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Memoization is a helper function, or we can say a technique for improving the efficiency of a program by Keeping track of the values that the function has already calculated in the past. In this article, we will discuss the Memoization Helper function with different examples and discuss all the examples in detail so that we can get a better understanding of Memoization.

Now let’s discuss the Memoization helper function in the deep in the below section and also see their implementation with an explanation.

Introduction to Memoization Helper Function

Memoization is a technique of programming that is used to improve the time complexity and space complexity of a program by Keeping track of the values that the function has already calculated in the past. By saving the outcomes of function calls in a cache, the program becomes more efficient. By repeatedly running the function with the same parameter that has previously been calculated, we frequently waste time. We can then cache the calculated value and just return it when the function is called with the same parameter.

Implementation of a Memoization helper function

Here, we’re going to explore a tonne of examples and explanations to help you understand the memoization helper function better.

Example1

Let’s see the working of the memoization helper function with this example in which we will discuss the code, output, and explanation to get a better understanding of the concept −

Here we define function add by passing two parameters in it num1 and num2 for doing the addition of integer num1 and num2. In this function, we have run for loop and after that, we have to return the sum of both the integer.

In this case, we have called the addition function but our function taking time because of for loop. And we are calling the function again and again with the same parameter. So, if there we are using memoization by storing the value of addition so we are able to save time then we will return the cached value. We don’t need to calculate the added value for the same parameters.

Example 2

Let’s see how long it took our function to determine the value of add (5,4) with the help of code and explanation −

function add(num1,num2){ for(let i=0;i<100000;i++){ } return num1+num2; } console.time("Time taken"); console.log(add(5, 4)); console.timeEnd("Time taken");

Our function taken 14.441ms time to add the integers 5 and 4.

By using the memoization technique, we may improve the efficiency of the function by caching the value that has already been calculated and then returning it when the function is called with the same parameter.

Example 3

Let’s now discuss how we can utilise the memoization technique to shorten the time it takes to repeatedly execute a function with the same parameter.

function memoizeFunction(func) { let storage = {}; return function (val1, val2) { const val = val1.toString() + val2.toString(); if (!storage[val]) { storage[val] = func(val1, val2); } return storage[val]; } } function add(num1, num2) { for (let i = 0; i < 10000000; i++) { } return num1 + num2; } console.time("First time, time taken"); let func = memoizeFunction(add); console.log(func(5, 4)); console.timeEnd("First time, time taken"); console.time("Second time, time taken"); func = memoizeFunction(add); console.log(func(5, 4)); console.timeEnd("Second time, time taken"); console.time("Third time, time taken"); func = memoizeFunction(add); console.log(func(5, 4)); console.timeEnd("Third time, time taken");

Note − the length of time needed to finish a task can change.

In this instance, we cached the value we earlier calculated using a memoized function. When we initially use func(4,5), the parameter is first converted to string form and then saved within the object ‘storage’ along with the calculated value.

Additionally, when the function is called with the same parameter, it first determines whether or not it already exists in the object ‘storage’. If it is already calculated, it won’t do it again and will instead simply return the value that is contained inside the object ‘storage’.

As we can see from the output, each time we used the function with the same parameter, the time it took to add 5 and 4 was less.

Time is taken each time −

98.885 ms 83.375 ms 13.071 ms

Therefore, it is evident from the output that the memoization technique assisted in shortening the time each time we repeatedly called the function with the same argument.

Example 4

Let’s discuss another example of a memorization helper function by the Fibonacci number.

function memoizeFunction(func) { let storage = {}; return function (val) { const value = val.toString(); if (!storage[value]) { storage[value] = func(val); } return storage[value]; } } function fibonacci(num) { return num; else return fibonacci(num - 1) + fibonacci(num - 2); } console.time("First time, time taken"); let func = memoizeFunction(fibonacci); console.log(func(6)); console.timeEnd("First time, time taken"); console.time("Second time, time taken"); func = memoizeFunction(fibonacci); console.log(func(6)); console.timeEnd("Second time, time taken"); console.time("Third time, time taken"); func = memoizeFunction(fibonacci); console.log(func(6)); console.timeEnd("Third time, time taken");

Fibonacci series take an exponential time to execute if all the steps are performed without the help of memorization technique. By storing the previous results, we can get the pre-defined results to reduce the further checking of the calculated results and can take number of steps to linear.

Conclusion

In this article, we have learned that Memoization is a helper function or a technique for improving the efficiency of a program by Keeping track of the values that the function has already calculated in the past. By saving the outcomes of function calls in a cache, the program becomes more efficient. We can then cache the calculated value and just return it when the function is called with the same parameter.

You're reading Explain Implement A Memoization Helper Function

## Invoke A Function With A Function Constructor In Javascript

A function constructor invocation creates a new object. The new object inherits the properties and methods from the constructor. This keyword in the constructor does not have a value. This value will be a new object created when the function is invoked. Function construction creates functions that execute the global scope only.

In JavaScript invoking a function with a function, the constructor is different from invoking a function as a method or invoking a function. Because it creates a new object that constructs properties and methods.

Syntax

Following is the snippet invoking a function with a function constructor. Here, we are creating a function and pa, passing parameters, and inheriting these two parameters to this keyword with var1 and var2.

Function functioName(arg1, arg2){ chúng tôi = arg1; chúng tôi = arg2; } var x = new functionName(1,2) Example: 1

In the following example, a function and a function constructor were created. And the function constructor inherits the properties of the function. Then we were passing the value as an object.

function funcDemo(arg1, arg2) { chúng tôi = arg1; chúng tôi = arg2; } var obj = new funcDemo(10, 20); document.write(JSON.stringify(obj)); document.write(obj.var1 + ” ” + obj.var2);

Example 2

In the following example, we are creating a function constructor and also passing the constructor method. And calling the method and variables with the help of an object.

function details() { chúng tôi = “Aman kumar”; chúng tôi = 23; this.greet = function () { document.write(“Hello! What is your name and age ?”); }; } var obj = new details(); obj.greet(); document.write(obj.name + ” ” + obj.age);

Example 3

In the following example, we are using a function constructor named User and printing the details of the user.

function Users(name, age, state, degree, designation) { chúng tôi = name; chúng tôi = age; this.state = state; this.degree = degree; this.designation = designation; } const obj = new Users( “Aman”, 23, “Jharkhand”, “B-Tech”, “technical Writer” ); document.write(JSON.stringify(obj));

Example 4

Let us see another example −

let areaFun = function (side1, side2) { this.length = side1; this.width = side2; }; let result = new areaFun(5, 5); document.getElementById(“getArea”).innerHTML = JSON.stringify(result);

## Examples To Implement Mysql Mod()

Introduction to MySQL MOD()

MySQL MOD() function allows us to divide a literal number with another numeric expression to return the remainder of it. The MOD() takes numeric values as arguments for the function to execute and produce the result. The arguments can be said as dividend and divisor as in Maths which works on the fractional numeric values to output the special remainder. If the divisor or, say, the second parameter of the MySQL MOD() function is set to 0 (zero), the result will be NULL. This function can be used safely with BIGINT data type values, but mainly it is used for fractional parts to perform the division and show the remainder after calculating logically.

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Syntax

The following syntax code introduces the structure to apply the Math function MOD() in MySQL:

MOD(a,b) a MOD b a % b

We have the above three syntaxes to use the MOD() function within the queries in MySQL.

Here, the ‘a’ and ‘b’ parameters are the required numeric values that can be either fractional part or BIGINT data type values with the help of the MOD() function’s division process. After the completion of the command query then, the return value is the remainder balanced within numbers as in Maths calculation problems.

Also, we can define the first argument value (a) as dividends and the other one-second argument (b) as the divisor for the MOD() calculation to produce the result of the division.

Thus, with the literal numbers, the MOD() function returns the remains of the division, and if the divisor is zero like MOD(dividend,0), the result will be NULL.

How does MySQL MOD() Function work?

As per the syntax above, dividing a numeric expression by another literal number will result in the remainder of this MOD() function in MySQL.

This performs the same work as Modulus calculation is calculated in Maths that we do manually using logic and basic concepts. In addition, we also use SELECT query statement to supplement the MOD() function used in MySQL, so we use the MOD() function as:

SELECT MOD(a,b);

Code: Let us take a query using the MOD() function for MySQL to illustrate the function syntaxes that can be executed in three forms-

SELECT MOD(152, 3);

Or,

SELECT 152 % 3;

Or,

SELECT 152 MOD 3;

Output:

Explanation: As you can see that the results show the remainder of each query form that is executed on the server. So, like this, the Math function implements the logic to produce the modulus process result.

Examples of implementing MOD() function in MySQL

Let us illustrate the MySQLMOD() function with the help of the following examples and show executions results generated simultaneously:

1. Some Simple Examples Using MySQL MOD() function

Code #1

SELECT MOD(21,2);

Output:

It can be noticed that MySQL permits to use of the modulus operator, i.e., %, which can be considered as the synonym for this MOD() function to give the result also like this:

Code #2

SELECT 21 % 2;

Output:

Also, look at the results gained using the MOD() function that accepts fractional elements to return the remainder of the division process. The query for this is as follows:

Code #3

SELECT MOD(15.8, 3);

Output:

By utilizing both a dividend and divisor with fractional values, the following code demonstrates how to obtain the result:

SELECT MOD(8.452, 2.14);

Output:

The result of the remainder is also with fractional values. Also, view the below query with a divisor in fractional value only:

Code #5

SELECT 25 MOD 2.3;

Output:

2. Using MOD() function for zero divisor value

Suppose we have set the divisor as zero and dividend like any literal numeric expression; then the MOD() function applied to it will produce the following result as a reminder:

Code #1

SELECT MOD(17,0);

Output:

You can view that the remainder of the above query with a 0 divisor value is NULL when executed on the MySQL server.

Let us try out the result of the MOD() function in MySQL when we set zero value for dividend and non-zero or, say, any literal number for divisor using the succeeding query:

Code #2

SELECT 0 % 5;

So, do remember that if we divide the 0 value with any numeric expression, mathematically, it will give the result zero, just the opposite if we reverse the statement values when the divisor is zero. If the dividend is non-zero, then the output will be NULL.

3. Using MOD() function on Database Table fields

Supposing we have created a table named ‘Books’ where we will implement the MOD() function in MySQL. To accomplish this, you can utilize the following code for table creation, which includes the fields BookID, BookName, Language, and Price:

CREATE TABLE Orders(BookID INT PRIMARY KEY AUTO_INCREMENT, BookNameVarchar(255) NOT NULL, LanguageVarchar(255) NOT NULL, Price INT NOT NULL);

Let us enter some data records in the table Books with the following SQL query:

Code #1

INSERT INTO TABLE (BookID, BookName, Language, Price) VALUES('101','Algebraic Maths','English','2057');

And so on.

Output: View the table here:

Now, we will use MySQLMOD() query on the table column ‘Price’ to check whether the values are even or odd:

Code #2

SELECT BookID, BookName, Language, Price, IF (MOD(Price,2), 'Odd_Price','Even_Price') AS Odd_Even_Prices FROM Books GROUP BY BookID ORDER BY BookID;

Output:

In this illustration:

In this case, we utilize the price column to fetch its values for performing the MOD() operation, specifically on numeric values.

When we apply the MOD() function, the values in the Price column will undergo division by 2 as the divisor, resulting in the retrieval of the remainder. The query will evaluate odd or even according to the results produced in zero or one.

At last, we have applied the IF() function that, when executed, will show Odd and Even prices string based on the result of the MOD() operation.

Conclusion

MySQL provides the MOD() function, which actively evaluates the given values and generates the remainder after dividing the specified arguments.

The MySQL MOD() thus implements a modulo operation on calling the function to provide the remainder of two numeric values passed in the function by the division method.

Recommended Articles

We hope that this EDUCBA information on “MySQL MOD()” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

## How Can We Implement Logistic Regression?

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

Introduction

In this article, we will be learning about how we can implement logistic regression by writing Python code. You must be wondering what is logistic regression and what is the theory behind it? What python packages are involved while implementing logistic regression? You must be coming up with many more questions but I will try to answer as many as questions possible. Well, you have chosen the right article. So let’s begin our journey for logistic regression.

Introduction to Logistic Regression

How can we implement it?

Sigmoid function

Calculating probability and making predictions

Calculating the cost

Reducting the cost using Gradient Descent

Testing you model

Predicting the values

Introduction to logistic regression

Now we proceed to see how this algorithm can be implemented.

How can we implement it?

This algorithm can be implemented in two ways. The first way is to write your own functions i.e. you code your own sigmoid function, cost function, gradient function, etc. instead of using some library. The second way is,  of course as I mentioned, to use the Scikit-Learn library. The Scikit-Learn library makes our life easier and pretty good. All functions are already built-in, you just need to call those functions by passing the required parameters into it. However, if you are learning logistic regression for the first time, then I would suggest you write your own code instead of using the sci-kit-learn library. It does not mean that the mentioned library is not useful, I only want to make you learn the core concepts of this algorithm.

Before you start working on a project, it is important for us to visualize the dataset. Once we have visualized your dataset, then we will move to the major part of the project. After visualization, we will train our dataset. The training process includes calculating the probability and the cost, and then reduce the cost on the available dataset. The given dataset helps us to train our model so that accurate predictions could be made. Once the model is trained, we check our accuracy on the validation set (this is the part of the dataset, usually we use 80% of our dataset as a training set and the rest 20% as a validation set.) A validation set is required to measure the accuracy of our trained model i.e. how our model will behave when it is exposed to similar unseen data. We compare the results of a validation set with their actual labels mentioned in the dataset.

So let’s visualize our data.

First load the data from the CSV file.

Then, have a look at the dataset with the following command:

﻿

The above image is an output of some dataset that aims to predict loan eligibility.

Of course, I recommend everyone who is learning ML and want to pursue a career in Data Science to learn plotting graphs using the Matplotlib library. It is going to be useful. Trust me!

By plotting your data on a graph, we can visualize the importance as well as the distribution of a particular factor.

Now in the next section, we’ll learn to make predictions using the sigmoid function.

Making predictions and implementing sigmoid function

Calculating probability and making predictions

Now you can clearly imagine what is sigmoid function from the above graph. The y-axis is the sigmoid function and the x-axis is the dot product of theta vector and X vector.

Now we’ll write a code for it:

def calc_sigmoid(z): p=1/(1+ np.exp(-z)) p=np.minimum(p, 0.9999) p = np.maximum(p, 0.0001) return p

This is a sigmoid function that accepts a parameter z which is a dot product from the following function:

def calc_pred_func(theta,x): y=np.dot(theta,np.transpose(x)) return calc_sigmoid(y)

The above function returns a probability value between [0,1].

Calculating the cost

Now the most important part is to reduce the cost of the predictions we made. So you must be wondering what is cost? It is the error in the calculations made from the existing labels. So we have to reduce our costs gradually. First, we calculate it using the given function:

def calc_error(y_pred, y_label): len_label=len(y_label) cost= (-y_label*np.log(y_pred) - (1-y_label)*np.log(1-y_pred)).sum()/len_label return cost

Now, we will work to reduce our cost using gradient descent

Reducing the cost using Gradient Descent

Just to tell you about the gradient descent, the graph looks like this for this technique:

Now we will implement the gradient descent technique:

def gradient_descent(y_pred,y_label,x, learning_rate, theta): len_label=len(y_label) J= (-(np.dot(np.transpose(x),(y_label-y_pred)))/len_label) theta-= learning_rate*J #print("theta_0 shape: ",np.shape(theta),np.shape(J)) return theta Training Your Model: def train(y_label,x, learning_rate, theta, iterations): list_cost=[] for i in range(iterations): y_pred=calc_pred_func(theta,x) theta=gradient_descent(y_pred,y_label,x, learning_rate, theta) if i%100==0: print("n iteration",i) print("y_label:",y_pred) print("theta:",theta) cost=calc_error(y_pred, y_label) list_cost.append(cost) print("final cost list: ",list_cost) return theta,list_cost,y_pred

We will call the train function to call all functions above and you’ll see the following graph when you plot costs per iterations:

We can see the graph declining i.e. the cost is reducing.

Now we will test our model on the validation set:

def classify(y_test): return np.around(y_test) def predict(x_test,theta): y_test=np.dot(theta,np.transpose(x_test)) #print(y_test) p=calc_sigmoid(y_test) print(p) return p #for calculating accuracy based on validation set ''' Description of variables: x_test:numpy array (no. of rows in dataset, no. of features/columns) z_test: is added as column in x_test for the purpose of theta_0 y_label_test: actual labels y_test= numpy array of probability (note: these are probabilities NOT classes) y_test_pred_labels= numpy array of classes based of probability (note: here it is classes NOT probability) ''' x_test=loan.iloc[500:, 1:10].values x_rows_test, x_columns_test= x_test.shape z_test = np.ones((x_rows_test,1), dtype=float) x_test=np.append(x_test,z_test,axis=1) y_label_test=loan.loc[500:,"Status_New" ].values y_label_test=y_label_test.astype(float) x_test=x_test.astype(float) y_test=predict(x_test,theta) y_test_pred_labels=classify(y_test)

This will make a prediction on the validation set and you will get your output as the label 0 or 1.

I hope you enjoyed my article. This was all about its implementation.

Hi! I am Sarvagya Agrawal. I am pursuing B.Tech. from the Netaji Subhas University Of Technology. ML is my passion and feels proud to contribute to the community of ML learners through this platform. Feel free to contact me by visiting my website: sarvagyaagrawal.github.io

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

Related

## What Is Queue In Python? Explain With Examples

A queue is a linear data structure that works on the First In First Out mechanism(FIFO).

The element which enters first in the queue is the first to be processed.

Example

The queue data structure can be understood with the help of a queue at a bus stand. The person who reaches first at the bus stand is the first person in the queue and other persons stand him as they reach the bus stand. When the bus arrives, the person who reached first at the bus stand will be the first person to enter the bus and the rest will enter in the order in which they reached the bus stand. Thus, the FIRST IN FIRST OUT mechanism is followed.

Implementation of Queue in Python

The Queue in Python can be implemented in various ways using other linear data structures or in-built modules in the Python library.

Method 1 − Implement using list

The queue in Python can be implemented using list. It is not very efficient since inserting or deleting an element at the beginning of the list takes O(n) time which is slow compared to an implementation using other ways.

Operations involved

append() − This function adds an element at the end of the queue.

pop(0) − This function removes and returns the first element in the queue.

Example

Live Demo

queue=[] queue.append(1) queue.append(2) queue.append(3) print("Initial queue",queue) print("Element popped from the queue") print(queue.pop(0)) print(queue.pop(0)) print("Queue after popping some elements",queue) Output Initial queue [1, 2, 3] Element popped from the queue 1 2 Queue after popping some elements [3]

You can’t remove more elements once the queue is empty. Doing so results in an exception.

queue.pop(0) IndexError: pop from empty list Method 2 − Implement using queue.Queue

This is the way to implement queue using inbuilt module from python. We need to import Queue from queue. We can initialize queue with some specific size. A size of zero means an infinite queue.

Operations involved

maxsize − maximum number of elements allowed in a queue

get() − remove and return the first element from the queue. If queue is empty, wait until queue has atleast one element.

get_nowait() − remove and return the first element from the chúng tôi queue is empty, raise an exception.

put(item) − append an element at the end of the chúng tôi queue is full, wait until a free slot is availabe.

put_nowait(item) − append an element at the end of the chúng tôi queue is full, raise an exception.

full() − returns true if the queue is full, else return false.

empty() − return True if the queue is empty, else false

qsize() − returns the number of elements present in the queue

Example

Live Demo

from queue import Queue q=Queue(maxsize=3) q.put(1) q.put(2) q.put(3) print("Is queue full",q.full()) print("Element popped from the queue") print(q.get()) print(q.get()) print("Number of elements in queue",q.qsize()) print("Is queue empty",q.empty()) Output Is queue full True Element popped from the queue 1 2 Number of elements in queue 1 Is queue empty False Method 3 − Implement using collections.deque

This is another way to implement a queue in Python. We need to import deque from the collections module.

Operations involved

append() − This function adds an element at the end of the queue.

popleft() − This function removes and returns the first element in the queue in O(1) time complexity.

Example

Live Demo

from collections import deque queue=deque() queue.append(1) queue.append(2) queue.append(3) print("Intial queue: ",queue) print("Element popped from the queue") print(queue.popleft()) print(queue.popleft()) print("Queue after popping some elements: ",queue) Output Intial queue: deque([1, 2, 3]) Element popped from the queue 1 2 Queue after popping some elements: deque([3])

Using popleft() function on an empty deque will raise an exception.

## How To Implement Autofill In Your Android Apps

How does autofill work?

Providing hints for autofill

If your app uses standard Views, then by default it should work with any autofill service that uses heuristics to determine the type of data that each View expects. However, not all autofill services use these kind of heuristics; some rely on the View itself to declare the type of data that it expects.

To ensure your app can communicate with the Autofill Framework regardless of the autofill service that the user has installed on their device, you’ll need to add an “android:autofillHints” attribute to every View that’s capable of sending and receiving autofill data.

Let’s take a look at how you’d update a project to provide autofill hints. Create a new project that targets Android Oreo, and then create a basic login screen consisting of two EditTexts that accept a username and a password:

Later in this article we’ll be covering different ways of optimizing your app for autofill, but since this is enough to provide basic autofill support, let’s look at how you’d put this updated application to the test.

Build and install Google’s Autofill Framework sample project

Android Studio will now import the Autofill Framework app as a new project. If Android Studio prompts you to upgrade your Gradle plugin, select ‘Update.’

At the time of writing, this project still uses the Java 8.0 support provided by the deprecated Jack compiler, so open the module-level build.gradle file and remove the following:

Code

jackOptions { enabled true }

If you look at the Manifest, you’ll see that this project has two launcher Activities:

Code

<application android:allowBackup="true" android:icon="@mipmap/ic_launcher" android:label="@string/app_name" android:supportsRtl="true" <activity android:name=".app.MainActivity" <activity ... ... ... <activity android:name=".multidatasetservice.settings.SettingsActivity" android:exported="true" android:label="@string/settings_name" Activate Android Oreo’s Autofill

Autofill is disabled by default; to enable it, you’ll need to specify the autofill service that you want to use:

Select ‘Multi-Dataset Autofill Service,’ which is Google’s autofill service application.

Supply some data

If we’re going to test our app’s ability to receive data from an autofill service, then the autofill service is going to need some data that it can supply to this application.

There’s an easy way to feed data to an autofill service:

Load any other application that expects the data in question – in this instance, that’s any application where we can enter a username and password.

Enter this data into the application.

When prompted, save this data to the autofill service.

Switch to the application that you want to test.

Select the View that you want to test, and then see whether autofill kicks in and offers to complete this View for you.

Launch the Autofill Sample app.

Launch the login screen application we created earlier in this tutorial.

Tap the ‘username’ View. At this point the autofill picker should appear.

Select the dataset you want to use, and all Views present in this dataset will be autofilled, so the username and password Views should be autofilled simultaneously.

While this is enough to implement basic autofill functionality in your app, there’s some additional steps you can take to ensure your application is providing the best possible autofill experience.

In this final section I’m going to look at several ways that you can optimize your app for autofill.

Is a View important, or unimportant?

“auto.” Android is free to decide whether this View is important for autofill – essentially, this is the system’s default behavior.

“yes.” This View and all of its child Views are important for autofill.

“no.” This View is unimportant for autofill. Occasionally, you may be able to improve the user experience by marking certain Views as unimportant, for example if your app includes a CAPTCHA, then focusing on this field could trigger the autofill picker menu, which is just unnecessary onscreen clutter, distracting the user from what they’re trying to accomplish. In this scenario, you can improve the user experience by marking this View as android:importantForAutofill=“no.”

“noExcludeDescendants.” The View and all of its children are unimportant for autofill.

“yesExcludeDescendants.” The View is important for autofill, but all of its child Views are unimportant.

Alternatively, you can use the setImportantForAutofill method, which accepts the following:

IMPORTANT_FOR_AUTOFILL_AUTO.

IMPORTANT_FOR_AUTOFILL_YES.

IMPORTANT_FOR_AUTOFILL_NO.

IMPORTANT_FOR_AUTOFILL_YES_EXCLUDE_DESCENDANTS

IMPORTANT_FOR_AUTOFILL_NO_EXCLUDE_DESCENDANTS.

For example:

Code

.setImportantForAutofill(View.IMPORTANT_FOR_AUTOFILL_NO_EXCLUDE_DESCENDANTS); Force an autofill request

Most of the time, the autofill lifecycle is started automatically in response to notifyViewEntered(View), which is called when the user enters a View that supports autofill. However, sometimes you may want to trigger an autofill request in response to user action, for example if the user long-presses a field.

You can force an autofill request using requestAutofill(), for example:

Code

public void eventHandler(View view) { AutofillManager afm = context.getSystemService(AutofillManager.class); if (afm != null) { afm.requestAutofill(); } } Check whether autofill is enabled

You may decide to offer additional features when autofill is enabled, for example an ‘Autofill’ item in your app’s contextual overflow menu. However, since it’s never a good idea to mislead users by offering features that your app can’t currently deliver, you should always check whether autofill is currently enabled and then adjust your application accordingly, for example removing ‘Autofill’ from your context menu if autofill is disabled.

You can check whether autofill is available, by calling the isEnabled() method of the AutofillManager object:

Code

if (getSystemService(android.view.autofill.AutofillManager.class).isEnabled()) { Sharing data between your website and application

Open the Android project that you want to associate with your website.

Enter the domain that you want to associate with your application.

Enter your app’s signing config, or select a keystore file. Note that if you use a debug config or keystore, then eventually you’ll need to generate and upload a new Digital Asset Links file that uses your app’s release key.

Wrapping Up

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