Random Class
[ This article is for Windows Phone 8 developers. If you’re developing for Windows 10, see the latest documentation. ]
Represents a pseudo-random number generator, a device that produces a sequence of numbers that meet certain statistical requirements for randomness.
Assembly: mscorlib (in mscorlib.dll)
The Random type exposes the following members.
| Name | Description | |
|---|---|---|
 | Random() | Initializes a new instance of the Random class, using a time-dependent default seed value. |
| Random(Int32) | Initializes a new instance of the Random class, using the specified seed value. |
| Name | Description | |
|---|---|---|
| Equals(Object) | Determines whether the specified Object is equal to the current Object. (Inherited from Object.) |
 | Finalize | Allows an object to try to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection. (Inherited from Object.) |
| GetHashCode | Serves as a hash function for a particular type. (Inherited from Object.) |
| GetType | Gets the Type of the current instance. (Inherited from Object.) |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) |
| Next() | Returns a nonnegative random number. |
| Next(Int32) | Returns a nonnegative random number less than the specified maximum. |
| Next(Int32, Int32) | Returns a random number within a specified range. |
| NextBytes | Fills the elements of a specified array of bytes with random numbers. |
| NextDouble | Returns a random number between 0.0 and 1.0. |
| Sample | Returns a random number between 0.0 and 1.0. |
| ToString | Returns a string that represents the current object. (Inherited from Object.) |
Pseudo-random numbers are chosen with equal probability from a finite set of numbers. The chosen numbers are not completely random because a definite 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 Donald E. Knuth's subtractive random number generator algorithm. For more information, see D. E. Knuth. "The Art of Computer Programming, volume 2: Seminumerical Algorithms". Addison-Wesley, Reading, MA, second edition, 1981.
The random number generation starts from a seed value. If the same seed is used repeatedly, the same series of numbers is generated. One way to produce different sequences is to make the seed value time-dependent, thereby producing a different series with each new instance of Random. By default, the parameterless constructor of the Random class uses the system clock to generate its seed value, while its parameterized constructor can take an Int32 value based on the number of ticks in the current time. However, 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 that two Random objects that are instantiated in close sucession generate an identical series of random numbers.
This problem can be avoided by creating a single Random object rather than multiple ones.
To improve performance, create one Random object to generate many random numbers over time, instead of repeatedly creating new Random objects to generate one random number.
To generate a cryptographically secure random number suitable for creating a random password, for example, use a class derived from System.Security.Cryptography::RandomNumberGenerator such as System.Security.Cryptography::RNGCryptoServiceProvider.
Notes to CallersThe implementation of the random number generator in the Random class is not guaranteed to remain the same across major versions of the .NET Framework for Windows Phone. As a result, your application code should not assume that the same seed will result in the same pseudo-random sequence in different versions of the .NET Framework for Windows Phone.
Notes to InheritorsThe Random::Next(), Random::Next(Int32, Int32), and NextBytes methods do not necessarily call the derived class implementation of the Sample method. As a result, classes derived from Random should override all three of these methods.