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pasml
A collection of machine learning algorithms for object pascal
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README
PasML
A collection of machine learning algorithms for object pascal.
Implemented algorithms
| Category | Type | Model | Notes |
|---|---|---|---|
| Unsupervised | Clustering | K-Means | Only random centroid initialization. Gladly accept your contribution. |
| Supervised | Classification | Decision Tree | C4.5 implementation. Continuous data handling only for now. |
| Supervised | Classification | Naive Bayes | Gaussian naive bayes |
| Supervised | Regression | MLP Regressor | Multi-layer perceptron regressor. Requires noe. |
Numerik compatibility
Dataset := ReadCSV('datasets/iris.csv');
X := Dataset[[_ALL_, [0, 1, 2, 3]]];
y := Dataset[[_ALL_, 4]];Clustering example
kmeans := TKMeans.Create(3);
kmeans.Fit(X);
WriteLn('Clustering result:');
PrintMultiArray(kmeans.Predict(X));Classification example
nb := TNaiveBayesClassifier.Create;
nb.Fit(X, y);
pred := nb.Predict(X);
WriteLn('Accuracy:');
WriteLn(Mean(pred = Ravel(y)).Item);Integration with gnuplot
PasML has gnuplot integration support. Please ensure that you have installed gnuplot and add the executable path to the environment variable. Currently only scatter and line plots are supported.
fig := TFigure.Create('Iris Flower Features', 'Petal length', 'Petal width');
fig.AddScatterPlot(X[[_ALL_, [2, 3]]].SliceBool([y = 0]), 'Iris Setosa');
fig.AddScatterPlot(X[[_ALL_, [2, 3]]].SliceBool([y = 1]), 'Iris Virginica');
fig.AddScatterPlot(X[[_ALL_, [2, 3]]].SliceBool([y = 2]), 'Iris Versicolor');
fig.LegendPosition := lpLeftTop;
fig.Show;
fig.Free;
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