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Added some tests to make sure the recent addition to svm_c_linear_trainer

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works right.
This commit is contained in:
Davis King 2012-05-19 17:15:59 -04:00
parent b7d8129013
commit ba33b82e83
1 changed files with 59 additions and 0 deletions
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59
dlib/test/oca.cpp
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@ -43,6 +43,12 @@ namespace
typedef matrix<double,0,1> w_type; typedef matrix<double,0,1> w_type;
w_type w; w_type w;
decision_function<linear_kernel<w_type> > df;
svm_c_linear_trainer<linear_kernel<w_type> > trainer;
trainer.set_c_class1(2);
trainer.set_c_class1(3);
trainer.set_learns_nonnegative_weights(true);
trainer.set_epsilon(1e-12);
std::vector<w_type> x; std::vector<w_type> x;
w_type temp(2); w_type temp(2);
@ -75,6 +81,13 @@ namespace
dlog << LINFO << "error: "<< max(abs(w-true_w)); dlog << LINFO << "error: "<< max(abs(w-true_w));
DLIB_TEST(max(abs(w-true_w)) < 1e-10); DLIB_TEST(max(abs(w-true_w)) < 1e-10);
df = trainer.train(x,y);
w = join_cols(df.basis_vectors(0), uniform_matrix<double>(1,1,-df.b));
true_w = 0, 1, 0;
dlog << LINFO << "error: "<< max(abs(w-true_w));
DLIB_TEST(max(abs(w-true_w)) < 1e-10);
print_spinner(); print_spinner();
// test the version with a non-negativity constraint on w. // test the version with a non-negativity constraint on w.
@ -132,6 +145,52 @@ namespace
true_w = 1, 0, 0; true_w = 1, 0, 0;
dlog << LINFO << "error: "<< max(abs(w-true_w)); dlog << LINFO << "error: "<< max(abs(w-true_w));
DLIB_TEST(max(abs(w-true_w)) < 1e-10); DLIB_TEST(max(abs(w-true_w)) < 1e-10);
df = trainer.train(x,y);
w = join_cols(df.basis_vectors(0), uniform_matrix<double>(1,1,-df.b));
true_w = 1, 0, 0;
dlog << LINFO << "error: "<< max(abs(w-true_w));
DLIB_TEST(max(abs(w-true_w)) < 1e-10);
x.clear();
y.clear();
temp = -2, 2;
x.push_back(temp);
temp = 0, -0;
x.push_back(temp);
y.push_back(+1);
y.push_back(-1);
trainer.set_c(10);
df = trainer.train(x,y);
w = join_cols(df.basis_vectors(0), uniform_matrix<double>(1,1,-df.b));
true_w = 0, 1, -1;
dlog << LINFO << "w: " << trans(w);
dlog << LINFO << "error: "<< max(abs(w-true_w));
DLIB_TEST(max(abs(w-true_w)) < 1e-10);
x.clear();
y.clear();
temp = -2, 2;
x.push_back(temp);
temp = 0, -0;
x.push_back(temp);
y.push_back(-1);
y.push_back(+1);
trainer.set_c(10);
df = trainer.train(x,y);
w = join_cols(df.basis_vectors(0), uniform_matrix<double>(1,1,-df.b));
true_w = 1, 0, 1;
dlog << LINFO << "w: " << trans(w);
dlog << LINFO << "error: "<< max(abs(w-true_w));
DLIB_TEST(max(abs(w-true_w)) < 1e-10);
} }
} a; } a;