System.Random.Sample 方法
方法描述
返回一个介于 0.0 和 1.0 之间的随机数。
语法定义(C# System.Random.Sample 方法 的用法)
protected virtual double Sample()
参数/返回值
| 参数值/返回值 | 参数类型/返回类型 | 参数描述/返回描述 |
|---|---|---|
| 返回值 | System.Double | 大于等于 0.0 并且小于 1.0 的双精度浮点数。 |
提示和注释
要另外生成一个随机分布或随机数生成器原则，需要从 Random 类派生一个类，并重写 Sample 方法。
重要事项
该 Sample 方法为 protected 时，这意味着它只可由 Random 类及其派生类的内部访问。 要生成一个介于 0 和 1 之间的随机数字（从 Random 实例生成），可调用 NextDouble 方法。
对继承者的说明
从 .NET Framework 2.0 版开始，如果您从 Random 派生类并重写 Sample 方法，则在调用下列方法的基类实现时将不使用由 Sample 方法的派生类实现提供的分布：
Random.NextBytes(Byte[]) 方法。
Random.Next() 方法。
Random.Next(Int32, Int32) 方法，如果（maxValue - minValue 大于 Int32.MaxValue。
而是将使用由 Random 基类提供的统一分布。 此行为提高了 Random 类的总体性能。 若要修改此行为以在派生类中调用 Sample 方法的实现，还必须重写这三个成员的行为。 该示例提供了说明。
System.Random.Sample 方法例子
该分布不同于基类的 Sample 方法所生成的统一分布。
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
*/
版本信息
.NET Framework 受以下版本支持：4、3.5、3.0、2.0、1.1、1.0 .NET Framework Client Profile 受以下版本支持：4、3.5 SP1 受以下版本支持：
适用平台
Windows 7, Windows Vista SP1 或更高版本, Windows XP SP3, Windows XP SP2 x64 Edition, Windows Server 2008（不支持服务器核心）, Windows Server 2008 R2（支持 SP1 或更高版本的服务器核心）, Windows Server 2003 SP2 .NET Framework 并不是对每个平台的所有版本都提供支持。有关支持的版本的列表，请参见.NET Framework 系统要求。