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Privacy policy. Thank you. Microsoft makes no warranties, express or implied, with respect to the information provided here. Represents a pseudo-random number generator, which is an algorithm that produces a sequence of numbers that meet certain statistical requirements for randomness. The following example creates a single random number generator and calls its NextBytes , Next , and NextDouble methods to generate sequences of random numbers within different ranges.

The following example generates a random integer that it uses as an index to retrieve a string value from an array. Pseudo-random numbers are chosen with equal probability from a finite set of numbers. The chosen numbers are not completely random because a mathematical algorithm is used to select them, but they are sufficiently random for practical purposes.

The current implementation of the Random class is based on a modified version of Donald E. Knuth’s subtractive random number generator algorithm. For more information, see D. Addison-Wesley, Reading, MA, third edition, To generate a cryptographically secure random number, such as one that’s suitable for creating a random password, use the RNGCryptoServiceProvider class or derive a class from System. Instantiating the random number generator Avoiding multiple instantiations The System. Random class and thread safety Generating different types of random numbers Substituting your own algorithm How do you use System.

Random to… Retrieve the same sequence of random values Retrieve unique sequences of random values Retrieve integers in a specified range Retrieve integers with a specified number of digits Retrieve floating-point values in a specified range Generate random Boolean values Generate random bit integers Retrieve bytes in a specified range Retrieve an element from an array or collection at random Retrieve a unique element from an array or collection.

You instantiate the random number generator by providing a seed value a starting value for the pseudo-random number generation algorithm to a Random class constructor.

You can supply the seed value either explicitly or implicitly:. The Random Int32 constructor uses an explicit seed value that you supply. The Random constructor uses the default seed value. This is the most common way of instantiating the random number generator. NET Framework, the default seed value is time-dependent.

NET Core, the default seed value is produced by the thread-static, pseudo-random number generator. If the same seed is used for separate Random objects, they will generate the same series of random numbers. This can be useful for creating a test suite that processes random values, or for replaying games that derive their data from random numbers.

However, note that Random objects in processes running under different versions of the. NET Framework may return different series of random numbers even if they’re instantiated with identical seed values. To produce different sequences of random numbers, you can make the seed value time-dependent, thereby producing a different series with each new instance of Random.

The parameterized Random Int32 constructor can take an Int32 value based on the number of ticks in the current time, whereas the parameterless Random constructor uses the system clock to generate its seed value.

However, on the. NET Framework only, because the clock has finite resolution, using the parameterless constructor to create different Random objects in close succession creates random number generators that produce identical sequences of random numbers.

The following example illustrates how two Random objects that are instantiated in close succession in a. NET Framework application generate an identical series of random numbers. On most Windows systems, Random objects created within 15 milliseconds of one another are likely to have identical seed values.

To avoid this problem, create a single Random object instead of multiple objects. Note that the Random class in. NET Core does not have this limitation. On the. NET Framework, initializing two random number generators in a tight loop or in rapid succession creates two random number generators that can produce identical sequences of random numbers.

In most cases, this is not the developer’s intent and can lead to performance issues, because instantiating and initializing a random number generator is a relatively expensive process.

Both to improve performance and to avoid inadvertently creating separate random number generators that generate identical numeric sequences, we recommend that you create one Random object to generate many random numbers over time, instead of creating new Random objects to generate one random number. However, the Random class isn’t thread safe.

If you call Random methods from multiple threads, follow the guidelines discussed in the next section. Instead of instantiating individual Random objects, we recommend that you create a single Random instance to generate all the random numbers needed by your app.

However, Random objects are not thread safe. If your app calls Random methods from multiple threads, you must use a synchronization object to ensure that only one thread can access the random number generator at a time. If you don’t ensure that the Random object is accessed in a thread-safe way, calls to methods that return random numbers return 0. The following example uses the C lock Statement , the F lock function and the Visual Basic SyncLock statement to ensure that a single random number generator is accessed by 11 threads in a thread-safe manner.

Each thread generates 2 million random numbers, counts the number of random numbers generated and calculates their sum, and then updates the totals for all threads when it finishes executing. The ThreadStaticAttribute attribute is used to define thread-local variables that track the total number of random numbers generated and their sum for each thread. A lock the lock statement in C , the lock function in F and the SyncLock statement in Visual Basic protects access to the variables for the total count and sum of all random numbers generated on all threads.

A semaphore the CountdownEvent object is used to ensure that the main thread blocks until all other threads complete execution.

The example checks whether the random number generator has become corrupted by determining whether two consecutive calls to random number generation methods return 0. If corruption is detected, the example uses the CancellationTokenSource object to signal that all threads should be canceled. Before generating each random number, each thread checks the state of the CancellationToken object.

If cancellation is requested, the example calls the CancellationToken. ThrowIfCancellationRequested method to cancel the thread. The following example is identical to the first, except that it uses a Task object and a lambda expression instead of Thread objects. The variables to keep track of the number of random numbers generated and their sum in each task are local to the task, so there is no need to use the ThreadStaticAttribute attribute. The static Task. WaitAll method is used to ensure that the main thread doesn’t complete before all tasks have finished.

There is no need for the CountdownEvent object. The exception that results from task cancellation is surfaced in the Task. WaitAll method. In the previous example, it is handled by each thread. The random number generator provides methods that let you generate the following kinds of random numbers:.

A series of Byte values. You determine the number of byte values by passing an array initialized to the number of elements you want the method to return to the NextBytes method. The following example generates 20 bytes. A single integer. You can choose whether you want an integer from 0 to a maximum value Int MaxValue – 1 by calling the Next method, an integer between 0 and a specific value by calling the Next Int32 method, or an integer within a range of values by calling the Next Int32, Int32 method.

In the parameterized overloads, the specified maximum value is exclusive; that is, the actual maximum number generated is one less than the specified value.

The following example calls the Next Int32, Int32 method to generate 10 random numbers between and Note that the second argument to the method specifies the exclusive upper bound of the range of random values returned by the method. In other words, the largest integer that the method can return is one less than this value. A single floating-point value from 0. The exclusive upper bound of the random number returned by the method is 1, so its actual upper bound is 0.

The following example generates 10 random floating-point numbers. The Next Int32, Int32 method allows you to specify the range of the returned random number. However, the maxValue parameter, which specifies the upper range returned number, is an exclusive, not an inclusive, value.

This means that the method call Next 0, returns a value between 0 and 99, and not between 0 and You can also use the Random class for such tasks as generating random T:System. Boolean values , generating random floating point values with a range other than 0 to 1 , generating random bit integers , and randomly retrieving a unique element from an array or collection.

For these and other common tasks, see the How do you use System. Random to… section. You can implement your own random number generator by inheriting from the Random class and supplying your random number generation algorithm. To supply your own algorithm, you must override the Sample method, which implements the random number generation algorithm. You don’t have to override the Next Int32 and NextDouble methods. For an example that derives from the Random class and modifies its default pseudo-random number generator, see the Sample reference page.

The following sections discuss and provide sample code for some of the ways you might want to use random numbers in your app. Sometimes you want to generate the same sequence of random numbers in software test scenarios and in game playing. Testing with the same sequence of random numbers allows you to detect regressions and confirm bug fixes. Using the same sequence of random number in games allows you to replay previous games.

You can generate the same sequence of random numbers by providing the same seed value to the Random Int32 constructor. The seed value provides a starting value for the pseudo-random number generation algorithm. The following example uses as an arbitrary seed value to instantiate the Random object, displays 20 random floating-point values, and persists the seed value.


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