public void DifrentInsertionCosts2()
{
var removalCalculator = A.Fake <IInsertionCalculator <string> >();
var correlater = new LevenshteinCorrelater <string>(new BasicDistanceCalculator <string>(10), new BasicRemovalCalculator <string>(10), removalCalculator);
A.CallTo(() => removalCalculator.InsertionCost(A <string> ._)).ReturnsLazily(
(string s) =>
{
if (s == "A")
{
return(1);
}
if (s == "B")
{
return(2);
}
return((uint)3);
});
var array1 = new string[0];
var array2 = new[] { "A", "B" };
var expectedResult = new CorrelaterResult <string>(3, new string[] { null, null }, array2);
correlater.AssertComparision(array1, array2, expectedResult);
}
[DataRow("1234", "5678", 0)] // Diff eveywhere
public void IContinuousCorrelaterTest_InnerCorrelaterIsContinuous(string string1, string string2, int extraTimesToCallOrResultUpdate)
{
var levenshteinCorrelater = new LevenshteinCorrelater <char>(missmatchCost, removalInsertionCost, removalInsertionCost);
var splitToChunksCorrelater = new SplitToChunksCorrelaterWrapper <char>(levenshteinCorrelater, 2);
var correlater = new IgnoreIdenticalBeginningAndEndCorrelaterWrapper <char>(splitToChunksCorrelater);
var match1 = new List <char>();
var match2 = new List <char>();
var totalDistance = 0L;
var totalUpdates = 0;
var innerUpdates = 0;
correlater.OnResultUpdate += result =>
{
match1.AddRange(result.BestMatch1);
match2.AddRange(result.BestMatch2);
totalDistance += result.Distance;
totalUpdates++;
};
splitToChunksCorrelater.OnResultUpdate += _ => innerUpdates++;
var actualResult = correlater.Correlate(string1, string2);
var resultFromEvent = new CorrelaterResult <char>(totalDistance, match1.ToArray(), match2.ToArray());
Assertions.AssertResultIsAsExpected(actualResult, resultFromEvent);
Assert.AreEqual(extraTimesToCallOrResultUpdate + innerUpdates, totalUpdates, $"Got wrong number of {nameof(totalUpdates)}");
}
public void DifrentDistnaceCosts()
{
var distanceCalculator = A.Fake <IDistanceCalculator <string> >();
var correlater = new LevenshteinCorrelater <string>(distanceCalculator, 2, 2);
A.CallTo(() => distanceCalculator.Distance(A <string> ._, A <string> ._)).ReturnsLazily(
(string s1, string s2) =>
{
if (s1 == s2)
{
return(0);
}
if (s2 == "A" && s1 == "E")
{
return(1);
}
return((uint)10);
});
var array1 = new[] { "V", "E" };
var array2 = new[] { "T", "A" };
var expectedResult = new CorrelaterResult <string>(5,
new[] { null, "V", "E" }, new[] { "T", null, "A" });
correlater.AssertComparision(array1, array2, expectedResult);
}
public CorrelaterResult <char> StringCorrelaterExsample()
{
uint removalCost = 1, insertionCost = 1;
// You'll need to implement your own IDistanceCalculator<char>.
// IDistanceCalculator defines the "distance" between any two elements.
IDistanceCalculator <char> distanceCalculator = new MyDistanceCalculator <char>();
// The library contains a number of ICorrelaters.
// LevenshteinCorrelater uses dynamic programing to find the Levenshtein-distance between the two collections.
// Since a string is actually an IEnumerable<char>, we need to use an ICorrelater<char>.
ICorrelater <char> correlater =
new LevenshteinCorrelater <char>(distanceCalculator, removalCost, insertionCost);
string string1 = "ABC";
string string2 = "ADC";
// Correlate the strings.
CorrelaterResult <char> result = correlater.Correlate(string1, string2);
// Print some of the result
Console.WriteLine(result.Distance);
Console.WriteLine(result.BestMatch1);
Console.WriteLine(result.BestMatch2);
return(result);
}
private void SetCorrelator()
{
ICorrelater <char> correlator;
switch (selectCorrelatorComboBox.Text)
{
case nameof(DamerauLevenshteinCorrelater <char>):
correlator = new DamerauLevenshteinCorrelater <char>(10, 12, 7, 7);
break;
case nameof(DynamicLcsCorrelater <char>):
correlator = new DynamicLcsCorrelater <char>();
break;
case nameof(MyersAlgorithmCorrelater <char>):
correlator = new MyersAlgorithmCorrelater <char>();
break;
case nameof(PatienceDiffCorrelater <char>):
correlator = new PatienceDiffCorrelater <char>();
break;
case nameof(HuntSzymanskiCorrelater <char>):
correlator = new HuntSzymanskiCorrelater <char>();
break;
case nameof(NullCorrelator <char>):
correlator = new NullCorrelator <char>();
break;
case nameof(LevenshteinCorrelater <char>):
default:
correlator = new LevenshteinCorrelater <char>(10, 7, 7);
break;
}
if (slowCompareCheclCheckBox.Checked)
{
correlator = new SlowCorrelater <char>(correlator, 500);
}
if (splitToChunksCheckBox.Checked)
{
correlator = new SplitToChunksCorrelaterWrapper <char>(correlator, 10);
}
if (IgnoreIdenticalBeginningAndEndCorrelaterWrapperCheckBox.Checked)
{
correlator = new IgnoreIdenticalBeginningAndEndCorrelaterWrapper <char>(correlator);
}
if (SplitByPatienceWrapperCheckBox.Checked)
{
correlator = new SplitByPatienceAlgorithmWrapper <char>(correlator);
}
stringCorrelatorUserControl1.Correlater = correlator;
}
public void CorrelateString()
{
var string1 = "abc";
var string2 = "ac";
var expectedResult = new CorrelaterResult <char>(removalCost, string1.ToCharArray(), "a\0c".ToCharArray());
var stringCorrelater = new LevenshteinCorrelater <char>(missmatchCost, removalCost, insertionCost);
stringCorrelater.AssertComparision(string1, string2, expectedResult);
}
public void OnProgressUpdate()
{
ICorrelater <string> correlater = new LevenshteinCorrelater <string>(10, 7, 7);
correlater.OnProgressUpdate += (int currentProgress, int totalProgress) =>
{
// Do something with the progress update here
myUi.UpdateProgress(currentProgress, totalProgress);
};
CorrelaterResult <string> result = correlater.Correlate(collection1, collection2);
}
public void DifrentInsertionCosts1()
{
var removalCalculator = A.Fake <IInsertionCalculator <string> >();
var correlater = new LevenshteinCorrelater <string>(new BasicDistanceCalculator <string>(10), new BasicRemovalCalculator <string>(10), removalCalculator);
A.CallTo(() => removalCalculator.InsertionCost(A <string> ._)).ReturnsLazily(
(string s) => s == "A" ? (uint)1 : 40);
var array1 = new[] { "C" };
var array2 = new[] { "A", "E" };
var expectedResult = new CorrelaterResult <string>(11, new[] { null, "C" }, array2);
correlater.AssertComparision(array1, array2, expectedResult);
}
public CorrelaterResult <char> SplitToChunksCorrelaterWrapper()
{
uint removalCost = 7;
uint insertionCost = 7;
uint missmatchCost = 10;
int chunkSize = 200; // Bigger chunks will result in a slower, albeit more accurate, correlations
ICorrelater <char> innerCorrelater =
new LevenshteinCorrelater <char>(missmatchCost, removalCost, insertionCost);
// The SplitToChunksCorrelaterWrapper wraps an inner ICorrelater
ICorrelater <char> splitToChunksCorrelaterWrapper =
new SplitToChunksCorrelaterWrapper <char>(innerCorrelater, chunkSize);
CorrelaterResult <char> result = splitToChunksCorrelaterWrapper.Correlate(collection1, collection2);
return(result);
}
public void OnProgressUpdatesHappensRightNumberOfTimes()
{
var correlater = new LevenshteinCorrelater <string>(missmatchCost, removalCost, insertionCost);
correlater.AssertProgressUpdateWasCalledRightNumberOfTimes();
}
