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Introduction to TensorFlow Federated

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This article will try to understand tensorflow federated, how we can use it, its Model, characteristics, computation API, and finally conclude our view.

What is TensorFlow federated?

The framework helps you perform machine learning on completely decentralized data. We train the models that are shared globally and include the clients participating in placing their data for training locally. One of the examples which will be helpful to understand where we make the use of tensorflow federation is for the training of keyboard prediction model on mobile phones and making sure at the same time that the sensitive and secured data is not being uploaded on the data server.

The developers can use and include Federated learning algorithms in their data and models. At the same time, the novel algorithms are available and open for any experimentation for the developers. Therefore, the people performing the research on this can find ample examples and the point where they can start for various experiment topics. Federated analytics is the computation that is non-learning based and can be implemented using the interface of tensorflow federated.

How and where to use TensorFlow federated?

We can make the use of federated learning in various ways that include –

By using FC API, design and create new federated learning algorithms.

Assisting the development and optimization of computation structures that are generated.

Apply the APIs of the federated learning to the models of TensorFlow that exist currently.

Integrate the Tensorflow Federated framework with other environments of development.

You can make use of it by following the below steps –

Installation of TFF –

This can be done by opening the terminal or command prompt and typing in the following command for execution –

pip install tensorflow-federated –upgrade

Create a notebook and import the package and other dependencies.

Prepare the dataset for simulation.

The data should be of NIST or MNIST format and is by default provided when you go for creating a leaf project.

Make the use of federated data to train the Model.

After that, you can train the Model and make it aware of various functionalities that it should perform and be aware as you do with any of the TensorFlow models.

Print the summarized information about the implementation of tensorflow federated.

Finally, you can print out the machine learning tensorflow federated model results.

TensorFlow federated Model

The two models used in TensorFlow federated FL API are tff.learning.Model and create_keras_model().

TensorFlow federated characteristics

The main characteristics are listed below –

Effort saving – Whenever any developer approaches to create a learning system of federated, the pain points where the developers mostly face the problem are targeted here, and the platform of tensorflow federated is designed keeping the mitigations of those points in mind for the convenience of developers. The challenges faced by most of the developers include local and global communication perspectives, logic interleaving of various types, and execution and construction order tension.

Architecture agnostic – It can compile the whole code and provide the representation of the same in an abstract way, which facilitates the developer to deploy its Model acrModel diverse environment.

Availability of many extensions – Quantization, compression, and differential privacy are some of the extensions available in Tensorflow Federated.

TensorFlow federated Computation API

There are two types of computation APIs, which are described below –

Federated Core API, also known as FC –

The low-level interface used at the system’s core part is included in this API. Federated algorithms can be concisely expressed along with the combination of TensorFlow using this API. It also consists of a functional programming environment that is typed strongly and includes the distributed operators for communication. This API layer is the base over which we have created the building of federated learning.

Federated Learning API, referred to as FL –

The developers can include the evaluation and federated training models to the existing models of TensorFlow by using the high-level interfaces provided in this federated learning API layer.

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Complete Guide To Tensorflow Opencl

Introduction to TensorFlow OpenCL

TensorFlow is a machine learning algorithm execution framework based on artificial intelligence concepts. We’re working on adding support for OpenCLTM devices to the TensorFlow framework using SYCLTM to give developers access to a wider range of processors. SYCL is an easy free, cross-platform C++ abstraction layer, while OpenCL(Open Computing Language) is a framework for building applications that execute across heterogeneous platforms. OpenCL is a standard parallel computing standard for event and data-based parallelism.

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Overview of TensorFlow OpenCL CUDA vs OpenCL

Comparison CUDA OpenCL

Developed by NVIDIA Corporation. developed by Khronos Group

Definition Compute Unified Device Architecture (CUDA) is a parallel computing design that supports applications that demand a lot of parallel processing. OpenCL is an open standard that may be used on a wide range of hardware, including desktop and laptop GPUs.

Multiple OS Support

e.g., Windows XP and later, macOS

OpenCL, on the other hand, can run on practically any operating system and on a wide range of hardware.

e.g., Android, FreeBSD, Windows, Linux, macOS e

GPU Support 2 GPUs Utilize 1GPU

Language support C, C++, fortran C, C++

Templates CUDA is a C API and also constructs. C++ bindings and has C99

Function Compiler- build kernels Kernels at run time.

Libraries Has a large number of high-performance libraries Although it has a large number of libraries that may be used on any OpenCL-compliant hardware, it is not as comprehensive as CUDA.

Performance

TensorFlow OpenCL examples

There are no known vulnerabilities in TensorFlow-OpenCL and no known vulnerabilities in its dependent libraries. The Apache-2.0 License applies to TensorFlow-OpenCL. This is a permissive license. Permissive licenses offer the fewest limitations and can be used in almost any project.

Blender’s most recent versions support OpenCL rendering. Using the container that has been provided to the Sylabs library, you can run Blender as a graphical programme that will use a local Radeon GPU for OpenCL compute:

$ singularity exec --rocm --bind /etc/OpenCL library://sylabs/demo/blend blender

Set-Up and Run the TensorFlow OpenCL

To add OpenCL support to TensorFlow, we need to use ComputeCpp to create an OpenCL version of TensorFlow. TensorFlow now includes OpenCL support, which can be implemented using SYCL, thanks to Codeplay. TensorFlow is based on the Eigen linear algebra C++ library.

OpenCL installation

clinfo

Install Packages

pip install -U –user numpy==1.14.5 wheel==0.31.1 six==1.11.0 mock==2.0.0 enum34==1.1.6

Configure Set-up

cd tensorflow

Environment variables Set-up

It’s a good idea to run the tests to ensure TensorFlow was constructed successfully. With the following command, you may perform a big set of roughly 1500 tests:

bazel test --test_lang_filters=cc,py --test_timeout 1500 --verbose_failures --jobs=1 --config=sycl --config=opt --

Build Tensor Flow

cd tensorflow

Set-Up operations

with tf.Session() as se1:

This line-up will build a new context manager, instructing TensorFlow to use the GPU to accomplish those tasks.

TensorFlow program

Program #1

>>> se1.close()

d_name = sys.argv[1] print(“n” * 6)

Explanation

Python chúng tôi gpu 1500

Output:

OpenCL Acceleration for TensorFlow

OpenCL allows a wide range of accelerators to be used, involving multi-core CPUs, GPUs, DSPs, FPGAs, and specialized hardware like inferencing engines. An OpenCL system is divided into host and device components, with host software developed in a general programming language like C or C++ and generated for running on a host CPU using a normal compiler. TensorFlow to OpenCL translation would necessitate scribbling the kernels in OpenCL C and distinct codebases, both of which would be difficult to maintain. All of it is single-source C++ when using SYCL, therefore it’s possible to integrate the SYCL back-end to TensorFlow in a non-intrusive way.

Let’s see the sample code for registration

}

Conclusion

In general, OpenCL is successful. As a standard, it contains all of the necessary parts, namely run-time code creation and sufficient support for heterogeneous computing. Therefore, in this article, we have seen how tensor flow is acted on OpenCL.

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Complete Guide On Pytorch Max In Detail

Introduction to PyTorch max

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What is PyTorch max? How to use PyTorch max?

Syntax

torch.max(specified input)

Explanation

By using the above syntax, we can implement the max() function into deep learning; in this syntax, we use a torch with max function as shown here; we only pass the specified input that we want. Then, finally, we get the max element from the tensor.

Now let’s see a different example of the max() function, so we will get more details as follows.

First, we need to import the torch, as shown below.

import torch

Now we need to create the tensor by using the following statement as follows.

input = torch.randn([3, 4]) print(input) max_e = torch.max(input) print(max_e)

Explanation

In the above example, we first import the torch; after that, we created a tensor by using the randn function as shown. After that, we pass the input value to the max() function and print the result. We illustrated the final output of the above program by using the following screenshot as follows.

PyTorch max over multiple dimensions

Now let’s see how we can use the max() function with multiple dimensions in Pytorch as follows.

Sometimes we need to get the maximum dimension as tensor instead of single; at that time, we can also use the max() function. We need to specify the dimension in multiple dimensions either by using an axis or dim variable. After execution, it returns the max element as well as max indices of the tensor.

Syntax

max_ele, max_indice = torch.max(specified input tensor, dim)

Explanation

In the above syntax, we use max() with two parameters such as max_ele and max_indices, as shown in the above syntax.

Example #1

Code:

import torch input = torch.randn([3, 4]) print(input) max_ele, max_indic = torch.max(input, dim=0) print(max_ele) print(max_indic)

Explanation

By using the above example, we try to implement the multiple dimension with max() function; in this example, we first import the torch as shown; after that, we create a tensor by using the randn function as shown. After that, we pass the input value to the max() function and print the result. In this example, we assign the maximum dimension that is dim = 0 as shown. Finally, we just print the max element and max indices. We illustrated the final output of the above program by using the following screenshot as follows.

Now let’s same example of max() function with dimension value is 1 as follows.

Example #2

Code:

import torch input = torch.randn([3, 4]) print(input) max_ele, max_indic = torch.max(input, dim=1) print(max_ele) print(max_indic)

Explanation

By using the above example, we try to implement the multiple dimension with max() function; in this example, we first import the torch as shown; after that, we create a tensor by using the randn function as shown. After that, we pass the input value to the max() function and print the result. In this example, we assign the maximum dimension that is dim = 1, as shown. Finally, we just print the max element and max indices. We illustrated the final output of the above program by using the following screenshot as follows.

Let’s consider we have two tensors, such P and Q, and they have the same dimension, and we need to compare this tensor and get the max element.

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

Example #3

Code:

import torch i_tensor1 = torch.randn([3, 4]) i_tensor2 = torch.randn([3, 4]) print("First Tensor:", i_tensor1) print("Second Tensor:", i_tensor2) max_ele = torch.max(i_tensor1, i_tensor2) print(max_ele)

Explanation

In the above program, first, we define the two tensors that are tensor1 and tensor2 as shown; here, the difference is only in the max() function; here, we need to send two tensors to the max() function. Then, we illustrated the final output of the above program by using the following screenshot as follows.

PyTorch max performance

Now let’s see the performance of the max() function as follows.

Using the max() function can speed up the operation because we can easily, or we can say that efficiently find out the max element from the input tensor as per requirement. Using the max() function, we can increase deep learning performance, or we can say that machine learning is as per our requirement.

Conclusion

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

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Complete Guide On How To Use Lightgbm In Python

This article was published as a part of the Data Science Blogathon

Introduction

A Gradient Boosting Decision tree or a GBDT is a very popular machine learning algorithm that has effective implementations like XGBoost and many optimization techniques are actually adopted from this algorithm. The efficiency and scalability of the model are not quite up to the mark when there are more features in the data. For this specific behavior, the major reason is that each feature should scan all the various data instances to make an estimate of all the possible split points which is very time-consuming and tedious.

r EFB. So GOSS will actually exclude the significant portion of the data part which have small gradients and only use the remaining data to estimate the overall information gain. The data instances which have large gradients actually play a greater role for computation on information gain. GOSS can get accurate results with a significant information gain despite using a smaller dataset than other models.

With the EFB, It puts the mutually exclusive features along with nothing but it will rarely take any non-zero value at the same time to reduce the number of features. This impacts the overall result for an effective feature elimination without compromising the accuracy of the split point.

By combining the two changes, it will fasten up the training time of any algorithm by 20 times. So LGBM can be thought of as gradient boosting trees with the combination for EFB and GOSS. You can access their official documentation here.

The main features of the LGBM model are as follows :

Higher accuracy and a faster training speed.

Low memory utilization

Comparatively better accuracy than other boosting algorithms and handles overfitting much better while working with smaller datasets.

Parallel Learning support.

Compatible with both small and large datasets

Demystifying the Maths behind LGBM

We use a concept known as verdict trees so that we can cram a function like for example, from the input space X, towards the gradient space G. A training set with the instances like x1,x2 and up to xn is assumed where each element is a vector with s dimensions in the space X. In each of the restatements of a gradient boosting, all the negative gradients of a loss function with respect towards the output model are denoted as g1, g2, and up to gn. The decision tree actually divides each and every node at the most revealing feature, it also gives rise to the largest evidence gain. In this type of model, the data improvement can be measured by the variance after segregating. It can be represented by the following formula :

“Y=Base_tree(X)-lr*Tree1(X)-lr*Tree2(X)-lr*Tree3(X)”

Explanation, Let O be a training dataset on a fixed node of a decision tree and then the variance gain of dividing measure j at a point d for a node is defined as :

Gradient One-Sided Sampling or GOSS utilizes every instance with a larger gradient and does the task of random sampling on the various instances with the small gradients. The training dataset is given by the notation of O for each particular node of the Decision tree. The variance gain of j or the dividing measure at the point d for the node is given by :

This is achieved by the method of GOSS in LightGBM models.

Coding an LGBM in Python

The LGBM model can be installed by using the Python pip function and the command is “pip install lightbgm” LGBM also has a custom API support in it and using it we can implement both Classifier and regression algorithms where both the models operate in a similar fashion. The Dataset used here is of the Titanic Passengers which will be used in the below code and can be found in my drive at this location.

Code :

Python Code:

Output :

Here we can see that there are 8 columns out of which the passenger ID will be dropped and the embarked will be finally chosen as a target variable for the following classification challenge.

Loading the variables: # To define the input and output feature x = data.drop(['Embarked','PassengerId'],axis=1) y = data.Embarked # train and test split x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.33,random_state=42) Loading and fitting the model:

The initial process of initializing a model is very similar to a normal model initializing and the main difference is that we will get much more parameter settings adjustments while we are initializing the model. We will define the max_depth, learning rate and random state in the following code. In the fit model, we have passed eval_matrix and eval_set to evaluate the model during training itself.

Code : model = lgb.LGBMClassifier(learning_rate=0.09,max_depth=-5,random_state=42) model.fit(x_train,y_train,eval_set=[(x_test,y_test),(x_train,y_train)], verbose=20,eval_metric='logloss') Output:

Since our model has very low instances, we need to first check for overfitting with the following code and then we will proceed for the next few steps :

Code : print('Training accuracy {:.4f}'.format(model.score(x_train,y_train))) print('Testing accuracy {:.4f}'.format(model.score(x_test,y_test))) Output : Training accuracy 0.9647 Testing accuracy 0.8163

As we can clearly see that there is absolutely no significant difference between both the accuracies and hence the model has made an estimation that is quite accurate.

LGBM also comes with additional plotting functions like plotting the various feature importance, metric evaluation and the tree plot.

Code : lgb.plot_importance(model)

Output :

If you do not mention the eval_set during the fitment, then you will actually get an error while plotting the metric evaluation

Code : lgb.plot_metric(model) Output

And as you can clearly see here, the validation curve will tend to increase after it has crossed the 100th evaluation. This can be totally fixed by tuning and setting the hyperparameters of the model. We can also plot the tree using a function.

Code:

lgb.plot_tree(model,figsize=(30,40))

Output:

Now we will plot a few metrics by using the sklearn library

Code :

metrics.plot_confusion_matrix(model,x_test,y_test,cmap='Blues_r')

Output :

Code :

print(metrics.classification_report(y_test,model.predict(x_test)))

Output :

Now as we can clearly see from the confusion matrix combined with the classification report, the model is struggling to predict class 1 because of the few instances that we have but if we compare the same result with the other various ensemble algorithm, then LGBM performs the best. We can also perform the same process for the regressor model but there we need to change the estimator to the LGBMRegressor()

End Notes:

From this article, we can see and understand how to use an LGBM model and how it can tackle the problem by using a GODD and EFB and then we implemented it for a real-life classification problem and the overall process is also very similar to the other ML algorithms. The in-built plotting functionality also makes the library much more attractive and reduces the overall effort for the evaluation side.

Stay Safe and get vaccinated everyone.

Arnab Mondal

Collab Notebook Link :

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Complete Guide To Mongodb Commands

Introduction to MongoDB Commands

MongoDB is a cross-platform, document-oriented, open-source database management system with high availability, performance, and scalability. MongoDB, a NoSQL database, finds extensive use in big data applications and other complex data processing tasks that do not align well with the relational database model. Instead of using the relational database notion of storing data in tables, MongoDB architecture is built on collections and documents. Here we discuss the MongoDB commands.

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Why MongoDB Commands?

It can easily control global data, ensuring fast performance and compliance.

It provides a flexible data model. This goes with the case where the app needs to be built from scratch or the case of updating a single record.

Scaling the application ensures that there is no downtime.

Features

MongoDB command uses a master-slave replication concept. To prevent database downtime, this replica feature is essential.

MongoDB command comes with the auto-sharding feature, which distributes data across multiple physical partitions known as shards. The result of which automatic load balancing happens.

It’s schema-less. Hence more efficient.

Basic of MongoDB Commands 1. Create database

In MongoDB use, DATABASE_NAME is used to create a database. If this name database doesn’t exist, it will get created, and else it will return the existing one.

To check the current database now:

By default, the MongoDB command comes with the database name “test.” Suppose you inserted a document without specifying the database; MongoDB will automatically store it in a “test” database.

2. Drop Database

If the database is not specified, it will delete the default database, “test.”

3. Create Collection

To create a collection, the MongoDB command used is: db.createCollection(name, options)

Here, the name is the Collection’s name & options are a document used to specify the Collection’s configuration. Though the “Options” parameter is optional, it’s good to provide it.

4. Drop Collection

5. Insert Document

To insert data into a database collection in MongoDB, you can use the “insert()” or “save()” method.

Here “mycol” is the collection name. If the Collection doesn’t exist, then the MongoDB command will create the database collection, which will be inserted.

6. Query Document

Querying Collection is done by the find() method.

As the find() method will show the findings in a non-structured way, a structured pretty() method is used to get the results.

Intermediate MongoDB Commands 1. Limit()

This MongoDB command limits the no. of records need to use in MongoDB. The argument of this function accepts only number types. The argument is the number of the Document that needs to be displayed.

2. Sort()

This is to the records of MongoDB. 1 & -1 are used to sort the documents. 1 is for ascending, whereas -1 is for descending.

3. Indexing is the concept that helps MongoDB to scan documents in an inefficient way

Advanced Commands of MongoDB 1. Aggregate ()

This MongoDB command helps process the data, which returns the calculated result. This can group values from multiple documents together.

2. Replication

Replication in MongoDB is achieved using a replication set. A replica set is a group of MongoDB processes with the same dataset. Replica set provides:

High availability

Redundancy hence faults tolerant/disaster recovery.

In replica, one node is the primary node, and the rest are the secondary node. All write operations remain with the primary node.

Let’s see; you can convert a standalone MongoDB instance into a replica set.

Here are the steps for that:

Close is already running the MongoDB server.

Now Start the MongoDB server by specifying — replSet option.

Syntax:

3. Create & restore Backup

To create the Backup, the mongodump command is used. The server’s data will be dumped into a dump directory(/bin/dump/). Options are there to limit the data.

To restore a backup in MongoDB, you would use the “mongorestore” command.

4. Monitor Deployment

To check the status of all your running processes/instances, a mongostat command is helpful. It tracks and returns the counter of database operations. These counters include inserts, updates, queries, deletes, and cursors. This MongoDB command is beneficial as it shows your status about low running memory, some performance issues, etc.

You must go to your MongoDB installation bin directory and run mongostat.

Tips and Tricks to Use MongoDB Commands

Pre-allocate space: When you know your Document will grow to a certain size. This is an optimization technique in MongoDB. Insert a document and add a garbage field.

Try fetching data in a single query.

As MongoDB is, by default, case sensitive.

Example:

db.people.find({name: ‘Russell’}) &

db.people.find({name: ‘russell’}) are different.

While performing a search, it’s a good habit to use regex. Like:

db.people.find({name: /russell/i})

Prefer Odd No. of Replica Sets: Using replica sets is an easy way to add redundancy and enhance read performance. All nodes replicate the data, and it can be retrieved in case of a primary node failure. They vote amongst themselves and elect a primary node. Using the odd number of the replica will make voting more accessible in case of failure.

Secure MongoDB using a firewall: As MongoDB itself doesn’t provide any authentication, it’s better to secure it with a firewall and mapping it to the correct interface.

No joins: MongoDB, a NoSQL database, does not support joins. One must write multiple queries to retrieve data from more than two collections. Writing queries can become hectic if the schema is not well organized. This may result in the re-designing of the schema. It’s always better to spend some extra time to design a schema.

Conclusion

MongoDB commands are the best practice solution to maintain high availability, efficient and scalable operations, which is today’s business demand.

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Complete Guide To Matlab Remainder

Introduction to Matlab Remainder

The following article provides an outline for Matlab Remainder. Remainder is obtained in division when 2 numbers can’t be divided exactly.

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In division 4 quantities are involved.

Dividend: The number which is to be divided.

Divisor: The number ‘by which’ the ‘Dividend’ is to be divided.

Quotient: The ‘multiplying factor’ by which ‘Divisor’ is multiplied to get it equal to or closest to the ‘Dividend’.

Remainder: If the product Divisor * Quotient is not equal to the ‘Dividend’, then the lag is referred as ‘Remainder.

In Matlab we use ‘rem’ function for the purpose of finding the remainder of a division.

Syntax:

R = rem (A, B)

Description:

R = rem (A, B) will return the remainder when ‘A’ is divided by ‘B’.

A is dividend and B is Divisor.

A range like A:B can also be passed as an argument. In this case, the entire range will be considered as ‘Dividends’ and we get an array of ‘Remainders’ respective to each dividend.

Examples of Matlab Remainder

Given below are the examples mentioned :

Example #1

For our first example, we will follow the following steps:

Initialize the Dividend.

Initialize the Divisor.

Pass both Dividend and Divisor to the rem function.

Code:

A = 15

[Initializing the Dividend]

B = 3

[Initializing the Divisor]

R = rem(A, B)

[Passing Dividend and Divisor as arguments to the rem function] [Mathematically, if we divide A with B, we will get ‘0’ as remainder. This is because 3 exactly divides 15, leaving no remainder]

Input:

R = rem(A, B)

Output:

As we can see in the output, we have obtained the remainder of 15 and 3 as ‘0’.

Example #2

In this example, we will take a non-integer dividend and divisor as an integer.

For this example, we will follow the following steps:

Initialize the Dividend.

Initialize the Divisor.

Pass both Dividend and Divisor to the rem function.

Code:

A = 6.7

[Initializing the Dividend]

B = 3

[Initializing the Divisor]

R = rem(A, B)

[Passing Dividend and Divisor as arguments to the rem function] [Mathematically, if we divide A with B, we will get ‘0.7’ as remainder. This is because 3 does not divide 6.7 exactly, and leaves 0.7 as remainder]

Input:

R = rem(A, B)

Output:

As we can see in the output, we have obtained the remainder of 6.7 and 3 as ‘0.7’.

Example #3

In this example, we will take both dividend and divisor as non-integers.

For this example, we will follow the following steps:

Initialize the Dividend.

Initialize the Divisor.

Pass both Dividend and Divisor to the rem function.

Code:

[Initializing the Dividend]

B = 4.32

[Initializing the Divisor]

R = rem(A, B)

[Passing Dividend and Divisor as arguments to the rem function] [Mathematically, if we divide A with B, we will get ‘0.12’ as remainder. This is because 4.32 does not divide 17.4 exactly and leaves 0.12 as remainder]

Input:

R = rem(A, B)

Output:

As we can see in the output, we have obtained the remainder of 17.4 and 4.32 as 0.12.

In the above 3 examples, we used rem function to get the remainder for single input.

Next, we will see how to use rem function for a range of dividends.

Passing a range of integers to the rem function will give an array output with remainder of each element when divided by the divisor.

Example #4

We will take a range of 5 to 10 and will use 4 as divisor.

For this example, we will follow the following steps:

Initialize the range as [5:10]

Initialize the Divisor

Pass both Dividend range and Divisor to the rem function

Code:

A = [5 : 10] [Initializing the range of Dividends]

B = 4

[Initializing the Divisor]

R = rem(A, B)

[Passing Dividend range and Divisor as arguments to the rem function] [Mathematically, if we divide every integer from 5 to 10 by 4, we will get the following remainders:

Please note that these remainders correspond to division of elements of A by 4]

Input:

A = [5 : 10] R = rem(A, B)

Output:

As we can see in the output, we have obtained the array of remainders for the range passed as an argument.

Example #5

Let us take another example and take a range of 10 to 15.

For this example, we will follow the following steps:

Initialize the range as [10:15].

Initialize the Divisor as 3.

Pass both Dividend range and Divisor to the rem function.

Code:

A = [10 : 15] [Initializing the range of Dividends]

B = 3

[Initializing the Divisor]

R = rem(A, B)

[Passing Dividend range and Divisor as arguments to the rem function] [Mathematically, if we divide every integer from 10 to 15 by 3, we will get following remainders:

1 2 0 1 2 0]

Input:

A = [10 : 15] R = rem(A, B)

Output:

As we can see in the output, we have obtained the array of remainders for the range passed as an argument.

Conclusion

‘rem’ function is used in Matlab to find the remainders during division. We can pass both single dividends or a range of dividends as argument to the ‘rem’ function.

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