MLKIT

A HEADER-ONLY LIBRARY PROVIDES SKLEARN-LIKE API WITH GPU SUPPORT.

Dependencies

• ArrayFire: a general purpose GPU library.
• Googletest: Google Testing and Mocking Framework.

Examples

#include "mlkit.hpp"

using namespace std;
using namespace mk;

int main(int argc, char **argv)
{
    int device = argc > 1 ? atoi(argv[1]) : -1; // default -1
    try {
        if(device >= 0)
            af::setBackend(AF_BACKEND_CUDA); // use gpu
        else
            af::setBackend(AF_BACKEND_CPU); // use cpu

        af::info();
        af::array X = af::randn(100, 3);
        af::array y = 1 * X.col(0) + 2 * X.col(1) + 3 * X.col(2) + 4 + af::randu(100, 1) * 0.5;
        linear_model::LinearRegression lr = linear_model::LinearRegression(true);
        lr.fit(X, y);
        cout << endl \
             << "[linear regression]" << endl \
             << "-----------------------------------------------" << endl \
             << "expect coef: [1, 2, 3], expect intercept: 4" << endl \
             << "-----------------------------------------------" << endl \
             << "fit result: " << endl;
        af_print(lr.coef_);
        af_print(lr.intercept_)
        cout << "-----------------------------------------------" << endl;
        lr.score(X, y);
    } catch (af::exception &ae) {
        cerr << ae.what() << endl;
    }
    return 0;
}

The output:

# compiler command
g++ -std=c++11 -g example.cpp -o test -I/opt/arrayfire/include -Imlkit/include -laf -L/opt/arrayfire/lib

# output
ArrayFire v3.7.0 (CPU, 64-bit Linux, build c30d5455)
[0] Intel: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz, 95293 MB, Max threads(20) GNU Compiler Collection(GCC/G++) 7.4.0

[linear regression]
-----------------------------------------------
expect coef: [1, 2, 3], expect intercept: 4
-----------------------------------------------
fit result:
lr.coef_
[3 1 1 1]
   Offset: 1
   Strides: [1 4 4 4]
    0.9999
    1.9851
    2.9896

lr.intercept_
[1 1 1 1]
   Offset: 0
   Strides: [1 4 4 4]
    4.2475
-----------------------------------------------
Mean Sqaure Error: 0.01791

Algorithms

• Statistical Learning：
  • linear_model.LinearRegression
  • linear_model.LogisticRegression
  • neighbors.KNeighborsClassifier
  • cluster.KMeans
  • decomposition.PCA
  • tree.DecisionTreeClassifier
  • mixture.GaussianMixture
  • svm.LinearSVC

Reference

• 李航. 统计学习方法[M]. 2012.
• Harrington P. Machine Learning in Action[M]. 2012.