Test coverage

[RenatoGeh]

GoSPN currently has little to no coverage of modules. Go has a native unit test library, which we should use.

Though marked as good first issue, we obviously do not expect a single PR with all changes. Instead, please send PRs addressing one checkbox at a time.

The following is our goal coverage list:

• app module:
  • Classification: should achieve at least a certain percentage of accuracy in an artificial dataset (e.g. DigitsX) (app/image.go)
  • Completion: should not pass a certain mean square error threshold of the original image's crop (app/image.go)
• common module:
  • HSV to RGB (common/color.go)
  • ApproxEqual (common/equal.go)
  • Queue tests (common/queue.go)
    • Enqueue
    • Dequeue
    • Stress test
  • Stack tests (common/stack.go)
    • Enqueue
    • Dequeue
    • Stress test
• conc module:
  • SingleQueue tests on race condition (conc/queue.go)
• data module:
  • Dataset manipulation (data/manipulate.go)
    • Cascade Rounding
    • ExtractLabels
    • Partition
    • PartitionByLabels
    • Split
    • Copy
    • Shuffle
    • Join
    • SubtractLabel
    • SubtractVariable
    • Identical
    • MergeLabel
    • Divide
  • Dataset download test
• io module:
  • ARFF dataset (io/arff.go)
    • ARFFToData
    • ParseArff
  • HTTP (io/http.go)
    • DownloadFromURL
  • Image I/O (io/images.go)
    • SplitHalf
  • DATA dataset (io/input.go)
    • ParseData
    • ParseDataNL
    • ParseEvidence
    • ParsePartitionedData
  • NPY dataset (io/npy.go)
    • ReadBalanced
    • ReadAll
    • Read
    • Reset
  • Output (io/output.go)
    • DrawGraphTools
    • DrawGraph
  • SPN I/O (io/spn.go)
    • SaveSPN
    • LoadSPN
• learn module:
  • SPN derivation (learn/derive.go)
    • DeriveSPN
    • DeriveWeights
    • DeriveWeightsBatch
    • DeriveApplyWeights
    • DeriveHard
  • Discriminative gradient descent (learn/discriminative.go)
    • DiscriminativeGD
    • DiscriminativeHardGD
    • DiscriminativeBGD
    • DiscriminativeHardBGD
    • applyDGD
    • storeDGD
    • applyDGDFrom
    • applyHDGD
  • Generative gradient descent (learn/generative.go)
    • GenerativeGD
    • GenerativeHardGD
    • GenerativeBGD
    • GenerativeHardBGD
    • applyFastGD
    • applyGD
    • applyFastHGD
    • applyHGD
  • Variable and scope (learn/variable.go)
    • GobEncode
    • GobDecode
    • ExtractInstance
    • CompleteDataToMatrix
    • DataToMatrix
    • MatrixToData
    • ReflectScope
    • CopyScope
    • DataToVarData
  • Dennis-Ventura clustering structure learning
  • Gens-Domingos LearnSPN structure learning
  • Poon-Domingos dense architecture
• score module:
  • Confusion matrix
  • Score registration
• spn module:
  • Gaussian node (spn/gaussian.go)
  • Product node (spn/product.go)
  • Sum node (spn/sum.go)
  • Indicator node (spn/indicator.go)
  • Multinomial node (spn/multinom.go)
  • Breadth and depth first search (spn/search.go)
  • SPN serialization (spn/serial.go)
  • Dynamic programming storing (spn/storer.go)
  • Topological sorting (spn/topo.go)
  • Exact inference bottom-up pass (spn/utils.go)
    • Inference
    • InferenceY
    • StoreInference
  • Max-product algorithm (spn/utils.go)
    • StoreMAP
    • TraceMAP
  • NormalizeSPN (spn/utils.go)
  • ComputeHeight (spn/utils.go)
  • ComputeScope (spn/utils.go)
  • Complete (spn/utils.go)
  • Decomposable (spn/utils.go)
• sys module:
  • RandComb (sys/rand.go)
• utils module:
  • Logarithmic operations (utils/log.go)
    • LogSumLog
    • LogSum
    • LogProd
    • LogSumPair
    • Trim
    • LogSumExp
    • LogSumExpPair
  • Statistic functions (utils/stats.go)
    • Mean
    • StdDev
    • MuSigma
    • PartitionQuantiles
  • UnionFind (utils/unionfind.go)
  • Clustering algorithms
    • Auxiliary clustering functions (utils/cluster.go)
    • DBSCAN
    • K-means
    • K-medoid
    • K-mode
    • OPTICS
  • Variable independence tests
    • Chi-square Pearson test
    • G-test
    • Independence graph