コード例 #1
0
const MatrixX& Jacobian::GetNullspace()
{
	if(computeNullSpace_)
	{
		computeNullSpace_ = false;
		/*jacobianInverseNoDls_ = jacobian_;
		PseudoInverse(jacobianInverseNoDls_); // tmp while figuring out how to chose lambda*/
		//ComputeSVD();
		MatrixX id = MatrixX::Identity(jacobian_.cols(), jacobian_.cols());
		ComputeSVD();
		//Eigen::JacobiSVD<MatrixX> svd(jacobian_, Eigen::ComputeThinU | Eigen::ComputeThinV);
		MatrixX res = MatrixX::Zero(id.rows(), id.cols());
		for(int i =0; i < svd_.matrixV().cols(); ++ i)
		{
			VectorX v = svd_.matrixV().col(i);
			res += v * v.transpose();
		}
		Identitymin_ = id - res;
		//Identitymin_ = id - (jacobianInverseNoDls_* jacobian_);
	}
	return Identitymin_;
}
TYPED_TEST(TestSecondOrderMultinomialLogisticRegression, MinimizerOverfitSmall) {
    MatrixX<TypeParam> X(2, 10);
    VectorXi y(10);

    X << 0.6097662 ,  0.53395565,  0.9499446 ,  0.67289898,  0.94173948,
         0.56675891,  0.80363783,  0.85303565,  0.15903886,  0.99518533,
         0.41655682,  0.29256121,  0.36103228,  0.29899503,  0.4957268 ,
         -0.04277318, -0.28038614, -0.12334621, -0.17497722,  0.1492248;
    y << 0, 0, 0, 0, 0, 1, 1, 1, 1, 1;
    std::vector<MatrixX<TypeParam> > X_var;
    for (int i=0; i<10; i++) {
        //X_var.push_back(MatrixX<TypeParam>::Random(2, 2).array().abs() * 0.01);
        //X_var.push_back(MatrixX<TypeParam>::Zero(2, 2));
        VectorX<TypeParam> a = VectorX<TypeParam>::Random(2).array() * 0.01;
        X_var.push_back(
            a * a.transpose()
        );
    }

    SecondOrderLogisticRegressionApproximation<TypeParam> mlr(X, X_var, y, 0);
    MatrixX<TypeParam> eta = MatrixX<TypeParam>::Zero(2, 3);

    GradientDescent<SecondOrderLogisticRegressionApproximation<TypeParam>, MatrixX<TypeParam>> minimizer(
        std::make_shared<
            ArmijoLineSearch<
                SecondOrderLogisticRegressionApproximation<TypeParam>,
                MatrixX<TypeParam>
            >
        >(),
        [](TypeParam value, TypeParam gradNorm, size_t iterations) {
            return iterations < 5000;
        }
    );
    minimizer.minimize(mlr, eta);

    EXPECT_GT(0.1, mlr.value(eta));
}