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Merge pull request #198 from Givikap120/better_accuracy_estimator

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Reworked hit result generator based on accuracy
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StanR 2024-08-31 13:48:27 +05:00 • committed by GitHub
commit 085a853461
Signed by: github
GPG key ID: B5690EEEBB952194
2 changed files with 120 additions and 22 deletions
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71
PerformanceCalculator/Simulate/OsuSimulateCommand.cs
View file

@ -62,19 +62,68 @@ namespace PerformanceCalculator.Simulate
} }
else else
{ {
// Let Great=6, Good=2, Meh=1, Miss=0. The total should be this. // Total result count excluding countMiss
var targetTotal = (int)Math.Round(accuracy * totalResultCount * 6); int relevantResultCount = totalResultCount - countMiss;
// Start by assuming every non miss is a meh // Accuracy excluding countMiss. We need that because we're trying to achieve target accuracy without touching countMiss
// This is how much increase is needed by greats and goods // So it's better to pretened that there were 0 misses in the 1st place
var delta = targetTotal - (totalResultCount - countMiss); double relevantAccuracy = accuracy * totalResultCount / relevantResultCount;
// Each great increases total by 5 (great-meh=5) // Clamp accuracy to account for user trying to break the algorithm by inputting impossible values
countGreat = delta / 5; relevantAccuracy = Math.Clamp(relevantAccuracy, 0, 1);
// Each good increases total by 1 (good-meh=1). Covers remaining difference.
countGood = delta % 5; // Main curve for accuracy > 25%, the closer accuracy is to 25% - the more 50s it adds
// Mehs are left over. Could be negative if impossible value of amountMiss chosen if (relevantAccuracy >= 0.25)
countMeh = totalResultCount - countGreat - countGood - countMiss; {
// Main curve. Zero 50s if accuracy is 100%, one 50 per 9 100s if accuracy is 75% (excluding misses), 4 50s per 9 100s if accuracy is 50%
double ratio50To100 = Math.Pow(1 - (relevantAccuracy - 0.25) / 0.75, 2);
// Derived from the formula: Accuracy = (6 * c300 + 2 * c100 + c50) / (6 * totalHits), assuming that c50 = c100 * ratio50to100
double count100Estimate = 6 * relevantResultCount * (1 - relevantAccuracy) / (5 * ratio50To100 + 4);
// Get count50 according to c50 = c100 * ratio50to100
double count50Estimate = count100Estimate * ratio50To100;
// Round it to get int number of 100s
countGood = (int?)Math.Round(count100Estimate);
// Get number of 50s as difference between total mistimed hits and count100
countMeh = (int?)(Math.Round(count100Estimate + count50Estimate) - countGood);
}
// If accuracy is between 16.67% and 25% - we assume that we have no 300s
else if (relevantAccuracy >= 1.0 / 6)
{
// Derived from the formula: Accuracy = (6 * c300 + 2 * c100 + c50) / (6 * totalHits), assuming that c300 = 0
double count100Estimate = 6 * relevantResultCount * relevantAccuracy - relevantResultCount;
// We only had 100s and 50s in that scenario so rest of the hits are 50s
double count50Estimate = relevantResultCount - count100Estimate;
// Round it to get int number of 100s
countGood = (int?)Math.Round(count100Estimate);
// Get number of 50s as difference between total mistimed hits and count100
countMeh = (int?)(Math.Round(count100Estimate + count50Estimate) - countGood);
}
// If accuracy is less than 16.67% - it means that we have only 50s or misses
// Assuming that we removed misses in the 1st place - that means that we need to add additional misses to achieve target accuracy
else
{
// Derived from the formula: Accuracy = (6 * c300 + 2 * c100 + c50) / (6 * totalHits), assuming that c300 = c100 = 0
double count50Estimate = 6 * relevantResultCount * relevantAccuracy;
// We have 0 100s, because we can't start adding 100s again after reaching "only 50s" point
countGood = 0;
// Round it to get int number of 50s
countMeh = (int?)Math.Round(count50Estimate);
// Fill the rest results with misses overwriting initial countMiss
countMiss = (int)(totalResultCount - countMeh);
}
// Rest of the hits are 300s
countGreat = (int)(totalResultCount - countGood - countMeh - countMiss);
} }
return new Dictionary<HitResult, int> return new Dictionary<HitResult, int>

71
PerformanceCalculatorGUI/RulesetHelper.cs
View file

@ -127,19 +127,68 @@ namespace PerformanceCalculatorGUI
} }
else else
{ {
// Let Great=6, Good=2, Meh=1, Miss=0. The total should be this. // Total result count excluding countMiss
var targetTotal = (int)Math.Round(accuracy * totalResultCount * 6); int relevantResultCount = totalResultCount - countMiss;
// Start by assuming every non miss is a meh // Accuracy excluding countMiss. We need that because we're trying to achieve target accuracy without touching countMiss
// This is how much increase is needed by greats and goods // So it's better to pretened that there were 0 misses in the 1st place
var delta = targetTotal - (totalResultCount - countMiss); double relevantAccuracy = accuracy * totalResultCount / relevantResultCount;
// Each great increases total by 5 (great-meh=5) // Clamp accuracy to account for user trying to break the algorithm by inputting impossible values
countGreat = delta / 5; relevantAccuracy = Math.Clamp(relevantAccuracy, 0, 1);
// Each good increases total by 1 (good-meh=1). Covers remaining difference.
countGood = delta % 5; // Main curve for accuracy > 25%, the closer accuracy is to 25% - the more 50s it adds
// Mehs are left over. Could be negative if impossible value of amountMiss chosen if (relevantAccuracy >= 0.25)
countMeh = totalResultCount - countGreat - countGood - countMiss; {
// Main curve. Zero 50s if accuracy is 100%, one 50 per 9 100s if accuracy is 75% (excluding misses), 4 50s per 9 100s if accuracy is 50%
double ratio50To100 = Math.Pow(1 - (relevantAccuracy - 0.25) / 0.75, 2);
// Derived from the formula: Accuracy = (6 * c300 + 2 * c100 + c50) / (6 * totalHits), assuming that c50 = c100 * ratio50to100
double count100Estimate = 6 * relevantResultCount * (1 - relevantAccuracy) / (5 * ratio50To100 + 4);
// Get count50 according to c50 = c100 * ratio50to100
double count50Estimate = count100Estimate * ratio50To100;
// Round it to get int number of 100s
countGood = (int?)Math.Round(count100Estimate);
// Get number of 50s as difference between total mistimed hits and count100
countMeh = (int?)(Math.Round(count100Estimate + count50Estimate) - countGood);
}
// If accuracy is between 16.67% and 25% - we assume that we have no 300s
else if (relevantAccuracy >= 1.0 / 6)
{
// Derived from the formula: Accuracy = (6 * c300 + 2 * c100 + c50) / (6 * totalHits), assuming that c300 = 0
double count100Estimate = 6 * relevantResultCount * relevantAccuracy - relevantResultCount;
// We only had 100s and 50s in that scenario so rest of the hits are 50s
double count50Estimate = relevantResultCount - count100Estimate;
// Round it to get int number of 100s
countGood = (int?)Math.Round(count100Estimate);
// Get number of 50s as difference between total mistimed hits and count100
countMeh = (int?)(Math.Round(count100Estimate + count50Estimate) - countGood);
}
// If accuracy is less than 16.67% - it means that we have only 50s or misses
// Assuming that we removed misses in the 1st place - that means that we need to add additional misses to achieve target accuracy
else
{
// Derived from the formula: Accuracy = (6 * c300 + 2 * c100 + c50) / (6 * totalHits), assuming that c300 = c100 = 0
double count50Estimate = 6 * relevantResultCount * relevantAccuracy;
// We have 0 100s, because we can't start adding 100s again after reaching "only 50s" point
countGood = 0;
// Round it to get int number of 50s
countMeh = (int?)Math.Round(count50Estimate);
// Fill the rest results with misses overwriting initial countMiss
countMiss = (int)(totalResultCount - countMeh);
}
// Rest of the hits are 300s
countGreat = (int)(totalResultCount - countGood - countMeh - countMiss);
} }
return new Dictionary<HitResult, int> return new Dictionary<HitResult, int>