Random.Sample 方法

定义

返回一个介于 0.0 和 1.0 之间的随机浮点数。Returns a random floating-point number between 0.0 and 1.0.

protected:
 virtual double Sample();
protected virtual double Sample ();
abstract member Sample : unit -> double
override this.Sample : unit -> double
Protected Overridable Function Sample () As Double

返回

大于或等于 0.0 且小于 1.0 的双精度浮点数。A double-precision floating point number that is greater than or equal to 0.0, and less than 1.0.

示例

下面的示例从 Random 派生一个类,并重写 Sample 方法以生成随机数的分布。The following example derives a class from Random and overrides the Sample method to generate a distribution of random numbers. 此分布不同于基类的 Sample 方法生成的统一分布。This distribution is different than the uniform distribution generated by the Sample method of the base class.

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
*/
using System;

// This derived class converts the uniformly distributed random 
// numbers generated by base.Sample( ) to another distribution.
public 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 override double Sample( )
    {
        return Math.Sqrt( base.Sample( ) );
    }
   
    public override int Next()
    {
       return (int) (Sample() * int.MaxValue);
    }   
}

public class RandomSampleDemo  
{
    static void Main( )
    {	
        const int rows = 4, cols = 6;
        const int runCount = 1000000;
        const int distGroupCount = 10;
        const double intGroupSize = 
            ( (double)int.MaxValue + 1.0 ) / (double)distGroupCount;

        RandomProportional randObj = new RandomProportional( );

        int[ ]      intCounts = new int[ distGroupCount ];
        int[ ]      realCounts = new 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 ] );
    }
}

/*
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
*/
' This derived class converts the uniformly distributed random 
' numbers generated by base.Sample( ) to another distribution.
Public Class RandomProportional
   Inherits Random

   ' The Sample method generates a distribution proportional to the value 
   ' of the random numbers, in the range [0.0, 1.0].
   Protected Overrides Function Sample( ) As Double
      Return Math.Sqrt( MyBase.Sample( ) )
   End Function
   
   Public Overrides Function [Next]() As Integer
      Return Sample() * Integer.MaxValue
   End Function 
End Class 

Module RandomSampleDemo
    Sub Main( )
        Const rows As Integer = 4, cols As Integer = 6
        Const runCount As Integer = 1000000
        Const distGroupCount As Integer = 10
        Const intGroupSize As Double = _
            ( CDbl( Integer.MaxValue ) + 1.0 ) / _
            CDbl( distGroupCount )
            
        Dim randObj As New RandomProportional( )
            
        Dim intCounts( distGroupCount ) As Integer
        Dim realCounts( distGroupCount ) As Integer
        Dim i As Integer, j As Integer 
            
        Console.WriteLine( vbCrLf & _
            "The derived RandomProportional class overrides " & _ 
            "the Sample method to " & vbCrLf & _
            "generate random numbers in the range " & _ 
            "[0.0, 1.0]. The distribution " & vbCrLf & _
            "of the numbers is proportional to their numeric " & _
            "values. For example, " & vbCrLf & _ 
            "numbers are generated in the vicinity of 0.75 " & _
            "with three times " & vbCrLf & "the " & _
            "probability of those generated near 0.25." )
        Console.WriteLine( vbCrLf & _
            "Random doubles generated with the NextDouble( ) " & _ 
            "method:" & vbCrLf )
            
        ' Generate and display [rows * cols] random doubles.
        For i = 0 To rows - 1
            For j = 0 To cols - 1
                Console.Write( "{0,12:F8}", randObj.NextDouble( ) )
            Next j
            Console.WriteLine( )
        Next i
            
        Console.WriteLine( vbCrLf & _
            "Random integers generated with the Next( ) " & _ 
            "method:" & vbCrLf )
            
        ' Generate and display [rows * cols] random integers.
        For i = 0 To rows - 1
            For j = 0 To cols - 1
                Console.Write( "{0,12}", randObj.Next( ) )
            Next j
            Console.WriteLine( )
        Next i
            
        Console.WriteLine( vbCrLf & _
            "To demonstrate the proportional distribution, " & _ 
            "{0:N0} random " & vbCrLf & _
            "integers and doubles are grouped into {1} " & _ 
            "equal value ranges. This " & vbCrLf & _
            "is the count of values in each range:" & vbCrLf, _
            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 i = 0 To runCount - 1
            intCounts( Fix( CDbl( randObj.Next( ) ) / _
                intGroupSize ) ) += 1
            realCounts( Fix( randObj.NextDouble( ) * _
                CDbl( distGroupCount ) ) ) += 1
        Next i
            
        ' Display the count of each group.
        For i = 0 To distGroupCount - 1
            Console.WriteLine( _
                "{0,10}-{1,10}{2,10:N0}{3,12:N5}-{4,7:N5}{5,10:N0}", _
                Fix( CDbl( i ) * intGroupSize ), _
                Fix( CDbl( i + 1 ) * intGroupSize - 1.0 ), _
                intCounts( i ), _
                CDbl( i ) / CDbl( distGroupCount), _
                CDbl( i + 1 ) / CDbl( distGroupCount ), _
                realCounts( i ) )
        Next i
    End Sub
End Module 
' This example of Random.Sample() generates 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 their numeric 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:
'    
'      0.28377004  0.75920598  0.33430371  0.66720626  0.97080243  0.27353772
'      0.17787962  0.54618410  0.08145080  0.56286100  0.99002910  0.64898614
'      0.27673277  0.99455281  0.93778966  0.76162002  0.70533771  0.44375798
'      0.55939883  0.87383136  0.66465779  0.77392566  0.42393411  0.82409159
'    
'    Random integers generated with the Next( ) method:
'    
'      1364479914  1230312341  1657373812  1526222928   988564704   700078020
'      1801013705  1541517421  1146312560   338318389  1558995993  2027260859
'       884520932  1320070465   570200106  1027684711   943035246  2088689333
'       630809089  1705728475  2140787648  2097858166  1863010875  1386804198
'    
'    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     9,892     0.00000-0.10000     9,928
'     214748364- 429496728    30,341     0.10000-0.20000    30,101
'     429496729- 644245093    49,958     0.20000-0.30000    49,964
'     644245094- 858993458    70,099     0.30000-0.40000    70,213
'     858993459-1073741823    90,801     0.40000-0.50000    89,553
'    1073741824-1288490187   109,699     0.50000-0.60000   109,427
'    1288490188-1503238552   129,438     0.60000-0.70000   130,339
'    1503238553-1717986917   149,886     0.70000-0.80000   150,000
'    1717986918-1932735282   170,338     0.80000-0.90000   170,128
'    1932735283-2147483647   189,548     0.90000-1.00000   190,347

注解

若要生成不同的随机分发或不同的随机数生成器原则,请从 Random 类中派生一个类,并重写 Sample 方法。To produce a different random distribution or a different random number generator principle, derive a class from the Random class and override the Sample method.

重要

protectedSample 方法,这意味着只能在 Random 类及其派生类中访问此方法。The Sample method is protected, which means that it is accessible only within the Random class and its derived classes. 若要从 Random 实例生成0到1之间的随机数字,请调用 NextDouble 方法。To generate a random number between 0 and 1 from a Random instance, call the NextDouble method.

继承者说明

从 .NET Framework 版本2.0 开始,如果从 Random 派生一个类并重写 Sample() 方法,则不会在调用以下方法的基类实现时使用 Sample() 方法的派生类实现提供的分布:Starting 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 following methods: - NextBytes(Byte[]) 方法。- The NextBytes(Byte[]) method.

- Next() 方法。- The Next() method.

-Next(Int32, Int32) 方法(如果为(maxValue - minValue)大于 MaxValue- The Next(Int32, Int32) method, if (maxValue - minValue) is greater than MaxValue.

相反,将使用基 Random 类提供的统一分布。Instead, the uniform distribution provided by the base Random class is used. 此行为可提高 Random 类的整体性能。This behavior improves the overall performance of the Random class. 若要修改此行为以调用派生类中 Sample() 方法的实现,还必须重写这三个成员的行为。To modify this behavior to call the implementation of the Sample() method in the derived class, you must also override the behavior of these three members. 说明如示例所示。The example provides an illustration.

适用于

另请参阅