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 import svm

clf = svm.LinearSVC(C=1., random_state=0)
clf.fit(X, y)


    

    




/opt/miniconda/envs/sklearn-porter/lib/python2.7/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)






    
Out[3]:





LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
     intercept_scaling=1, loss='squared_hinge', max_iter=1000,
     multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
     verbose=0)

In [4]:

    

from sklearn_porter import Porter

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

print(output)


    

    




<?php

class LinearSVC {

    public function __construct($coefficients, $intercepts) {
        $this->coefficients = $coefficients;
        $this->intercepts = $intercepts;
    }

    public function predict($features) {
        $classIdx = -1;
        $classVal = null;
        for ($i = 0, $il = count($this->intercepts); $i < $il; $i++) {
            $prob = 0.;
            for ($j = 0, $jl = count($this->coefficients[0]); $j < $jl; $j++) {
                $prob += $this->coefficients[$i][$j] * $features[$j];
            }
            if (is_null($classVal) || $prob + $this->intercepts[$i] > $classVal) {
                $classVal = $prob + $this->intercepts[$i];
                $classIdx = $i;
            }
        }
        return $classIdx;
    }

}

if ($argc > 1) {

    // Features:
    array_shift($argv);
    $features = $argv;

    // Parameters:
    $coefficients = [[0.1842387105153816, 0.45123226735416666, -0.8079442418237148, -0.4507120994138801], [0.05538075463135362, -0.9011091317807134, 0.40964725305179495, -0.9616708418438406], [-0.8507797840387784, -0.9866797559823655, 1.380978948733964, 1.865367212912733]];
    $intercepts = [0.10956037385996253, 1.6860176665898299, -1.7096500573837932];

    // Prediction:
    $clf = new LinearSVC($coefficients, $intercepts);
    $prediction = $clf->predict($features);
    fwrite(STDOUT, $prediction);

}