| softmax(float[]) |  | 0% | | 0% | 6 | 6 | 12 | 12 | 1 | 1 |
| getWeights() | | 0% | | 0% | 3 | 3 | 6 | 6 | 1 | 1 |
| save(OutputStream) | | 0% | | n/a | 1 | 1 | 11 | 11 | 1 | 1 |
| entropy(float[]) | | 0% | | 0% | 3 | 3 | 5 | 5 | 1 | 1 |
| transposeToBucketMajor(byte[][], int, int) | | 0% | | 0% | 3 | 3 | 6 | 6 | 1 | 1 |
| loadRaw(InputStream) |  | 67% | | 50% | 2 | 3 | 3 | 16 | 0 | 1 |
| LinearModel(int, int, String[], float[], float[], byte[][]) | | 0% | | n/a | 1 | 1 | 8 | 8 | 1 | 1 |
| writeLabels(DataOutputStream) | | 0% | | 0% | 2 | 2 | 5 | 5 | 1 | 1 |
| writeFloats(DataOutputStream, float[]) | | 0% | | 0% | 2 | 2 | 3 | 3 | 1 | 1 |
| loadFromPath(Path) | | 0% | | n/a | 1 | 1 | 3 | 3 | 1 | 1 |
| load(InputStream) | | 69% | | 33% | 3 | 4 | 1 | 10 | 0 | 1 |
| loadFromClasspath(String) | | 64% | | 50% | 1 | 2 | 1 | 4 | 0 | 1 |
| predict(int[]) | | 0% | | n/a | 1 | 1 | 1 | 1 | 1 | 1 |
| getNumClasses() | | 0% | | n/a | 1 | 1 | 1 | 1 | 1 | 1 |
| getLabels() | | 0% | | n/a | 1 | 1 | 1 | 1 | 1 | 1 |
| getScales() | | 0% | | n/a | 1 | 1 | 1 | 1 | 1 | 1 |
| getBiases() | | 0% | | n/a | 1 | 1 | 1 | 1 | 1 | 1 |
| predictLogits(int[]) | | 100% | | 100% | 0 | 8 | 0 | 20 | 0 | 1 |
| readLabels(DataInputStream, int) | | 100% | | 100% | 0 | 2 | 0 | 7 | 0 | 1 |
| LinearModel(int, int, String[], float[], float[], byte[]) | | 100% | | n/a | 0 | 1 | 0 | 8 | 0 | 1 |
| readFloats(DataInputStream, int) | | 100% | | 100% | 0 | 2 | 0 | 4 | 0 | 1 |
| getLabel(int) | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| getNumBuckets() | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |