public static FeatureMatch CreateFeatureMatch(this DbFeature feature, string searchTerm)
{
var match = new FeatureMatch()
{
FeatureName = feature.Title,
ShortenedDescription = feature.Description?.Substring(0, Math.Min(feature.Description.Length, 100)) + "...",
ProductName = feature.Product,
GroupName = feature.Group,
Version = feature.Versions.OrderBy(v => v, new SemanticVersionComparer()).FirstOrDefault(),
Tags = feature.Tags.ToList()
};
string[] searchTerms = searchTerm.Split(" ", StringSplitOptions.RemoveEmptyEntries);
string matchingText = FindMatchingText(feature.Description, searchTerms);
if (String.IsNullOrEmpty(matchingText) && feature.Background != null)
{
matchingText = FindMatchingText(feature.Background.ToString(), searchTerms);
}
var enumerator = feature.Scenarios.GetEnumerator();
while (String.IsNullOrEmpty(matchingText) && enumerator.MoveNext())
{
matchingText = FindMatchingText(enumerator.Current.ToString(), searchTerms);
}
match.MatchingText = matchingText;
return(match);
}
public void FindMatchTest()
{
long matchTime;
using (Mat modelImage = CvInvoke.Imread(@"C:\\imgs\Model.jpeg", ImreadModes.Color))
using (Mat observedImage = CvInvoke.Imread(@"C:\\imgs\Test.jpg", ImreadModes.Color))
{
Mat result = FeatureMatch.Draw(modelImage, observedImage, out matchTime);
}
}
/// <summary>
/// Matches a list of features to a list of mass tags.
/// </summary>
/// <param name="features"></param>
/// <param name="tags"></param>
/// <returns></returns>
public List <FeatureMatch <T, U> > MatchFeatures(List <T> features,
List <U> tags,
PeakMatcherOptions options)
{
var matches = new List <FeatureMatch <T, U> >();
// Construct a large array of features so we can do searching in linear time.
var allFeatures = new List <FeatureLight>();
foreach (var copyFeature in features)
{
allFeatures.Add(copyFeature);
}
foreach (var copyTag in tags)
{
allFeatures.Add(copyTag);
}
// Sort by mass, gives us the best search time.
allFeatures.Sort(FeatureLight.MassComparison);
var netTolerance = options.Tolerances.Net;
var massTolerance = options.Tolerances.Mass;
var driftTolerance = options.Tolerances.DriftTime;
var shift = options.DaltonShift;
var N = allFeatures.Count;
var elementNumber = 0;
// This was a linear search, now O(N^2). Need to improve.
while (elementNumber < N)
{
var feature = allFeatures[elementNumber];
var massTag = feature as U;
if (massTag == null)
{
var lowerNET = feature.Net - netTolerance;
var higherNET = feature.Net + netTolerance;
var lowerDritfTime = feature.DriftTime - driftTolerance;
var higherDriftTime = feature.DriftTime + driftTolerance;
var currentMassTolerance = feature.MassMonoisotopicAligned * massTolerance / 1000000.0;
var lowerMass = feature.MassMonoisotopicAligned - currentMassTolerance;
var higherMass = feature.MassMonoisotopicAligned + currentMassTolerance;
var matchIndex = elementNumber - 1;
while (matchIndex >= 0)
{
var toMatchFeature = allFeatures[matchIndex];
if (toMatchFeature.MassMonoisotopicAligned < lowerMass)
{
break;
}
var tag = toMatchFeature as U;
if (tag != null)
{
if (lowerNET <= tag.Net && tag.Net <= higherNET)
{
if (lowerDritfTime <= tag.DriftTime && tag.DriftTime <= higherDriftTime)
{
var match = new FeatureMatch <T, U>(feature as T, tag, false, false);
matches.Add(match);
}
}
}
matchIndex--;
}
matchIndex = elementNumber + 1;
while (matchIndex < N)
{
var toMatchFeature = allFeatures[matchIndex];
if (toMatchFeature.MassMonoisotopicAligned > higherMass)
{
break;
}
var tag = toMatchFeature as U;
if (tag != null)
{
if (lowerNET <= tag.Net && tag.Net <= higherNET)
{
if (lowerDritfTime <= tag.DriftTime && tag.DriftTime <= higherDriftTime)
{
var match = new FeatureMatch <T, U>(feature as T, tag, false, false);
matches.Add(match);
}
}
}
matchIndex++;
}
}
elementNumber++;
}
return(matches);
}
/// <summary>
/// Find a list of matches between two lists.
/// </summary>
/// <param name="shortObservedList">List of observed features. Possibly a subset of the entire list corresponding to a particular charge state.</param>
/// <param name="shortTargetList">List of target features. Possibly a subset of the entire list corresponding to a particular charge state.</param>
/// <param name="tolerances">Tolerances to be used for matching.</param>
/// <param name="shiftAmount">A fixed shift amount to use for populating the shifted match list.</param>
/// <returns>A list of type FeatureMatch containing matches within the defined region.</returns>
public List <FeatureMatch <TObserved, TTarget> > FindMatches(List <TObserved> shortObservedList, List <TTarget> shortTargetList, FeatureMatcherTolerances tolerances, double shiftAmount)
{
// Create a list to hold the matches until they are returned.
var matchList = new List <FeatureMatch <TObserved, TTarget> >();
// Set indices to use when iterating over the lists.
var observedIndex = 0;
var lowerBound = 0;
// Sort both lists by mass.
if (!double.IsNaN(shortObservedList[0].MassMonoisotopicAligned) && shortObservedList[0].MassMonoisotopicAligned > 0.0)
{
shortObservedList.Sort(FeatureLight.MassAlignedComparison);
}
else
{
shortObservedList.Sort(FeatureLight.MassComparison);
}
if (!double.IsNaN(shortTargetList[0].MassMonoisotopicAligned) && shortTargetList[0].MassMonoisotopicAligned > 0.0)
{
shortTargetList.Sort(FeatureLight.MassAlignedComparison);
}
else
{
shortTargetList.Sort(FeatureLight.MassComparison);
}
// Locally store the tolerances.
var massTolerancePpm = tolerances.MassTolerancePPM;
var netTolerance = tolerances.NETTolerance;
var driftTimeTolerance = tolerances.DriftTimeTolerance;
// Iterate through the list of observed features.
while (observedIndex < shortObservedList.Count)
{
// Store the current observed feature locally.
var observedFeature = shortObservedList[observedIndex];
// Flag variable that gets set to false when the observed mass is greater than the current mass tag by more than the tolerance.
var continueLoop = true;
// Set the target feature iterator to the current lower bound.
var targetIndex = lowerBound;
// Iterate through the list of target featrues or until the observed feature is too great.
while (targetIndex < shortTargetList.Count && continueLoop)
{
// Add any shift to the mass tag.
var targetFeature = shortTargetList[targetIndex];
// Check to see that the features are within the mass tolearance of one another.
double massDifference;
if (WithinMassTolerance(observedFeature, targetFeature, massTolerancePpm, shiftAmount, out massDifference))
{
var withinTolerances = WithinNETTolerance(observedFeature, targetFeature, netTolerance);
if (m_matchParameters.UseDriftTime)
{
withinTolerances = withinTolerances & WithinDriftTimeTolerance(observedFeature, targetFeature, driftTimeTolerance);
withinTolerances = withinTolerances & (observedFeature.ChargeState == targetFeature.ChargeState);
}
// Create a temporary match between the two and check it against all tolerances before adding to the match list.
if (withinTolerances)
{
var match = new FeatureMatch <TObserved, TTarget>();
match.AddFeatures(observedFeature, targetFeature, m_matchParameters.UseDriftTime, (shiftAmount > 0));
matchList.Add(match);
}
}
else
{
// Increase the lower bound if the the MassTag masses are too low or set the continueLoop flag to false if they are too high.
if (massDifference < massTolerancePpm)
{
lowerBound++;
}
else
{
continueLoop = false;
}
}
// Increment the target index.
targetIndex++;
}
// Increment the observed index.
observedIndex++;
}
// Return the list of matches.
return(matchList);
}