[mlpack-git] master: Accidentally checked in unstable code. (92508aa)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Thu Mar 5 22:04:19 EST 2015
Repository : https://github.com/mlpack/mlpack
On branch : master
Link : https://github.com/mlpack/mlpack/compare/904762495c039e345beba14c1142fd719b3bd50e...f94823c800ad6f7266995c700b1b630d5ffdcf40
>---------------------------------------------------------------
commit 92508aa9ac2acf43229b0f331e06277f7505faf9
Author: Ryan Curtin <ryan at ratml.org>
Date: Wed Nov 26 16:47:57 2014 +0000
Accidentally checked in unstable code.
>---------------------------------------------------------------
92508aa9ac2acf43229b0f331e06277f7505faf9
src/mlpack/tests/softmax_regression_test.cpp | 15 ++++-----------
1 file changed, 4 insertions(+), 11 deletions(-)
diff --git a/src/mlpack/tests/softmax_regression_test.cpp b/src/mlpack/tests/softmax_regression_test.cpp
index 006988e..07ad79d 100644
--- a/src/mlpack/tests/softmax_regression_test.cpp
+++ b/src/mlpack/tests/softmax_regression_test.cpp
@@ -174,8 +174,8 @@ BOOST_AUTO_TEST_CASE(SoftmaxRegressionTwoClasses)
const double lambda = 0.5;
// Generate two-Gaussian dataset.
- GaussianDistribution g1(arma::vec("1.0 1.0 1.0"), arma::eye<arma::mat>(3, 3));
- GaussianDistribution g2(arma::vec("9.0 9.0 9.0"), arma::eye<arma::mat>(3, 3));
+ GaussianDistribution g1(arma::vec("1.0 9.0 1.0"), arma::eye<arma::mat>(3, 3));
+ GaussianDistribution g2(arma::vec("4.0 3.0 4.0"), arma::eye<arma::mat>(3, 3));
arma::mat data(inputSize, points);
arma::vec labels(points);
@@ -190,19 +190,13 @@ BOOST_AUTO_TEST_CASE(SoftmaxRegressionTwoClasses)
data.col(i) = g2.Random();
labels(i) = 1;
}
- arma::rowvec ones;
- ones.ones(points);
- data.insert_rows(0, ones);
// Train softmax regression object.
- SoftmaxRegression<> sr(data, labels, inputSize + 1, numClasses, lambda);
+ SoftmaxRegression<> sr(data, labels, inputSize, numClasses, lambda);
// Compare training accuracy to 100.
const double acc = sr.ComputeAccuracy(data, labels);
- Log::Debug << acc << " acc\n";
- Log::Debug << sr.Lambda() << " lambda\n";
- Log::Debug << sr.Parameters().t() << "\n";
- BOOST_CHECK_CLOSE(acc, 100.0, 0.5);
+ BOOST_REQUIRE_CLOSE(acc, 100.0, 0.3);
// Create test dataset.
for (size_t i = 0; i < points/2; i++)
@@ -218,7 +212,6 @@ BOOST_AUTO_TEST_CASE(SoftmaxRegressionTwoClasses)
// Compare test accuracy to 100.
const double testAcc = sr.ComputeAccuracy(data, labels);
- Log::Debug << testAcc << " acc\n";
BOOST_REQUIRE_CLOSE(testAcc, 100.0, 0.6);
}
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