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Skip metric regularization if adapt window=0 #3037

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Skip metric regularization if adapt window=0 #3037

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10 changes: 6 additions & 4 deletions src/stan/mcmc/covar_adaptation.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -23,10 +23,12 @@ class covar_adaptation : public windowed_adaptation {

estimator_.sample_covariance(covar);

double n = static_cast<double>(estimator_.num_samples());
covar = (n / (n + 5.0)) * covar
+ 1e-3 * (5.0 / (n + 5.0))
* Eigen::MatrixXd::Identity(covar.rows(), covar.cols());
if (estimator_.num_samples() > 1) {
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Because this behavior probably won't be obvious I think we need to wire a message to the logger. Both here and in var_adaptation can you add a std::string& msg argument which passes the message "Warning: inverse metric is not updated for window sizes less than 2". Then we can pass that to the logger in all of the transition implementations.

double n = static_cast<double>(estimator_.num_samples());
covar = (n / (n + 5.0)) * covar
+ 1e-3 * (5.0 / (n + 5.0))
* Eigen::MatrixXd::Identity(covar.rows(), covar.cols());
}

estimator_.restart();

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8 changes: 5 additions & 3 deletions src/stan/mcmc/var_adaptation.hpp
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Expand Up @@ -23,9 +23,11 @@ class var_adaptation : public windowed_adaptation {

estimator_.sample_variance(var);

double n = static_cast<double>(estimator_.num_samples());
var = (n / (n + 5.0)) * var
+ 1e-3 * (5.0 / (n + 5.0)) * Eigen::VectorXd::Ones(var.size());
if (estimator_.num_samples() > 1) {
double n = static_cast<double>(estimator_.num_samples());
var = (n / (n + 5.0)) * var
+ 1e-3 * (5.0 / (n + 5.0)) * Eigen::VectorXd::Ones(var.size());
}

estimator_.restart();

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29 changes: 29 additions & 0 deletions src/test/unit/mcmc/covar_adaptation_test.cpp
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Expand Up @@ -27,3 +27,32 @@ TEST(McmcCovarAdaptation, learn_covariance) {
}
EXPECT_EQ(0, logger.call_count());
}

TEST(McmcCovarAdaptation, learn_covariance_one_sample) {
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Corresponding test in var_adaptation_test.cpp?

stan::test::unit::instrumented_logger logger;

const int n = 10;
Eigen::VectorXd q = Eigen::VectorXd::Zero(n);
Eigen::MatrixXd covar(Eigen::MatrixXd::Identity(n, n));

const int n_learn = 1;

Eigen::MatrixXd target_covar(Eigen::MatrixXd::Identity(n, n));

stan::mcmc::covar_adaptation adapter(n);
adapter.set_window_params(50, 0, 0, n_learn, logger);

bool update = false;

for (int i = 0; i < n_learn; ++i)
update = adapter.learn_covariance(covar, q);

EXPECT_TRUE(update);

for (int i = 0; i < n; ++i) {
for (int j = 0; j < n; ++j) {
EXPECT_EQ(target_covar(i, j), covar(i, j));
}
}
EXPECT_EQ(0, logger.call_count());
}