Random::Next Method
Returns a nonnegative random number.
Assembly: mscorlib (in mscorlib.dll)
Random::Next generates a random number whose value ranges from zero to less than Int32::MaxValue. To generate a random number whose value ranges from zero to some other positive number, use the Random::Next(Int32) method overload. To generate a random number within a different range, use the Random::Next(Int32, Int32) method overload.
Notes to InheritorsStarting with the .NET Framework version 2.0, if you derive a class from Random and override the Sample method, the distribution provided by the derived class implementation of the Sample method is not used in calls to the base class implementation of the Random::Next method. Instead, the uniform distribution returned by the base Random class is used. This behavior improves the overall performance of the Random class. To modify this behavior to call the Sample method in the derived class, you must also override the Random::Next method.
The following example generates random integers with various overloads of the Next method.
// Example of the Random::Next( ) methods. using namespace System; // Generate random numbers with no bounds specified. void NoBoundsRandoms( int seed ) { Console::WriteLine( "\nRandom object, seed = {0}, no bounds:", seed ); Random^ randObj = gcnew Random( seed ); // Generate six random integers from 0 to int.MaxValue. for ( int j = 0; j < 6; j++ ) Console::Write( "{0,11} ", randObj->Next() ); Console::WriteLine(); } // Generate random numbers with an upper bound specified. void UpperBoundRandoms( int seed, int upper ) { Console::WriteLine( "\nRandom object, seed = {0}, upper bound = {1}:", seed, upper ); Random^ randObj = gcnew Random( seed ); // Generate six random integers from 0 to the upper bound. for ( int j = 0; j < 6; j++ ) Console::Write( "{0,11} ", randObj->Next( upper ) ); Console::WriteLine(); } // Generate random numbers with both bounds specified. void BothBoundsRandoms( int seed, int lower, int upper ) { Console::WriteLine( "\nRandom object, seed = {0}, lower = {1}, upper = {2}:", seed, lower, upper ); Random^ randObj = gcnew Random( seed ); // Generate six random integers from the lower to // upper bounds. for ( int j = 0; j < 6; j++ ) Console::Write( "{0,11} ", randObj->Next( lower, upper ) ); Console::WriteLine(); } int main() { Console::WriteLine( "This example of the Random::Next( ) methods\n" "generates the following output.\n" ); Console::WriteLine( "Create Random objects all with the same seed and " "generate\nsequences of numbers with different " "bounds. Note the effect\nthat the various " "combinations of bounds have on the sequences." ); NoBoundsRandoms( 234 ); UpperBoundRandoms( 234, Int32::MaxValue ); UpperBoundRandoms( 234, 2000000000 ); UpperBoundRandoms( 234, 200000000 ); BothBoundsRandoms( 234, 0, Int32::MaxValue ); BothBoundsRandoms( 234, Int32::MinValue, Int32::MaxValue ); BothBoundsRandoms( 234, -2000000000, 2000000000 ); BothBoundsRandoms( 234, -200000000, 200000000 ); BothBoundsRandoms( 234, -2000, 2000 ); } /* This example of the Random::Next( ) methods generates the following output. Create Random objects all with the same seed and generate sequences of numbers with different bounds. Note the effect that the various combinations of bounds have on the sequences. Random object, seed = 234, no bounds: 2091148258 1024955023 711273344 1081917183 1833298756 109460588 Random object, seed = 234, upper bound = 2147483647: 2091148258 1024955023 711273344 1081917183 1833298756 109460588 Random object, seed = 234, upper bound = 2000000000: 1947533580 954563751 662424922 1007613896 1707392518 101943116 Random object, seed = 234, upper bound = 200000000: 194753358 95456375 66242492 100761389 170739251 10194311 Random object, seed = 234, lower = 0, upper = 2147483647: 2091148258 1024955023 711273344 1081917183 1833298756 109460588 Random object, seed = 234, lower = -2147483648, upper = 2147483647: 2034812868 -97573602 -724936960 16350718 1519113864 -1928562472 Random object, seed = 234, lower = -2000000000, upper = 2000000000: 1895067160 -90872498 -675150156 15227793 1414785036 -1796113767 Random object, seed = 234, lower = -200000000, upper = 200000000: 189506716 -9087250 -67515016 1522779 141478503 -179611377 Random object, seed = 234, lower = -2000, upper = 2000: 1895 -91 -676 15 1414 -1797 */
The following example derives a class from Random to generate a sequence of random numbers whose distribution differs from the uniform distribution generated by the Sample method of the base class. It overrides the Sample method to provide the distribution of random numbers, and overrides the Random::Next method to use series of random numbers.
using namespace System; // This derived class converts the uniformly distributed random // numbers generated by base.Sample( ) to another distribution. public ref class RandomProportional : Random { // The Sample method generates a distribution proportional to the value // of the random numbers, in the range [0.0, 1.0]. protected: virtual double Sample() override { return Math::Sqrt(Random::Sample()); } public: RandomProportional() {} virtual int Next() override { return (int) (Sample() * Int32::MaxValue); } }; int main(array<System::String ^> ^args) { const int rows = 4, cols = 6; const int runCount = 1000000; const int distGroupCount = 10; const double intGroupSize = ( (double) Int32::MaxValue + 1.0 ) / (double)distGroupCount; RandomProportional ^randObj = gcnew RandomProportional(); array<int>^ intCounts = gcnew array<int>(distGroupCount); array<int>^ realCounts = gcnew array<int>(distGroupCount); Console::WriteLine( "\nThe derived RandomProportional class overrides " + "the Sample method to \ngenerate random numbers " + "in the range [0.0, 1.0]. The distribution \nof " + "the numbers is proportional to their numeric values. " + "For example, \nnumbers are generated in the " + "vicinity of 0.75 with three times the \n" + "probability of those generated near 0.25." ); Console::WriteLine( "\nRandom doubles generated with the NextDouble( ) " + "method:\n" ); // Generate and display [rows * cols] random doubles. for( int i = 0; i < rows; i++ ) { for( int j = 0; j < cols; j++ ) Console::Write( "{0,12:F8}", randObj->NextDouble( ) ); Console::WriteLine( ); } Console::WriteLine( "\nRandom integers generated with the Next( ) " + "method:\n" ); // Generate and display [rows * cols] random integers. for( int i = 0; i < rows; i++ ) { for( int j = 0; j < cols; j++ ) Console::Write( "{0,12}", randObj->Next( ) ); Console::WriteLine( ); } Console::WriteLine( "\nTo demonstrate the proportional distribution, " + "{0:N0} random \nintegers and doubles are grouped " + "into {1} equal value ranges. This \n" + "is the count of values in each range:\n", runCount, distGroupCount ); Console::WriteLine( "{0,21}{1,10}{2,20}{3,10}", "Integer Range", "Count", "Double Range", "Count" ); Console::WriteLine( "{0,21}{1,10}{2,20}{3,10}", "-------------", "-----", "------------", "-----" ); // Generate random integers and doubles, and then count // them by group. for( int i = 0; i < runCount; i++ ) { intCounts[ (int)( (double)randObj->Next( ) / intGroupSize ) ]++; realCounts[ (int)( randObj->NextDouble( ) * (double)distGroupCount ) ]++; } // Display the count of each group. for( int i = 0; i < distGroupCount; i++ ) Console::WriteLine( "{0,10}-{1,10}{2,10:N0}{3,12:N5}-{4,7:N5}{5,10:N0}", (int)( (double)i * intGroupSize ), (int)( (double)( i + 1 ) * intGroupSize - 1.0 ), intCounts[ i ], ( (double)i ) / (double)distGroupCount, ( (double)( i + 1 ) ) / (double)distGroupCount, realCounts[ i ] ); return 0; } /* This example of Random.Sample() displays the following output: The derived RandomProportional class overrides the Sample method to generate random numbers in the range [0.0, 1.0). The distribution of the numbers is proportional to the number values. For example, numbers are generated in the vicinity of 0.75 with three times the probability of those generated near 0.25. Random doubles generated with the NextDouble( ) method: 0.59455719 0.17589882 0.83134398 0.35795862 0.91467727 0.54022658 0.93716947 0.54817519 0.94685080 0.93705478 0.18582318 0.71272428 0.77708682 0.95386216 0.70412393 0.86099417 0.08275804 0.79108316 0.71019941 0.84205103 0.41685082 0.58186880 0.89492302 0.73067715 Random integers generated with the Next( ) method: 1570755704 1279192549 1747627711 1705700211 1372759203 1849655615 2046235980 1210843924 1554274149 1307936697 1480207570 1057595022 337854215 844109928 2028310798 1386669369 2073517658 1291729809 1537248240 1454198019 1934863511 1640004334 2032620207 534654791 To demonstrate the proportional distribution, 1,000,000 random integers and doubles are grouped into 10 equal value ranges. This is the count of values in each range: Integer Range Count Double Range Count ------------- ----- ------------ ----- 0- 214748363 10,079 0.00000-0.10000 10,148 214748364- 429496728 29,835 0.10000-0.20000 29,849 429496729- 644245093 49,753 0.20000-0.30000 49,948 644245094- 858993458 70,325 0.30000-0.40000 69,656 858993459-1073741823 89,906 0.40000-0.50000 90,337 1073741824-1288490187 109,868 0.50000-0.60000 110,225 1288490188-1503238552 130,388 0.60000-0.70000 129,986 1503238553-1717986917 149,231 0.70000-0.80000 150,428 1717986918-1932735282 170,234 0.80000-0.90000 169,610 1932735283-2147483647 190,381 0.90000-1.00000 189,813 */
Windows 7, Windows Vista SP1 or later, Windows XP SP3, Windows XP SP2 x64 Edition, Windows Server 2008 (Server Core not supported), Windows Server 2008 R2 (Server Core supported with SP1 or later), Windows Server 2003 SP2
The .NET Framework does not support all versions of every platform. For a list of the supported versions, see .NET Framework System Requirements.