sklearn-porter

In [1]:

    

import sys
sys.path.append('../../../../..')


    

In [2]:

    

from sklearn.datasets import load_iris

iris_data = load_iris()

X = iris_data.data
y = iris_data.target

print(X.shape, y.shape)


    

    




((150, 4), (150,))

In [3]:

    

from sklearn.naive_bayes import GaussianNB

clf = GaussianNB()
clf.fit(X, y)


    

    
Out[3]:





GaussianNB(priors=None, var_smoothing=1e-09)

In [4]:

    

from sklearn_porter import Porter

porter = Porter(clf, language='java')
output = porter.export()

print(output)


    

    




class GaussianNB {

    private double[] priors;
    private double[][] sigmas;
    private double[][] thetas;

    public GaussianNB(double[] priors, double[][] sigmas, double[][] thetas) {
        this.priors = priors;
        this.sigmas = sigmas;
        this.thetas = thetas;
    }

    public int predict(double[] features) {
        double[] likelihoods = new double[this.sigmas.length];
    
        for (int i = 0, il = this.sigmas.length; i < il; i++) {
            double sum = 0.;
            for (int j = 0, jl = this.sigmas[0].length; j < jl; j++) {
                sum += Math.log(2. * Math.PI * this.sigmas[i][j]);
            }
            double nij = -0.5 * sum;
            sum = 0.;
            for (int j = 0, jl = this.sigmas[0].length; j < jl; j++) {
                sum += Math.pow(features[j] - this.thetas[i][j], 2.) / this.sigmas[i][j];
            }
            nij -= 0.5 * sum;
            likelihoods[i] = Math.log(this.priors[i]) + nij;
        }
    
        int classIdx = 0;
        for (int i = 0, l = likelihoods.length; i < l; i++) {
            classIdx = likelihoods[i] > likelihoods[classIdx] ? i : classIdx;
        }
        return classIdx;
    }

    public static void main(String[] args) {
        if (args.length == 4) {

            // Features:
            double[] features = new double[args.length];
            for (int i = 0, l = args.length; i < l; i++) {
                features[i] = Double.parseDouble(args[i]);
            }

            // Parameters:
            double[] priors = {0.3333333333333333, 0.3333333333333333, 0.3333333333333333};
            double[][] sigmas = {{0.12176400309550259, 0.14081600309550263, 0.029556003095502676, 0.010884003095502673}, {0.2611040030955028, 0.09650000309550268, 0.21640000309550278, 0.03832400309550265}, {0.39625600309550263, 0.10192400309550273, 0.2984960030955029, 0.07392400309550265}};
            double[][] thetas = {{5.005999999999999, 3.428000000000001, 1.4620000000000002, 0.2459999999999999}, {5.936, 2.7700000000000005, 4.26, 1.3259999999999998}, {6.587999999999998, 2.9739999999999998, 5.552, 2.026}};

            // Prediction:
            GaussianNB clf = new GaussianNB(priors, sigmas, thetas);
            int estimation = clf.predict(features);
            System.out.println(estimation);

        }
    }
}

In [5]:

    

# Save classifier:
# with open('GaussianNB.java', 'w') as f:
#     f.write(output)

# Compile model:
# $ javac -cp . GaussianNB.java

# Run classification:
# $ java GaussianNB 1 2 3 4