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Update tests to changed batch function API.

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This commit is contained in:
Ryan Curtin
2017-10-04 18:26:11 -04:00
parent b76cc5d256
commit 28631e415b
2 changed files with 53 additions and 53 deletions
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src/mlpack/tests/logistic_regression_test.cpp
+37 -37
View File
@@ -196,38 +196,38 @@ BOOST_AUTO_TEST_CASE(LogisticRegressionSeparableEvaluate)
0.0 /* no regularization */);
// These were hand-calculated using Octave.
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("1 1 1"), 0), 4.85873516e-2,
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("1 1 1"), 0, 1), 4.85873516e-2,
1e-5);
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("1 1 1"), 1), 6.71534849e-3,
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("1 1 1"), 1, 1), 6.71534849e-3,
1e-5);
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("1 1 1"), 2), 7.00091146645,
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("1 1 1"), 2, 1), 7.00091146645,
1e-5);
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("0 0 0"), 0), 0.6931471805,
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("0 0 0"), 0, 1), 0.6931471805,
1e-5);
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("0 0 0"), 1), 0.6931471805,
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("0 0 0"), 1, 1), 0.6931471805,
1e-5);
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("0 0 0"), 2), 0.6931471805,
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("0 0 0"), 2, 1), 0.6931471805,
1e-5);
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("-1 -1 -1"), 0), 3.0485873516,
1e-5);
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("-1 -1 -1"), 1), 5.0067153485,
1e-5);
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("-1 -1 -1"), 2),
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("-1 -1 -1"), 0, 1),
3.0485873516, 1e-5);
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("-1 -1 -1"), 1, 1),
5.0067153485, 1e-5);
BOOST_REQUIRE_CLOSE(lrf.Evaluate(arma::rowvec("-1 -1 -1"), 2, 1),
9.1146645377e-4, 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -40 -40"), 0), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -40 -40"), 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -40 -40"), 2), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -40 -40"), 0, 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -40 -40"), 1, 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -40 -40"), 2, 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -80 0"), 0), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -80 0"), 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -80 0"), 2), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -80 0"), 0, 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -80 0"), 1, 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -80 0"), 2, 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -100 20"), 0), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -100 20"), 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -100 20"), 2), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -100 20"), 0, 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -100 20"), 1, 1), 1e-5);
BOOST_REQUIRE_SMALL(lrf.Evaluate(arma::rowvec("200 -100 20"), 2, 1), 1e-5);
}
/**
@@ -271,10 +271,10 @@ BOOST_AUTO_TEST_CASE(LogisticRegressionFunctionRegularizationSeparableEvaluate)
for (size_t j = 0; j < points; ++j)
{
BOOST_REQUIRE_CLOSE(lrfNoReg.Evaluate(parameters, j) + smallRegTerm,
lrfSmallReg.Evaluate(parameters, j), 1e-5);
BOOST_REQUIRE_CLOSE(lrfNoReg.Evaluate(parameters, j) + bigRegTerm,
lrfBigReg.Evaluate(parameters, j), 1e-5);
BOOST_REQUIRE_CLOSE(lrfNoReg.Evaluate(parameters, j, 1) + smallRegTerm,
lrfSmallReg.Evaluate(parameters, j, 1), 1e-5);
BOOST_REQUIRE_CLOSE(lrfNoReg.Evaluate(parameters, j, 1) + bigRegTerm,
lrfBigReg.Evaluate(parameters, j, 1), 1e-5);
}
}
}
@@ -295,20 +295,20 @@ BOOST_AUTO_TEST_CASE(LogisticRegressionFunctionSeparableGradient)
arma::rowvec gradient;
// If the model is at the optimum, then the gradient should be zero.
lrf.Gradient(arma::rowvec("200 -40 -40"), 0, gradient);
lrf.Gradient(arma::rowvec("200 -40 -40"), 0, gradient, 1);
BOOST_REQUIRE_EQUAL(gradient.n_elem, 3);
BOOST_REQUIRE_SMALL(gradient[0], 1e-15);
BOOST_REQUIRE_SMALL(gradient[1], 1e-15);
BOOST_REQUIRE_SMALL(gradient[2], 1e-15);
lrf.Gradient(arma::rowvec("200 -40 -40"), 1, gradient);
lrf.Gradient(arma::rowvec("200 -40 -40"), 1, gradient, 1);
BOOST_REQUIRE_EQUAL(gradient.n_elem, 3);
BOOST_REQUIRE_SMALL(gradient[0], 1e-15);
BOOST_REQUIRE_SMALL(gradient[1], 1e-15);
BOOST_REQUIRE_SMALL(gradient[2], 1e-15);
lrf.Gradient(arma::rowvec("200 -40 -40"), 2, gradient);
lrf.Gradient(arma::rowvec("200 -40 -40"), 2, gradient, 1);
BOOST_REQUIRE_EQUAL(gradient.n_elem, 3);
BOOST_REQUIRE_SMALL(gradient[0], 1e-15);
BOOST_REQUIRE_SMALL(gradient[1], 1e-15);
@@ -317,19 +317,19 @@ BOOST_AUTO_TEST_CASE(LogisticRegressionFunctionSeparableGradient)
// Perturb two elements in the wrong way, so they need to become smaller. For
// the first two data points, classification is still correct so the gradient
// should be zero.
lrf.Gradient(arma::rowvec("200 -30 -30"), 0, gradient);
lrf.Gradient(arma::rowvec("200 -30 -30"), 0, gradient, 1);
BOOST_REQUIRE_EQUAL(gradient.n_elem, 3);
BOOST_REQUIRE_SMALL(gradient[0], 1e-15);
BOOST_REQUIRE_SMALL(gradient[1], 1e-15);
BOOST_REQUIRE_SMALL(gradient[2], 1e-15);
lrf.Gradient(arma::rowvec("200 -30 -30"), 1, gradient);
lrf.Gradient(arma::rowvec("200 -30 -30"), 1, gradient, 1);
BOOST_REQUIRE_EQUAL(gradient.n_elem, 3);
BOOST_REQUIRE_SMALL(gradient[0], 1e-15);
BOOST_REQUIRE_SMALL(gradient[1], 1e-15);
BOOST_REQUIRE_SMALL(gradient[2], 1e-15);
lrf.Gradient(arma::rowvec("200 -30 -30"), 2, gradient);
lrf.Gradient(arma::rowvec("200 -30 -30"), 2, gradient, 1);
BOOST_REQUIRE_EQUAL(gradient.n_elem, 3);
BOOST_REQUIRE_GE(gradient[1], 0.0);
BOOST_REQUIRE_GE(gradient[2], 0.0);
@@ -337,18 +337,18 @@ BOOST_AUTO_TEST_CASE(LogisticRegressionFunctionSeparableGradient)
// Perturb two elements in the other wrong way, so they need to become larger.
// For the first and last data point, classification is still correct so the
// gradient should be zero.
lrf.Gradient(arma::rowvec("200 -60 -60"), 0, gradient);
lrf.Gradient(arma::rowvec("200 -60 -60"), 0, gradient, 1);
BOOST_REQUIRE_EQUAL(gradient.n_elem, 3);
BOOST_REQUIRE_SMALL(gradient[0], 1e-15);
BOOST_REQUIRE_SMALL(gradient[1], 1e-15);
BOOST_REQUIRE_SMALL(gradient[2], 1e-15);
lrf.Gradient(arma::rowvec("200 -30 -30"), 1, gradient);
lrf.Gradient(arma::rowvec("200 -30 -30"), 1, gradient, 1);
BOOST_REQUIRE_EQUAL(gradient.n_elem, 3);
BOOST_REQUIRE_LE(gradient[1], 0.0);
BOOST_REQUIRE_LE(gradient[2], 0.0);
lrf.Gradient(arma::rowvec("200 -60 -60"), 2, gradient);
lrf.Gradient(arma::rowvec("200 -60 -60"), 2, gradient, 1);
BOOST_REQUIRE_EQUAL(gradient.n_elem, 3);
BOOST_REQUIRE_SMALL(gradient[0], 1e-15);
BOOST_REQUIRE_SMALL(gradient[1], 1e-15);
@@ -456,9 +456,9 @@ BOOST_AUTO_TEST_CASE(LogisticRegressionFunctionRegularizationSeparableGradient)
// Test separable gradient for each point. Regularization will be the same.
for (size_t k = 0; k < points; ++k)
{
lrfNoReg.Gradient(parameters, k, gradient);
lrfSmallReg.Gradient(parameters, k, smallRegGradient);
lrfBigReg.Gradient(parameters, k, bigRegGradient);
lrfNoReg.Gradient(parameters, k, gradient, 1);
lrfSmallReg.Gradient(parameters, k, smallRegGradient, 1);
lrfBigReg.Gradient(parameters, k, bigRegGradient, 1);
// Check sizes of gradients.
BOOST_REQUIRE_EQUAL(gradient.n_elem, parameters.n_elem);
@@ -515,7 +515,7 @@ BOOST_AUTO_TEST_CASE(LogisticRegressionSGDSimpleTest)
// Create a logistic regression object using a custom SGD object with a much
// smaller tolerance.
StandardSGD sgd(0.005, 32, 500000, 1e-10);
StandardSGD sgd(0.005, 1, 500000, 1e-10);
LogisticRegression<> lr(data, responses, sgd, 0.001);
// Test sigmoid function.
src/mlpack/tests/nca_test.cpp
+16 -16
View File
@@ -161,12 +161,12 @@ BOOST_AUTO_TEST_CASE(SoftmaxSeparableObjective)
// his notebook). As a result of lack of precision of the by-hand result, the
// tolerance is fairly high.
arma::mat coordinates = arma::eye<arma::mat>(2, 2);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 0), -0.22480, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 1), -0.30613, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 2), -0.22480, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 3), -0.22480, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 4), -0.30613, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 5), -0.22480, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 0, 1), -0.22480, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 1, 1), -0.30613, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 2, 1), -0.22480, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 3, 1), -0.22480, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 4, 1), -0.30613, 0.01);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 5, 1), -0.22480, 0.01);
}
/**
@@ -185,10 +185,10 @@ BOOST_AUTO_TEST_CASE(OptimalSoftmaxSeparableObjective)
// Use a very close tolerance for optimality; we need to be sure this function
// gives optimal results correctly.
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 0), -1.0, 1e-10);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 1), -1.0, 1e-10);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 2), -1.0, 1e-10);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 3), -1.0, 1e-10);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 0, 1), -1.0, 1e-10);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 1, 1), -1.0, 1e-10);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 2, 1), -1.0, 1e-10);
BOOST_REQUIRE_CLOSE(sef.Evaluate(coordinates, 3, 1), -1.0, 1e-10);
}
/**
@@ -206,42 +206,42 @@ BOOST_AUTO_TEST_CASE(SoftmaxSeparableGradient)
arma::mat coordinates = arma::eye<arma::mat>(2, 2);
arma::mat gradient(2, 2);
sef.Gradient(coordinates, 0, gradient);
sef.Gradient(coordinates, 0, gradient, 1);
BOOST_REQUIRE_CLOSE(gradient(0, 0), -2.0 * 0.0069708, 0.01);
BOOST_REQUIRE_CLOSE(gradient(0, 1), -2.0 * -0.0101707, 0.01);
BOOST_REQUIRE_CLOSE(gradient(1, 0), -2.0 * -0.0101707, 0.01);
BOOST_REQUIRE_CLOSE(gradient(1, 1), -2.0 * -0.14359, 0.01);
sef.Gradient(coordinates, 1, gradient);
sef.Gradient(coordinates, 1, gradient, 1);
BOOST_REQUIRE_CLOSE(gradient(0, 0), -2.0 * 0.008496, 0.01);
BOOST_REQUIRE_SMALL(gradient(0, 1), 1e-5);
BOOST_REQUIRE_SMALL(gradient(1, 0), 1e-5);
BOOST_REQUIRE_CLOSE(gradient(1, 1), -2.0 * -0.12238, 0.01);
sef.Gradient(coordinates, 2, gradient);
sef.Gradient(coordinates, 2, gradient, 1);
BOOST_REQUIRE_CLOSE(gradient(0, 0), -2.0 * 0.0069708, 0.01);
BOOST_REQUIRE_CLOSE(gradient(0, 1), -2.0 * 0.0101707, 0.01);
BOOST_REQUIRE_CLOSE(gradient(1, 0), -2.0 * 0.0101707, 0.01);
BOOST_REQUIRE_CLOSE(gradient(1, 1), -2.0 * -0.1435886, 0.01);
sef.Gradient(coordinates, 3, gradient);
sef.Gradient(coordinates, 3, gradient, 1);
BOOST_REQUIRE_CLOSE(gradient(0, 0), -2.0 * 0.0069708, 0.01);
BOOST_REQUIRE_CLOSE(gradient(0, 1), -2.0 * 0.0101707, 0.01);
BOOST_REQUIRE_CLOSE(gradient(1, 0), -2.0 * 0.0101707, 0.01);
BOOST_REQUIRE_CLOSE(gradient(1, 1), -2.0 * -0.1435886, 0.01);
sef.Gradient(coordinates, 4, gradient);
sef.Gradient(coordinates, 4, gradient, 1);
BOOST_REQUIRE_CLOSE(gradient(0, 0), -2.0 * 0.008496, 0.01);
BOOST_REQUIRE_SMALL(gradient(0, 1), 1e-5);
BOOST_REQUIRE_SMALL(gradient(1, 0), 1e-5);
BOOST_REQUIRE_CLOSE(gradient(1, 1), -2.0 * -0.12238, 0.01);
sef.Gradient(coordinates, 5, gradient);
sef.Gradient(coordinates, 5, gradient, 1);
BOOST_REQUIRE_CLOSE(gradient(0, 0), -2.0 * 0.0069708, 0.01);
BOOST_REQUIRE_CLOSE(gradient(0, 1), -2.0 * -0.0101707, 0.01);