train(Column[], Column[], double, int, int, int, double) |   | 93% |   | 92% | 2 | 14 | 1 | 26 | 0 | 1 |
takeStep(int, int, double[][], double[]) |   | 98% |   | 95% | 1 | 11 | 1 | 48 | 0 | 1 |
error(Column[], Column[], int, int) |   | 97% |   | 64% | 5 | 8 | 0 | 8 | 0 | 1 |
validatePreconditions(Column[], Column[], int, int, int, double) |  | 96% |   | 50% | 1 | 2 | 0 | 3 | 0 | 1 |
getDeltaB(double[][], double[], int, double, double, double, int, double, double, double) |  | 100% |  | 100% | 0 | 8 | 0 | 22 | 0 | 1 |
examineExample(int, double[][], double[]) |  | 100% |  | 100% | 0 | 13 | 0 | 18 | 0 | 1 |
getRange(double, double, double, double) |  | 100% |  | 100% | 0 | 4 | 0 | 14 | 0 | 1 |
findMaxDifference(double, double[]) |  | 100% |  | 100% | 0 | 5 | 0 | 11 | 0 | 1 |
learnedFunction(Matrix) |  | 100% |  | 100% | 0 | 3 | 0 | 6 | 0 | 1 |
precomputeKernels() |  | 100% |  | 100% | 0 | 3 | 0 | 5 | 0 | 1 |
partialError(int, int, double, double[]) |  | 100% |  | 100% | 0 | 3 | 0 | 3 | 0 | 1 |
SVM(Kernel, Random) |  | 100% | | n/a | 0 | 1 | 0 | 7 | 0 | 1 |
setTrainingParam(int, double) |  | 100% |   | 75% | 1 | 4 | 0 | 8 | 0 | 1 |
simulate(Column) |  | 100% | | n/a | 0 | 1 | 0 | 3 | 0 | 1 |
simulate(Column, Column) |  | 100% | | n/a | 0 | 1 | 0 | 2 | 0 | 1 |
SVM(Kernel) |  | 100% | | n/a | 0 | 1 | 0 | 2 | 0 | 1 |
static {...} |  | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |