public PivotTable GetSparseSimilarites(PivotTableEntry baseVector, PivotTable vectors, bool logarithm, bool onlyBase)
{
this.pbarUpdate(vectors.Count, 0, 0);
PivotTable outMap = new PivotTable();
int i = 0;
foreach (PivotTableEntry b in vectors)
{
PivotTableAnalysisResult similarity = GetSparseSimilarity(baseVector, b, logarithm, onlyBase);
similarity.Data.Add("cos_sim", similarity.prob);
Dictionary <String, double> diffData = CalculateDiffs(baseVector, b);
foreach (String key in diffData.Keys)
{
if (!similarity.Data.ContainsKey(key))
{
similarity.Data.Add(key, diffData[key]);
}
}
outMap.Add(similarity);
this.pbarValueUpdate(i);
i++;
}
return(outMap);
}
/// <summary>
/// Returns a PivotTableAnalysisResult with an empty Data dictionary but with a prob
/// score set to the cosine similarity value of the two input vector spaces.
/// Look here for more info: https://upload.wikimedia.org/math/4/e/4/4e45dc7ae582130813e804f793f24ead.png
///</summary>
///
/// <param name="a"></param>The base vectors
/// <param name="b"></param>
/// <param name="logarithm"></param>
/// <param name="onlyBase"></param>
/// <returns></returns>
public PivotTableAnalysisResult GetSparseSimilarity(PivotTableEntry a, PivotTableEntry b, bool logarithm, bool onlyBase)
{
if (a == null || b == null)
{
throw new Exception("neither a nor b are allowed to be null");
}
PivotTableAnalysisResult prob = new PivotTableAnalysisResult();
Double aSoS = 0d, bSoS = 0d, dotProd = 0d;
HashSet <string> keys = new HashSet <string>();
foreach (string k in a.Data.Keys)
{
if (k != "OBJECTID")
{
keys.Add(k);
}
}
if (!onlyBase)
{
foreach (string k in b.Data.Keys)
{
if (k != "OBJECTID")
{
keys.Add(k);
}
}
}
foreach (string key in keys)
{
if (key == "OBJECTID")
{
continue;
}
double x = 0d;
double y = 0d;
if (a.Data.ContainsKey(key))
{
x = a.Data[key];
}
if (b.Data.ContainsKey(key))
{
y = b.Data[key];
}
if (logarithm)
{
x = Math.Log10(x + 1);
y = Math.Log10(y + 1);
}
aSoS += x * x;
bSoS += y * y;
dotProd += x * y;
}
if (dotProd == 0)
{
return(new PivotTableAnalysisResult()
{
prob = -1d, RowKey = b.RowKey, Context = b.Context
});
}
double div = (Math.Sqrt(aSoS) * Math.Sqrt(bSoS));
if (div == 0d)
{
return(new PivotTableAnalysisResult()
{
prob = -1d, RowKey = b.RowKey, Context = b.Context
});
}
Double similarity = dotProd / div;
PivotTableAnalysisResult idprob = new PivotTableAnalysisResult()
{
prob = similarity, RowKey = b.RowKey, Context = b.Context
};
return(idprob);
}
/// <summary>
/// Compares two pivot tables. Do not pass in columns that don't make sense to compare. This method encapsulates a cosine similarity
/// calculation on geohash cell pairs, and subsequently, each pair also calculates a diff between each col pair as a quasi percentage diff.
///
/// </summary>
/// <param name="timeA"></param>
/// <param name="timeB"></param>A PivotTable that is full
/// <returns></returns>
public PivotTable DetectChange(PivotTable ptA, PivotTable ptB, string label, bool diffs)
{
PivotTable outList = new PivotTable();
//each dictionary below is a geohash agg layer, key=aGeoHashPrefix,value=anAggVectorOfThatBox
Dictionary <string, PivotTableEntry> a = new Dictionary <string, PivotTableEntry>();
Dictionary <string, PivotTableEntry> b = new Dictionary <string, PivotTableEntry>();
HashSet <string> hashset = new HashSet <string>();
//union the key sets into hashset variable
foreach (PivotTableEntry av in ptA)
{
a.Add(av.RowKey, av);
hashset.Add(av.RowKey);
}
foreach (PivotTableEntry av in ptB)
{
b.Add(av.RowKey, av);
hashset.Add(av.RowKey);
}
this.pbarUpdate.Invoke(hashset.Count, 0, 0);
//now hashset variable is a unique list of strings
Dictionary <string, double> empty = new Dictionary <string, double>();
foreach (String s in hashset)
{
empty.Add(s, 0d);
}
int x = 0;
foreach (string geohash in hashset)
{
this.pbarValueUpdate.Invoke(x);
x++;
PivotTableEntry ava = null;
PivotTableEntry avb = null;
if (a.ContainsKey(geohash))
{
ava = a[geohash];
}
if (b.ContainsKey(geohash))
{
avb = b[geohash];
}
if (ava == null || avb == null)
{
outList.Add(new PivotTableAnalysisResult()
{
RowKey = geohash, prob = 0d, Data = empty, Label = label
});
}
else
{
PivotTableAnalysisResult p = GetSparseSimilarity(ava, avb, true, false);
p.RowKey = geohash;
p.Label = label;
if (diffs)
{
p.Data = CalculateDiffs(ava, avb);
}
else
{
p.Data = new Dictionary <string, double>();
}
p.Data.Add("cos_sim", p.prob);
p.Data.Add("percent_change", Math.Abs(p.prob - 1) * 100);
outList.Add(p);
}
}
return(outList);
}
/// <summary>
/// Returns a PivotTableAnalysisResult with an empty Data dictionary but with a prob
/// score set to the cosine similarity value of the two input vector spaces.
/// Look here for more info: https://upload.wikimedia.org/math/4/e/4/4e45dc7ae582130813e804f793f24ead.png
///</summary>
///
/// <param name="a"></param>The base vectors
/// <param name="b"></param>
/// <param name="logarithm"></param>
/// <param name="onlyBase"></param>
/// <returns></returns>
public PivotTableAnalysisResult GetSparseSimilarity(PivotTableEntry a, PivotTableEntry b, bool logarithm, bool onlyBase)
{
if (a == null || b == null) {
throw new Exception("neither a nor b are allowed to be null");
}
PivotTableAnalysisResult prob = new PivotTableAnalysisResult();
Double aSoS = 0d, bSoS = 0d, dotProd = 0d;
HashSet<string> keys = new HashSet<string>();
foreach (string k in a.Data.Keys) {
if (k != "OBJECTID") {
keys.Add(k);
}
}
if (!onlyBase) {
foreach (string k in b.Data.Keys) {
if (k != "OBJECTID") {
keys.Add(k);
}
}
}
foreach (string key in keys) {
if (key == "OBJECTID") {
continue;
}
double x = 0d;
double y = 0d;
if (a.Data.ContainsKey(key)) { x = a.Data[key]; }
if (b.Data.ContainsKey(key)) { y = b.Data[key]; }
if (logarithm) {
x = Math.Log10(x + 1);
y = Math.Log10(y + 1);
}
aSoS += x * x;
bSoS += y * y;
dotProd += x * y;
}
if (dotProd == 0) {
return new PivotTableAnalysisResult() { prob = -1d, RowKey = b.RowKey, Context = b.Context };
}
double div = (Math.Sqrt(aSoS) * Math.Sqrt(bSoS));
if (div == 0d) {
return new PivotTableAnalysisResult() { prob = -1d, RowKey = b.RowKey, Context = b.Context };
}
Double similarity = dotProd / div;
PivotTableAnalysisResult idprob = new PivotTableAnalysisResult() { prob = similarity, RowKey = b.RowKey, Context = b.Context };
return idprob;
}