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What is an Investment Partnership?

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How does Investment Partnership work?

In an investment partnership, a fund manager becomes a partner in the business by investing cash and only then earns the authority to exercise influence on the decision-making of the business. Therefore, the fund manager’s capital contribution to the business should be higher than that of the other general limited partners, who are mere investors and have no say in the investment decisions. The fund manager becomes eligible for additional profit distribution over and above the interest holding (owing to invested capital) if the profits derived from the business exceed a certain level of returns, which is also known as the hurdle rate.

Examples of Investment Partnership

Investment partnership businesses usually invest in alternative investment funds. Some of the most common examples of such investments are mentioned below:

1. Hedge Fund

Typically, institutional investors, high-net-worth individuals, and other accredited investors invest in this type of financial instrument. This investment vehicle is usually used to hedge risk by simultaneously buying and shorting assets in a long-short equity strategy, hence the “hedge fund.”

2. Private Equity

It is a type of alternative investment class that comprises capital that is invested in unlisted companies. Generally, this type of fund either directly invests in private companies or buyout public companies leading to their delisting from the stock exchange. It is considered to be a high-risk, high-return investment.

3. Venture Capital 4. Mutual Fund

In this type of financial instrument, the fund manager pools money from a large number of investors and then invests the raised capital in different types of financial securities, such as equity stocks, bonds, short-term debt funds, etc. The collective holdings of a mutual fund are popularly called its portfolio.

How is an Investment Partnership taxed?

An investment partnership enjoys favorable tax treatment, as reflected from the instances mentioned below.

In North Carolina, a firm operating as an investment partnership is not considered to be doing business. Hence, such a firm is not mandated to file an income tax return, nor is it liable to pay any income tax on behalf of its non-resident partners.

In Illinois, an investment partnership is not required to pay replacement tax for tax years ending on or after December 31, 2004. Also, its non-resident partner needs to pay tax on the income passed through the investment partnership, only if the partner’s contribution to the business is made in connection with a business conducted only partially within Illinois.

Advantages of Investment Partnership

An investment partnership falls in the category of alternative investment funds, which means the investments go into risky securities that offer higher return potential.

The regulations are limited for these investment funds. Hence, the fund managers enjoy a greater degree of discretion regarding managing the investments to generate higher returns.

These investment funds, in many cases, put their money into companies that are just in their initial phase of business. These financing options help these start-ups secure their growth funding.

One of the best things about investment partnerships is that the general partners or investors outsource the fund management to professionally trained fund managers.

The profit distribution generated from the business enjoys favorable tax treatment.

First, in most cases, the general partners don’t possess much knowledge about the business, especially financial statements. Likewise, the investors have a very limited idea about how the fund managers are managed.

Given these are high-risk, high-return investments, a wrong move with regard to an investment strategy can prove lethal and may wipe out the entire wealth accumulated over the years.

Small retail investors seldom get an opportunity to invest in an investment partnership business as the fund managers usually seek funding from wealthy and accredited investors only.

Key Takeaways

Some of the key takeaways of the article are:

In investment partnerships, more than 90% of the business assets are held in the form of investments in financial instruments. As a result, more than 90% of its income comes from these financial assets.

The fund manager invests cash in the business to become one of the partners, after which he/ she can exercise influence on the decision-making. Typically, the fund manager’s contribution is higher than that of the other general limited partners.

Investment partnership is classified as alternative investment funds. These funds invest in risky securities that offer higher return potential. Hence, even a slight mistake can wipe out the entire wealth accumulated over the years.

Usually, fund managers prefer wealthy and accredited investors over small retail investors.

Conclusion

Apart from providing the required growth funding to start-ups, investment partnerships help investors earn amazing returns on their investments. In this way, this business model facilitates better efficiency in the financial market. However, the general partners or investors remain exposed to significant risk due to the investments’ nature and lack of transparency.

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How Does Cybersecurity Assessment Work?

Cybersecurity is now the greatest concern in this digital age. We’ve seen 160 million data compromise victims According to the most recent reports, the number of records was much higher than in the previous year. Unsecured cloud databases are the main reason for this rapid rise.

Do you not think this is a warning to all companies on the market? It is, but it doesn’t mean that everything is secure online. It all comes down to your cybersecurity program and security protocols.

Conducting a cybersecurity audit is all you have to do. Many people confuse cybersecurity audits with cybersecurity assessments. The terms mean different things and have different processes.

This blog will help you to understand the differences between audit and cyber assessment. You will also learn when to implement it. Let’s get started.

What’s a Cybersecurity Assessment?

Cybersecurity assessment is an in-depth investigation of cyber security risks and recommendations for best security practices. This assessment is intended for IT-related and IT-related businesses only.

In some cases it can also be used to assess business units. This process is used by companies to assess how secure their systems and organization are, and to identify the areas that need attention. This assessment will be performed by a cybersecurity analyst or consultant.

What Does Cybersecurity Assessment Look Like?

This is the general approach to conducting a cybersecurity assessment:

First, identify all relevant systems, processes, data.

Do a cybersecurity risk assessment to determine vulnerabilities and threats as well as the likelihood of them happening in the future.

Recommendations for the best security practices should be made.

Communication between the management, IT team, security, and the analyst performing the assessment should be maintained.

It is important to establish a timeline for cybersecurity assessments. They can take several days or even weeks depending on the scale of the assessment and the methodology used.

This is because you can assess how secure your company is against cyber threats. You can also estimate the risk and cost.

When is a Cybersecurity Assessment conducted?

Although cybersecurity assessments are ongoing, they can be done at any time. It is done, however, for the following:

Before you apply a new IT system, or network security technology.

Before you start a new operation in any area of your company.

Before outsourcing or hiring employees with access to critical information.

If you have to conform with industry standards or regulatory agencies.

If your organization has undergone a major infrastructure change.

Cybersecurity Assessment Benefits:

Companies can identify cybersecurity gaps and then work to fix them.

Estimates financial losses due to poor security practices and a lack of cybersecurity precautions.

Provides guidance on how to create a solid strategy against cyberattacks.

Learn about the downsides of cybersecurity assessment.

– This is an expensive process that is often not affordable for small businesses.

What is a Cybersecurity Audit?

Cybersecurity audit is a process that is mostly used to assess IT systems. It includes the assessment of records, logs and change management controls. Physical security access controls can also be applied.

Configuration parameters, policies, standards, and policies are all included. This includes penetration testing to determine if vulnerabilities exist to give organizations an objective opinion on whether current security controls are sufficient or need to be improved. It is an independent evaluation of the IT infrastructure and systems.

What Does a Cybersecurity Audit Look Like?

Certified internal auditors, information security professionals or an external third party can conduct a cybersecurity audit. The audit is performed in two phases.

Phase 1: Internal Audit

This phase is performed by internal auditors or information security specialists. This phase is extremely detailed and can result in high company costs if it’s implemented.

– This phase includes an evaluation of current systems. Additionally, vulnerability at different levels are considered.

Phase II – Third-Party Audit

Independent auditors are independent from the company and perform this phase. It’s an objective assessment of IT systems to validate security controls.

When is a Cybersecurity Audit Conducted

A cybersecurity audit is usually done when IT systems are affected by changes in policies or functions. Depending on the frequency of system changes, policies and procedures, the company might opt to have it done at intervals such as annually or quarterly.

Cybersecurity Audit Benefits:

This tool allows you to find vulnerabilities and fix them.

Determines the effectiveness of controls.

Helps to identify procedures for monitoring or handling security incidents.

Offers an objective view of your business.

Cybersecurity Audit Drawbacks:

– This is not recommended for small businesses that do not have the resources to conduct proper testing.

It can take time and delay new products or projects.

What’s the Difference between Cybersecurity Audit and Assessment?

It’s now time to understand the differences between cybersecurity audit and assessment. We have listed the main points that will help you quickly understand the difference.

Cybersecurity assessment and audit are two different types of security compliance processes. However, they differ in the focus areas that they cover. An audit, on the other hand, is more specific.

Cybersecurity assessment includes areas such as vulnerability scanning, risk analysis and network access controls. Cyber audit, on the other hand, focuses exclusively on IT systems that store or process company information.

– Internal staff are responsible for assessment, while an external auditor conducts audits.

– An audit may be more detailed than an assessment.

Assessment can be used to assess the security of your organization. An audit is used to validate the effectiveness and efficiency of security controls.

You can save money by performing a cybersecurity assessment. Some steps can be skipped, or reduced. An audit, on the other hand, is more thorough and may result in higher costs for the company.

– An auditor will only be concerned with IT security systems.

-The assessment covers a variety of areas, such as vulnerability scanning, risk analysis and access controls for networks & system. An audit does not assess infrastructure and IT systems.

Conclusion:

This article should have helped you to understand the differences between audit and cybersecurity assessment. Both processes are different and you don’t need to be done together. An audit is also a good idea if you are new to information security. It helps to validate security controls.

If you are an expert in the field, it would suffice to conduct a review of the entire process before making any major changes. The costs of an audit will be cheaper if you are able to do the assessment correctly.

How Does Mysql Average Work? (Examples)

Introduction to MySQL Average

To find the average of a field in various records, we use MySQL Average. We can find the average of the records of a column by using the “Group by” clause.MySQL AVG() function is an aggregate function that allows you to calculate the average value of column values. To calculate the average of the distinct values from a column, we can use the “DISTINCT” operator. AVG function will ignore “NULL” values.

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AVG function can be used in the subqueries. It can be used along with the control flow functions like IF, IFNULL, NULLIF, and CASE.

Syntax:

Here above is the syntax of the Average function. The average function returns the data of the INT datatype.

How Does MySQL Average Work?

Now let us create a table, perform the average function on the column, and retrieve the data.

Query:

create table Freelancer_data ( Freelancer_id INT, Freelancer_Name VARCHAR(20), Type_of_work VARCHAR(30), No_of_submission INT, No_of_pages_submitted INT, EMAIL varchar(30) ); 1. Insert data Into the Table

Query:

2. Select the Data from the Table select * from freelancer_data;

Output:

3. Now let us find the average Pages Submitted by the Freelancer

Query:

select AVG(No_of_pages_submitted) as "Average papers submitted" from freelancer_data;

Output:

Now let us find the average pages submitted by the freelancer based on the Type_of_work using the “GROUP BY” clause:

Query:

select AVG(No_of_pages_submitted) as "Average papers submitted", Type_of_work from freelancer_data group by 2;

Output:

or

Query:

select AVG(No_of_pages_submitted) as "Average papers submitted", Type_of_work from freelancer_data group by Type_of_work;

Output:

4. Using AVG() With “HAVING” Clause

To set conditions for the output of the average values, we use the “HAVING” clause.

Query:

select AVG(No_of_pages_submitted) as "Average papers submitted", Type_of_work from freelancer_data group by 2

or

Query:

select AVG(No_of_pages_submitted) as "Average papers submitted", Type_of_work from freelancer_data group by Type_of_work

Output:

5. Using AVG() with sub-query

In the sub-query, we find the average based on “type_of_work”. The outer query gets the average for the output of the inner query.

Query:

SELECT AVG(AVG_PAGES) as "average pages"/* Outer query*/ FROM (select AVG(NO_OF_PAGES_SUBMITTED) AS "AVG_PAGES" /* -- inner query --*/ from freelancer_data GROUP BY TYPE_OF_WORK) AVG;

Output:

6. Using AVG() with Control functions

Let us find the average of the pages submitted if the “no_of_submission” is 3. Else, consider it as “null”. As AVG ignores the NULL values, the below output is average for only the submission count is =3.

SELECT AVG(IF(No_of_submission= 3, No_of_pages_submitted, NULL))/No_of_submission 'Avg pages' FROM freelancer_data;

Output:

Example to Implement MySQL Average

Now let us consider other simple examples below:

Query:

create table sample_AVG ( ID INT, NAME VARCHAR(30), DEPT_NO INT, SALARY FLOAT(10,2) ); 1. Insert data Into the Table

Query:

insert into SAMPLE_AVG values (1278,'Jack', 2, 90000); insert into SAMPLE_AVG values (2278,'Will', 2, 80000); insert into SAMPLE_AVG values (3278,'Rose', 3, 78000); insert into SAMPLE_AVG values (4278,'Ben', 3, 45000); insert into SAMPLE_AVG values (5278,'Stuart', 3, 67000); insert into SAMPLE_AVG values (6278,'Rample', 4, 57000); insert into SAMPLE_AVG values (7278,'Jackern', 4, 47000); insert into SAMPLE_AVG values (8278,'fred', 4, 68000); insert into SAMPLE_AVG values (9278,'Gram', 4,86000);

Query:

select * from SAMPLE_AVG;

Output:

2. Now let us find the average of salary from the Table

Query:

select AVG(salary) as "Average salary" from sample_avg;

Output:

3. Now let us find the average SALARY based on the DEPT_NO using the “GROUP BY” Clause

Query:

select AVG(salary) as "Average salary", Dept_no from sample_avg group by 2; /* - - Position of the column - -*/

Output:

or

Query:

select AVG(salary) as "Average salary", Dept_no from sample_avg group by dept_no;

4. Using AVG() With “HAVING” Clause

To set conditions for the output of the average values, we use the “HAVING” clause.

Query:

select AVG(salary) as "Average salary", Dept_no from sample_avg group by 2

or

Output:

Query:

select AVG(salary) as "Average salary", Dept_no from sample_avg group by dept_no

Output:

5. Using AVG() with sub-query

In the sub-query, we find the average based on “dept_no”. The outer query gets the average for the output of the inner query.

Query:

SELECT AVG(AVG_SAL) as "average salary" FROM (select AVG(salary) as "Avg_sal", Dept_no from sample_avg group by 2 )AVG;

Output:

6. Using AVG() with Control Functions

Here let us find the SALARY average if the “DEPT_NO” is in 3, 4 else, consider it “null”. As AVG ignores the NULL values, the below output is average for only the submission count is 3 and 4.

Query:

SELECT AVG(IF(DEPT_NO IN (3,4), SALARY, NULL))/COUNT(DEPT_NO) 'AVGSALARY' FROM SAMPLE_AVG;

Output: 

Conclusion

To find the average of a field in various records, we use MySQL Average. We can find the average of the records of a column by using the “Group by” clause.

MySQL AVG() function is an aggregate function that allows you to calculate the average value of column values.

To calculate the average of the distinct values from a column, we can use the “DISTINCT” operator. AVG function will ignore “NULL” values.

AVG function can be used in the subqueries. It can be used along with the control flow functions like IF, IFNULL, NULLIF, and CASE.

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How Does Unnest Function Work In Postgresql?

Introduction to PostgreSQL Unnest

PostgreSQL unnest is the type of array functions; the unnest function in PostgreSQL is basically used to expand the array into rows. Unnest function is converting an array into a table-like structure; we can also generate a table structure of an array using unnest function in PostgreSQL. Unnest array function is very useful in PostgreSQL for expanding the array into the set of values or converting the array into the table structure. If we need a table-like structure of array simultaneously, we have to use the unnest function on the array on which we have converted array data into the table-like structure.

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Syntax:

Below is the syntax:

Unnest(Any_array_number (Any array element number which was we have used with unnest function) Unnest (Any_array_text (Any array element text which was we have used with unnest function) Select unnest (any_array (Number of text value which was used in array.)) Select unnest (any_array (Number of text value which was used in array.)) limit number;

Below is the parameter description syntax of Unnest array function:

Unnest: This function we defined in PostgreSQL to set the element in a table-like structure. We have used unnest element with text as well as number array. We must define the number or text element with the unnest array function in PostgreSQL.

Any array text: This is defined as using an array of text values to convert the array into the table-like structure in PostgreSQL by using unnest array function.

Any array number: This is defined as a number. Using the unnest array function, we have used any number values to convert an array into the table-like structure in PostgreSQL.

Select: This operation selects the array’s value using the unnest array function in PostgreSQL. Select operations are very useful and important while using unnest array function in PostgreSQL.

Limit: In PostgreSQL, we have also using the limit clause with unnest function in PostgreSQL. While using the limit clause will show the number as per the limit we have used with unnest function.

How does Unnest Function work in PostgreSQL?

The first overload of usefulness aims to transform the values from an array into a single array.

In the old version of PostgreSQL, when we have to convert an array into the table structure, we have to use an array with the cross join. After joining the array with cross join, we generated the same series.

After generating the series, all the array elements will be structured into the table using cross join and generate series function.

The below example shows that in the old version, we used cross join with generate series function to convert the array into the table structure.

Code:

SELECT test_unnest[A] as array_element FROM  (SELECT ARRAY[11, 12, 13, 14, 15, 16, 17, 18, 19, 20] As test_unnest) as gen_ser CROSS JOIN generate_series(1, 10) As A;

Output:

The above example shows that we have used array element as [11, 12, 13, 14, 15, 16, 17, 18, 19, 20]; we have used this array element with generate series function for converting an array into the table like structure.

We have also used cross join with the array element and generated a series function for converting an array into a table-like structure.

By using unnest function, we do not need to use a cross join or generate series function to convert an array into a table like structure. We have simply using unnest function with an array.

In PostgreSQL, we have also utilised an array for a number or text to create a table-like structure.

We can also use unnest function with an order by clause. Using an unnest function, we have an order by clause with a 1 in it.

We have also use the limit clause with unnest function. After using a limit clause, it will show the output as per the number which was we have used with the limit.

Examples of PostgreSQL Unnest

Different examples are mentioned below:

Example #1 – Unnest function with array as a number.

Below example shows that unnest function with array as a number. In the below example, we use an array of numbers as [1, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10].

Code:

SELECT unnest(ARRAY[1, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10]);

Output:

Example #2 – Unnest function with array as text.

Below example shows that unnest function with array as text. In the below example, we are using an array of text as [‘ABC’, ‘PQR’, ‘XYZ’, ‘ABC’, ‘PQR’, ‘XYZ’, ‘ABC’, ‘PQR’, ‘XYZ’, ‘ABC’, ‘PQR’, ‘XYZ’].

Code:

SELECT unnest(ARRAY['ABC', 'PQR', 'XYZ', 'ABC', 'PQR', 'XYZ', 'ABC', 'PQR', 'XYZ', 'ABC', 'PQR', 'XYZ']);

Output:

Example #3 – Unnest function with an order by clause.

Below example shows that unnest function with an order by clause. In the below example, we use an array of numbers as [1, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10].

SELECT unnest(ARRAY[1, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10]) order by 1;

Output:

Example #4 – Unnest function with limit clause.

Below example shows that unnest function with a limit clause. In the below example, we use an array of numbers as [1, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10].

Code:

SELECT unnest(ARRAY[1, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10]) limit 5; SELECT unnest(ARRAY[1, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10]) limit 8;

Output:

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How Does Agg Operation Work In Pyspark?

Introduction to PySpark AGG

PYSPARK AGG is an aggregate function that is functionality provided in PySpark that is used for operations. The aggregate operation operates on the data frame of a PySpark and generates the result for the same. It operates on a group of rows and the return value is then calculated back for every group. The function works on certain column values that work out and the result is displayed over the PySpark operation.

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There are certain aggregate functions in PySpark that are used for the operation in the Python PySpark model. Some of them include the count, max ,min,avg that are used for the operation over columns in the data frame. The function calculates on the set of values given and returns a single value.

Syntax for PySpark Agg

The syntax is as given below:

c.agg({'ID':'sum'}).show()

c: The new data frame post group by.

agg: The Aggregate function to be used based on column value.

sum: Aggregation valueas Sum.

Output:

How does AGG Operation Work in PySpark?

Aggregation is a function that aggregates the data based on several logical rules over the PySpark data frame. It operates over a group of rows and calculates the single return value based on every group. The aggregate function returns the same values every time when they are called. We have a defined set of aggregate functions that operate on a group of data in PySpark and the result is then returned back in memory.

We have several defined aggregate function having a defined functionality for several functions, some of the aggregate function includes avg , max , min ,count , the sum that are used for various data level operation.

Let’s check the creation and working of the Aggregate function with some coding examples.

Examples of PySpark AGG

Let us see some examples of how PYSPARK  AGG operation works. Let’s start by creating simple data in PySpark.

data1  = [{'Name':'Jhon','ID':21.528,'Add':'USA'},{'Name':'Joe','ID':3.69,'Add':'USA'},{'Name':'Tina','ID':2.48,'Add':'IND'},{'Name':'Jhon','ID':22.22, 'Add':'USA'},{'Name':'Joe','ID':5.33,'Add':'INA'}]

A sample data is created with Name, ID, and ADD as the field.

a = sc.parallelize(data1)

RDD is created using sc.parallelize.

b = spark.createDataFrame(a) b.show()

Created Data Frame using Spark.createDataFrame.

Output:

Let us try to aggregate the data of this PySpark Data frame.

We will start by grouping up the data using data.groupBy() with the name of the column that needs to be grouped by.

c = b.groupBy('Name') c.count() c.count().show()

Output:

The chúng tôi function takes up the column name and the aggregate function to be used. Let’s check this with examples. The SUM function sums up the grouped data based on column value.

c.agg({'ID':'sum'}).show()

The MAX function checks out the maximum value of the function based on the column name provided.

c.agg({'ID':'max'}).show()

The COUNT function count of the total grouped data was included.

c.agg({'ID':'count'}).show()

Output:

The first function aggregates the data and collects the first element from the PySpark data frame.

c.agg({'ID':'first'}).show()

The last function aggregates the data and fetches out the last value.

c.agg({'ID':'last'}).show() c.agg({'ID':'avg'}).show()

Output:

The MEAN function computes the mean of the column in PySpark. It is an aggregate function.

c.agg({'ID':'mean'}).show()

The STDDEV function computes the standard deviation of a given column.

c.agg({'ID':'stddev'}).show()

The collect_list function collects the column of a data frame as LIST element.

c.agg({'ID':'collect_list'}).show()

The collect_set function collects the data of the data frame into the set and the result is displayed.

c.agg({'ID':'collect_set'}).show()

Output:

Note:

PySpark AGG is a function used for aggregation of the data in PySpark using several column values.

PySpark AGG function returns a single value out of it post aggregation.

PySpark AGG function is used after grouping of columns in PySpark.

PySpark AGG functions are having a defined set of operations for a list of columns passed to them.

PySpark AGG involves data shuffling and movement.

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How Does C++ Shuffle Work With Examples

Definition of C++ shuffle()

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Syntax:

void shuffle (RandomAccessIterator first, RandomAccessIterator last, URNG&& g);

Let us check what each keyword and parameter signify in this function

We call the RandomAccessIterator. The first parameter points to the position of the first element in the range, which will be rearranged.

The second parameter points to the last element in the range, which will be rearranged. For this also, it will be pointing to random access iterator.

The last parameter, g, is a special function object that helps us generate random numbers. It is called a uniform random number generator.

The return value of this function will be none.

How does C++ shuffle Work?

Using the C++ shuffle function is easy. Let us check how it works.

Code:

{ for (int i: vec) { std::cout << i << ‘ ‘; } } int main() { std::shuffle(vec.begin(), vec.end()); shuf(vec); return 0; }

We must import the vector library to use the shuffle() function. The user-defined function displays the shuffled vectors. We have created a vector with a few numbers in the main function. The shuffle() function has a beginning and an end which takes the vector elements and shuffles them. Once this is done, we call the function to print the shuffled array. We have not specified the random generation function; hence it will take the default function, which can be used. It will rearrange the elements in the vector. The function will swap the value of each element with any other randomly picked element from the same vector. It works with generators that work like the rand() function. To use this function without a generator, we can use the random_shuffle(). Let us check a few examples to help us understand the function better.

Examples of C++ shuffle()

Following are the examples given below:

Example #1

using namespace std; int main () { unsigned num = chrono::system_clock::now().time_since_epoch().count(); shuffle (shuf.begin(), shuf.end(), default_random_engine(num)); cout << “The numbers after shuffling are:”; for (int& x: shuf) cout << ‘ ‘ << x; cout << ‘n’; return 0; }

Output:

Code Explanation: The above code is an example of a shuffle function. We have used the iostream, array, random, and Chrono libraries. Here the Chrono library is used to create a random generator. We have taken an array with a size of 8 integers. Here we have defined this array, and then we are using the random generator function using the Chrono library. We are generating a random number using the epoch() and now() function which is a part of the clock library. It creates a pattern using which the numbers are shuffled. Then we have called the shuffle function, where we define the start and end of the array, and the third parameter is the variable that stores the calculation for random number generation. We then print the randomly shuffled array at the end of the program. Below is the output of the above program.

Example #2

Code:

using namespace std; void edu_shuffle(int arr[], int n) { unsigned rnd = 0; shuffle(arr, arr + n, default_random_engine(rnd)); for (int i = 0; i < n; ++i) cout << arr[i] << " "; cout << endl; } int main() { int arr[] = { 18, 23, 30, 47, 87, 49}; int num = sizeof(arr) / sizeof(arr[0]); edu_shuffle(arr, num); return 0; }

Code Explanation: In this program, we have imported a library and created a user-defined function, edu_shuffle. This function first creates an unsigned integer variable that will store the random generation calculation. We then use the shuffle() function, passing the start and end of elements between which the shuffling should occur. In place of random generation, we have used an inbuilt function default_random_engine to create a random number. In the main function, we calculated the end of the elements sent to the edu_shuffle function. We have used the sizeof function. We have sent these as parameters to the user-defined function, which helps execute the shuffle() function. The output of the above function will be as below:

Advantages of C++ shuffle()

The shuffle function helps in generating a random sequence of numbers easily.

This function swaps numbers with internal elements quickly.

If no random generator function is specified, the shuffle() function default will be taken.

It is fast and efficient, which makes it easy to use

The randomness of numbers can be built and used with the C++98/03 standard.

Conclusion

The shuffle() function is an easy way of rearranging the elements of a vector or array. A random generator variable can be used to generate the pattern of random numbers. The library plays a role in defining the “else” pattern by utilizing the default functionality provided by the function. It actively swaps the elements within a given range. This range can be between any elements in the array. This function is similar to the random_shuffle() function. The only difference is shuffle() uses a uniform random number generator.

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