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='c')
output = porter.export()
print(output)
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#define N_FEATURES 4
#define N_CLASSES 3
#define N_VECTORS 104
#define N_ROWS 3
#define N_COEFFICIENTS 2
#define N_INTERCEPTS 3
#define KERNEL_TYPE 'r'
#define KERNEL_GAMMA 0.001
#define KERNEL_COEF 0.0
#define KERNEL_DEGREE 3
double vectors[104][4] = {{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}};
double coefficients[2][104] = {{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}};
double intercepts[3] = {0.09572808365772528, 0.049757317370245795, -0.08418168966801846};
int weights[3] = {28, 49, 27};
int predict (double features[]) {
int i, j, k, d, l;
double kernels[N_VECTORS];
double kernel;
switch (KERNEL_TYPE) {
case 'l':
// <x,x'>
for (i = 0; i < N_VECTORS; i++) {
kernel = 0.;
for (j = 0; j < N_FEATURES; j++) {
kernel += vectors[i][j] * features[j];
}
kernels[i] = kernel;
}
break;
case 'p':
// (y<x,x'>+r)^d
for (i = 0; i < N_VECTORS; i++) {
kernel = 0.;
for (j = 0; j < N_FEATURES; j++) {
kernel += vectors[i][j] * features[j];
}
kernels[i] = pow((KERNEL_GAMMA * kernel) + KERNEL_COEF, KERNEL_DEGREE);
}
break;
case 'r':
// exp(-y|x-x'|^2)
for (i = 0; i < N_VECTORS; i++) {
kernel = 0.;
for (j = 0; j < N_FEATURES; j++) {
kernel += pow(vectors[i][j] - features[j], 2);
}
kernels[i] = exp(-KERNEL_GAMMA * kernel);
}
break;
case 's':
// tanh(y<x,x'>+r)
for (i = 0; i < N_VECTORS; i++) {
kernel = 0.;
for (j = 0; j < N_FEATURES; j++) {
kernel += vectors[i][j] * features[j];
}
kernels[i] = tanh((KERNEL_GAMMA * kernel) + KERNEL_COEF);
}
break;
}
int starts[N_ROWS];
int start;
for (i = 0; i < N_ROWS; i++) {
if (i != 0) {
start = 0;
for (j = 0; j < i; j++) {
start += weights[j];
}
starts[i] = start;
} else {
starts[0] = 0;
}
}
int ends[N_ROWS];
for (i = 0; i < N_ROWS; i++) {
ends[i] = weights[i] + starts[i];
}
if (N_CLASSES == 2) {
for (i = 0; i < N_VECTORS; i++) {
kernels[i] = -kernels[i];
}
double decision = 0.;
for (k = starts[1]; k < ends[1]; k++) {
decision += kernels[k] * coefficients[0][k];
}
for (k = starts[0]; k < ends[0]; k++) {
decision += kernels[k] * coefficients[0][k];
}
decision += intercepts[0];
if (decision > 0) {
return 0;
}
return 1;
}
double decisions[N_INTERCEPTS];
double tmp;
for (i = 0, d = 0, l = N_ROWS; i < l; i++) {
for (j = i + 1; j < l; j++) {
tmp = 0.;
for (k = starts[j]; k < ends[j]; k++) {
tmp += kernels[k] * coefficients[i][k];
}
for (k = starts[i]; k < ends[i]; k++) {
tmp += kernels[k] * coefficients[j - 1][k];
}
decisions[d] = tmp + intercepts[d];
d = d + 1;
}
}
int votes[N_INTERCEPTS];
for (i = 0, d = 0, l = N_ROWS; i < l; i++) {
for (j = i + 1; j < l; j++) {
votes[d] = decisions[d] > 0 ? i : j;
d = d + 1;
}
}
int amounts[N_CLASSES];
for (i = 0, l = N_CLASSES; i < l; i++) {
amounts[i] = 0;
}
for (i = 0; i < N_INTERCEPTS; i++) {
amounts[votes[i]] += 1;
}
int classVal = -1;
int classIdx = -1;
for (i = 0; i < N_CLASSES; i++) {
if (amounts[i] > classVal) {
classVal = amounts[i];
classIdx= i;
}
}
return classIdx;
}
int main(int argc, const char * argv[]) {
/* Features: */
double features[argc-1];
int i;
for (i = 1; i < argc; i++) {
features[i-1] = atof(argv[i]);
}
/* Prediction: */
printf("%d", predict(features));
return 0;
}
In [5]:
# Save model:
# with open('nusvc.c', 'w') as f:
# f.write(output)
# Compile model:
# $ gcc nusvc.c -std=c99 -lm -o nusvc
# Run classification:
# $ ./nusvc 1 2 3 4
Content source: nok/sklearn-porter
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