Repository: https://github.com/nok/sklearn-porter
Documentation: sklearn.svm.NuSVC
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.NuSVC(gamma=0.001, kernel='rbf', random_state=0)
clf.fit(X, y)
Out[3]:
NuSVC(cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',
max_iter=-1, nu=0.5, probability=False, random_state=0, shrinking=True,
tol=0.001, verbose=False)
In [4]:
from sklearn_porter import Porter
porter = Porter(clf, language='php')
output = porter.export()
print(output)
<?php
class NuSVC {
public function __construct($nClasses, $nRows, $vectors, $coefficients, $intercepts, $weights, $kernel, $gamma, $coef0, $degree) {
$this->nClasses = $nClasses;
$this->classes = array_fill(0, $nClasses, 0);
for ($i = 0; $i < $nClasses; $i++) {
$this->classes[$i] = $i;
}
$this->nRows = $nRows;
$this->vectors = $vectors;
$this->coefficients = $coefficients;
$this->intercepts = $intercepts;
$this->weights = $weights;
$this->kernel = strtoupper($kernel);
$this->gamma = $gamma;
$this->coef0 = $coef0;
$this->degree = $degree;
}
public function predict($features) {
$kernels = array_fill(0, count($this->vectors), 0);
$kernel;
switch ($this->kernel) {
case 'LINEAR':
// <x,x'>
for ($i = 0; $i < count($this->vectors); $i++) {
$kernel = 0.;
for ($j = 0; $j < count($this->vectors[$i]); $j++) {
$kernel += $this->vectors[$i][$j] * $features[$j];
}
$kernels[$i] = $kernel;
}
break;
case 'POLY':
// (y<x,x'>+r)^d
for ($i = 0; $i < count($this->vectors); $i++) {
$kernel = 0.;
for ($j = 0; $j < count($this->vectors[$i]); $j++) {
$kernel += $this->vectors[$i][$j] * $features[$j];
}
$kernels[$i] = pow(($this->gamma * $kernel) + $this->coef0, $this->degree);
}
break;
case 'RBF':
// exp(-y|x-x'|^2)
for ($i = 0; $i < count($this->vectors); $i++) {
$kernel = 0.;
for ($j = 0; $j < count($this->vectors[$i]); $j++) {
$kernel += pow($this->vectors[$i][$j] - $features[$j], 2);
}
$kernels[$i] = exp(-$this->gamma * $kernel);
}
break;
case 'SIGMOID':
// tanh(y<x,x'>+r)
for ($i = 0; $i < count($this->vectors); $i++) {
$kernel = 0.;
for ($j = 0; $j < count($this->vectors[$i]); $j++) {
$kernel += $this->vectors[$i][$j] * $features[$j];
}
$kernels[$i] = tanh(($this->gamma * $kernel) + $this->coef0);
}
break;
}
$starts = array_fill(0, $this->nRows, 0);
for ($i = 0; $i < $this->nRows; $i++) {
if ($i != 0) {
$start = 0;
for ($j = 0; $j < $i; $j++) {
$start += $this->weights[$j];
}
$starts[$i] = $start;
} else {
$starts[0] = 0;
}
}
$ends = array_fill(0, $this->nRows, 0);
for ($i = 0; $i < $this->nRows; $i++) {
$ends[$i] = $this->weights[$i] + $starts[$i];
}
if ($this->nClasses == 2) {
for ($i = 0; $i < count($kernels); $i++) {
$kernels[$i] = -$kernels[$i];
}
$decision = 0.;
for ($k = $starts[1]; $k < $ends[1]; $k++) {
$decision += $kernels[$k] * $this->coefficients[0][$k];
}
for ($k = $starts[0]; $k < $ends[0]; $k++) {
$decision += $kernels[$k] * $this->coefficients[0][$k];
}
$decision += $this->intercepts[0];
if ($decision > 0) {
return 0;
}
return 1;
}
$decisions = array_fill(0, count($this->intercepts), 0);
for ($i = 0, $d = 0, $l = $this->nRows; $i < $l; $i++) {
for ($j = $i + 1; $j < $l; $j++) {
$tmp = 0.;
for ($k = $starts[$j]; $k < $ends[$j]; $k++) {
$tmp += $this->coefficients[$i][$k] * $kernels[$k];
}
for ($k = $starts[$i]; $k < $ends[$i]; $k++) {
$tmp += $this->coefficients[$j - 1][$k] * $kernels[$k];
}
$decisions[$d] = $tmp + $this->intercepts[$d];
$d++;
}
}
$votes = array_fill(0, count($this->intercepts), 0);
for ($i = 0, $d = 0, $l = $this->nRows; $i < $l; $i++) {
for ($j = $i + 1; $j < $l; $j++) {
$votes[$d] = $decisions[$d] > 0 ? $i : $j;
$d++;
}
}
$amounts = array_fill(0, $this->nClasses, 0);
for ($i = 0, $l = count($votes); $i < $l; $i++) {
$amounts[$votes[$i]] += 1;
}
$classVal = -1;
$classIdx = -1;
for ($i = 0, $l = count($amounts); $i < $l; $i++) {
if ($amounts[$i] > $classVal) {
$classVal = $amounts[$i];
$classIdx = $i;
}
}
return $this->classes[$classIdx];
}
}
if ($argc > 1) {
// Features:
array_shift($argv);
$features = $argv;
// Parameters:
$vectors = [[4.9, 3.0, 1.4, 0.2], [4.6, 3.1, 1.5, 0.2], [5.4, 3.9, 1.7, 0.4], [5.0, 3.4, 1.5, 0.2], [4.9, 3.1, 1.5, 0.1], [5.4, 3.7, 1.5, 0.2], [4.8, 3.4, 1.6, 0.2], [5.7, 4.4, 1.5, 0.4], [5.7, 3.8, 1.7, 0.3], [5.1, 3.8, 1.5, 0.3], [5.4, 3.4, 1.7, 0.2], [5.1, 3.7, 1.5, 0.4], [5.1, 3.3, 1.7, 0.5], [4.8, 3.4, 1.9, 0.2], [5.0, 3.0, 1.6, 0.2], [5.0, 3.4, 1.6, 0.4], [5.2, 3.5, 1.5, 0.2], [4.7, 3.2, 1.6, 0.2], [4.8, 3.1, 1.6, 0.2], [5.4, 3.4, 1.5, 0.4], [4.9, 3.1, 1.5, 0.2], [5.1, 3.4, 1.5, 0.2], [4.5, 2.3, 1.3, 0.3], [5.0, 3.5, 1.6, 0.6], [5.1, 3.8, 1.9, 0.4], [4.8, 3.0, 1.4, 0.3], [5.1, 3.8, 1.6, 0.2], [5.3, 3.7, 1.5, 0.2], [7.0, 3.2, 4.7, 1.4], [6.4, 3.2, 4.5, 1.5], [6.9, 3.1, 4.9, 1.5], [5.5, 2.3, 4.0, 1.3], [6.5, 2.8, 4.6, 1.5], [5.7, 2.8, 4.5, 1.3], [6.3, 3.3, 4.7, 1.6], [4.9, 2.4, 3.3, 1.0], [6.6, 2.9, 4.6, 1.3], [5.2, 2.7, 3.9, 1.4], [5.0, 2.0, 3.5, 1.0], [5.9, 3.0, 4.2, 1.5], [6.0, 2.2, 4.0, 1.0], [6.1, 2.9, 4.7, 1.4], [5.6, 2.9, 3.6, 1.3], [6.7, 3.1, 4.4, 1.4], [5.6, 3.0, 4.5, 1.5], [5.8, 2.7, 4.1, 1.0], [6.2, 2.2, 4.5, 1.5], [5.6, 2.5, 3.9, 1.1], [5.9, 3.2, 4.8, 1.8], [6.1, 2.8, 4.0, 1.3], [6.3, 2.5, 4.9, 1.5], [6.1, 2.8, 4.7, 1.2], [6.6, 3.0, 4.4, 1.4], [6.8, 2.8, 4.8, 1.4], [6.7, 3.0, 5.0, 1.7], [6.0, 2.9, 4.5, 1.5], [5.7, 2.6, 3.5, 1.0], [5.5, 2.4, 3.8, 1.1], [5.5, 2.4, 3.7, 1.0], [5.8, 2.7, 3.9, 1.2], [6.0, 2.7, 5.1, 1.6], [5.4, 3.0, 4.5, 1.5], [6.0, 3.4, 4.5, 1.6], [6.7, 3.1, 4.7, 1.5], [6.3, 2.3, 4.4, 1.3], [5.6, 3.0, 4.1, 1.3], [5.5, 2.5, 4.0, 1.3], [5.5, 2.6, 4.4, 1.2], [6.1, 3.0, 4.6, 1.4], [5.8, 2.6, 4.0, 1.2], [5.0, 2.3, 3.3, 1.0], [5.6, 2.7, 4.2, 1.3], [5.7, 3.0, 4.2, 1.2], [5.7, 2.9, 4.2, 1.3], [6.2, 2.9, 4.3, 1.3], [5.1, 2.5, 3.0, 1.1], [5.7, 2.8, 4.1, 1.3], [5.8, 2.7, 5.1, 1.9], [6.3, 2.9, 5.6, 1.8], [4.9, 2.5, 4.5, 1.7], [6.5, 3.2, 5.1, 2.0], [6.4, 2.7, 5.3, 1.9], [5.7, 2.5, 5.0, 2.0], [5.8, 2.8, 5.1, 2.4], [6.4, 3.2, 5.3, 2.3], [6.5, 3.0, 5.5, 1.8], [6.0, 2.2, 5.0, 1.5], [5.6, 2.8, 4.9, 2.0], [6.3, 2.7, 4.9, 1.8], [6.2, 2.8, 4.8, 1.8], [6.1, 3.0, 4.9, 1.8], [7.2, 3.0, 5.8, 1.6], [6.3, 2.8, 5.1, 1.5], [6.1, 2.6, 5.6, 1.4], [6.4, 3.1, 5.5, 1.8], [6.0, 3.0, 4.8, 1.8], [6.9, 3.1, 5.4, 2.1], [6.9, 3.1, 5.1, 2.3], [5.8, 2.7, 5.1, 1.9], [6.7, 3.0, 5.2, 2.3], [6.3, 2.5, 5.0, 1.9], [6.5, 3.0, 5.2, 2.0], [6.2, 3.4, 5.4, 2.3], [5.9, 3.0, 5.1, 1.8]];
$coefficients = [[4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 0.0, 4.680538527007988, 0.0, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 0.0, -0.0, -0.0, -0.0, -4.680538527007988, -0.0, -0.0, -0.0, -4.680538527007988, -0.0, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -0.0, -4.680538527007988, -0.0, -0.0, -4.680538527007988, -0.0, -4.680538527007988, -0.0, -4.680538527007988, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -0.0, -0.0, -0.0, -0.0, -0.0, -4.680538527007988, -4.680538527007988, -4.680538527007988, -0.0, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -0.0, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -0.0, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366], [0.0, 0.0, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 0.0, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 47.52934177369389, 47.52934177369389, 47.52934177369389, 0.0, 47.52934177369389, 47.52934177369389, 47.52934177369389, 0.0, 47.52934177369389, 0.0, 0.0, 0.0, 0.0, 47.52934177369389, 0.0, 47.52934177369389, 47.52934177369389, 0.0, 47.52934177369389, 0.0, 47.52934177369389, 0.0, 47.52934177369389, 47.52934177369389, 47.52934177369389, 47.52934177369389, 47.52934177369389, 47.52934177369389, 0.0, 0.0, 0.0, 0.0, 47.52934177369389, 47.52934177369389, 47.52934177369389, 47.52934177369389, 47.52934177369389, 0.0, 0.0, 47.52934177369389, 47.52934177369389, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -0.0, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -0.0, -47.52934177369389]];
$intercepts = [0.09572808365772528, 0.049757317370245795, -0.08418168966801846];
$weights = [28, 49, 27];
// Prediction:
$clf = new NuSVC(3, 3, $vectors, $coefficients, $intercepts, $weights, "rbf", 0.001, 0.0, 3);
$prediction = $clf->predict($features);
fwrite(STDOUT, $prediction);
}
Content source: nok/sklearn-porter
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