internal bool HasItems(ItemKey itemKey)
{
int count = 0;
foreach (string itemId in _itemIds)
{
if (itemKey.HasItem(itemId))
{
count++;
}
}
return(count == itemKey.Count);
}
public int GetSupport(ItemKey itemId)
{
int count = 0;
foreach (ShoppingRecord record in _dataset.Values)
{
if (record.HasItems(itemId))
{
count++;
}
}
return(count);
}
private ItemKey GetKey(ItemKey itemKey1, ItemKey itemKey2)
{
SortedSet <string> itemKeys = new SortedSet <string>();
foreach (string key in itemKey1.Ids)
{
itemKeys.Add(key);
}
foreach (string key in itemKey2.Ids)
{
itemKeys.Add(key);
}
return(itemKeys.Count != itemKey1.Count + 1 ? null : new ItemKey(itemKeys));
}
// https://stackoverflow.com/questions/7802822/all-possible-combinations-of-a-list-of-values
// 取得組合後的 ItemKey
public List <ItemKey> GetAllCombinationIds2()
{
var result = new SortedSet <ItemKey>(new ItemKeyComparar());
for (int i = 0; i < _records.Count; i++)
{
for (int j = i + 1; j < _records.Count; j++)
{
ItemKey key = GetKey(_records[i].ItemKey, _records[j].ItemKey);
if (key != null)
{
result.Add(key);
}
}
}
return(result.ToList());
}
public double GetResult(double minSupport, List <string> boughtItemIds, List <string> forecastItemIds)
{
CalculateSupport(minSupport);
_sw.Reset();
_sw.Start();
try
{
// todo: 排序項目, 計算機率
var boughtKey = new ItemKey(boughtItemIds);
int boughtCount = _resultDict.ContainsKey(boughtKey.CombinationId) ?
_resultDict[boughtKey.CombinationId].Count : 0;
if (boughtCount == 0)
{
return(0);
}
var allIds = boughtItemIds.Concat(forecastItemIds).ToList();
var allKey = new ItemKey(allIds);
if (_resultDict.ContainsKey(allKey.CombinationId) == false)
{
return(0);
}
int allCount = _resultDict.ContainsKey(boughtKey.CombinationId) ?
_resultDict[allKey.CombinationId].Count : 0;
return(allCount / (boughtCount / 1d)); // 轉成 double
}
finally
{
_sw.Stop();
_timeDict[nameof(GetResult)] = _sw.Elapsed.TotalMilliseconds;
}
}
public double GetResult(double minSupport, List <string> boughtItemIds, List <string> forecastItemIds)
{
// Scan Data
var itemKeys = _dataset.GetSortHeaderTable();
var supDataset = _dataset.GetFilter(itemKeys);
FPNode tree = CreateFPTree(supDataset);
CalculateSupport(tree, minSupport);
_sw.Reset();
_sw.Start();
try
{
// todo: 排序項目, 計算機率
var boughtKey = new ItemKey(boughtItemIds);
int boughtCount = _resultDict.ContainsKey(boughtKey.CombinationId) ?
_resultDict[boughtKey.CombinationId].Count : 0;
if (boughtCount == 0)
{
return(0);
}
var allIds = boughtItemIds.Concat(forecastItemIds).ToList();
var allKey = new ItemKey(allIds);
int allCount = _resultDict.ContainsKey(boughtKey.CombinationId) ?
_resultDict[allKey.CombinationId].Count : 0;
return(allCount / (boughtCount / 1d)); // 轉成 double
}
finally
{
_sw.Stop();
_timeDict[nameof(GetResult)] = _sw.Elapsed.TotalMilliseconds;
}
}
public AprioriRecord(ItemKey itemKey, int count)
{
ItemKey = itemKey;
Count = count;
}
