public List <T> Analyse1 <T>(FactorTrendAnalyseDto <T> dto)
{
//分析历史趋势,排除最后一位号码，（最后一位号码分析当前要分析的可能号码）
var historicalNumbers = dto.Numbers.Take(dto.Numbers.Count - 1).ToList();
//分析每个因子
var factor = dto.Factor;
//统计因子在记录中的趋势
var trendResult = CountConsecutiveDistribution(dto.Numbers, factor.Left, factor.Right);
//行明细结果集
var rowDetailses = trendResult.FactorDistributions;
if (rowDetailses == null || rowDetailses.Count == 0)
{
return(null);
}
var lastIndexResult = rowDetailses[rowDetailses.Count - 1];
//因子不包含最后一个号码，（连续次数为0）
if (lastIndexResult.ConsecutiveTimes == 0)
{
return(null);
}
//大于等于指定的连续次和小于等于指定的间隔数
if (lastIndexResult.ConsecutiveTimes >= dto.AddConsecutiveTimes && lastIndexResult.MaxConsecutiveTimesInterval <= dto.AddInterval)
{
//返回的可能因子
return(factor.Right);
}
return(null);
}
public List <Factor <T> > Analyse <T>(FactorsTrendAnalyseDto <T> dto)
{
//预测的可能因子
var predictiveFactors = new List <Factor <T> >();
var factorTrend = new FactorTrend();
//分析每个因子
foreach (var factor in dto.Factors)
{
var analyseDto = new FactorTrendAnalyseDto <T>
{
Factor = factor,
Numbers = dto.Numbers,
AllowMinTimes = dto.AllowMinTimes,
AnalyseHistoricalTrendEndIndex = dto.AnalyseHistoricalTrendEndIndex,
AddConsecutiveTimes = dto.AddConsecutiveTimes,
AddInterval = dto.AddInterval
};
var predictiveFactor = factorTrend.Analyse(analyseDto);
if (predictiveFactor != null)
{
predictiveFactors.Add(factor);
}
}
return(predictiveFactors);
}
public List <T> Analyse <T>(FactorTrendAnalyseDto <T> dto)
{
//分析历史趋势,排除最后一位号码，（最后一位号码分析当前要分析的可能号码）
var historicalNumbers = dto.Numbers.Take(dto.Numbers.Count - 1).ToList();
//分析每个因子
var factor = dto.Factor;
//统计每个因子在记录中的趋势
var trendResult = CountConsecutiveDistribution(dto.Numbers, factor.Left, factor.Right);
//行明细结果集
var rowDetailses = trendResult.FactorDistributions;
if (rowDetailses == null || rowDetailses.Count == 0)
{
return(null);
}
var lastIndexResult = rowDetailses[rowDetailses.Count - 1];
//因子不包含最后一个号码，（连续次数为0）
if (lastIndexResult.ConsecutiveTimes == 0)
{
return(null);
}
var historicalTrends = GetCorrectRates(historicalNumbers, trendResult, dto.AnalyseHistoricalTrendEndIndex, factor.Right);
//筛选正确100%的历史趋势，如没有不记录
//historicalTrends = historicalTrends.Where(h => h.CorrectRate == 1).OrderBy(h => h.AllowInterval).ThenByDescending(h => h.AllowConsecutiveTimes).ToList();
historicalTrends = historicalTrends.Where(h => h.CorrectRate == 1).OrderByDescending(h => h.AllowConsecutiveTimes).ThenBy(h => h.AllowInterval).ToList();
if (historicalTrends.Count == 0)
{
return(null);
}
var firstHistoricalTrend = historicalTrends.FirstOrDefault();
if (firstHistoricalTrend == null)
{
return(null);
}
//可以考虑加大连续次数和间隔数
if (lastIndexResult.ConsecutiveTimes >= firstHistoricalTrend.AllowConsecutiveTimes + dto.AddConsecutiveTimes && lastIndexResult.MaxConsecutiveTimesInterval <= firstHistoricalTrend.AllowInterval - dto.AddInterval)
{
//返回的可能因子
return(factor.Right);
}
return(null);
}
public void TestMethod_Analyse_Ten_Digit_By_Random_ASC()
{
var numbers = GetTestNumbers(0, 10, 100000);
//因子
var factors = FactorGenerator.Create(new List <byte> {
0, 1, 2, 3, 4, 5, 6, 7, 8, 9
}.ToList());
var watch = new Stopwatch();
watch.Start();
var resultString = new StringBuilder();
var defaultTakeCount = 1000;
var testCount = 2500;
var trend = new FactorTrend();
var factorsTrendCorrectRates = new Dictionary <Factor <byte>, List <FactorTrendCorrectRate> >();
var firstrFactor = factors[0];
foreach (var factor in factors)
{
var strFactor = string.Join(",", factor.Right);
var trendCorrectRates = new List <FactorTrendCorrectRate>();
for (var c = 3; c < 22; c++)
{
for (var interval = 0; interval > -9; interval--)
{
var hasCount = 0;
var resultCount = 0;
for (var i = testCount; i >= 0; i--)
{
var number = numbers[i];
var curNumbers = numbers.Skip(i + 1).Take(defaultTakeCount).ToList();
curNumbers.Reverse();
var dto = new FactorTrendAnalyseDto <byte>
{
AddConsecutiveTimes = c,
AddInterval = interval,
Numbers = curNumbers,
AnalyseHistoricalTrendEndIndex = 1,
Factor = factor
};
var result = trend.Analyse1(dto);
var success = false;
if (result == null)
{
continue;
}
if (result.Count > 0)
{
resultCount++;
if (result.Contains(number))
{
success = true;
}
}
if (success)
{
hasCount++;
}
}
var correctRate = resultCount == 0 ? 0 : (double)hasCount / resultCount;
if (correctRate == 1)
{
trendCorrectRates.Add(new FactorTrendCorrectRate
{
AllowConsecutiveTimes = c,
AllowInterval = interval,
AnalyticalCount = resultCount,
CorrectCount = hasCount,
TypeDescription = strFactor,
CorrectRate = correctRate
});
}
}
}
if (trendCorrectRates.Count > 0)
{
factorsTrendCorrectRates.Add(factor, trendCorrectRates);
}
break;
}
watch.Stop();
var usedSeconds = watch.ElapsedMilliseconds / 1000;
var realHasCount = 0;
var realResultCount = 0;
var consecutiveTimes = 12;
var curInterval = -2;
testCount = 99000;
for (var i = testCount; i >= 0; i--)
{
var number = numbers[i];
var curNumbers = numbers.Skip(i + 1).Take(200).ToList();
curNumbers.Reverse();
var dto = new FactorTrendAnalyseDto <byte>
{
AddConsecutiveTimes = consecutiveTimes,
AddInterval = curInterval,
Numbers = curNumbers,
AnalyseHistoricalTrendEndIndex = 1,
Factor = firstrFactor
};
var result = trend.Analyse1(dto);
var success = false;
if (result == null)
{
continue;
}
if (result.Count > 0)
{
realResultCount++;
if (result.Contains(number))
{
success = true;
}
}
if (success)
{
realHasCount++;
resultString.AppendLine("期次：" + i + ",号码：" + number + ",分析结果：" + (success ? "-Yes- " : " ") + string.Join(";", result));
}
}
watch.Stop();
usedSeconds = watch.ElapsedMilliseconds / 1000;
var str = resultString.ToString();
}
