Fix/diagnose num chains

src/cmdstan/diagnose.cpp

@@ -76,7 +76,7 @@ int main(int argc, const char *argv[]) {
   stan::io::stan_csv_metadata metadata = csv_parsed[0].metadata;
   std::vector<std::string> param_names = csv_parsed[0].header;
   size_t num_params = param_names.size();
-  int num_samples = chains.num_samples();
+  int num_samples = chains.num_samples() * chains.num_chains();
   std::vector<std::string> bad_n_eff_names;
   std::vector<std::string> bad_rhat_names;
   bool has_errors = false;

src/test/interface/diagnose_test.cpp

@@ -96,3 +96,20 @@ TEST(CommandDiagnose, mix) {
   ss << expected_output.rdbuf();
   EXPECT_EQ(1, count_matches(ss.str(), out.output));
 }
+
+TEST(CommandDiagnose, divergences) {
+  std::string path_separator;
+  path_separator.push_back(get_path_separator());
+  std::string command = "bin" + path_separator + "diagnose";
+  std::string csv_file = "src" + path_separator + "test" + path_separator
+                         + "interface" + path_separator + "example_output"
+                         + path_separator + "div_output*.csv";
+
+  run_command_output out = run_command(command + " " + csv_file);
+  ASSERT_FALSE(out.hasError) << "\"" << out.command << "\" quit with an error";
+
+  std::ifstream expected_output("src/test/interface/example_output/div.nom");
+  std::stringstream ss;
+  ss << expected_output.rdbuf();
+  EXPECT_EQ(1, count_matches(ss.str(), out.output));
+}

src/test/interface/example_output/div.nom

@@ -0,0 +1,20 @@
+Checking sampler transitions treedepth.
+Treedepth satisfactory for all transitions.
+
+Checking sampler transitions for divergences.
+77 of 400 (19.25%) transitions ended with a divergence.
+These divergent transitions indicate that HMC is not fully able to explore the posterior distribution.
+Try increasing adapt delta closer to 1.
+If this doesn't remove all divergences, try to reparameterize the model.
+
+Checking E-BFMI - sampler transitions HMC potential energy.
+E-BFMI satisfactory.
+
+Rank-normalized split effective sample size satisfactory for all parameters.
+
+The following parameters had rank-normalized split R-hat greater than 1.01:
+  x[1], x[2], x[3], x[4], x[5]
+Such high values indicate incomplete mixing and biased estimation.
+You should consider regularizating your model with additional prior information or a more effective parameterization.
+
+Processing complete.