| predictLogits(int[]) |  | 0% | | 0% | 11 | 11 | 27 | 27 | 1 | 1 |
| createExtractor() | | 0% | | 0% | 8 | 8 | 24 | 24 | 1 | 1 |
| load(InputStream) | | 0% | | 0% | 5 | 5 | 19 | 19 | 1 | 1 |
| softmax(float[]) | | 0% | | 0% | 6 | 6 | 12 | 12 | 1 | 1 |
| save(OutputStream) | | 0% | | n/a | 1 | 1 | 12 | 12 | 1 | 1 |
| getWeights() | | 0% | | 0% | 3 | 3 | 6 | 6 | 1 | 1 |
| entropy(float[]) | | 0% | | 0% | 3 | 3 | 5 | 5 | 1 | 1 |
| transposeToBucketMajor(byte[][], int, int) | | 0% | | 0% | 3 | 3 | 6 | 6 | 1 | 1 |
| readLabels(DataInputStream, int) | | 0% | | 0% | 2 | 2 | 7 | 7 | 1 | 1 |
| CharSoupModel(int, int, String[], float[], float[], byte[][], int) | | 0% | | n/a | 1 | 1 | 9 | 9 | 1 | 1 |
| withFeatureFlags(int) | | 0% | | n/a | 1 | 1 | 2 | 2 | 1 | 1 |
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| loadFromClasspath(String) | | 0% | | 0% | 2 | 2 | 5 | 5 | 1 | 1 |
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| CharSoupModel(int, int, String[], float[], float[], byte[][]) | | 0% | | n/a | 1 | 1 | 2 | 2 | 1 | 1 |
| predict(int[]) | | 0% | | n/a | 1 | 1 | 2 | 2 | 1 | 1 |
| getLabel(int) | | 0% | | n/a | 1 | 1 | 1 | 1 | 1 | 1 |
| getNumBuckets() | | 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 |
| getFeatureFlags() | | 0% | | n/a | 1 | 1 | 1 | 1 | 1 | 1 |