inside the LOOP or else it will be an infinite loop if the condition always evaluates false.

Example

In order to get clarity in implementation of EXIT WHEN statement in PL/ SQL let us consider one example. Let us try to get the same output as that of the above program but instead of using just EXIT and the conditional IF statement, we will now make the use of EXIT WHEN statement as shown below –

DECLARE count_variable number(2) := 5; BEGIN WHILE count_variable < 15 LOOP count_variable := count_variable + 1; END LOOP; END;

The output of the above code is as shown below which is the same as that of the first example –

The condition mentioned after the WHEN statement is evaluated for each and every time the WHILE LOOP is traversed. Till the counter variable has a value less than 11 it evaluates false and so the EXIT WHEN statement is considered as the NULL statement. As soon as the value of the counter variable becomes 11 which is greater than 10 the condition becomes true and then the EXIT statement terminates the execution of the while loop and the flow of control is transferred to the statement placed just below the END LOOP statement.

Conclusion – PL/SQL exit

The EXIT and EXIT WHEN statement in PL/ SQL helps to specify the condition in which we can help the termination of the loop on a conditional basis.

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Javascript Program For Finding The Length Of Loop In Linked List

In this program, we will be given a linked list that may consist of a loop and we have to find if the loop exists then what will be the size of the loop. Let’s a very famous approach to finding the length of a loop with the help of a code and discuss its time and space complexity.

Introduction to Problem

In this problem, as we have seen above we are given a linked list that may or may not contain a loop in it and we have to find the length of the loop if it exits otherwise we have to return zero as there is no loop present. We will use the Floyd loop method to find the loop and then check its size. For example, if we are given a linked list as −

And there is a loop from the node containing 8 to the node containing 4, which means 8 is connected to 4 making the loop of length 5 and we have to detect it.

Approach

In this problem, we will use the Floyd loop method to detect the loop and then we will use the concept of length finding to find the length of the loop. Let’s first see the basic steps of the problem then we will move to Floyd’s method and length method.

First, we will create the class to provide the basic structure of the nodes of the linked list and define constructors in it to initialize the node values.

Then we created a function to push the elements in the given linked list.

We have created a linked list using the above method and then we have linked the last node to another node to make a cycle in it.

Floyd’s Algorithm

In this algorithm, we traverse over the linked list and once we have entered the linked list then we cannot go out from any node. This means if we have two pointers in that linked list loop part and one pointer is moving forward with one node at a time and another is at the pace of two nodes at a time, they will meet at a certain point.

After implementing the algorithm, we will call that function and check if the loop is present or not

If the loop is present than we will call to the anther function to find the length of the loop.

Another wise we will return and print no loop is present.

Example

In the below example, we define a linked list and add 8 nodes to it. We make loop in a linked list by connecting node 8 to node 4. So it makes a loop of five nodes.

class Node{ constructor(data) { this.value = data chúng tôi = null; } } function push(data, head) { var new_node = new Node(data); if(head == null) { head = new_node; return head; } var temp = head while(temp.next != null) { temp = temp.next; } chúng tôi = new_node; return head; } function length(loop_node) { var count = 1; var temp = loop_node; while(temp.next != loop_node) { count++; temp = temp.next; } console.log("The length of the loop in the given linked list is: " + count); } function find_node(head) { var slow_ptr = head; var fast_ptr = head; while(slow_ptr != null && fast_ptr != null && fast_ptr.next != null) { slow_ptr = slow_ptr.next; fast_ptr = fast_ptr.next.next; if(slow_ptr == fast_ptr) { length(slow_ptr); return; } } console.log("There is no loop present in the given linked list"); } var head = null; head = push(1,head) head = push(2,head) head = push(3,head) head = push(4,head) head = push(5,head) head = push(6,head) head = push(7,head) head = push(8,head) var temp = head; while(temp.value != 4){ temp = temp.next; } var temp2 = head; while(temp2.next != null){ temp2 = temp2.next } temp2.next = temp find_node(head) Time and Space Complexity

In the above code, we have traversed over the complete linked list only once and for the loop part maximum of three times which makes the time complexity linear. So the time complexity of the above code is linear that is O(N) where N is the size of the linked list.

As we are not using any extra space makes the time complexity of the program O(1).

Conclusion

In this tutorial, we have learned how to find the length of the loop present in the linked list by implementing the concepts in the JavaScript language. We have used Floyd’s loop-finding algorithm to find the loop in the given linked list and then we have just used the while loop to traverse over the loop and find its length. The time complexity of the above code is O(N) and the space complexity is O(1).

Introduction, Syntax, And Different Examples Of Matlab Fit

Introduction to Matlab fit

MATLAB fit method can be used to fit a curve or a surface to a data set. Fitting a curve to data is a common technique used in Artificial intelligence and Machine learning models to predict the values of various attributes.

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For example, if we compare the weight of an item like rice with its price; ideally, it should increase linearly (Price will increase as the weight of rice will increase). If we fit a curve to this data of weight and price, we will get mostly a linear curve. Now someone looking at this linear curve can easily interpret the relation between the 2 attributes (weight and price in our example), without looking at the data.

Syntax:

fitobject = fit (a, b, fitType) is used to fit a curve to the data represented by the attributes ‘a’ and ‘b’. The type of model or curve to be fit is given by the argument ‘fitType’

Various values which the argument ‘fitType’ can take are given in the table below:

Model Name Description

‘poly1’

A polynomial curve with linear nature

‘poly11’

A polynomial surface with linear nature

‘poly2’

A polynomial curve with quadratic nature

‘linearinterp’

Linear piecewise interpolation

‘cubicinterp’

Cubic piecewise interpolation

‘smoothingspline’

A curve of the nature smoothing spline

Table 1

Let us now understand how to fit a curve or a surface to data in MATLAB:

We will need some data to which we will fit the curve, for our examples, we will use some inbuilt data sets provided by MATLAB like ‘carsmall’ and ‘census’.

Examples of Matlab fit

Let us discuss examples of Matlab fit.

Example #1

In this example, we will use the ‘carsmall’ data provided by MATLAB. The data is of various attributes of cars manufactured over the years 1970, 1976, and 1982. It has attributes like ‘Acceleration’, ‘Cylinders’, ‘Horsepower’ etc. which represent various features of a car. We will load this data to our workspace and will fit a curve to its attributes ‘Acceleration’ and ‘Displacement’. The steps to be followed for this example are:

Load the ‘carsmall’ data to the workspace

View the file loaded above to understand its attributes

Use the ‘fit’ function to fit a curve to the loaded data

Plot the model created in above step

1. load carsmall

[Using the ‘load’ command to load the ‘carsmall’ data set to our workspace]

[Using the ‘load’ command to load the ‘carsmall’ data set to our workspace]

2. whos -file carsmall

[Using ‘whos’ command to view the file loaded above]

[Using ‘whos’ command to view the file loaded above]

3. C = fit(Acceleration, Displacement, ‘poly2’)

[Using the ‘fit’ command to fit a curve to the data. The first 2 parameters represent the attributes to which we want to fit the curve and the 3rd parameter represents the type of curve which we want to fit (please refer to Table 1 for this)]

[Using the ‘fit’ command to fit a curve to the data. The first 2 parameters represent the attributes to which we want to fit the curve and the 3parameter represents the type of curve which we want to fit (please refer to Table 1 for this)]

4. plot(C, Acceleration, Displacement)

[Using ‘plot’ command to plot the model created in above step]

[Using ‘plot’ command to plot the model created in above step]

This is how our input and output will look like in MATLAB command window:

Input 1:

Loading the carsmall data set:

Input 2:

Fitting the curve to the data:

Input 3:

Plotting the model created above:

Output 1:

Output 2:

Output 3:

As we can see in Output 3, we have obtained a curve that fits our data. Output 1 and Output 2 represent the data attributes and the model respectively.

In the same example, we can also fit a different type of curve as per our requirement. Let us try to fit ‘smoothingspline’ curve to the above data.

The code will be similar as in the above example with a change in line 3

Code:

 1. load carsmall

[Using the ‘load’ command to load the ‘carsmall’ file to our workspace]

[Using the ‘load’ command to load the ‘carsmall’ file to our workspace]

2. whos -file carsmall

[Using ‘whos’ command to view the file loaded above]

[Using ‘whos’ command to view the file loaded above]

 3. C = fit(Acceleration, Displacement, ‘smoothingspline’)

[Please note that the 3rd argument is now ‘smoothingspline’]

[Please note that the 3argument is now ‘smoothingspline’]

4. plot(C, Acceleration, Displacement)

[Using ‘plot’ command to plot the model created in above step]

Input 1:

Loading the carsmall data set:

Input 2:

Fitting the curve to the data:

Input 3:

Plotting the model created above:

Output 1:

Output 2:

Output 3:

As we can see in Output 3, we have obtained a smoothing spline curve that fits our data.

Example #2

In this example, we will use the ‘census’ data provided by MATLAB. The data is of the US and gives the population of the country in a particular year. It has 2 attributes ‘cdate’ and ‘pop’ representing ‘census date’ and ‘population’. We will load this data to our workspace and will fit a curve to it. The steps to be followed for this example are:

Load the census data to the workspace

View the file loaded above to understand its attributes

Use the ‘fit’ function to fit a curve to the loaded data

Plot the model created in above step

Code:

load census

[Using the ‘load’ command to load the census file to our workspace]

[Using ‘whos’ command to view the file loaded above]

[Using ‘whos’ command to view the file loaded above]

C = fit(cdate, pop, 'poly2')

[Using the ‘fit’ command to fit a curve to the data. The first 2 parameters represent the attributes to which we want to fit the curve and the 3rd parameter represents the type of curve which we want to fit (please refer to Table 1 for this)]

[Using the ‘fit’ command to fit a curve to the data. The first 2 parameters represent the attributes to which we want to fit the curve and the 3parameter represents the type of curve which we want to fit (please refer to Table 1 for this)]

plot(C, cdate, pop)

[Using ‘plot’ command to plot the model created in above step]

[Using ‘plot’ command to plot the model created in above step]

This is how our input and output will look like in MATLAB command window:

Input 1:

Loading the census data set:

Input 2:

Fitting the curve to the data:

Input 3:

Plotting the model created above:

Output 1:

Output 2:

Output 3:

As we can see in Output 3, we have obtained a curve that fits our data. Output 1 and Output 2 represent the data attributes and the model respectively.

Conclusion

We use ‘fit’ function in MATLAB to fit a curve to our data set

Fitting a curve is very useful technique used in Machine learning

We can control the type of curve that we want to fit to our data by using the ‘fitType’ argument.

Recommended Articles

This is a guide to Matlab fit. Here we also discuss the introduction, syntax, and different examples with code implementation. You may also have a look at the following articles to learn more –

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