是否有任何人知道为什么RNGCryptoServiceProvider试图获得更大的数字,然后3亿失败时,卡方检验。
Does any one know why the RNGCryptoServiceProvider fail chi-square test when trying to get numbers bigger then 300,000,000.
我试图让范围内的0-1,000,000,000和我收到故障卡方检验结果的随机数,在范围0-300,000,000的数字比其它数字出现了。
I tried to get random number in the range 0-1,000,000,000 and the result that I received fail chi-square test, the numbers in the range 0-300,000,000 appeared more than the other numbers.
最后,我合并的大数字形式更小的数字(0-99 * 100M + 0-99,999,999)和卡方检验合格。
eventually i combined the big number form to smaller numbers (0-99 *100M + 0-99,999,999) and the chi-square test pass.
谁能解释这种异常大的数字?
can anyone explain this anomaly in big numbers?
我用下面的code得到的数字
I used the following code to get the numbers
[Timeout(TestTimeout.Infinite), TestMethod]
public void TestMethodStatistic()
{
Dictionary<long, long> appearances = new Dictionary<long, long>();
UInt64 tenBillion = 10000000000;
for (UInt64 i = 0; i < 10000000; i++)
{
UInt64 random = GetSIngleRandomNumberInternal() % tenBillion;
UInt64 bucket = random /10000000;
if (!appearances.ContainsKey(Convert.ToInt64(bucket)))
{
appearances.Add(Convert.ToInt64(bucket), 0);
}
appearances[Convert.ToInt64(bucket)]++;
}
string results = "\nBucket Id\tcount\n";
foreach (var appearance in appearances)
{
results += appearance.Key+"\t"+ appearance.Value +"\n";
}
File.AppendAllText(@"C:\Result.txt",results);
}
private RNGCryptoServiceProvider rngCsp = new RNGCryptoServiceProvider();
private UInt64 GetSIngleRandomNumberInternal()
{
byte[] randomNumBytes = new byte[sizeof(UInt64)];
rngCsp.GetBytes(randomNumBytes);
return BitConverter.ToUInt64(randomNumBytes, 0);
}
取的Result.txt文件和复制内容到Excel。 使它成为一个表,并添加2列1与价值100000,第二个预期的结果是卡方检验值为= CHISQ.TEST([计数],[预计])
Take the Result.txt file and copy the content to an excel. make it a table and add 2 columns 1 is the expected result with the value 100000 and the second one is the Chi-square test the value is "=CHISQ.TEST([count],[[expected ]])"
在卡方检验值小于0.1,我们有一个问题。
when the value of the chi-square test is less than 0.1 we have a problem.
最有可能的问题是,你推出一个偏见,当您使用剩余技术。请参见的余技术是多少偏见出台? 一个解释。
Most likely the problem is that you're introducing a bias when you use the remainder technique. See How much bias is introduced by the remainder technique? for an explanation.