A linear model library by C++(with C interface). including:
- Linear Regression
- Lasso
- Ridge
- Elastic Net
- Logistic Regression
You can use liblm like scikit-learn.
import numpy as np
import liblm
X = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
y = np.array([1, 2, 3, 4])
X_test = X + 5
clf = liblm.Lasso(lamda=0.2)
clf.fit(X, y)
print(clf.predict(X_test))#include <stdio.h>
#include <liblm/c_api.h>
int main() {
double *X = (double *) malloc(sizeof(double) * 5);
double *y = (double *) malloc(sizeof(double) * 5);
double *X_test = (double *) malloc(sizeof(double) * 5);
for (int i = 0; i < 5; ++i)
{
X[i] = i;
y[i] = 2 * i - 1;
X_test[i] = i + 5;
}
lm_problem *prob = (lm_problem *) malloc(sizeof(lm_problem));
dmatrix data;
data.data = X;
data.row = 5;
data.col = 1;
prob->X = data;
prob->y = y;
lm_param param;
param.alg = REG;
param.regu = NONE;
lm_model *model = lm_train(prob, ¶m);
dmatrix d;
d.data = X_test;
d.row = 5;
d.col = 1;
y = lm_predict(model, &d);
for (int i = 0; i < 5; ++i)
printf_s("%.3f ", y[i]);
printf_s("\n");
free_model(model);
return 0;
}#include <liblm/regressor.h>
#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
int main()
{
MatrixXd X(4, 1);
X << 1, 2, 3, 4;
VectorXd y(4);
y << 1, 2, 3, 4;
MatrixXd X_test = X.array() + 5;
Regressor *clf = new LinearRegression();
clf->fit(X, y);
std::cout << clf->predict(X_test) << std::endl;
delete clf;
return 0;
}Use cmake GUI to generate Visual Studio project, and build it by VS.
$ cmake .$ make
After building, you will get a library file and a executable file.
Put library file(liblm.dll or libliblm.so or libliblm.dylib) into python/liblm.
$ cd python$ python setup.py install