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Introduction to PyTorch SGD

In PyTorch, we can implement the different optimization algorithms. The most common technique we know that and more methods used to optimize the objective for effective implementation of an algorithm that we call SGD is stochastic gradient descent. In other words, we can say that it is a class of optimization algorithms. It very well may be viewed as a stochastic guess of angle plunge enhancement since it replaces the genuine slope (determined from the whole informational collection) by a gauge thereof (determined from an arbitrarily chosen subset of the information). This decreases the computational weight, particularly in high-dimensional streamlining issues, accomplishing quicker emphases in exchange for a lower intermingling rate.

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What is PyTorch SGD?

First, how about we talk about what you mean by upgrading a model. We just need the model to arrive at the condition of greatest precision is given asset requirements like time, processing power, memory, and so on. Streamlining has an expansive extension, and you can likewise change the engineering to improve the model. However, that is something that accompanies instinct created by the experience.

The SGD is only Stochastic Gradient Descent; It is an analyzer that goes under angle plunge, which is a renowned enhancement procedure utilized in AI and profound learning. The SGD enhancer where “stochastic” signifies a framework that is associated or connected up with irregular likelihood. In the SGD analyzer, a couple of tests are being gotten, or we can say a couple of tests are being chosen in an arbitrary way rather than taking up the entire dataset for every cycle. We will utilize torch.optim, which is a bundle, executes various improvement calculations for improving a capacity. The few usually utilized strategies are as of now upheld, and the interface is general enough with the goal that more useful ones can be likewise effortlessly incorporated in the future.

Stochastic Gradient Descent is amazingly fundamental and is only occasionally utilized at this point. One issue is with the overall learning rate identified with the same. Henceforth it doesn’t function admirably when the boundaries are in a few scales since an espresso learning rate will make the preparation slow, while an outsized learning rate may cause motions. Likewise, Stochastic Gradient Descent, for the most part, struggles getting away from the seat focuses. Adagrad, Adadelta, RMSprop, and ADAM, for the most part, handle saddle focus better. SGD with force delivers some speed to the improvement and furthermore helps get away from neighborhood minima better.

Using PyTorch SGD Implementation PyTorch SGD

Now let’s see how we can implement the SGD in PyTorch as follows.

Syntax

Explanation

Using the above syntax, we can implement the SGD optimization algorithm as per our requirement; here, we use different parameters.

specified parameters: Specified parameter means iterable parameters that are used to define the distinct group of parameters.

lrv: lrv is nothing but the learning rate value of the optimized algorithm.

mf: This is an afloat and optional part of this syntax, and mf means momentum factor. The default value of mf is 0.

dm: This is also a float and optional part of the syntax. The default value of dampening is 0.

nm: This is a Boolean and optional part of the syntax, and Nesterov momentum’s default value is false.

PyTorch SGD Examples

Now let’s see different examples of SGD in PyTorch for better understanding as follows.

First, we need to import the library that we require as follows.

import torch

After that, we need to define the different parameters that we want as follows.

btch, dm_i, dm_h, dm_o = 74, 900, 90, 12

Here we define the different parameters as shown in the above code; here, btch means batch size, dm_i means input dimension, dm_o output dimension, and h for hidden.

Now create a random tensor by using the following code as follows.

input_a = torch.randn(btch, dm_i) result_b = torch.randn(btch, dm_o) SGD_model = torch.nn.Sequential( torch.nn.Linear(dm_i, dm_h), torch.nn.ReLU(), torch.nn.Linear(dm_h, dm_o), ) l_fun = torch.nn.MSELoss(reduction='sum')

In the next line, we need to define the learning rate value as per our requirements.

r_l = 0.2

In the next step, we need to initialize the optimizer we want with a forwarding pass.

optm = torch.optim.SGD(SGD_model.parameters(), lr=r_l, momentum=0.9) for values in range(600): p_y = SGD_model(input_a) loss = l_fun(p_y, result_b) if values % 100 == 98: print(values, loss.item())

We illustrated the final output of the above code by using the following screenshot as follows.

Conclusion

We hope from this article you learn more about the PyTorch SGD. From the above article, we have taken in the essential idea of the PyTorch SGD, and we also see the representation and example of PyTorch SGD. Furthermore, from this article, we learned how and when we sequential SGD.

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Learn The Essential Idea Of The Pytorch Gan

Introduction to PyTorch GAN What is PyTorch GAN?

A generative ill-disposed network (GAN) is a class of AI systems imagined in 2014 by Ian Good fellow and his partners. Two neural networks (Generator and Discriminator) rival each other like in a game. This procedure determines how to produce new information utilizing the same measurements as that of the preparation set, given a preparation set.

GANs are a system for showing a DL model to catch the preparation information’s dispersion so we can create new information from that equivalent appropriation. GANs were developed by Ian Good fellow in 2014 and first depicted in the paper Generative Adversarial Nets. They are made of two particular models, a generator and a discriminator. The work of the generator is to bring forth ‘counterfeit’ pictures that appear as though the preparation pictures. The work of the discriminator is to check out a picture and yield whether it is a genuine preparing picture or a phony picture from the generator.

During preparation, the generator is continually attempting to outmaneuver the discriminator by creating better and better fakes, while the discriminator is attempting to improve as an analyst and accurately arrange the genuine and phony pictures. The harmony of this game is the point at which the generator is creating amazing fakes that look as though they came straightforwardly from the preparation information, and the discriminator is left to consistently speculate half certainty that the generator yield is genuine or counterfeit.

Two neural networks

Now let’s have two neural network types of GAN as follows.

1. Discriminator

This is a classifier that examines information given by the generator and attempts to recognize if it is either phony created or genuine information. Preparing is performed utilizing genuine information occurrences, sure models, and phony information cases from the generator, which are used as bad models.

2. Generator

The generator figures out how to make counterfeit information with input from the discriminator. It will likely cause the discriminator to arrange its yield as genuine.

To prepare the generator, you’ll need to firmly incorporate it with the discriminator. Preparing includes taking arbitrary information, changing it into an information occurrence, taking care of it to the discriminator and getting an arrangement, and figuring generator misfortune, which punishes for a right judgment by the discriminator.

This should be remembered for backpropagation: it needs to begin at the yield and stream back from the discriminator to the generator. Therefore, keep the discriminator static during generator preparation.

To prepare the generator, utilize the accompanying general method:

Get an underlying irregular commotion test and use it to create generator yield

Get discriminator arrangement of the irregular commotion yield

Compute discriminator misfortune

Back propagate utilizing both the discriminator and the generator to get slopes

This will guarantee that with each preparation cycle, the generator will improve at making yields that will trick the current age of the discriminator.

PyTorch GAN code example

Now let’s see the example of GAN for better understanding as follows.

First, we need to import the different packages that we require as follows.

import torch import argparse import numpy as nup import chúng tôi as tnn import torch.optim as optm from torchvision import datasets, transforms from torch.autograd import Variable from torchvision.utils import save_image from torchvision.utils import make_grid from torch.utils.tensorboard import SummaryWriter

After that, we need a dataset, so by using the following, we can load and process the dataset as follows.

t_data = transforms.Compose([transforms.Resize((64, 64)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) train_dataset = datasets.ImageFolder(root='anime', transform= t_data) train_loader = torch.utils.data.DataLoader(set=train_dataset, batch_size=batch_size, shuffle=True) def weights_init(m): cname = m.__class__.__name__ if cname.find('Conv') != -1: torch.nn.init.normal_(m.weight, 0.0, 0.02) elif cname.find('BatchNorm') != -1: torch.nn.init.normal_(m.weight, 1.0, 0.02) torch.nn.init.zeros_(m.bias)

Now we need to create the generator network by using the following code as follows.

class Generator(nn.Module): def __init__(self): super(Generator, self).__init__() chúng tôi = tnn.Sequential( tnn.ConvT2d(l_dim, 32* 6, 4, 1, 0, bias=False), tnn.BNorm2d(32 * 6), tnn.ReLU(True), tnn.ConvT2d(32 * 6, 32 * 4, 4, 2, 1, bias=False), tnn.BNorm2d(32 * 4), tnn.ReLU(True), tnn.ConvT2d(32 * 4, 32 * 2, 4, 2, 1, bias=False), tnn.BNorm2d(32 * 2), tnn.ReLU(True), tnn.ConvT2d(32 * 2, 64, 4, 2, 1, bias=False), tnn.BNorm2d(32), tnn.ReLU(True), tnn.ConvT2d(32, 3, 4, 2, 1, bias=False), tnn.Tanh() ) def forward(self, input): output = self.main(input) return output

Explanation

The final output of the above program we illustrated by using the following screenshot as follows.

Conclusion

We hope from this article you learn more about the Pytorch gan. From the above article, we have taken in the essential idea of the Pytorch gan, and we also see the representation of the Pytorch gan. From this article, we learned how and when we use the Pytorch gan.

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Learn The Different Methods Of Junit Assert

Introduction to JUnit assertEquals

JUnit assertEquals is one of the methods in the JUnit Library to verify the equality of two objects. For example, one of the objects may be the program output, and the other may be externally supplied to the code. The intention of the verification is to ensure that the program works as per the desired manner during the test and take action if it is not so.

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JUnit is a very powerful testing tool, in an open-source format, used for testing small as well as large blocks of code, and it has a huge collection of libraries to make testing easy and vibrant. assertEquals is one such library, and there are many more libraries under the assert family, and in this article, we will analyze them.

JUnit Assert Equals Class

The earlier version of JUnit, i.e., JUnit 3, had two different classes: the main class junit.framework.Testcase and an inherited class junit.framework.Assert. All the assert methods are called through the junit.framework.Assert class.

From JUnit version 4 onwards, Annotations were used to mark the tests, and there was no need to extend Testcase class which means that the assert methods are not directly available, but these methods could be imported in static mode from the new Assert class, and that is the reason all such methods under the new class are static methods.

The Assert methods can be imported using the statements

import static org.junit.Assert.*;

Post this import; these static methods can be used without prefix.

JUnit assertEquals Usage

Here the two objects are compared for equality. These objects may have integer, floating-point, and Boolean values. If the values are not equal, then AssertError will be thrown, and if the values are equal, it returns true.

assertEquals(a, b) is the exact code used in the comparison of the objects.

The objects A and B should be of the same data type

Objects A and B will get converted to their wrapper object type

Value in object A will be compared with B.

If values are equal, it will return true; otherwise, it will trigger failure.

JUnit Assert Methods

The class org.junit.Assert class contains the following methods in it, which can be called in to test various conditions.

assertArrayEquals

Method Name Access Specifier Return Type Details

assertArrayEquals – byte[ ] Static void Verifies bytes arrays (a, b) are equal

assertArrayEquals – char[ ] Static void Verifies char arrays (a, b) are equal

assertArrayEquals – double[ ] Static void Verifies double arrays (a, b) are equal

assertArrayEquals – float[ ] Static void Verifies float arrays (a, b) are equal

assertArrayEquals – int[ ] Static void Verifies integer arrays (a, b) are equal

assertArrayEquals – long[ ] Static void Verifies long arrays (a, b) are equal

assertArrayEquals – objects[ ] Static void Verifies object arrays (a, b) are equal

assertArrayEquals – short[ ] Static void Verifies short arrays (a, b) are equal

The exact syntax is

assertArrayEquals ( xxxx, expecteds, xxxx, actuals ) xxxx – Byte, Char, Double, Float, Int, Long, Objects, Short assertArrayEquals ( String, message, xxxx, expecteds, xxxx, actuals) - With message xxxx – Byte, Char, Double, Float, Int, Long, Objects, Short assertEquals

Access Specifier Return Type Method Name /

Detail of the method

static Void assertEquals ( double expected, double actual )

Verifies whether variables (a, b) with floating-point type are equal.

This functionality is not in use. Instead of this, a new function

assertEquals ( double expected, double actual, double epsilon ) is used

static void assertEquals ( double expected, double actual, double delta )

Verifies whether variables (a, b) with floating-point type are equal within a positive delta value

static void assertEquals ( long expected, long actual )

Verifies whether variables (a,b) with long integers type are equal

static void

Verifies whether multiple objects(a, b) are equal. It is not in use. Instead, it is recommended to use assertArrayEquals

static void assertEquals (object expected, object actual )

Verifies whether objects (a, b) are equal.

assertEquals (With Message)

Access Specifier Return Type

Method Name / Detail of the method

static Void assertEquals ( String message, double expected, double actual )

Verifies whether variables (a, b) with floating-point type are equal.

This functionality is not in use. Instead of this, a new function

assertEquals ( String message, double expected, double actual, double epsilon ) is used

static void assertEquals ( String message, double expected, double actual, double delta )

Verifies whether variables (a, b) with floating-point type are equal within a positive delta value

static void assertEquals ( String message, long expected, long actual )

Verifies whether variables (a,b) with long integers type are equal

static void assertEquals ( String message, objects [ ] expected, objects [] actuals )

Verifies whether multiple objects(a, b) are equal. It is not in use. Instead, it is recommended to use assertArrayEquals

static void assertEquals ( String message, object expected, object actual )

Verifies whether objects (a, b) are equal.

Others

Access Specifier Return Type

static void assertFalse (boolean condition)

Verifies whether the given condition is false

static void assertFalse (String message, boolean condition)

Verifies whether the given condition is false (with a message)

static void assertNotNull(String message, Object object)

Verifies whether the given object is not null

static void assertNotsame(String message, Object unexpected Object actual)

Verifies whether the two objects referred are not the same

static void assertNull(String message, Object object)

Verifies whether the given object is null

static void assertsame(String message, Object expected Object actual)

Verifies whether the two objects referred are the same

static void assertTrue (String message, boolean condition)

Verifies whether the given condition is true

Example of assertEquals import org.junit.Test; import static org,junit.Assert.assertEquals; String firstobject = "Jupiter"; String secondobject = "Jupiter"; assertEquals(firstobject , secondobject);

The above code will return true because both the strings have the same value.

Conclusion – JUnit assertEquals

Methods in Assert class, especially assertEquals, facilitate testing of equality conditions and ensure that an error-free product is delivered on time.

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Learn The Implementation Of Db2 Cast With Examples

Introduction to DB2 CAST

DB2 CAST is a function available in DB2 that is used for explicit conversion of the data type of a particular value to another datatype. DB2 comes with the default facility of trying to convert the datatypes of the values if they are not mentioned properly to an expected data type value in all the functions and the instructions that are issued and where there is a necessity of doing so. This is called implicit casting or conversion of a datatype. In this article, we will study how we can convert the value of one data type to another with the help of the CAST() function available in DB2, the syntax of the CAST() function, and the implementation with the help of certain examples.

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Syntax

CAST(any expression or value AS the required datatype)

In the above expression, the expression or value can be any field value of a particular table column or a constant value, or even a variable that holds a certain value. The required data type is the data type that you want the value mentioned as the expression to be converted to. We can specify any data type, we want the data to convert to such as int, string, etc.

Implicit casting in DB2 Examples

Consider the following example where we are trying to add 6 integer value with ‘16’ string value using the plus(+) operator and retrieve the result –

6 + ’16’ as result ;

The execution of the above query statement gives the following output with the resultant value being an integer which is 22. Over here, DB2 firstly converted the string ‘16’ to an integer value and then went for doing the addition.

Let us consider one more example where we will be using the concatenation operator which is a string operator and works only with string. When we use the same values specified in the above example 6 and ‘16’ which are an integer and a string, using the following query statement –

The execution of the above query statement gives the following output with the resultant value being a string which is 616. Over here, DB2 firstly converted the integer 6 to string value and then goes for doing the concatenation.

Now, let us see, how we can cast the values explicitly using the CAST() function. If the values cannot be cast by the DB2 due to incompatible type of values, it throws an error saying as shown in the below image –

Now, let us try to convert a decimal value to an integer value using the DB2 CAST() function which will do explicit datatype conversion for us. Consider the following query statement where we are trying to convert a decimal number 14.562 to and integer value to get a rounded whole number. We can do this by using the following query statement with the CAST() function in it –

The output of the above query statement is a rounded integer value of 14.562 which is 14 as shown below –

Now, consider a decimal number 16.5454 which we want to cast to a decimal number itself but a lower scale value to it. We can even do this by using the CAST() function and our query statement, in this case, will look as follows –

CAST ( 16.5454 DEC (4,2)) AS result;

The output of the above query statement’s execution is as shown below with the number rounded and cast to a decimal value of two places after a decimal point has a different scale than the original one –

Let us try to convert a value of the TIMESTAMP datatype to the TIME datatype, in order to get only the time value in the output. We will convert the current timestamp value to time using the cast function in the following query statement to retrieve the current time value of the system –

CAST (CURRENT TIMESTAMP AS TIME) as result;

The execution of the above query statement gives the following result where we get the value of the current time of the system from the current TIMESTAMP value which is as shown below –

We can even convert the current timestamp value to the date datatype in order to retrieve today’s date of my system and using the following query statement and the CAST() function –

CAST (CURRENT TIMESTAMP AS DATE) as result;

The output of the execution of the above query statement is as shown below with the date value in it which is the current system date.

Let us try one last example where we will cast the value of string data type to a DATE datatype explicitly by using the CAST() function in DB2 RDBMS. Let us consider a random date value say ‘2030-01-27’. We will try to convert this string to DATE datatype by using the following query statement –

CAST(‘2030-01-27’ AS DATE) result;

The output of the execution of the above query statement in DB2 DBMS gives the following resultant value with the date specified being cast to a DATE data type as shown below –

Conclusion

IN DB2 RDBMS, the datatypes are internally converted into required datatypes while using the functions and manipulations and while doing operations. However, if we want to explicitly convert a particular value to a required datatype then we can make use of the CAST() function to convert the value to a different datatype explicitly in DB2 RDBMS. We can convert the data type f the values to any in-built and user-defined datatype using the CAST() function.

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Learn The Examples And Advantages

Introduction to PostgreSQL STRING_AGG()

PostgreSQL supports various kinds of aggregate functions, The STRING_AGG() function is one of the aggregate functions which is used to concatenate the list of strings, and it will add a place to a delimiter symbol or a separator between all of the strings. The separator or a delimiter symbol will not be included at the end of the output string. The PostgreSQL STRING_AGG() function is supported by PostgreSQL 9.0 version, which performs the aggregate option related to the string. We can use various separators or delimiter symbols to concatenate the strings.

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Syntax

Explanation:

The STRING_AGG() function takes input ORDER BY clause is an optional and other two arguments as follows:

expression: This is a character string that is any valid expression.

separator/delimiter: This defines the separator/delimiter used for string concatenation.

The ORDER BY clause is optional and defines the order of concatenated string results.

The ORDER BY has the syntax as follows:

How does PostgreSQL STRING_AGG() function works?

The input expression needed should be a character string data type. We can also use other data types but only need to ensure that we have explicitly cast other data types to the character string data type.

The PostgreSQL STRING_AGG() returns us the result in string type.

The STRING_AGG() is generally used with the GROUP BY clause like we use other PostgreSQL aggregate functions such as MIN(), MAX(), AVG(), SUM(), COUNT(), etc.

Examples to Implement PostgreSQL STRING_AGG() function

We will create a table named ‘student’ and ‘course’ by using the CREATE TABLE statement as follows:

STUDENT TABLE:

create table student ( stud_id serial PRIMARY KEY, stud_name VARCHAR(80) NOT NULL, stud_grade CHAR(1) NOT NULL, stud_country VARCHAR(80) NOT NULL, course_id int NOT NULL );

COURSE TABLE:

create table course ( course_id serial PRIMARY KEY, course_name VARCHAR(80) NOT NULL );

Now, we will insert some data into the ‘course’ table by using the INSERT INTO statement as follows:

INSERT INTO course(course_name) VALUES ('Computer'), ('Mechanical'), ('Civil'), ('Electrical');

Illustrate the above INSERT statement’s result using the following SQL statement and snapshot.

select * from course;

INSERT INTO student(stud_name,stud_grade,stud_country,course_id) VALUES ('Smith','A','USA',1), ('Johnson','B','USA',2), ('Williams','C','USA',3), ('Jones','C','Canada',1), ('Brown','B','Canada',2), ('Davis','A','Canada',3), ('Aarnav','A','India',1), ('Aarush','B','India',2), ('Aayush','C','India',3), ('Abdul','C','UAE',1), ('Ahmed','A','UAE',3), ('Ying', 'A','China',1), ('Yue','B','China',2), ('Feng', 'C','China',3), ('Mian','C','South Korea',1), ('Fei','B','South Korea',2), ('Hong','A','South Korea',3);

Illustrate the above INSERT statement’s result using the following SQL statement and snapshot.

select * from student;

SELECT c.course_name AS "course name", s.stud_name AS "student name" FROM course c RIGHT JOIN student s ON c.course_id = s.course_id ORDER BY 1;

Illustrate the result of the above statement by using the following snapshot.

We can concatenate the student names by using the STRING_AGG() function by modifying the above SQL statement as follows:

SELECT crs.course_name AS "course name", string_agg(stud.stud_name, ', ') AS "student list" FROM course crs JOIN student stud ON crs.course_id = stud.course_id GROUP BY 1 ORDER BY 1;

Illustrate the result of the above statement by using the following snapshot.

SELECT stud_grade, STRING_AGG(stud_name,', ') AS StudentsPerGrade FROM student GROUP BY stud_grade ORDER BY 1 ;

Illustrate the result of the above statement by using the following snapshot.

In the above example, the resulting snapshot shows us the students concatenated by a comma separator with a similar grade obtained.

SELECT STRING_AGG(stud_name, ', ') AS "student_names", stud_country FROM student GROUP BY stud_country;

Illustrate the result of the above statement by using the following snapshot.

In the above example, we observe that the code groups and concatenates all students from the same country, utilizing a comma separator.

Advantages

We can control the order of the result by using the ORDER BY clause.

The PostgreSQL STRING_AGG() function returns the result in string format.

We can use the STRING_AGG() function to concatenate all strings and add a delimiter symbol or separator between them.

The PostgreSQL STRING_AGG() supports various types of delimiter symbols or separators and does not include delimiter symbols or separators at the end of the string.

Conclusion

From the above article, we hope you understand how to use the PostgreSQL STRING_AGG() function and how the PostgreSQL STRING_AGG() function works. Also, we have added several examples of the PostgreSQL STRING_AGG() function to understand it in detail.

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Learn The Examples For Implementation

Introduction to PostgreSQL REGEXP_REPLACE

The regular expression is a sequence of characters, the short name for the list of strings. If any string matches with any of the strings, which is part of a list of the strings defined by the regular expression. PostgreSQL supports the regular expression, and the function provided by PostgreSQL is used to replace substrings with a new substring that matches a POSIX regular expression. The PostgreSQL REGEXP_REPLACE() function uses a POSIX regular expression pattern.

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Syntax:

REGEXP_REPLACE(input_string, regex_pattern, replace_string,[, flags])

Explanation:

input_string: This defines the input string in which replacement should be taken place for a specified pattern, a POSIX regular expression.

regex_pattern: This defines the POSIX regular expression to match the string.

flags: This flag is used to control the behavior of the REGEXP_REPLACE() function, This can have the value of one or more characters.

Replace_string: This string defines a string that replaces the substring, which matches the POSIX regular expression pattern.

Examples of PostgreSQL REGEXP_REPLACE

Here are the following examples of implementing the PostgreSQL REGEXP_REPLACE function

Consider the following name format like the first name and then last name:

‘Jacob David’

Suppose we want to re-arrange the last name and the first name for purposes like the last name and then the first name. So, we can use the PostgreSQL REGEXP_REPLACE() function to do this as follows:

SELECT REGEXP_REPLACE('Jacob David', '(.*) (.*)', '2, 1');

Illustrate the result of the above statement by using the following snapshot.

Example #2

Example, to remove the string, consider we have a string in the following as follows:

"xyz54321ABC"

Now, we will remove all alphabets characters from the above string by using the following statement:

SELECT REGEXP_REPLACE('xyz54321ABC', '[[:alpha:]]', '', 'g');

Illustrate the result of the above statement by using the following snapshot.

Now, we will remove all digits from the above string by using the following statement:

SELECT REGEXP_REPLACE('xyz54321ABC', '[[:digit:]]', '', 'g');

Illustrate the result of the above statement by using the following snapshot.

In the above examples, we have used the following regular expressions.

'[[:alpha:]]' '[[:digit:]]'

Also, we have used the replacement string as ‘’ and the flag ‘g’ we have used to instruct the PostgreSQL REGEXP_REPLACE function to replace all of the occurrences of the matched string and not just the first occurrence.

Example #3

Remove multiple occurrences of the spaces. Consider the following example, which removes more than one space that occurred in a string. Consider the following statement to do the same.

SELECT REGEXP_REPLACE('PostgreSQL is awesome database', '( ){2,}', ' ', 'g');

Illustrate the result of the above statement by using the following snapshot.

Example #4

We will create a table named ‘student’ by using the CREATE TABLE statement as follows:

create table student ( stud_id serial PRIMARY KEY, stud_fname VARCHAR(80) NOT NULL, stud_lname VARCHAR(80) NOT NULL );

Now, we will insert some data into the student table by using the INSERT INTO statement as follows

INSERT INTO student(stud_fname,stud_lname) VALUES ('Smith','Johnson'), ('Williams','Jones'), ('Brown','Davis');

Illustrate the above INSERT statement’s result using the following SQL statement and snapshot.

select * from student;

Consider the following SQL statement where we are checking whether the stud_lname is having ‘Jo’ substring, and if it exists, then we replace it with ‘K.’

SELECT REGEXP_REPLACE(stud_lname , 'Jo', 'K') AS "New Name" FROM student;

Also, consider the other example,

Consider the following SQL statement where we are checking whether the stud_lname is having ‘s’ substring, and if it exists, then we replace it with ‘K’

SELECT REGEXP_REPLACE(stud_lname , 's', 'K') AS "New Name" FROM student;

Illustrate the result of the above SQL statement by using the following snapshot.

Advantages of using PostgreSQL REGEXP_REPLACE () function

1. The PostgreSQL REGEXP_REPLACE () function supports various flags,

Consider examples like:

flag ‘i’ : match case-insensitively

flag ‘g’: search globally for each occurrence.

2. The PostgreSQL REGEXP_REPLACE() replaces all occurrences of the substring with the new string.

3. We can use The PostgreSQL REGEXP_REPLACE() function, the substring in variable length or dynamic strings.

Conclusion

From the above article, we hope you understand how to use the PostgreSQL REGEXP_REPLACE() function and how the PostgreSQL REGEXP_REPLACE() function works. Also, we have added several examples of the PostgreSQL REGEXP_REPLACE() function to understand it in detail.