README
¶
sklearn
Partial port of scikit-learn to go
Examples
cluster
datasets
LoadIris LoadBreastCancer LoadDiabetes LoadBoston LoadExamScore LoadMicroChipTest LoadMnist LoadMnistWeights MakeRegression MakeBlobs
interpolate
gaussian_process/kernels
ConstantKernel WhiteKernel RBF DotProduct
linear_model
LinearRegression BayesianRidge MultiTaskElasticNet MultiTaskLasso ElasticNet Lasso LassoPath LogisticRegression Ridge
metrics
AccuracyScore ConfusionMatrix PrecisionScore RecallScore F1Score FBetaScore PrecisionRecallFScoreSupport ROCCurve AUC ROCAUCScore PrecisionRecallCurve AveragePrecisionScore R2Score
model_selection
neighbors
KNeighborsClassifier MinkowskiDistance EuclideanDistance KDTree NearestCentroid KNeighborsRegressor NearestNeighbors NearestNeighbors.KNeighborsGraph NearestNeighbors.Tree
neural_network
MLPClassifier.Unmarshal MLPClassifier.Fit.mnist MLPClassifier.Predict.mnist MLPClassifier.Fit.breast.cancer MLPRegressor.Fit.boston
pipeline
preprocessing
MinMaxScaler StandardScaler RobustScaler AddDummyFeature OneHotEncoder Shuffler MaxAbsScaler Binarizer Normalizer Scale KernelCenterer QuantileTransformer PowerTransformer PowerTransformer.boxcox KBinsDiscretizer FunctionTransformer Imputer LabelBinarizer MultiLabelBinarizer LabelEncoder PCA
svm
This is a personal project to get a deeper understanding of how all of this magic works
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linted with
gofmt, golint, go vetrevive -
unit tested but coverage should reach 90%
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underdocumented but scikit-learn doc is your friend
Many thanks to gonum and scikit-learn authors and contributors
PRs are welcome
Documentation
Source Files
Directories
¶
| Path | Synopsis |
|---|---|
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Package base contains miscellaneous utilities common to other packages
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Package base contains miscellaneous utilities common to other packages |
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Package cluster gathers popular unsupervised clustering algorithms.
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Package cluster gathers popular unsupervised clustering algorithms. |
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Package datasets includes utilities to load datasets, including methods to load and fetch popular reference datasets.
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Package datasets includes utilities to load datasets, including methods to load and fetch popular reference datasets. |
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data
Package data is a dummy package for folder of data files used in tests
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Package data is a dummy package for folder of data files used in tests |
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Package linearmodel implements generalized linear models.
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Package linearmodel implements generalized linear models. |
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Package metrics includes score functions, performance metrics and pairwise metrics and distance computations.
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Package metrics includes score functions, performance metrics and pairwise metrics and distance computations. |
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Package modelselection contains KFold, GridSearchCV, CrossValidate
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Package modelselection contains KFold, GridSearchCV, CrossValidate |
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Package neighbors implements the k-nearest neighbors algorithm.
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Package neighbors implements the k-nearest neighbors algorithm. |
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Package neuralnetwork reproduces multilayer perceptron based on Andreas Mueller implementation + float 32 implementation + weight decay + batch normalization
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Package neuralnetwork reproduces multilayer perceptron based on Andreas Mueller implementation + float 32 implementation + weight decay + batch normalization |
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Package preprocessing includes scaling, centering, normalization, binarization and imputation methods.
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Package preprocessing includes scaling, centering, normalization, binarization and imputation methods. |
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Package svm includes Support Vector Machine algorithms.
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Package svm includes Support Vector Machine algorithms. |