Random.Sample Metoda
Definicja
Ważne
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Zwraca losową liczbę zmiennoprzecinkową z zakresu od 0,0 do 1,0.
protected:
virtual double Sample();protected virtual double Sample ();abstract member Sample : unit -> double
override this.Sample : unit -> doubleProtected Overridable Function Sample () As DoubleZwraca
Liczba zmiennoprzecinkowa o podwójnej precyzji większa lub równa 0,0 i mniejsza niż 1,0.
Przykłady
Poniższy przykład tworzy klasę z Random i zastępuje Sample metodę w celu wygenerowania rozkładu liczb losowych. Rozkład ten różni się od równomiernego rozkładu generowanego przez Sample metodę klasy bazowej.
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 output similar to the following:
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 output similar to the following:
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
*/
open System
// This derived class converts the uniformly distributed random
// numbers generated by base.Sample() to another distribution.
type RandomProportional() =
inherit Random()
// The Sample method generates a distribution proportional to the value
// of the random numbers, in the range [0.0, 1.0].
override _.Sample() =
sqrt (base.Sample())
override this.Next() =
this.Sample() * float Int32.MaxValue
|> int
let [<Literal>] rows = 4
let [<Literal>] cols = 6
let [<Literal>] runCount = 1000000
let [<Literal>] distGroupCount = 10
let intGroupSize =
(float Int32.MaxValue + 1.0) / float distGroupCount
let randObj = RandomProportional()
printfn """
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."""
printfn "\nRandom doubles generated with the NextDouble() method:\n"
// Generate and display [rows * cols] random doubles.
for _ = 1 to rows do
for _ = 1 to cols do
printf $"{randObj.NextDouble(),12:F8}"
printfn ""
printfn "\nRandom integers generated with the Next() method:\n"
// Generate and display [rows * cols] random integers.
for _ = 1 to rows do
for _ = 1 to cols do
printf $"{randObj.Next(),12}"
printfn ""
printfn $"""
To demonstrate the proportional distribution, {runCount:N0} random
integers and doubles are grouped into {distGroupCount} equal value ranges. This
is the count of values in each range:
"""
printfn $"""{"Integer Range",21}{"Count",10}{"Double Range",20}{"Count",10}"""
printfn $"""{"-------------",21}{"-----",10}{"------------",20}{"-----",10}"""
// Generate random integers and doubles, and then count them by group.
let intCounts =
Array.init runCount (fun _ ->
(randObj.Next() |> float) / float intGroupSize
|> int )
|> Array.countBy id
|> Array.map snd
let realCounts =
Array.init runCount (fun _ ->
randObj.NextDouble() * float distGroupCount
|> int )
|> Array.countBy id
|> Array.map snd
// Display the count of each group.
for i = 0 to distGroupCount - 1 do
Console.WriteLine(
"{0,10}-{1,10}{2,10:N0}{3,12:N5}-{4,7:N5}{5,10:N0}",
int(float i * intGroupSize),
int(float (i + 1) * intGroupSize - 1.0),
intCounts.[i],
(float i) / float distGroupCount,
float (i + 1) / float distGroupCount,
realCounts.[i])
(*
This example of Random.Sample() displays output similar to the following:
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 output similar to the following:
'
' 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
Uwagi
Aby utworzyć inny rozkład losowy lub inną regułę generatora liczb losowych, należy utworzyć klasę z Random klasy i zastąpić metodę Sample .
Ważne
Metoda Sample to protected, co oznacza, że jest dostępna tylko w Random klasie i jej klasach pochodnych. Aby wygenerować losową liczbę z zakresu od 0 do 1 z Random wystąpienia, wywołaj metodę NextDouble .
Uwagi dotyczące dziedziczenia
Począwszy od .NET Framework w wersji 2.0, jeśli uzyskasz klasę z Random i zastąpisz Sample() metodę, rozkład dostarczony przez implementację klasy pochodnej Sample() metody nie jest używany w wywołaniach do implementacji klasy bazowej następujących metod:
Metoda NextBytes(Byte[]) .
Metoda Next() .
Metoda Next(Int32, Int32) , jeśli (
minValuemaxValue- ) jest większa niż Int32.MaxValue.
Zamiast tego jest używany jednolity rozkład dostarczony przez klasę bazową Random . To zachowanie poprawia ogólną wydajność Random klasy. Aby zmodyfikować to zachowanie w celu wywołania implementacji Sample() metody w klasie pochodnej, należy również zastąpić zachowanie tych trzech elementów członkowskich. Przykład stanowi ilustrację.
Dotyczy
Zobacz też
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