LinearModel

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total562 of 92339%64 of 9834%60801041872031
softmax(float[])720%100%66121211
predictLogitsDense(float[])710%60%44101011
save(OutputStream)530%20%22161611
getWeights()400%40%336611
predictCalibratedLogits(int[])380%60%446611
entropy(float[])370%40%335511
transposeToBucketMajor(byte[][], int, int)350%40%336611
LinearModel(int, int, String[], float[], float[], byte[][], float[], float[])330%n/a11111111
validateCalibration()292849%7750%783701
loadRaw(InputStream)257875%5550%5632301
writeLabels(DataOutputStream)230%20%225511
writeFloats(DataOutputStream, float[])200%20%223311
loadFromClasspath(String)170%20%224411
loadFromPath(Path)130%n/a113311
load(InputStream)122769%4233%3411001
LinearModel(int, int, String[], float[], float[], byte[][])110%n/a112211
hasCalibration()100%40%331111
predict(int[])50%n/a111111
sanitizeStd(float[])32990%2466%241601
getClassMean()30%n/a111111
getClassStd()30%n/a111111
getLabels()30%n/a111111
getScales()30%n/a111111
getBiases()30%n/a111111
predictLogits(int[])112100%12100%0702001
LinearModel(int, int, String[], float[], float[], byte[], float[], float[])30100%n/a0101101
readLabels(DataInputStream, int)29100%2100%020701
readFloats(DataInputStream, int)17100%2100%020401
getLabel(int)5100%n/a010101
getNumBuckets()3100%n/a010101
getNumClasses()3100%n/a010101