113 lines
3.5 KiB
C
113 lines
3.5 KiB
C
#include "lizfcm.test.h"
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Matrix_double *eigen_test_matrix() {
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// produces a matrix that has eigenvalues [5 + sqrt{17}, 2, 5 - sqrt{17}]
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Matrix_double *m = InitMatrixWithSize(double, 3, 3, 0.0);
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m->data[0]->data[0] = 2.0;
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m->data[0]->data[1] = 2.0;
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m->data[0]->data[2] = 4.0;
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m->data[1]->data[0] = 1.0;
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m->data[1]->data[1] = 4.0;
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m->data[1]->data[2] = 7.0;
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m->data[2]->data[1] = 2.0;
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m->data[2]->data[2] = 6.0;
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return m;
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}
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UTEST(eigen, least_dominant_eigenvalue) {
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Matrix_double *m = eigen_test_matrix();
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double expected_least_dominant_eigenvalue = 0.87689; // 5 - sqrt(17)
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double tolerance = 0.0001;
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uint64_t max_iterations = 64;
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Array_double *v_guess = InitArrayWithSize(double, 3, 1.0);
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double approx_least_dominant_eigenvalue =
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least_dominant_eigenvalue(m, v_guess, tolerance, max_iterations);
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EXPECT_NEAR(expected_least_dominant_eigenvalue,
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approx_least_dominant_eigenvalue, tolerance);
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}
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UTEST(eigen, dominant_eigenvalue) {
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Matrix_double *m = InitMatrixWithSize(double, 2, 2, 0.0);
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m->data[0]->data[0] = 2.0;
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m->data[0]->data[1] = -12.0;
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m->data[1]->data[0] = 1.0;
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m->data[1]->data[1] = -5.0;
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Array_double *v_guess = InitArrayWithSize(double, 2, 1.0);
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double tolerance = 0.0001;
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uint64_t max_iterations = 64;
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double expect_dominant_eigenvalue = -2.0;
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double approx_dominant_eigenvalue =
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dominant_eigenvalue(m, v_guess, tolerance, max_iterations);
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EXPECT_NEAR(expect_dominant_eigenvalue, approx_dominant_eigenvalue,
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tolerance);
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free_matrix(m);
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free_vector(v_guess);
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}
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UTEST(eigen, shifted_eigenvalue) {
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Matrix_double *m = eigen_test_matrix();
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double least_dominant_eigenvalue = 0.87689; // 5 - sqrt{17}
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double dominant_eigenvalue = 9.12311; // 5 + sqrt{17}
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double expected_middle_eigenvalue = 2.0;
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double shift = (dominant_eigenvalue + least_dominant_eigenvalue) / 2.0;
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double tolerance = 0.0001;
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uint64_t max_iterations = 64;
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Array_double *v_guess = InitArray(double, {0.5, 1.0, 0.75});
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double approx_middle_eigenvalue = shift_inverse_power_eigenvalue(
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m, v_guess, shift, tolerance, max_iterations);
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EXPECT_NEAR(approx_middle_eigenvalue, expected_middle_eigenvalue, tolerance);
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}
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UTEST(eigen, leslie_matrix_dominant_eigenvalue) {
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Array_double *felicity = InitArray(double, {0.0, 1.5, 0.8});
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Array_double *survivor_ratios = InitArray(double, {0.8, 0.55});
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Matrix_double *leslie = leslie_matrix(survivor_ratios, felicity);
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Array_double *v_guess = InitArrayWithSize(double, 3, 2.0);
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double tolerance = 0.0001;
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uint64_t max_iterations = 64;
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double expect_dominant_eigenvalue = 1.22005;
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double approx_dominant_eigenvalue =
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dominant_eigenvalue(leslie, v_guess, tolerance, max_iterations);
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EXPECT_NEAR(expect_dominant_eigenvalue, approx_dominant_eigenvalue,
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tolerance);
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free_vector(v_guess);
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free_vector(survivor_ratios);
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free_vector(felicity);
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free_matrix(leslie);
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}
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UTEST(eigen, leslie_matrix) {
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Array_double *felicity = InitArray(double, {0.0, 1.5, 0.8});
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Array_double *survivor_ratios = InitArray(double, {0.8, 0.55});
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Matrix_double *m = InitMatrixWithSize(double, 3, 3, 0.0);
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m->data[0]->data[0] = 0.0;
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m->data[0]->data[1] = 1.5;
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m->data[0]->data[2] = 0.8;
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m->data[1]->data[0] = 0.8;
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m->data[2]->data[1] = 0.55;
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Matrix_double *leslie = leslie_matrix(survivor_ratios, felicity);
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EXPECT_TRUE(matrix_equal(leslie, m));
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free_matrix(leslie);
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free_matrix(m);
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free_vector(felicity);
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free_vector(survivor_ratios);
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}
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