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updated tests 

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extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403661
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Davis King 2010-05-30 19:45:21 +00:00
parent c543a025ba
commit 90c29847e2
1 changed files with 38 additions and 17 deletions
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55
dlib/test/linear_manifold_regularizer.cpp
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@ -54,29 +54,17 @@ namespace
const unsigned long num_points = 200;
// create a large dataset with two concentric circles. There will be 100000 points on each circle
// for a total of 200000 samples.
// create a large dataset with two concentric circles.
generate_circle(samples, 1, num_points); // circle of radius 1
generate_circle(samples, 5, num_points); // circle of radius 5
// Create a set of sample_pairs that tells us which samples are "close" and should thus
// be classified similarly. These edges will be used to define the manifold regularizer.
// To find these edges we use a simple function that samples point pairs randomly and
// returns the top 5% with the shortest edges.
std::vector<sample_pair> edges;
find_k_nearest_neighbors(samples, squared_euclidean_distance(), 2, edges);
find_percent_shortest_edges_randomly(samples, squared_euclidean_distance(0.1, 4), 1, 10000, "random seed", edges);
dlog << LTRACE << "number of edges generated: " << edges.size();
empirical_kernel_map<kernel_type> ekm;
// Since the circles are not linearly separable we will use an empirical kernel map to
// map them into a space where they are separable. So we create an empirical_kernel_map
// using a random subset of our data samples as basis samples. Note, however, that even
// though the circles are linearly separable in this new space given by the empirical_kernel_map
// we still won't be able to correctly classify all the points given just the 2 labeled examples.
// We will need to make use of the nearest neighbor information stored in edges. To do that
// we will use the linear_manifold_regularizer next.
ekm.load(kern, randomly_subsample(samples, 100));
// Project all the samples into the span of our 50 basis samples
@ -108,7 +96,7 @@ namespace
W(edges[i].index2(), edges[i].index1()) = use_gaussian_weights(0.1)(edges[i]);
}
matrix<double> L = diagm(sum_rows(W)) - W;
matrix<double> trueT = inv_lower_triangular(chol(identity_matrix<double>(X.nr()) + 10000.0/edges.size()*X*L*trans(X)));
matrix<double> trueT = inv_lower_triangular(chol(identity_matrix<double>(X.nr()) + (10000.0/sum(lowerm(W)))*X*L*trans(X)));
dlog << LTRACE << "T error: "<< max(abs(T - trueT));
DLIB_TEST(max(abs(T - trueT)) < 1e-7);
@ -214,6 +202,21 @@ namespace
DLIB_TEST(edges[2] == sample_pair(0,3,0));
DLIB_TEST(edges[3] == sample_pair(0,4,0));
find_k_nearest_neighbors(samples, squared_euclidean_distance(), 3, edges);
DLIB_TEST(edges.size() == 8);
find_k_nearest_neighbors(samples, squared_euclidean_distance(3.9, 4.1), 3, edges);
DLIB_TEST(edges.size() == 4);
std::sort(edges.begin(), edges.end(), &order_by_index);
DLIB_TEST(edges[0] == sample_pair(1,2,0));
DLIB_TEST(edges[1] == sample_pair(1,3,0));
DLIB_TEST(edges[2] == sample_pair(2,4,0));
DLIB_TEST(edges[3] == sample_pair(3,4,0));
find_k_nearest_neighbors(samples, squared_euclidean_distance(30000, 4.1), 3, edges);
DLIB_TEST(edges.size() == 0);
}
void test_knn1_approx()
@ -229,8 +232,6 @@ namespace
test = -1,-1; samples.push_back(test);
std::vector<sample_pair> edges;
// For this simple graph and high number of samples we will do we should obtain the exact
// knn solution.
find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(), 1, 10000, seed, edges);
DLIB_TEST(edges.size() == 4);
@ -241,6 +242,21 @@ namespace
DLIB_TEST(edges[2] == sample_pair(0,3,0));
DLIB_TEST(edges[3] == sample_pair(0,4,0));
find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(), 3, 10000, seed, edges);
DLIB_TEST(edges.size() == 8);
find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(3.9, 4.1), 3, 10000, seed, edges);
DLIB_TEST(edges.size() == 4);
std::sort(edges.begin(), edges.end(), &order_by_index);
DLIB_TEST(edges[0] == sample_pair(1,2,0));
DLIB_TEST(edges[1] == sample_pair(1,3,0));
DLIB_TEST(edges[2] == sample_pair(2,4,0));
DLIB_TEST(edges[3] == sample_pair(3,4,0));
find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(30000, 4.1), 3, 10000, seed, edges);
DLIB_TEST(edges.size() == 0);
}
void test_knn2()
@ -265,6 +281,8 @@ namespace
DLIB_TEST(edges[2] == sample_pair(1,3,0));
DLIB_TEST(edges[3] == sample_pair(2,3,0));
find_k_nearest_neighbors(samples, squared_euclidean_distance(), 200, edges);
DLIB_TEST(edges.size() == 4*3/2);
}
void test_knn2_approx()
@ -291,6 +309,9 @@ namespace
DLIB_TEST(edges[2] == sample_pair(1,3,0));
DLIB_TEST(edges[3] == sample_pair(2,3,0));
find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(), 200, 10000, seed, edges);
DLIB_TEST(edges.size() == 4*3/2);
}
void perform_test (