diff --git a/src/stan/analyze/mcmc/compute_effective_sample_size.hpp b/src/stan/analyze/mcmc/compute_effective_sample_size.hpp index 39eb636c71..e06c89f7a1 100644 --- a/src/stan/analyze/mcmc/compute_effective_sample_size.hpp +++ b/src/stan/analyze/mcmc/compute_effective_sample_size.hpp @@ -12,6 +12,8 @@ namespace stan { namespace analyze { /** + * \deprecated use split_rank_normalized_ess instead + * * Computes the effective sample size (ESS) for the specified * parameter across all kept samples. The value returned is the * minimum of ESS and the number_total_draws * @@ -138,6 +140,8 @@ inline double compute_effective_sample_size(std::vector draws, } /** + * \deprecated use split_rank_normalized_ess instead + * * Computes the effective sample size (ESS) for the specified * parameter across all kept samples. The value returned is the * minimum of ESS and the number_total_draws * @@ -164,6 +168,8 @@ inline double compute_effective_sample_size(std::vector draws, } /** + * \deprecated use split_rank_normalized_ess instead + * * Computes the split effective sample size (ESS) for the specified * parameter across all kept samples. The value returned is the * minimum of ESS and the number_total_draws * @@ -199,6 +205,8 @@ inline double compute_split_effective_sample_size( } /** + * \deprecated use split_rank_normalized_ess instead + * * Computes the split effective sample size (ESS) for the specified * parameter across all kept samples. The value returned is the * minimum of ESS and the number_total_draws * diff --git a/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp b/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp index e40253ebb4..baa686e63d 100644 --- a/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp +++ b/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp @@ -18,6 +18,8 @@ namespace stan { namespace analyze { /** + * \deprecated use `rhat` instead + * * Computes the potential scale reduction (Rhat) for the specified * parameter across all kept samples. * @@ -102,6 +104,8 @@ inline double compute_potential_scale_reduction( } /** + * \deprecated use split_rank_normalized_rhat instead + * * Computes the potential scale reduction (Rhat) for the specified * parameter across all kept samples. * @@ -125,6 +129,8 @@ inline double compute_potential_scale_reduction( } /** + * \deprecated use split_rank_normalized_rhat instead + * * Computes the split potential scale reduction (Rhat) for the * specified parameter across all kept samples. When the number of * total draws N is odd, the (N+1)/2th draw is ignored. @@ -157,6 +163,8 @@ inline double compute_split_potential_scale_reduction( } /** + * \deprecated use split_rank_normalized_rhat instead + * * Computes the split potential scale reduction (Rhat) for the * specified parameter across all kept samples. When the number of * total draws N is odd, the (N+1)/2th draw is ignored. diff --git a/src/stan/analyze/mcmc/ess.hpp b/src/stan/analyze/mcmc/ess.hpp new file mode 100644 index 0000000000..33342ce68a --- /dev/null +++ b/src/stan/analyze/mcmc/ess.hpp @@ -0,0 +1,108 @@ +#ifndef STAN_ANALYZE_MCMC_ESS_HPP +#define STAN_ANALYZE_MCMC_ESS_HPP + +#include +#include +#include +#include +#include +#include + +namespace stan { +namespace analyze { + +/** + * Computes the effective sample size (ESS) for the specified + * parameter across all chains. The number of draws per chain must be > 3, + * and the values across all draws must be finite and not constant. + * See https://arxiv.org/abs/1903.08008, section 3.2 for discussion. + * + * Sample autocovariance is computed using the implementation in this namespace + * which normalizes lag-k autocorrelation estimators by N instead of (N - k), + * yielding biased but more stable estimators as discussed in Geyer (1992); see + * https://projecteuclid.org/euclid.ss/1177011137. + * + * @param chains matrix of draws across all chains + * @return effective sample size for the specified parameter + */ +double ess(const Eigen::MatrixXd& chains) { + const Eigen::Index num_chains = chains.cols(); + const Eigen::Index draws_per_chain = chains.rows(); + Eigen::MatrixXd acov(draws_per_chain, num_chains); + Eigen::VectorXd chain_mean(num_chains); + Eigen::VectorXd chain_var(num_chains); + + // compute the per-chain autocovariance + for (size_t i = 0; i < num_chains; ++i) { + chain_mean(i) = chains.col(i).mean(); + Eigen::Map draw_col(chains.col(i).data(), + draws_per_chain); + Eigen::VectorXd cov_col(draws_per_chain); + autocovariance(draw_col, cov_col); + acov.col(i) = cov_col; + chain_var(i) = cov_col(0) * draws_per_chain / (draws_per_chain - 1); + } + + // compute var_plus, eqn (3) + double w_chain_var = math::mean(chain_var); // W (within chain var) + double var_plus + = w_chain_var * (draws_per_chain - 1) / draws_per_chain; // \hat{var}^{+} + if (num_chains > 1) { + var_plus += math::variance(chain_mean); // B (between chain var) + } + + // Geyer's initial positive sequence, eqn (11) + Eigen::VectorXd rho_hat_t = Eigen::VectorXd::Zero(draws_per_chain); + double rho_hat_even = 1.0; + rho_hat_t(0) = rho_hat_even; // lag 0 + + Eigen::VectorXd acov_t(num_chains); + for (size_t i = 0; i < num_chains; ++i) { + acov_t(i) = acov(1, i); + } + double rho_hat_odd = 1 - (w_chain_var - acov_t.mean()) / var_plus; + rho_hat_t(1) = rho_hat_odd; // lag 1 + + // compute autocorrelation at lag t for pair (t, t+1) + // paired autocorrelation is guaranteed to be positive, monotone and convex + size_t t = 1; + while (t < draws_per_chain - 4 && (rho_hat_even + rho_hat_odd > 0) + && !std::isnan(rho_hat_even + rho_hat_odd)) { + for (size_t i = 0; i < num_chains; ++i) { + acov_t(i) = acov.col(i)(t + 1); + } + rho_hat_even = 1 - (w_chain_var - acov_t.mean()) / var_plus; + for (size_t i = 0; i < num_chains; ++i) { + acov_t(i) = acov.col(i)(t + 2); + } + rho_hat_odd = 1 - (w_chain_var - acov_t.mean()) / var_plus; + if ((rho_hat_even + rho_hat_odd) >= 0) { + rho_hat_t(t + 1) = rho_hat_even; + rho_hat_t(t + 2) = rho_hat_odd; + } + // convert initial positive sequence into an initial monotone sequence + if (rho_hat_t(t + 1) + rho_hat_t(t + 2) > rho_hat_t(t - 1) + rho_hat_t(t)) { + rho_hat_t(t + 1) = (rho_hat_t(t - 1) + rho_hat_t(t)) / 2; + rho_hat_t(t + 2) = rho_hat_t(t + 1); + } + t += 2; + } + + auto max_t = t; // max lag, used for truncation + // see discussion p. 8, par "In extreme antithetic cases, " + if (rho_hat_even > 0) { + rho_hat_t(max_t + 1) = rho_hat_even; + } + + double draws_total = num_chains * draws_per_chain; + // eqn (13): Geyer's truncation rule, w/ modification + double tau_hat = -1 + 2 * rho_hat_t.head(max_t).sum() + rho_hat_t(max_t + 1); + // safety check for negative values and with max ess equal to ess*log10(ess) + tau_hat = std::max(tau_hat, 1 / std::log10(draws_total)); + return (draws_total / tau_hat); +} + +} // namespace analyze +} // namespace stan + +#endif diff --git a/src/stan/analyze/mcmc/mcse.hpp b/src/stan/analyze/mcmc/mcse.hpp new file mode 100644 index 0000000000..040b94d9ea --- /dev/null +++ b/src/stan/analyze/mcmc/mcse.hpp @@ -0,0 +1,67 @@ +#ifndef STAN_ANALYZE_MCMC_MCSE_HPP +#define STAN_ANALYZE_MCMC_MCSE_HPP + +#include +#include +#include +#include +#include +#include +#include + +namespace stan { +namespace analyze { + +/** + * Computes the mean Monte Carlo error estimate for the central 90% interval. + * See https://arxiv.org/abs/1903.08008, section 4.4. + * Follows implementation in the R posterior package. + * + * @param chains matrix of draws across all chains + * @return mcse + */ +inline double mcse_mean(const Eigen::MatrixXd& chains) { + const Eigen::Index num_draws = chains.rows(); + if (chains.rows() < 4 || !is_finite_and_varies(chains)) + return std::numeric_limits::quiet_NaN(); + + double sample_var + = (chains.array() - chains.mean()).square().sum() / (chains.size() - 1); + return std::sqrt(sample_var / ess(chains)); +} + +/** + * Computes the standard deviation of the Monte Carlo error estimate + * https://arxiv.org/abs/1903.08008, section 4.4. + * Follows implementation in the R posterior package: + * https://github.com/stan-dev/posterior/blob/98bf52329d68f3307ac4ecaaea659276ee1de8df/R/convergence.R#L478-L496 + * + * @param chains matrix of draws across all chains + * @return mcse + */ +inline double mcse_sd(const Eigen::MatrixXd& chains) { + if (chains.rows() < 4 || !is_finite_and_varies(chains)) + return std::numeric_limits::quiet_NaN(); + + // center the data, take abs value + Eigen::MatrixXd draws_ctr = (chains.array() - chains.mean()).abs().matrix(); + + // posterior pkg fn `ess_mean` computes on split chains + double ess_mean = ess(split_chains(draws_ctr)); + + // estimated variance (2nd moment) + double Evar = draws_ctr.array().square().mean(); + + // variance of variance, adjusted for ESS + double fourth_moment = draws_ctr.array().pow(4).mean(); + double varvar = (fourth_moment - std::pow(Evar, 2)) / ess_mean; + + // variance of standard deviation - use Taylor series approximation + double varsd = varvar / Evar / 4.0; + return std::sqrt(varsd); +} + +} // namespace analyze +} // namespace stan + +#endif diff --git a/src/stan/analyze/mcmc/rank_normalization.hpp b/src/stan/analyze/mcmc/rank_normalization.hpp index d53a9acf56..f645b153bd 100644 --- a/src/stan/analyze/mcmc/rank_normalization.hpp +++ b/src/stan/analyze/mcmc/rank_normalization.hpp @@ -12,8 +12,9 @@ namespace stan { namespace analyze { /** - * Computes normalized average ranks for pooled draws. Normal scores computed - * using inverse normal transformation and a fractional offset. Based on paper + * Computes normalized average ranks for pooled draws. The values across + * all draws be finite and not constant. Normal scores computed using + * inverse normal transformation and a fractional offset. Based on paper * https://arxiv.org/abs/1903.08008 * * @param chains matrix of draws, one column per chain diff --git a/src/stan/analyze/mcmc/rhat.hpp b/src/stan/analyze/mcmc/rhat.hpp new file mode 100644 index 0000000000..ed5ed5cfc7 --- /dev/null +++ b/src/stan/analyze/mcmc/rhat.hpp @@ -0,0 +1,50 @@ +#ifndef STAN_ANALYZE_MCMC_RHAT_HPP +#define STAN_ANALYZE_MCMC_RHAT_HPP + +#include +#include +#include +#include +#include + +namespace stan { +namespace analyze { + +/** + * Computes square root of marginal posterior variance of the estimand by the + * weighted average of within-chain variance W and between-chain variance B. + * + * @param chains stores chains in columns + * @return square root of ((N-1)/N)W + B/N + */ +inline double rhat(const Eigen::MatrixXd& chains) { + const Eigen::Index num_chains = chains.cols(); + const Eigen::Index num_draws = chains.rows(); + + Eigen::RowVectorXd within_chain_means = chains.colwise().mean(); + double across_chain_mean = within_chain_means.mean(); + double between_variance + = num_draws + * (within_chain_means.array() - across_chain_mean).square().sum() + / (num_chains - 1); + double within_variance = + // Divide each row by chains and get sum of squares for each chain + // (getting a vector back) + ((chains.rowwise() - within_chain_means) + .array() + .square() + .colwise() + // divide each sum of square by num_draws, sum the sum of squares, + // and divide by num chains + .sum() + / (num_draws - 1.0)) + .sum() + / num_chains; + + return sqrt((between_variance / within_variance + num_draws - 1) / num_draws); +} + +} // namespace analyze +} // namespace stan + +#endif diff --git a/src/stan/analyze/mcmc/split_chains.hpp b/src/stan/analyze/mcmc/split_chains.hpp index a7fb1b8d61..fa89f6dc29 100644 --- a/src/stan/analyze/mcmc/split_chains.hpp +++ b/src/stan/analyze/mcmc/split_chains.hpp @@ -20,8 +20,9 @@ namespace analyze { inline Eigen::MatrixXd split_chains(const std::vector& chains, const int index) { size_t num_chains = chains.size(); - size_t num_samples = chains[0].rows(); - size_t half = std::floor(num_samples / 2.0); + size_t num_draws = chains[0].rows(); + size_t half = std::floor(num_draws / 2.0); + size_t tail_start = std::floor((num_draws + 1) / 2.0); Eigen::MatrixXd split_draws_matrix(half, num_chains * 2); int split_i = 0; @@ -29,7 +30,7 @@ inline Eigen::MatrixXd split_chains(const std::vector& chains, Eigen::Map head_block(chains[i].col(index).data(), half); Eigen::Map tail_block( - chains[i].col(index).data() + half, half); + chains[i].col(index).data() + tail_start, half); split_draws_matrix.col(split_i) = head_block; split_draws_matrix.col(split_i + 1) = tail_block; @@ -47,15 +48,16 @@ inline Eigen::MatrixXd split_chains(const std::vector& chains, */ inline Eigen::MatrixXd split_chains(const Eigen::MatrixXd& samples) { size_t num_chains = samples.cols(); - size_t num_samples = samples.rows(); - size_t half = std::floor(num_samples / 2.0); + size_t num_draws = samples.rows(); + size_t half = std::floor(num_draws / 2.0); + size_t tail_start = std::floor((num_draws + 1) / 2.0); Eigen::MatrixXd split_draws_matrix(half, num_chains * 2); int split_i = 0; for (std::size_t i = 0; i < num_chains; ++i) { Eigen::Map head_block(samples.col(i).data(), half); - Eigen::Map tail_block(samples.col(i).data() + half, - half); + Eigen::Map tail_block( + samples.col(i).data() + tail_start, half); split_draws_matrix.col(split_i) = head_block; split_draws_matrix.col(split_i + 1) = tail_block; diff --git a/src/stan/analyze/mcmc/split_rank_normalized_ess.hpp b/src/stan/analyze/mcmc/split_rank_normalized_ess.hpp index 6a26490147..e0c6316cc7 100644 --- a/src/stan/analyze/mcmc/split_rank_normalized_ess.hpp +++ b/src/stan/analyze/mcmc/split_rank_normalized_ess.hpp @@ -2,108 +2,20 @@ #define STAN_ANALYZE_MCMC_SPLIT_RANK_NORMALIZED_ESS_HPP #include +#include #include #include #include -#include -#include -#include -#include -#include -#include -#include #include -#include namespace stan { namespace analyze { -/** - * Computes the effective sample size (ESS) for the specified - * parameter across all chains. The number of draws per chain must be > 3, - * and the values across all draws must be finite and not constant. - * The value returned is the minimum of ESS and (sample_sz * log10(sample_sz). - * Sample autocovariance is computed using Stan math library implmentation. - * See https://arxiv.org/abs/1903.08008, section 3.2 for discussion. - * - * @param chains matrix of draws across all chains - * @return effective sample size for the specified parameter - */ -double ess(const Eigen::MatrixXd& chains) { - const Eigen::Index num_chains = chains.cols(); - const Eigen::Index num_draws = chains.rows(); - Eigen::MatrixXd acov(num_draws, num_chains); - Eigen::VectorXd chain_mean(num_chains); - Eigen::VectorXd chain_var(num_chains); - - // compute the per-chain autocovariance - for (size_t i = 0; i < num_chains; ++i) { - Eigen::Map chain_col(chains.col(i).data(), - num_draws); - Eigen::Map cov_col(acov.col(i).data(), num_draws); - stan::math::autocovariance(chain_col, cov_col); - chain_mean(i) = chain_col.mean(); - chain_var(i) = cov_col(0) * num_draws / (num_draws - 1); - } - - // compute var_plus, eqn (3) - double w_chain_var = math::mean(chain_var); // W (within chain var) - double var_plus = w_chain_var * (num_draws - 1) / num_draws; // \hat{var}^{+} - if (num_chains > 1) { - var_plus += math::variance(chain_mean); // B (between chain var) - } - - // Geyer's initial positive sequence, eqn (11) - Eigen::VectorXd rho_hat_t = Eigen::VectorXd::Zero(num_draws); - Eigen::VectorXd acov_t(num_chains); - double rho_hat_even = 1.0; - rho_hat_t(0) = rho_hat_even; // lag 0 - double rho_hat_odd = 1 - (w_chain_var - acov.row(1).mean()) / var_plus; - rho_hat_t(1) = rho_hat_odd; // lag 1 - - // compute autocorrelation at lag t for pair (t, t+1) - // paired autocorrelation is guaranteed to be positive, monotone and convex - size_t t = 1; - while (t < num_draws - 4 && (rho_hat_even + rho_hat_odd > 0) - && !std::isnan(rho_hat_even + rho_hat_odd)) { - for (size_t i = 0; i < num_chains; ++i) { - acov_t(i) = acov.col(i)(t + 1); - } - rho_hat_even = 1 - (w_chain_var - acov_t.mean()) / var_plus; - for (size_t i = 0; i < num_chains; ++i) { - acov_t(i) = acov.col(i)(t + 2); - } - rho_hat_odd = 1 - (w_chain_var - acov_t.mean()) / var_plus; - if ((rho_hat_even + rho_hat_odd) >= 0) { - rho_hat_t(t + 1) = rho_hat_even; - rho_hat_t(t + 2) = rho_hat_odd; - } - // convert initial positive sequence into an initial monotone sequence - if (rho_hat_t(t + 1) + rho_hat_t(t + 2) > rho_hat_t(t - 1) + rho_hat_t(t)) { - rho_hat_t(t + 1) = (rho_hat_t(t - 1) + rho_hat_t(t)) / 2; - rho_hat_t(t + 2) = rho_hat_t(t + 1); - } - t += 2; - } - - auto max_t = t; // max lag, used for truncation - // see discussion p. 8, par "In extreme antithetic cases, " - if (rho_hat_even > 0) { - rho_hat_t(max_t + 1) = rho_hat_even; - } - - double num_samples = num_chains * num_draws; - // eqn (13): Geyer's truncation rule, w/ modification - double tau_hat = -1 + 2 * rho_hat_t.head(max_t).sum() + rho_hat_t(max_t + 1); - // safety check for negative values and with max ess equal to ess*log10(ess) - tau_hat = std::max(tau_hat, 1 / std::log10(num_samples)); - return (num_samples / tau_hat); -} - /** * Computes the split effective sample size (split ESS) using rank based * diagnostic for a set of per-chain draws. Based on paper - * https://arxiv.org/abs/1903.08008 + * https://arxiv.org/abs/1903.08008 Computes bulk ESS over entire sample, + * and tail ESS over the 0.05 and 0.95 quantiles. * * When the number of total draws N is odd, the last draw is ignored. * @@ -111,7 +23,7 @@ double ess(const Eigen::MatrixXd& chains) { * Scale Reduction". http://mc-stan.org/users/documentation * @param chains matrix of per-chain draws, num_iters X chain - * @return potential scale reduction + * @return pair ESS_bulk, ESS_tail */ inline std::pair split_rank_normalized_ess( const Eigen::MatrixXd& chains) { diff --git a/src/stan/analyze/mcmc/split_rank_normalized_rhat.hpp b/src/stan/analyze/mcmc/split_rank_normalized_rhat.hpp index b071668801..50cce0b64d 100644 --- a/src/stan/analyze/mcmc/split_rank_normalized_rhat.hpp +++ b/src/stan/analyze/mcmc/split_rank_normalized_rhat.hpp @@ -1,56 +1,17 @@ #ifndef STAN_ANALYZE_MCMC_SPLIT_RANK_NORMALIZED_RHAT_HPP #define STAN_ANALYZE_MCMC_SPLIT_RANK_NORMALIZED_RHAT_HPP -#include #include #include +#include #include -#include -#include -#include -#include -#include -#include -#include +#include #include +#include namespace stan { namespace analyze { -/** - * Computes square root of marginal posterior variance of the estimand by the - * weighted average of within-chain variance W and between-chain variance B. - * - * @param chains stores chains in columns - * @return square root of ((N-1)/N)W + B/N - */ -inline double rhat(const Eigen::MatrixXd& chains) { - const Eigen::Index num_chains = chains.cols(); - const Eigen::Index num_draws = chains.rows(); - - Eigen::RowVectorXd within_chain_means = chains.colwise().mean(); - double across_chain_mean = within_chain_means.mean(); - double between_variance - = num_draws - * (within_chain_means.array() - across_chain_mean).square().sum() - / (num_chains - 1); - double within_variance = - // Divide each row by chains and get sum of squares for each chain - // (getting a vector back) - ((chains.rowwise() - within_chain_means) - .array() - .square() - .colwise() - // divide each sum of square by num_draws, sum the sum of squares, - // and divide by num chains - .sum() - / (num_draws - 1.0)) - .sum() - / num_chains; - - return sqrt((between_variance / within_variance + num_draws - 1) / num_draws); -} - /** * Computes the split potential scale reduction (split Rhat) using rank based * diagnostic for a set of per-chain draws. Based on paper diff --git a/src/stan/io/stan_csv_reader.hpp b/src/stan/io/stan_csv_reader.hpp index a5dfc9f0d0..22a11947a4 100644 --- a/src/stan/io/stan_csv_reader.hpp +++ b/src/stan/io/stan_csv_reader.hpp @@ -377,7 +377,7 @@ class stan_csv_reader { if (!read_samples(in, data.samples, data.timing)) { if (out) - *out << "Unable to parse sample" << std::endl; + *out << "No draws found" << std::endl; } return data; } diff --git a/src/stan/mcmc/chainset.hpp b/src/stan/mcmc/chainset.hpp new file mode 100644 index 0000000000..9eb9369621 --- /dev/null +++ b/src/stan/mcmc/chainset.hpp @@ -0,0 +1,486 @@ +#ifndef STAN_MCMC_CHAINSET_HPP +#define STAN_MCMC_CHAINSET_HPP + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace stan { +namespace mcmc { + +/** + * A mcmc::chainset object manages the post-warmup draws + * across a set of MCMC chains, which all have the same number of samples. + * + * @note samples are stored in column major, i.e., each column corresponds to + * an output variable (element). + * + */ +class chainset { + private: + size_t num_samples_; + std::vector param_names_; + std::vector chains_; + + public: + /* Construct a chainset from a single sample. + * Throws execption if sample is empty. + */ + explicit chainset(const stan::io::stan_csv& stan_csv) { + if (chains_.size() > 0) { + throw std::invalid_argument("Cannot re-initialize chains object"); + } + if (stan_csv.header.size() == 0 || stan_csv.samples.rows() == 0) { + throw std::invalid_argument("Error: empty sample"); + } + param_names_ = stan_csv.header; + num_samples_ = stan_csv.samples.rows(); + chains_.push_back(stan_csv.samples); + } + + /* Construct a chainset from a set of samples. + * Throws execption if sample column names and shapes don't match. + */ + explicit chainset(const std::vector& stan_csv) { + if (stan_csv.empty()) + return; + if (chains_.size() > 0) { + throw std::invalid_argument("Cannot re-initialize chains object"); + } + if (stan_csv[0].header.size() == 0 || stan_csv[0].samples.rows() == 0) { + throw std::invalid_argument("Error: empty sample"); + } + param_names_ = stan_csv[0].header; + num_samples_ = stan_csv[0].samples.rows(); + chains_.push_back(stan_csv[0].samples); + std::stringstream ss; + for (size_t i = 1; i < stan_csv.size(); ++i) { + if (stan_csv[i].header.size() != param_names_.size()) { + ss << "Error: chain " << (i + 1) << " missing or extra columns"; + throw std::invalid_argument(ss.str()); + } + for (int j = 0; j < param_names_.size(); j++) { + if (param_names_[j] != stan_csv[i].header[j]) { + ss << "Error: chain " << (i + 1) << " header column " << (j + 1) + << " doesn't match chain 1 header, found: " + << stan_csv[i].header[j] << " expecting: " << param_names_[j]; + throw std::invalid_argument(ss.str()); + } + } + if (stan_csv[i].samples.rows() != num_samples_) { + ss << "Error: chain " << (i + 1) << ", missing or extra rows."; + throw std::invalid_argument(ss.str()); + } + chains_.push_back(stan_csv[i].samples); + } + } + + /** + * Report number of chains in chainset. + * @return chainset size. + */ + inline int num_chains() const { return chains_.size(); } + + /** + * Report number of parameters per chain. + * @return size of parameter names vector. + */ + inline int num_params() const { return param_names_.size(); } + + /** + * Report number of samples (draws) per chain. + * @return rows per chain + */ + inline int num_samples() const { return num_samples_; } + + /** + * Get parameter names. + * @return vector of parameter names. + */ + const std::vector& param_names() const { return param_names_; } + + /** + * Get name of parameter at specified column index. + * Throws exception if index is out of bounds. + * + * @param index column index + * @return parameter name + */ + const std::string& param_name(int index) const { + if (index < 0 || index >= param_names_.size()) { + std::stringstream ss; + ss << "Bad index " << index << ", should be between 0 and " + << (param_names_.size() - 1); + throw std::invalid_argument(ss.str()); + } + return param_names_[index]; + } + + /** + * Get column index for specified parameter name. + * Throws exception if name not found. + * + * @param name parameter name + * @return column index + */ + int index(const std::string& name) const { + auto it = std::find(param_names_.begin(), param_names_.end(), name); + if (it == param_names_.end()) { + std::stringstream ss; + ss << "Unknown parameter name " << name; + throw std::invalid_argument(ss.str()); + } + return std::distance(param_names_.begin(), it); + } + + /** + * Assemble samples (draws) from specified column index across all chains + * into a matrix of samples X chain. + * Throws exception if column index is out of bounds. + * + * @param index column index + * @return matrix of draws across all chains + */ + Eigen::MatrixXd samples(const int index) const { + Eigen::MatrixXd result(num_samples(), chains_.size()); + if (index < 0 || index >= param_names_.size()) { + std::stringstream ss; + ss << "Bad index " << index << ", should be between 0 and " + << (param_names_.size() - 1); + throw std::invalid_argument(ss.str()); + } + for (int i = 0; i < chains_.size(); ++i) { + result.col(i) = chains_[i].col(index); + } + return result; + } + + /** + * Assemble samples (draws) from specified parameter name across all chains + * into a matrix of samples X chain. + * Throws exception if parameter name is not found. + * + * @param name parameter name + * @return matrix of draws across all chains + */ + Eigen::MatrixXd samples(const std::string& name) const { + return samples(index(name)); + } + + /** + * Compute mean value for specified parameter across all chains. + * + * @param index parameter index + * @return mean parameter value + */ + double mean(const int index) const { return samples(index).mean(); } + + /** + * Compute mean value for specified parameter across all chains. + * + * @param name parameter name + * @return mean parameter value + */ + double mean(const std::string& name) const { return mean(index(name)); } + + /** + * Compute sample variance for specified parameter across all chains. + * 1 / (N - 1) * sum((theta_n - mean(theta))^2) + * + * @param index parameter index + * @return sample variance + */ + double variance(const int index) const { + Eigen::MatrixXd draws = samples(index); + return (draws.array() - draws.mean()).square().sum() / (draws.size() - 1); + } + + /** + * Compute sample variance for specified parameter across all chains. + * 1 / (N - 1) * sum((theta_n - mean(theta))^2) + * + * @param name parameter name + * @return sample variance + */ + double variance(const std::string& name) const { + return variance(index(name)); + } + + /** + * Compute standard deviation for specified parameter across all chains. + * + * @param index parameter index + * @return sample sd + */ + double sd(const int index) const { return std::sqrt(variance(index)); } + + /** + * Compute standard deviation for specified parameter across all chains. + * + * @param name parameter name + * @return sample sd + */ + double sd(const std::string& name) const { return sd(index(name)); } + + /** + * Compute median value of specified parameter across all chains. + * + * @param index parameter index + * @return median + */ + double median(const int index) const { return (quantile(index, 0.5)); } + + /** + * Compute median value of specified parameter across all chains. + * + * @param name parameter name + * @return median + */ + double median(const std::string& name) const { return median(index(name)); } + + /** + * Compute maximum absolute deviation (mad) for specified parameter. + * + * Follows R implementation: constant * median(abs(x - center)) + * where the value of center is median(x) and the constant is 1.4826, + * a scale factor for asymptotically normal consistency: `1/qnorm(3/4)`. + * (R stats version 3.6.2) + * + * @param index parameter index + * @return sample mad + */ + double max_abs_deviation(const int index) const { + Eigen::MatrixXd draws = samples(index); + auto center = median(index); + Eigen::MatrixXd abs_dev = (draws.array() - center).abs(); + Eigen::Map map(abs_dev.data(), abs_dev.size()); + return 1.4826 * stan::math::quantile(map, 0.5); + } + + /** + * Compute maximum absolute deviation (mad) for specified parameter. + * + * Follows R implementation: constant * median(abs(x - center)) + * where the value of center is median(x) and the constant is 1.4826, + * a scale factor for asymptotically normal consistency: `1/qnorm(3/4)`. + * (R stats version 3.6.2) + * + * @param name parameter name + * @return sample mad + */ + double max_abs_deviation(const std::string& name) const { + return max_abs_deviation(index(name)); + } + + /** + * Compute the quantile value of the specified parameter + * at the specified probability. + * + * Calls stan::math::quantile which throws + * std::invalid_argument If any element of samples_vec is NaN, or size 0. + * and std::domain_error If `p<0` or `p>1`. + * + * @param index parameter index + * @param prob probability + * @return parameter value at quantile + */ + double quantile(const int index, const double prob) const { + // Ensure the probability is within [0, 1] + Eigen::MatrixXd draws = samples(index); + Eigen::Map map(draws.data(), draws.size()); + return stan::math::quantile(map, prob); + } + + /** + * Compute the quantile value of the specified parameter + * at the specified probability. + * + * @param name parameter name + * @param prob probability + * @return parameter value at quantile + */ + double quantile(const std::string& name, const double prob) const { + return quantile(index(name), prob); + } + + /** + * Compute the quantile values of the specified parameter + * for a set of specified probabilities. + * + * @param index parameter index + * @param probs vector of probabilities + * @return vector of parameter values for quantiles + */ + Eigen::VectorXd quantiles(const int index, + const Eigen::VectorXd& probs) const { + if (probs.size() == 0) + return Eigen::VectorXd::Zero(0); + Eigen::MatrixXd draws = samples(index); + Eigen::Map map(draws.data(), draws.size()); + std::vector probs_vec(probs.data(), probs.data() + probs.size()); + std::vector quantiles = stan::math::quantile(map, probs_vec); + return Eigen::Map(quantiles.data(), quantiles.size()); + } + + /** + * Compute the quantile values of the specified parameter + * for a set of specified probabilities. + * + * @param name parameter name + * @param probs vector of probabilities + * @return vector of parameter values for quantiles + */ + Eigen::VectorXd quantiles(const std::string& name, + const Eigen::VectorXd& probs) const { + return quantiles(index(name), probs); + } + + /** + * Computes the split potential scale reduction (split Rhat) using rank based + * diagnostic for a set of per-chain draws, for bulk and tail Rhat. + * Based on paper https://arxiv.org/abs/1903.08008 + * + * @param index parameter index + * @return pair (bulk_rhat, tail_rhat) + */ + std::pair split_rank_normalized_rhat(const int index) const { + return analyze::split_rank_normalized_rhat(samples(index)); + } + + /** + * Computes the split potential scale reduction (split Rhat) using rank based + * diagnostic for a set of per-chain draws, for bulk and tail Rhat. + * Based on paper https://arxiv.org/abs/1903.08008 + * + * @param name parameter name + * @return pair (bulk_rhat, tail_rhat) + */ + std::pair split_rank_normalized_rhat( + const std::string& name) const { + return split_rank_normalized_rhat(index(name)); + } + + /** + * Computes the effective sample size (ESS) for the specified + * parameter across all chains, according to the algorithm presented in + * https://arxiv.org/abs/1903.08008, section 3.2 for folded (split) + * rank-normalized ESS for both bulk and tail ESS. + * + * @param index parameter index + * @return pair (bulk_ess, tail_ess) + */ + std::pair split_rank_normalized_ess(const int index) const { + return analyze::split_rank_normalized_ess(samples(index)); + } + + /** + * Computes the effective sample size (ESS) for the specified + * parameter across all chains, according to the algorithm presented in + * https://arxiv.org/abs/1903.08008, section 3.2 for folded (split) + * rank-normalized ESS for both bulk and tail ESS. + * + * @param name parameter name + * @return pair (bulk_ess, tail_ess) + */ + std::pair split_rank_normalized_ess( + const std::string& name) const { + return split_rank_normalized_ess(index(name)); + } + + /** + * Computes the mean Monte Carlo error estimate for the central 90% interval. + * See https://arxiv.org/abs/1903.08008, section 4.4. + * Follows implementation in the R posterior package + * + * @param index parameter index + * @return mcse + */ + double mcse_mean(const int index) const { + return analyze::mcse_mean(samples(index)); + } + + /** + * Computes the mean Monte Carlo error estimate for the central 90% interval. + * See https://arxiv.org/abs/1903.08008, section 4.4. + * Follows implementation in the R posterior package. + * + * @param name parameter name + * @return mcse + */ + double mcse_mean(const std::string& name) const { + return mcse_mean(index(name)); + } + + /** + * Computes the standard deviation of the Monte Carlo error estimate + * https://arxiv.org/abs/1903.08008, section 4.4. + * Follows implementation in the R posterior package. + * + * @param index parameter index + * @return mcse_sd + */ + double mcse_sd(const int index) const { + return analyze::mcse_sd(samples(index)); + } + + /** + * Computes the standard deviation of the Monte Carlo error estimate + * https://arxiv.org/abs/1903.08008, section 4.4. + * Follows implementation in the R posterior package + * + * @param name parameter name + * @return mcse_sd + */ + double mcse_sd(const std::string& name) const { return mcse_sd(index(name)); } + + /** + * Compute autocorrelation for one column of one chain. + * Throws exception if column index is out of bounds. + * Autocorrelation is computed using Stan math library implmentation. + * + * @param chain chain index + * @param index column index + * @return vector of chain autocorrelation at all lags + */ + Eigen::VectorXd autocorrelation(const int chain, const int index) const { + Eigen::MatrixXd s = samples(index); + Eigen::Map chain_col(samples(chain).data(), + num_samples()); + Eigen::VectorXd autocorr_col(num_samples()); + stan::math::autocorrelation(s.col(chain), autocorr_col); + return autocorr_col; + } + + /** + * Compute autocorrelation for one column of one chain. + * Throws exception if column index is out of bounds. + * Autocorrelation is computed using Stan math library implmentation. + * + * @param chain chain index + * @param name column name + * @return vector of chain autocorrelation at all lags + */ + Eigen::VectorXd autocorrelation(const int chain, + const std::string name) const { + return autocorrelation(chain, index(name)); + } +}; + +} // namespace mcmc +} // namespace stan + +#endif diff --git a/src/test/unit/analyze/mcmc/check_chains_test.cpp b/src/test/unit/analyze/mcmc/check_chains_test.cpp index 4f3f9c579b..7d4c9107b1 100644 --- a/src/test/unit/analyze/mcmc/check_chains_test.cpp +++ b/src/test/unit/analyze/mcmc/check_chains_test.cpp @@ -41,7 +41,7 @@ TEST(CheckChains, good_and_bad) { EXPECT_TRUE(stan::analyze::is_finite_and_varies(chains)); } - // above test shows that column 7 is - make it non-finite + // above test shows that column 7 is OK - make it non-finite chain_1.col(0) = eight_schools_1.samples.col(7); chain_1(0, 0) = std::numeric_limits::infinity(); EXPECT_FALSE(stan::analyze::is_finite_and_varies(chain_1)); diff --git a/src/test/unit/analyze/mcmc/ess_basic_test.cpp b/src/test/unit/analyze/mcmc/ess_basic_test.cpp new file mode 100644 index 0000000000..2a1e910f59 --- /dev/null +++ b/src/test/unit/analyze/mcmc/ess_basic_test.cpp @@ -0,0 +1,53 @@ +#include +#include +#include +#include +#include +#include +#include + +class EssBasic : public testing::Test { + public: + void SetUp() { + chains_lp.resize(1000, 4); + chains_theta.resize(1000, 4); + draws_theta.resize(4); + draws_lp.resize(4); + sizes.resize(4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_lp.col(i) = bern_csv.samples.col(0); + chains_theta.col(i) = bern_csv.samples.col(7); + draws_lp[i] = chains_lp.col(i).data(); + draws_theta[i] = chains_theta.col(i).data(); + sizes[i] = 1000; + } + } + + void TearDown() {} + + std::stringstream out; + Eigen::MatrixXd chains_lp; + Eigen::MatrixXd chains_theta; + std::vector draws_theta; + std::vector draws_lp; + std::vector sizes; +}; + +TEST_F(EssBasic, test_basic_ess) { + // computed via cmdstan 2.35.0 stansummary + double ess_lp_expect = 1335.41366787; + double ess_theta_expect = 1377.5030282; + + auto ess_basic_lp = stan::analyze::ess(chains_lp); + auto ess_basic_theta = stan::analyze::ess(chains_theta); + + EXPECT_NEAR(ess_lp_expect, ess_basic_lp, 1e-8); + EXPECT_NEAR(ess_theta_expect, ess_basic_theta, 1e-8); +} diff --git a/src/test/unit/analyze/mcmc/mcse_test.cpp b/src/test/unit/analyze/mcmc/mcse_test.cpp new file mode 100644 index 0000000000..5b962ec3eb --- /dev/null +++ b/src/test/unit/analyze/mcmc/mcse_test.cpp @@ -0,0 +1,73 @@ +#include +#include +#include +#include +#include +#include +#include + +class MonteCarloStandardError : public testing::Test { + public: + void SetUp() { + chains_lp.resize(1000, 4); + chains_theta.resize(1000, 4); + chains_divergent.resize(1000, 4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_lp.col(i) = bern_csv.samples.col(0); + chains_theta.col(i) = bern_csv.samples.col(7); + chains_divergent.col(i) = bern_csv.samples.col(5); + } + } + + void TearDown() {} + + std::stringstream out; + Eigen::MatrixXd chains_lp; + Eigen::MatrixXd chains_theta; + Eigen::MatrixXd chains_divergent; +}; + +TEST_F(MonteCarloStandardError, test_mcse) { + // computed via R pkg posterior + double mcse_mean_lp_expect = 0.02016; + double mcse_mean_theta_expect = 0.00323; + + double mcse_sd_lp_expect = 0.03553; + double mcse_sd_theta_expect = 0.00216; + EXPECT_NEAR(mcse_mean_lp_expect, stan::analyze::mcse_mean(chains_lp), 1e-4); + EXPECT_NEAR(mcse_mean_theta_expect, stan::analyze::mcse_mean(chains_theta), + 1e-4); + + EXPECT_NEAR(mcse_sd_lp_expect, stan::analyze::mcse_sd(chains_lp), 1e-4); + EXPECT_NEAR(mcse_sd_theta_expect, stan::analyze::mcse_sd(chains_theta), 1e-4); +} + +TEST_F(MonteCarloStandardError, const_fail) { + auto mcse_mean = stan::analyze::mcse_mean(chains_divergent); + auto mcse_sd = stan::analyze::mcse_sd(chains_divergent); + EXPECT_TRUE(std::isnan(mcse_mean)); + EXPECT_TRUE(std::isnan(mcse_sd)); +} + +TEST_F(MonteCarloStandardError, inf_fail) { + chains_theta(0, 0) = std::numeric_limits::infinity(); + auto mcse_mean = stan::analyze::mcse_mean(chains_theta); + auto mcse_sd = stan::analyze::mcse_sd(chains_theta); + EXPECT_TRUE(std::isnan(mcse_mean)); + EXPECT_TRUE(std::isnan(mcse_sd)); +} + +TEST_F(MonteCarloStandardError, short_chains_fail) { + chains_theta.resize(3, 4); + auto mcse_mean = stan::analyze::mcse_mean(chains_theta); + auto mcse_sd = stan::analyze::mcse_sd(chains_theta); + EXPECT_TRUE(std::isnan(mcse_mean)); + EXPECT_TRUE(std::isnan(mcse_sd)); +} diff --git a/src/test/unit/analyze/mcmc/rank_normalization_test.cpp b/src/test/unit/analyze/mcmc/rank_normalization_test.cpp new file mode 100644 index 0000000000..f4776d3443 --- /dev/null +++ b/src/test/unit/analyze/mcmc/rank_normalization_test.cpp @@ -0,0 +1,85 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include + +class RankNormalization : public testing::Test { + public: + void SetUp() { + chains_theta.resize(1000, 4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_theta.col(i) = bern_csv.samples.col(7); + } + } + + void TearDown() {} + + std::stringstream out; + Eigen::MatrixXd chains_theta; +}; + +TEST_F(RankNormalization, test_min_max) { + Eigen::Index maxRow, maxCol; + Eigen::Index minRow, minCol; + double max_val = chains_theta.maxCoeff(&maxRow, &maxCol); + double min_val = chains_theta.minCoeff(&minRow, &minCol); + chains_theta(maxRow, maxCol) = 0.9999; + chains_theta(minRow, minCol) = 0.001; + + auto rank_norm_theta = stan::analyze::rank_transform(chains_theta); + + Eigen::Index maxRowRankNorm, maxColRankNorm; + Eigen::Index minRowRankNorm, minColRankNorm; + max_val = rank_norm_theta.maxCoeff(&maxRowRankNorm, &maxColRankNorm); + min_val = rank_norm_theta.minCoeff(&minRowRankNorm, &minColRankNorm); + EXPECT_EQ(maxRow, maxRowRankNorm); + EXPECT_EQ(minRow, minRowRankNorm); + EXPECT_EQ(maxCol, maxColRankNorm); + EXPECT_EQ(minCol, minColRankNorm); +} + +TEST_F(RankNormalization, test_symmetry) { + Eigen::MatrixXd foo(8, 2); + int val = 0; + for (size_t col = 0; col < 2; ++col) { + for (size_t row = 0; row < 8; ++row) { + val++; + foo(row, col) = val; + } + } + auto bar = stan::analyze::rank_transform(foo); + size_t rev_col = 2; + for (size_t col = 0; col < 2; ++col) { + --rev_col; + size_t rev_row = 8; + for (size_t row = 0; row < 8; ++row) { + --rev_row; + EXPECT_NEAR(bar(row, col), -bar(rev_row, rev_col), 1e-10); + } + } + + Eigen::MatrixXd baz(2, 8); + for (size_t col = 0; col < 8; ++col) { + int val = 0; + for (size_t row = 0; row < 2; ++row) { + val++; + baz(row, col) = val; + } + } + auto boz = stan::analyze::rank_transform(baz); + for (size_t col = 0; col < 8; ++col) { + EXPECT_NEAR(boz(0, col), -boz(1, col), 1e-10); + } +} diff --git a/src/test/unit/analyze/mcmc/rhat_basic_test.cpp b/src/test/unit/analyze/mcmc/rhat_basic_test.cpp new file mode 100644 index 0000000000..a825ecb820 --- /dev/null +++ b/src/test/unit/analyze/mcmc/rhat_basic_test.cpp @@ -0,0 +1,54 @@ +#include +#include +#include +#include +#include +#include + +class RhatBasic : public testing::Test { + public: + void SetUp() { + chains_lp.resize(1000, 4); + chains_theta.resize(1000, 4); + draws_theta.resize(4); + draws_lp.resize(4); + sizes.resize(4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_lp.col(i) = bern_csv.samples.col(0); + chains_theta.col(i) = bern_csv.samples.col(7); + draws_lp[i] = chains_lp.col(i).data(); + draws_theta[i] = chains_theta.col(i).data(); + sizes[i] = 1000; + } + } + + void TearDown() {} + + std::stringstream out; + Eigen::MatrixXd chains_lp; + Eigen::MatrixXd chains_theta; + std::vector draws_theta; + std::vector draws_lp; + std::vector sizes; +}; + +TEST_F(RhatBasic, test_basic_rhat) { + // computed via cmdstan 2.35.0 stansummary + double rhat_lp_basic_expect = 1.00035489482; + double rhat_theta_basic_expect = 1.00721797217; + + auto rhat_basic_lp + = stan::analyze::rhat(stan::analyze::split_chains(chains_lp)); + auto rhat_basic_theta + = stan::analyze::rhat(stan::analyze::split_chains(chains_theta)); + + EXPECT_NEAR(rhat_lp_basic_expect, rhat_basic_lp, 1e-10); + EXPECT_NEAR(rhat_theta_basic_expect, rhat_basic_theta, 1e-10); +} diff --git a/src/test/unit/analyze/mcmc/split_chains_test.cpp b/src/test/unit/analyze/mcmc/split_chains_test.cpp index 55fb46725f..a027937da7 100644 --- a/src/test/unit/analyze/mcmc/split_chains_test.cpp +++ b/src/test/unit/analyze/mcmc/split_chains_test.cpp @@ -109,3 +109,49 @@ TEST_F(SplitChains, split_chains_convenience) { } } } + +TEST_F(SplitChains, split_draws_matrix_odd_rows) { + // When the number of total draws N is odd, the (N+1)/2th draw is ignored. + Eigen::MatrixXd foo(7, 2); + int val = 0; + for (size_t col = 0; col < 2; ++col) { + for (size_t row = 0; row < 7; ++row) { + val += 1; + foo(row, col) = val; + } + } + auto bar = stan::analyze::split_chains(foo); + EXPECT_EQ(4, bar.cols()); + EXPECT_EQ(3, bar.rows()); + EXPECT_EQ(bar(0, 0), 1); + EXPECT_EQ(bar(1, 0), 2); + EXPECT_EQ(bar(2, 0), 3); + EXPECT_EQ(bar(0, 1), 5); + EXPECT_EQ(bar(1, 1), 6); + EXPECT_EQ(bar(2, 1), 7); + EXPECT_EQ(bar(0, 2), 8); + EXPECT_EQ(bar(1, 2), 9); + EXPECT_EQ(bar(2, 2), 10); + EXPECT_EQ(bar(0, 3), 12); + EXPECT_EQ(bar(1, 3), 13); + EXPECT_EQ(bar(2, 3), 14); + + Eigen::MatrixXd baz(4, 2); + for (size_t col = 0; col < 2; ++col) { + for (size_t row = 0; row < 4; ++row) { + val += 1; + baz(row, col) = val; + } + } + auto boz = stan::analyze::split_chains(baz); + EXPECT_EQ(4, boz.cols()); + EXPECT_EQ(2, boz.rows()); + EXPECT_EQ(boz(0, 0), 15); + EXPECT_EQ(boz(1, 0), 16); + EXPECT_EQ(boz(0, 1), 17); + EXPECT_EQ(boz(1, 1), 18); + EXPECT_EQ(boz(0, 2), 19); + EXPECT_EQ(boz(1, 2), 20); + EXPECT_EQ(boz(0, 3), 21); + EXPECT_EQ(boz(1, 3), 22); +} diff --git a/src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp b/src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp index 654a2fd168..44c823f5f2 100644 --- a/src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp +++ b/src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp @@ -6,63 +6,68 @@ #include #include -TEST(RankNormalizedEss, compute_split_rank_normalized_ess) { +class RankNormalizedEss : public testing::Test { + public: + void SetUp() { + chains_lp.resize(1000, 4); + chains_theta.resize(1000, 4); + chains_divergent.resize(1000, 4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_lp.col(i) = bern_csv.samples.col(0); + chains_theta.col(i) = bern_csv.samples.col(7); + chains_divergent.col(i) = bern_csv.samples.col(5); + } + } + + void TearDown() {} + std::stringstream out; - std::ifstream eight_schools_1_stream, eight_schools_2_stream; - stan::io::stan_csv eight_schools_1, eight_schools_2; - eight_schools_1_stream.open( - "src/test/unit/mcmc/test_csv_files/eight_schools_1.csv", - std::ifstream::in); - eight_schools_1 - = stan::io::stan_csv_reader::parse(eight_schools_1_stream, &out); - eight_schools_1_stream.close(); + Eigen::MatrixXd chains_lp; + Eigen::MatrixXd chains_theta; + Eigen::MatrixXd chains_divergent; +}; - eight_schools_2_stream.open( - "src/test/unit/mcmc/test_csv_files/eight_schools_2.csv", - std::ifstream::in); - eight_schools_2 - = stan::io::stan_csv_reader::parse(eight_schools_2_stream, &out); - eight_schools_2_stream.close(); +TEST_F(RankNormalizedEss, test_bulk_tail_ess) { + // computed via R pkg posterior + double ess_lp_bulk_expect = 1512.7684; + double ess_lp_tail_expect = 1591.9707; - // test against R implementation in pkg posterior (via cmdstanr) - Eigen::VectorXd ess_8_schools_bulk(10); - ess_8_schools_bulk << 348, 370, 600, 638, 765, 608, 629, 274, 517, 112; - Eigen::VectorXd ess_8_schools_tail(10); - ess_8_schools_tail << 845, 858, 874, 726, 620, 753, 826, 628, 587, 108; + double ess_theta_bulk_expect = 1407.5124; + double ess_theta_tail_expect = 1291.7131; - Eigen::MatrixXd chains(eight_schools_1.samples.rows(), 2); - for (size_t i = 0; i < 10; ++i) { - chains.col(0) = eight_schools_1.samples.col(i + 7); - chains.col(1) = eight_schools_2.samples.col(i + 7); - auto ess = stan::analyze::split_rank_normalized_ess(chains); - EXPECT_NEAR(ess.first, ess_8_schools_bulk(i), 5); - EXPECT_NEAR(ess.second, ess_8_schools_tail(i), 5); - } + auto ess_lp = stan::analyze::split_rank_normalized_ess(chains_lp); + auto ess_theta = stan::analyze::split_rank_normalized_ess(chains_theta); + + EXPECT_NEAR(ess_lp_bulk_expect, ess_lp.first, 1e-4); + EXPECT_NEAR(ess_lp_tail_expect, ess_lp.second, 1e-4); + + EXPECT_NEAR(ess_theta_bulk_expect, ess_theta.first, 1e-4); + EXPECT_NEAR(ess_theta_tail_expect, ess_theta.second, 1e-4); } -TEST(RankNormalizedEss, short_chains_fail) { - std::stringstream out; - std::ifstream eight_schools_5iters_1_stream, eight_schools_5iters_2_stream; - stan::io::stan_csv eight_schools_5iters_1, eight_schools_5iters_2; - eight_schools_5iters_1_stream.open( - "src/test/unit/mcmc/test_csv_files/eight_schools_5iters_1.csv", - std::ifstream::in); - eight_schools_5iters_1 - = stan::io::stan_csv_reader::parse(eight_schools_5iters_1_stream, &out); - eight_schools_5iters_1_stream.close(); - eight_schools_5iters_2_stream.open( - "src/test/unit/mcmc/test_csv_files/eight_schools_5iters_2.csv", - std::ifstream::in); - eight_schools_5iters_2 - = stan::io::stan_csv_reader::parse(eight_schools_5iters_2_stream, &out); - eight_schools_5iters_2_stream.close(); +TEST_F(RankNormalizedEss, const_fail) { + auto ess = stan::analyze::split_rank_normalized_ess(chains_divergent); + EXPECT_TRUE(std::isnan(ess.first)); + EXPECT_TRUE(std::isnan(ess.second)); +} - Eigen::MatrixXd chains(eight_schools_5iters_1.samples.rows(), 2); - for (size_t i = 0; i < 10; ++i) { - chains.col(0) = eight_schools_5iters_1.samples.col(i + 7); - chains.col(1) = eight_schools_5iters_2.samples.col(i + 7); - auto ess = stan::analyze::split_rank_normalized_ess(chains); - EXPECT_TRUE(std::isnan(ess.first)); - EXPECT_TRUE(std::isnan(ess.second)); - } +TEST_F(RankNormalizedEss, inf_fail) { + chains_theta(0, 0) = std::numeric_limits::infinity(); + auto ess = stan::analyze::split_rank_normalized_ess(chains_theta); + EXPECT_TRUE(std::isnan(ess.first)); + EXPECT_TRUE(std::isnan(ess.second)); +} + +TEST_F(RankNormalizedEss, short_chains_fail) { + chains_theta.resize(3, 4); + auto ess = stan::analyze::split_rank_normalized_ess(chains_theta); + EXPECT_TRUE(std::isnan(ess.first)); + EXPECT_TRUE(std::isnan(ess.second)); } diff --git a/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp b/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp index f0a46f2530..c9fd1ddb8d 100644 --- a/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp +++ b/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp @@ -1,65 +1,63 @@ #include #include #include +#include #include #include #include #include +#include + +class RankNormalizedRhat : public testing::Test { + public: + void SetUp() { + chains_lp.resize(1000, 4); + chains_theta.resize(1000, 4); + chains_divergent.resize(1000, 4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_lp.col(i) = bern_csv.samples.col(0); + chains_theta.col(i) = bern_csv.samples.col(7); + chains_divergent.col(i) = bern_csv.samples.col(5); + } + } + + void TearDown() {} -TEST(RankNormalizedRhat, compute_split_rank_normalized_rhat) { std::stringstream out; - std::ifstream eight_schools_1_stream; - stan::io::stan_csv eight_schools_1; - eight_schools_1_stream.open( - "src/test/unit/mcmc/test_csv_files/eight_schools_1.csv", - std::ifstream::in); - eight_schools_1 - = stan::io::stan_csv_reader::parse(eight_schools_1_stream, &out); - eight_schools_1_stream.close(); + Eigen::MatrixXd chains_lp; + Eigen::MatrixXd chains_theta; + Eigen::MatrixXd chains_divergent; +}; - // test against R implementation in pkg posterior - Eigen::VectorXd rhat_8_schools_1_bulk(10); - rhat_8_schools_1_bulk << 1.0012958313, 1.0046136496, 1.0085723580, - 1.0248629375, 1.0111456620, 1.0004458336, 0.9987162973, 1.0339773469, - 0.9985612618, 1.0281667351; +TEST_F(RankNormalizedRhat, test_bulk_tail_rhat) { + // computed via R pkg posterior + double rhat_lp_expect = 1.00073; + double rhat_theta_expect = 1.006789; - Eigen::VectorXd rhat_8_schools_1_tail(10); - rhat_8_schools_1_tail << 1.005676523, 1.009670999, 1.00184184, 1.002222679, - 1.004148161, 1.003218528, 1.009195353, 1.001426744, 1.003984381, - 1.025817745; + auto rhat_lp = stan::analyze::split_rank_normalized_rhat(chains_lp); + auto rhat_theta = stan::analyze::split_rank_normalized_rhat(chains_theta); - Eigen::MatrixXd chains(eight_schools_1.samples.rows(), 1); - for (size_t i = 0; i < 10; ++i) { - chains.col(0) = eight_schools_1.samples.col(i + 7); - auto rhats = stan::analyze::split_rank_normalized_rhat(chains); - EXPECT_NEAR(rhats.first, rhat_8_schools_1_bulk(i), 0.05); - EXPECT_NEAR(rhats.second, rhat_8_schools_1_tail(i), 0.05); - } + EXPECT_NEAR(rhat_lp_expect, std::max(rhat_lp.first, rhat_lp.second), 1e-5); + EXPECT_NEAR(rhat_theta_expect, std::max(rhat_theta.first, rhat_theta.second), + 1e-5); } -TEST(RankNormalizedRhat, const_fail) { - std::stringstream out; - std::ifstream bernoulli_const_1_stream, bernoulli_const_2_stream; - stan::io::stan_csv bernoulli_const_1, bernoulli_const_2; - bernoulli_const_1_stream.open( - "src/test/unit/mcmc/test_csv_files/bernoulli_const_1.csv", - std::ifstream::in); - bernoulli_const_1 - = stan::io::stan_csv_reader::parse(bernoulli_const_1_stream, &out); - bernoulli_const_1_stream.close(); - bernoulli_const_2_stream.open( - "src/test/unit/mcmc/test_csv_files/bernoulli_const_2.csv", - std::ifstream::in); - bernoulli_const_2 - = stan::io::stan_csv_reader::parse(bernoulli_const_2_stream, &out); - bernoulli_const_2_stream.close(); +TEST_F(RankNormalizedRhat, const_fail) { + auto rhat = stan::analyze::split_rank_normalized_rhat(chains_divergent); + EXPECT_TRUE(std::isnan(rhat.first)); + EXPECT_TRUE(std::isnan(rhat.second)); +} - Eigen::MatrixXd chains(bernoulli_const_1.samples.rows(), 2); - chains.col(0) - = bernoulli_const_1.samples.col(bernoulli_const_1.samples.cols() - 1); - chains.col(1) - = bernoulli_const_2.samples.col(bernoulli_const_2.samples.cols() - 1); - auto rhat = stan::analyze::split_rank_normalized_rhat(chains); +TEST_F(RankNormalizedRhat, inf_fail) { + chains_theta(0, 0) = std::numeric_limits::infinity(); + auto rhat = stan::analyze::split_rank_normalized_rhat(chains_theta); EXPECT_TRUE(std::isnan(rhat.first)); EXPECT_TRUE(std::isnan(rhat.second)); } diff --git a/src/test/unit/analyze/mcmc/test_csv_files/bern1.csv b/src/test/unit/analyze/mcmc/test_csv_files/bern1.csv new file mode 100644 index 0000000000..4daa3fc0f7 --- /dev/null +++ b/src/test/unit/analyze/mcmc/test_csv_files/bern1.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-17 18:13:44 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = /Users/mitzi/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 86520 +# output +# file = /Users/mitzi/github/stan-dev/cmdstanpy/test_csv_files/bernoulli-20241017141344_1.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.883328 +# Diagonal elements of inverse mass matrix: +# 0.512256 +-6.80778,0.991604,0.883328,2,3,0,6.81551,0.208532 +-6.80778,0.676659,0.883328,1,3,0,9.01984,0.208532 +-11.053,0.593184,0.883328,3,7,0,11.0857,0.0272877 +-10.8526,1,0.883328,1,1,0,11.3097,0.0293594 +-10.9957,0.926907,0.883328,2,7,0,11.355,0.661812 +-7.85065,1,0.883328,1,1,0,10.0946,0.457561 +-7.05998,1,0.883328,1,1,0,7.63241,0.356179 +-6.74806,0.998884,0.883328,2,3,0,7.0771,0.251099 +-6.77871,0.995524,0.883328,2,3,0,6.78513,0.281783 +-6.77684,1,0.883328,1,1,0,6.78461,0.28078 +-7.52399,0.771045,0.883328,1,3,0,8.22042,0.119146 +-8.05976,0.94003,0.883328,1,1,0,8.06699,0.0905514 +-6.75013,0.966227,0.883328,1,3,0,8.50437,0.241949 +-6.75182,0.99883,0.883328,1,3,0,6.75883,0.261 +-6.77286,0.985494,0.883328,2,3,0,6.84503,0.222854 +-6.74967,0.998274,0.883328,1,3,0,6.7864,0.257228 +-6.79941,0.964756,0.883328,2,3,0,6.97244,0.211425 +-6.91419,0.957459,0.883328,1,3,0,7.12266,0.326215 +-6.78633,0.974476,0.883328,1,3,0,7.07508,0.216511 +-6.78573,0.995433,0.883328,2,3,0,6.84195,0.216764 +-7.85073,0.799746,0.883328,1,3,0,8.22993,0.100264 +-7.98366,0.988934,0.883328,2,7,0,7.98371,0.470442 +-7.21863,1,0.883328,2,3,0,7.7957,0.382034 +-7.21863,0.213664,0.883328,2,3,0,13.1076,0.382034 +-6.80613,0.951776,0.883328,1,3,0,7.51243,0.209083 +-6.7796,0.989672,0.883328,1,3,0,6.90754,0.282255 +-6.92089,0.94385,0.883328,2,3,0,7.12942,0.181708 +-7.47031,0.931841,0.883328,2,3,0,7.67456,0.122833 +-6.85826,0.941179,0.883328,1,3,0,8.17326,0.311509 +-6.81941,1,0.883328,1,1,0,6.85828,0.299087 +-6.81618,1,0.883328,2,7,0,6.81777,0.205848 +-6.82389,0.92142,0.883328,2,3,0,7.4697,0.203538 +-6.76346,0.99761,0.883328,2,3,0,6.84805,0.228476 +-7.43915,0.862823,0.883328,1,3,0,7.66908,0.12507 +-7.08252,1,0.883328,1,1,0,7.38678,0.158088 +-8.35252,0.851521,0.883328,1,3,0,8.52579,0.0791085 +-6.90852,0.962927,0.883328,1,3,0,8.48705,0.184002 +-7.16205,0.978312,0.883328,2,7,0,7.1761,0.373355 +-8.37042,0.703747,0.883328,1,1,0,8.37741,0.50435 +-6.76884,0.912248,0.883328,2,3,0,9.00843,0.276067 +-6.76884,0.390809,0.883328,1,3,0,11.1779,0.276067 +-6.82492,0.824204,0.883328,2,3,0,7.99312,0.203239 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(Warm-up) +# 0.009 seconds (Sampling) +# 0.012 seconds (Total) +# diff --git a/src/test/unit/analyze/mcmc/test_csv_files/bern2.csv b/src/test/unit/analyze/mcmc/test_csv_files/bern2.csv new file mode 100644 index 0000000000..a92c9624ac --- /dev/null +++ b/src/test/unit/analyze/mcmc/test_csv_files/bern2.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-17 18:13:44 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 2 +# data +# file = /Users/mitzi/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 86520 +# output +# file = /Users/mitzi/github/stan-dev/cmdstanpy/test_csv_files/bernoulli-20241017141344_2.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.911571 +# Diagonal elements of inverse mass matrix: +# 0.568283 +-9.4699,1,0.911571,2,3,0,9.88038,0.0495852 +-8.60156,1,0.911571,1,1,0,9.43576,0.0708923 +-6.90138,0.862296,0.911571,2,7,0,9.70197,0.323078 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/dev/null +++ b/src/test/unit/analyze/mcmc/test_csv_files/bern3.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-17 18:13:44 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 3 +# data +# file = /Users/mitzi/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 86520 +# output +# file = /Users/mitzi/github/stan-dev/cmdstanpy/test_csv_files/bernoulli-20241017141344_3.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.889047 +# Diagonal elements of inverse mass matrix: +# 0.568698 +-8.55171,0.835828,0.889047,2,3,0,8.79251,0.0724419 +-9.14968,0.951918,0.889047,1,1,0,9.22155,0.0563692 +-6.74914,0.962544,0.889047,1,3,0,9.97683,0.244117 +-7.2825,0.895266,0.889047,2,3,0,7.54537,0.39127 +-6.89863,1,0.889047,1,1,0,7.17236,0.322391 +-6.89863,0.686169,0.889047,1,3,0,8.52937,0.322391 +-7.04084,0.883091,0.889047,1,3,0,7.5211,0.163334 +-7.15265,0.980207,0.889047,1,1,0,7.1846,0.150147 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2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-17 18:13:44 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 4 +# data +# file = /Users/mitzi/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 86520 +# output +# file = /Users/mitzi/github/stan-dev/cmdstanpy/test_csv_files/bernoulli-20241017141344_4.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.933117 +# Diagonal elements of inverse mass matrix: +# 0.524582 +-7.19125,0.977404,0.933117,1,1,0,7.22452,0.146161 +-9.45173,0.793941,0.933117,2,3,0,9.72516,0.0499428 +-8.92419,1,0.933117,1,1,0,9.52673,0.0618385 +-8.93603,0.999061,0.933117,1,1,0,9.22216,0.0615352 +-8.55245,1,0.933117,1,1,0,9.03101,0.0724187 +-7.16094,0.95946,0.933117,1,3,0,8.4516,0.149269 +-7.1408,1,0.933117,1,1,0,7.23097,0.151421 +-6.87906,0.985653,0.933117,2,3,0,7.32273,0.18992 +-6.81664,1,0.933117,1,1,0,6.87006,0.205706 +-6.93808,0.97414,0.933117,1,1,0,6.93857,0.178676 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+-7.05621,1,0.933117,1,1,0,7.17359,0.161348 +-7.00444,1,0.933117,1,1,0,7.08692,0.168318 +-9.45746,0.611648,0.933117,1,3,0,10.5904,0.580969 +-8.53314,1,0.933117,1,1,0,9.60608,0.517339 +-7.03412,1,0.933117,2,3,0,8.356,0.164225 +-6.85584,0.958689,0.933117,2,7,0,7.33865,0.310802 +-6.79446,0.981094,0.933117,1,3,0,6.97699,0.213254 +-6.79756,0.98851,0.933117,1,3,0,6.89726,0.29065 +-6.92037,0.944995,0.933117,1,3,0,7.11814,0.181803 +-7.58146,0.908818,0.933117,1,3,0,7.61075,0.115411 +-6.88438,0.987375,0.933117,1,3,0,7.49441,0.188796 +-6.89473,0.997839,0.933117,1,1,0,6.92264,0.186684 +-6.85119,1,0.933117,1,1,0,6.8991,0.196278 +-7.00353,0.989641,0.933117,2,3,0,7.005,0.168448 +-7.04386,0.992385,0.933117,1,1,0,7.0871,0.162939 +-7.61613,0.919816,0.933117,1,3,0,7.61852,0.113254 +-6.74817,1,0.933117,2,3,0,7.47342,0.247859 +-7.47698,0.838765,0.933117,1,3,0,7.58682,0.416618 +-9.18756,0.559437,0.933117,1,1,0,9.26535,0.563898 +-7.87144,1,0.933117,2,7,0,8.89897,0.0992328 +-6.77539,0.925126,0.933117,1,3,0,8.29713,0.279974 +-7.8816,0.819045,0.933117,2,3,0,7.8955,0.0987325 +-6.78475,1,0.933117,2,3,0,7.77764,0.217185 +-6.80888,0.98569,0.933117,1,3,0,6.89968,0.2952 +-7.14637,0.931089,0.933117,1,3,0,7.14649,0.370854 +-6.74851,0.983012,0.933117,2,3,0,7.25722,0.246125 +-6.76365,0.996602,0.933117,1,3,0,6.76531,0.228345 +# +# Elapsed Time: 0.003 seconds (Warm-up) +# 0.009 seconds (Sampling) +# 0.012 seconds (Total) +# diff --git a/src/test/unit/analyze/mcmc/test_csv_files/cmdstan-2-35-0-stansummary.csv b/src/test/unit/analyze/mcmc/test_csv_files/cmdstan-2-35-0-stansummary.csv new file mode 100644 index 0000000000..7537b4cb0b --- /dev/null +++ b/src/test/unit/analyze/mcmc/test_csv_files/cmdstan-2-35-0-stansummary.csv @@ -0,0 +1,18 @@ +name,Mean,MCSE,StdDev,5%,50%,95%,N_Eff,N_Eff/s,R_hat +"lp__",-7.2867579275,0.0202120721354,0.738616062616,-8.7825,-6.99001,-6.7503,1335.41366787,35142.4649438,1.00035489482 +"accept_stat__",0.92587778475,0.00177759974411,0.116280973122,0.672417,0.974476,1,4279.0696651,112607.09645,1.00155471132 +"stepsize__",0.90426575,0.0139260619795,0.0197240339958,0.883328,0.911571,0.933117,2.00601805416,52.7899487937,6.91946682912e+12 +"treedepth__",1.3515,0.00798399449516,0.479066932987,1,1,2,3600.40978064,94747.6258062,1.00017741661 +"n_leapfrog__",2.515,0.0230857538371,1.31423240019,1,3,3,3240.82976278,85284.9937574,1.00632559786 +"divergent__",0,nan,0,0,0,0,nan,nan,nan +"energy__",7.76890199,0.0271304159659,1.00790019677,6.80125,7.46009,9.72873,1380.13686038,36319.3910627,1.00204956973 +"theta",0.251297410482,0.00327489095264,0.121546686658,0.0769898,0.237539,0.473863,1377.5030282,36250.0796895,1.00721797217 +# Inference for Stan model: bernoulli_model +# 4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved. +# +# Warmup took (0.0030, 0.0030, 0.0040, 0.0030) seconds, 0.013 seconds total +# Sampling took (0.0090, 0.010, 0.010, 0.0090) seconds, 0.038 seconds total +# Samples were drawn using hmc with nuts. +# For each parameter, N_Eff is a crude measure of effective sample size, +# and R_hat is the potential scale reduction factor on split chains (at +# convergence, R_hat=1). diff --git a/src/test/unit/io/stan_csv_reader_test.cpp b/src/test/unit/io/stan_csv_reader_test.cpp index ec91cf8684..864bd1512e 100644 --- a/src/test/unit/io/stan_csv_reader_test.cpp +++ b/src/test/unit/io/stan_csv_reader_test.cpp @@ -578,7 +578,7 @@ TEST_F(StanIoStanCsvReader, no_samples) { stan::io::stan_csv no_samples = stan::io::stan_csv_reader::parse(no_samples_stream, &out); no_samples_stream.close(); - ASSERT_EQ(out.str(), "Unable to parse sample\n"); + ASSERT_EQ(out.str(), "No draws found\n"); } TEST_F(StanIoStanCsvReader, variational) { diff --git a/src/test/unit/mcmc/chainset_test.cpp b/src/test/unit/mcmc/chainset_test.cpp new file mode 100644 index 0000000000..5647652485 --- /dev/null +++ b/src/test/unit/mcmc/chainset_test.cpp @@ -0,0 +1,209 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +class McmcChains : public testing::Test { + public: + void SetUp() override { + bernoulli_500_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_500.csv", + std::ifstream::in); + bernoulli_default_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_default.csv", + std::ifstream::in); + bernoulli_thin_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_thin.csv", + std::ifstream::in); + bernoulli_warmup_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_warmup.csv", + std::ifstream::in); + eight_schools_1_stream.open( + "src/test/unit/mcmc/test_csv_files/eight_schools_1.csv", + std::ifstream::in); + eight_schools_2_stream.open( + "src/test/unit/mcmc/test_csv_files/eight_schools_2.csv", + std::ifstream::in); + + if (!bernoulli_500_stream || !bernoulli_default_stream + || !bernoulli_thin_stream || !bernoulli_warmup_stream + || !eight_schools_1_stream || !eight_schools_2_stream) { + FAIL() << "Failed to open one or more test files"; + } + bernoulli_500_stream.seekg(0, std::ios::beg); + bernoulli_default_stream.seekg(0, std::ios::beg); + bernoulli_thin_stream.seekg(0, std::ios::beg); + bernoulli_warmup_stream.seekg(0, std::ios::beg); + eight_schools_1_stream.seekg(0, std::ios::beg); + eight_schools_2_stream.seekg(0, std::ios::beg); + + bernoulli_500 + = stan::io::stan_csv_reader::parse(bernoulli_500_stream, &out); + bernoulli_default + = stan::io::stan_csv_reader::parse(bernoulli_default_stream, &out); + bernoulli_thin + = stan::io::stan_csv_reader::parse(bernoulli_thin_stream, &out); + bernoulli_warmup + = stan::io::stan_csv_reader::parse(bernoulli_warmup_stream, &out); + eight_schools_1 + = stan::io::stan_csv_reader::parse(eight_schools_1_stream, &out); + eight_schools_2 + = stan::io::stan_csv_reader::parse(eight_schools_2_stream, &out); + } + + void TearDown() override { + bernoulli_500_stream.close(); + bernoulli_default_stream.close(); + bernoulli_thin_stream.close(); + bernoulli_warmup_stream.close(); + eight_schools_1_stream.close(); + eight_schools_2_stream.close(); + } + + std::stringstream out; + + std::ifstream bernoulli_500_stream, bernoulli_default_stream, + bernoulli_thin_stream, bernoulli_warmup_stream, eight_schools_1_stream, + eight_schools_2_stream; + + stan::io::stan_csv bernoulli_500, bernoulli_default, bernoulli_thin, + bernoulli_warmup, eight_schools_1, eight_schools_2; +}; + +TEST_F(McmcChains, constructor) { + stan::mcmc::chainset chain_1(eight_schools_1); + EXPECT_EQ(1, chain_1.num_chains()); + EXPECT_EQ(eight_schools_1.header.size(), chain_1.num_params()); + EXPECT_EQ( + eight_schools_1.metadata.num_samples / eight_schools_1.metadata.thin, + chain_1.num_samples()); + + std::vector eight_schools; + eight_schools.push_back(eight_schools_1); + eight_schools.push_back(eight_schools_2); + stan::mcmc::chainset chain_2(eight_schools); + EXPECT_EQ(2, chain_2.num_chains()); + EXPECT_EQ(eight_schools_1.header.size(), chain_2.num_params()); + EXPECT_EQ( + eight_schools_1.metadata.num_samples / eight_schools_1.metadata.thin, + chain_2.num_samples()); + + std::vector bernoulli; + bernoulli.push_back(bernoulli_default); + bernoulli.push_back(bernoulli_thin); + bernoulli.push_back(bernoulli_warmup); + stan::mcmc::chainset chain_3(bernoulli); + EXPECT_EQ(3, chain_3.num_chains()); + EXPECT_EQ(bernoulli_default.header.size(), chain_3.num_params()); + EXPECT_EQ(bernoulli_default.metadata.num_samples, chain_3.num_samples()); +} + +TEST_F(McmcChains, addFail) { + std::vector bad; + bad.push_back(bernoulli_default); + bad.push_back(bernoulli_500); + EXPECT_THROW(stan::mcmc::chainset fail(bad), std::invalid_argument); + + bad.clear(); + bad.push_back(bernoulli_default); + bad.push_back(eight_schools_1); + EXPECT_THROW(stan::mcmc::chainset fail(bad), std::invalid_argument); +} + +TEST_F(McmcChains, paramNameIndex) { + stan::mcmc::chainset chains_csv(eight_schools_1); + EXPECT_EQ(1, chains_csv.num_chains()); + for (int i = 0; i < eight_schools_1.header.size(); i++) { + EXPECT_EQ(eight_schools_1.header[i], chains_csv.param_name(i)); + EXPECT_EQ(i, chains_csv.index(eight_schools_1.header[i])); + } + EXPECT_THROW(chains_csv.param_name(-5000), std::invalid_argument); + EXPECT_THROW(chains_csv.param_name(5000), std::invalid_argument); + EXPECT_THROW(chains_csv.index("foo"), std::invalid_argument); +} + +TEST_F(McmcChains, eight_schools_samples) { + std::vector eight_schools; + eight_schools.push_back(eight_schools_1); + eight_schools.push_back(eight_schools_2); + stan::mcmc::chainset chain_2(eight_schools); + Eigen::MatrixXd mu_all = chain_2.samples("mu"); + EXPECT_EQ(chain_2.num_chains() * chain_2.num_samples(), mu_all.size()); + mu_all = chain_2.samples(7); + EXPECT_EQ(chain_2.num_chains() * chain_2.num_samples(), mu_all.size()); + + EXPECT_THROW(chain_2.samples(5000), std::invalid_argument); + EXPECT_THROW(chain_2.samples("foo"), std::invalid_argument); +} + +TEST_F(McmcChains, summary_stats) { + std::stringstream out; + std::vector bern_csvs(4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + bern_csvs[i] = bern_csv; + } + stan::mcmc::chainset bern_chains(bern_csvs); + EXPECT_EQ(4, bern_chains.num_chains()); + + Eigen::MatrixXd theta = bern_chains.samples("theta"); + // default summary statistics - via R pkg posterior + double theta_mean_expect = 0.251297; + EXPECT_NEAR(theta_mean_expect, bern_chains.mean("theta"), 1e-5); + + double theta_median_expect = 0.237476; + EXPECT_NEAR(theta_median_expect, bern_chains.median("theta"), 1e-5); + + double theta_sd_expect = 0.121546; + EXPECT_NEAR(theta_sd_expect, bern_chains.sd("theta"), 1e-5); + + double theta_mad_expect = 0.12309; + EXPECT_NEAR(theta_mad_expect, bern_chains.max_abs_deviation("theta"), 1e-5); + + double theta_mcse_mean_expect = 0.003234; + EXPECT_NEAR(theta_mcse_mean_expect, bern_chains.mcse_mean("theta"), 1e-4); + + double theta_mcse_sd_expect = 0.002164; + EXPECT_NEAR(theta_mcse_sd_expect, bern_chains.mcse_sd("theta"), 1e-4); + + Eigen::VectorXd probs(6); + probs << 0.0, 0.01, 0.05, 0.95, 0.99, 1.0; + Eigen::VectorXd quantiles_expect(6); + quantiles_expect << 0.004072, 0.046281, 0.077169, 0.473885, 0.574524, + 0.698401; + Eigen::VectorXd theta_quantiles = bern_chains.quantiles("theta", probs); + for (size_t i = 0; i < probs.size(); ++i) { + EXPECT_NEAR(quantiles_expect(i), theta_quantiles(i), 1e-5); + } + + double theta_rhat_expect = 1.00679; + auto rhat = bern_chains.split_rank_normalized_rhat("theta"); + EXPECT_NEAR(theta_rhat_expect, std::max(rhat.first, rhat.second), 1e-5); + + double theta_ess_bulk_expect = 1407.5124; + double theta_ess_tail_expect = 1291.7131; + auto ess = bern_chains.split_rank_normalized_ess("theta"); + EXPECT_NEAR(theta_ess_bulk_expect, ess.first, 1e-4); + EXPECT_NEAR(theta_ess_tail_expect, ess.second, 1e-4); + + // autocorrelation - first 10 lags + Eigen::VectorXd theta_ac_expect(10); + theta_ac_expect << 1.00000, 0.42204, 0.20683, 0.08383, 0.037326, 0.02507, + 0.02003, 0.01347, 0.00476, 0.029495; + auto theta_ac = bern_chains.autocorrelation(0, "theta"); + for (size_t i = 0; i < 10; ++i) { + EXPECT_NEAR(theta_ac(i), theta_ac_expect(i), 0.0005); + } +} diff --git a/src/test/unit/mcmc/test_csv_files/bernoulli_500.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_500.csv new file mode 100644 index 0000000000..f7c479a86e --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_500.csv @@ -0,0 +1,557 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-20 18:55:04 UTC +# method = sample (Default) +# sample +# num_samples = 500 +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3634950897 (Default) +# output +# file = bernoulli_1.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.908714 +# Diagonal elements of inverse mass matrix: +# 0.533354 +-6.77412,0.987824,0.908714,2,3,0,6.8785,0.279254 +-6.89323,0.977402,0.908714,1,3,0,6.89338,0.321021 +-6.78182,1,0.908714,2,3,0,6.8661,0.218479 +-6.78182,0.773777,0.908714,1,3,0,7.98348,0.218479 +-7.21517,0.892133,0.908714,1,3,0,7.43887,0.381517 +-9.70031,0.447016,0.908714,1,1,0,9.70243,0.59546 +-7.64929,1,0.908714,2,3,0,9.25311,0.111255 +-7.80318,0.993575,0.908714,2,3,0,7.9051,0.102697 +-6.75708,0.98594,0.908714,1,3,0,7.94373,0.233437 +-6.74807,1,0.908714,2,3,0,6.75624,0.251184 +-6.76508,0.961627,0.908714,2,3,0,6.97493,0.227399 +-6.76508,0.694403,0.908714,1,3,0,8.48868,0.227399 +-7.46,0.856941,0.908714,1,3,0,7.64341,0.123565 +-6.84767,0.988752,0.908714,1,3,0,7.39531,0.197147 +-6.76112,0.937435,0.908714,2,3,0,7.4025,0.270583 +-6.76723,0.993298,0.908714,2,3,0,6.80703,0.226049 +-6.75849,1,0.908714,1,1,0,6.7661,0.232214 +-6.75856,0.916923,0.908714,2,3,0,7.37221,0.232155 +-6.76412,0.905032,0.908714,2,3,0,7.46252,0.228033 +-6.75246,0.98558,0.908714,2,3,0,6.86086,0.261903 +-7.14063,0.806926,0.908714,2,3,0,7.89708,0.15144 +-7.65515,0.922931,0.908714,1,1,0,7.65525,0.110908 +-6.85804,0.996431,0.908714,2,3,0,7.69222,0.194631 +-6.7956,0.992136,0.908714,2,3,0,6.94247,0.212823 +-6.75011,0.941177,0.908714,2,3,0,7.20539,0.241987 +-6.83571,0.977386,0.908714,1,3,0,6.86933,0.304611 +-7.04002,0.894555,0.908714,1,3,0,7.42018,0.163442 +-6.91021,0.955935,0.908714,1,3,0,7.47326,0.325253 +-6.75575,1,0.908714,2,3,0,6.89311,0.234677 +-6.74979,0.982019,0.908714,2,3,0,6.87707,0.25749 +-6.75266,0.999273,0.908714,1,1,0,6.75273,0.262168 +-6.75854,0.989314,0.908714,2,3,0,6.81651,0.232168 +-7.35696,0.872376,0.908714,1,3,0,7.52038,0.131354 +-7.17209,1,0.908714,1,1,0,7.3618,0.148109 +-6.76514,0.974208,0.908714,1,3,0,7.33856,0.273592 +-6.81293,0.933943,0.908714,2,3,0,7.16788,0.206861 +-6.78344,0.990552,0.908714,2,3,0,6.90852,0.284208 +-6.75189,1,0.908714,1,3,0,6.77606,0.261105 +-6.91381,0.79852,0.908714,2,3,0,8.08135,0.183007 +-7.36903,0.965086,0.908714,2,3,0,7.37005,0.403004 +-7.24594,1,0.908714,1,1,0,7.45634,0.38605 +-7.10956,1,0.908714,1,1,0,7.2858,0.3648 +-7.1841,0.701565,0.908714,2,3,0,8.86758,0.376798 +-6.75487,0.962329,0.908714,2,3,0,7.41982,0.264819 +-6.91264,0.952408,0.908714,1,3,0,7.01286,0.183226 +-6.74841,0.935239,0.908714,2,3,0,7.4253,0.253501 +-7.01744,0.836819,0.908714,2,3,0,7.72885,0.166491 +-6.76439,0.998239,0.908714,1,3,0,6.993,0.227849 +-6.77316,0.99526,0.908714,2,3,0,6.8081,0.222694 +-7.23311,0.91075,0.908714,1,3,0,7.31151,0.142098 +-6.93024,1,0.908714,2,3,0,7.17757,0.180038 +-7.0312,0.98107,0.908714,1,1,0,7.04685,0.164616 +-6.76558,0.997454,0.908714,2,3,0,7.04984,0.22708 +-8.32852,0.675198,0.908714,1,3,0,8.99917,0.0799677 +-7.54902,1,0.908714,1,1,0,8.23363,0.117493 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+-6.80467,0.99303,0.908714,1,3,0,7.49888,0.209581 +-6.86328,0.995849,0.908714,2,3,0,6.86601,0.193412 +-6.7527,1,0.908714,2,3,0,6.84135,0.262221 +-6.89757,0.968179,0.908714,1,3,0,6.90889,0.322123 +-6.7829,0.971235,0.908714,2,3,0,7.09147,0.283942 +-6.76687,1,0.908714,1,1,0,6.78086,0.274774 +-7.56409,0.874398,0.908714,2,3,0,7.57617,0.116518 +# +# Elapsed Time: 0.005 seconds (Warm-up) +# 0.005 seconds (Sampling) +# 0.01 seconds (Total) +# diff --git a/src/test/unit/mcmc/test_csv_files/bernoulli_const_1.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_const_1.csv deleted file mode 100644 index b4a727ec85..0000000000 --- a/src/test/unit/mcmc/test_csv_files/bernoulli_const_1.csv +++ /dev/null @@ -1,1057 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 35 -# stan_version_patch = 0 -# model = bernoulli_model -# start_datetime = 2024-07-20 18:56:30 UTC -# method = sample (Default) -# sample -# num_samples = 1000 (Default) -# num_warmup = 1000 (Default) -# save_warmup = false (Default) -# thin = 1 (Default) -# adapt -# engaged = true (Default) -# gamma = 0.05 (Default) -# delta = 0.8 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# save_metric = false (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# num_chains = 1 (Default) -# id = 1 (Default) -# data -# file = examples/bernoulli/bernoulli.data.json -# init = 2 (Default) -# random -# seed = 3635036313 (Default) -# output -# file = bernoulli_out.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -# sig_figs = -1 (Default) -# profile_file = profile.csv (Default) -# save_cmdstan_config = false (Default) -# num_threads = 1 (Default) -# stanc_version = stanc3 v2.35.0-25-gbb9ce42 -# stancflags = -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,zeta -# Adaptation terminated -# Step size = 0.932037 -# Diagonal elements of inverse mass matrix: -# 0.591014 --7.81355,0.761218,0.932037,1,3,0,8.13669,0.15 --8.71049,0.894869,0.932037,1,1,0,8.72268,0.15 --7.3531,0.957519,0.932037,1,3,0,8.53469,0.15 --7.02007,1,0.932037,1,1,0,7.29505,0.15 --8.49033,0.716959,0.932037,1,3,0,9.20407,0.15 --7.18724,1,0.932037,1,1,0,8.06459,0.15 --10.0621,0.636916,0.932037,2,3,0,10.0852,0.15 --8.11851,1,0.932037,1,1,0,9.65805,0.15 --6.86936,0.948809,0.932037,2,3,0,8.60097,0.15 --7.13756,0.935492,0.932037,2,3,0,7.43717,0.15 --7.09122,1,0.932037,1,1,0,7.19518,0.15 --6.91669,1,0.932037,1,1,0,7.06383,0.15 --6.76371,0.840542,0.932037,2,3,0,8.08838,0.15 --6.84805,0.983351,0.932037,1,3,0,6.84861,0.15 --8.3727,0.653843,0.932037,2,3,0,9.53079,0.15 --7.25928,0.806553,0.932037,1,3,0,9.43963,0.15 --6.95987,0.853051,0.932037,2,3,0,9.71446,0.15 --6.95334,1,0.932037,1,1,0,7.00708,0.15 --6.98417,0.993206,0.932037,1,1,0,7.02663,0.15 --8.9535,0.727406,0.932037,1,3,0,9.27425,0.15 --7.08162,0.995936,0.932037,2,3,0,9.00617,0.15 --7.16691,0.98306,0.932037,1,1,0,7.22238,0.15 --6.8898,1,0.932037,1,1,0,7.10684,0.15 --7.8902,0.846547,0.932037,1,3,0,7.96857,0.15 --7.24239,1,0.932037,1,1,0,7.78446,0.15 --7.94748,0.888836,0.932037,1,1,0,7.94857,0.15 --6.83694,0.864212,0.932037,2,3,0,8.91495,0.15 --6.83694,0.425574,0.932037,1,3,0,9.01249,0.15 --6.80131,0.985852,0.932037,1,3,0,6.93894,0.15 --6.74824,0.978446,0.932037,2,3,0,6.95179,0.15 --6.74832,0.945401,0.932037,2,3,0,7.0277,0.15 --6.85442,0.972221,0.932037,1,3,0,6.87104,0.15 --6.86406,0.996818,0.932037,1,1,0,6.89618,0.15 --7.11238,0.918504,0.932037,1,1,0,7.12695,0.15 --7.11238,0.431368,0.932037,1,1,0,8.53302,0.15 --6.79843,1,0.932037,1,1,0,7.00834,0.15 --6.74804,0.996585,0.932037,2,3,0,6.81732,0.15 --9.50952,0.509265,0.932037,1,3,0,9.67564,0.15 --8.27778,1,0.932037,1,1,0,9.48419,0.15 --7.2527,1,0.932037,2,3,0,8.04703,0.15 --6.75205,0.907555,0.932037,2,3,0,8.25548,0.15 --7.3411,0.857468,0.932037,1,3,0,7.42326,0.15 --7.07094,1,0.932037,1,1,0,7.32247,0.15 --7.04767,0.556206,0.932037,2,3,0,9.56585,0.15 --6.87625,1,0.932037,2,3,0,6.9975,0.15 --7.12535,0.945777,0.932037,1,1,0,7.12652,0.15 --7.12535,0.913353,0.932037,1,1,0,7.53634,0.15 --6.78755,1,0.932037,2,3,0,7.04258,0.15 --7.7545,0.780448,0.932037,1,3,0,7.76329,0.15 --6.77502,0.859809,0.932037,2,3,0,8.49845,0.15 --7.19668,0.726665,0.932037,2,3,0,8.64609,0.15 --9.64819,0.775506,0.932037,2,3,0,10.0305,0.15 --6.86272,0.7808,0.932037,1,3,0,10.9403,0.15 --6.99329,0.696273,0.932037,2,3,0,8.30815,0.15 --6.99329,0.700745,0.932037,1,1,0,7.63428,0.15 --7.11558,0.957857,0.932037,1,1,0,7.18108,0.15 --6.80487,1,0.932037,2,3,0,7.09376,0.15 --6.97334,0.955154,0.932037,2,3,0,7.16146,0.15 --7.2664,0.98044,0.932037,2,3,0,7.27318,0.15 --7.06819,1,0.932037,2,3,0,7.25815,0.15 --6.74803,0.991749,0.932037,1,3,0,7.05576,0.15 --7.05542,0.823118,0.932037,2,3,0,7.78068,0.15 --6.89251,1,0.932037,1,1,0,7.02757,0.15 --6.76584,0.932575,0.932037,2,3,0,7.49343,0.15 --6.75064,0.997924,0.932037,2,3,0,6.78013,0.15 --6.91371,0.96173,0.932037,1,3,0,6.93351,0.15 --6.74953,0.994131,0.932037,1,3,0,6.91533,0.15 --8.33302,0.635332,0.932037,1,3,0,9.05105,0.15 --8.04729,1,0.932037,1,1,0,8.43889,0.15 --8.42015,0.956552,0.932037,1,1,0,8.53727,0.15 --7.91154,1,0.932037,1,1,0,8.4304,0.15 --7.65791,1,0.932037,1,1,0,7.97836,0.15 --7.97869,0.955317,0.932037,1,1,0,8.06587,0.15 --6.81845,1,0.932037,1,3,0,7.88771,0.15 --6.76575,0.914099,0.932037,2,3,0,7.34829,0.15 --6.74964,0.996733,0.932037,2,3,0,6.78491,0.15 --6.77106,0.99494,0.932037,1,3,0,6.77141,0.15 --6.77106,0.945747,0.932037,1,3,0,6.92649,0.15 --6.77106,0.856062,0.932037,1,3,0,7.35372,0.15 --6.88028,0.948865,0.932037,2,3,0,7.03829,0.15 --7.33958,0.635769,0.932037,2,3,0,9.861,0.15 --8.06974,0.890942,0.932037,1,1,0,8.07292,0.15 --6.97186,0.998838,0.932037,1,3,0,7.91764,0.15 --6.76939,0.981956,0.932037,1,3,0,7.03238,0.15 --7.44177,0.80944,0.932037,1,3,0,7.79154,0.15 --8.01591,0.972061,0.932037,2,3,0,8.03736,0.15 --9.92045,0.865258,0.932037,2,3,0,9.92397,0.15 --7.31963,1,0.932037,2,3,0,10.3209,0.15 --6.9789,1,0.932037,1,1,0,7.25409,0.15 --7.21981,0.951312,0.932037,1,1,0,7.23197,0.15 --7.47612,0.930523,0.932037,2,3,0,8.12075,0.15 --6.81131,0.990375,0.932037,2,3,0,7.55719,0.15 --6.80278,0.920389,0.932037,2,3,0,7.43635,0.15 --6.88803,0.979103,0.932037,1,1,0,6.89063,0.15 --7.02902,0.795987,0.932037,2,3,0,8.6993,0.15 --6.74805,0.993012,0.932037,1,3,0,7.01716,0.15 --6.93322,0.952905,0.932037,1,3,0,6.95228,0.15 --6.76781,0.904559,0.932037,2,3,0,7.33629,0.15 --6.76232,0.985564,0.932037,2,3,0,6.83656,0.15 --6.87137,0.974516,0.932037,1,3,0,6.87148,0.15 --7.21556,0.953187,0.932037,2,3,0,7.22785,0.15 --6.76367,0.998041,0.932037,1,3,0,7.1984,0.15 --6.82308,0.981349,0.932037,1,3,0,6.86896,0.15 --6.82308,0.352125,0.932037,1,3,0,10.8402,0.15 --6.99474,0.738309,0.932037,2,3,0,8.0486,0.15 --6.99474,0.963463,0.932037,1,1,0,7.1311,0.15 --6.8637,0.713515,0.932037,2,3,0,8.29356,0.15 --6.94178,0.974091,0.932037,1,1,0,6.96832,0.15 --6.80481,1,0.932037,2,3,0,6.9081,0.15 --6.92327,0.971351,0.932037,1,1,0,6.92425,0.15 --7.1233,0.957499,0.932037,1,1,0,7.13244,0.15 --6.76778,0.973133,0.932037,1,3,0,7.19571,0.15 --6.90939,0.888041,0.932037,2,3,0,7.29419,0.15 --7.3757,0.927908,0.932037,2,3,0,7.37586,0.15 --7.33194,1,0.932037,1,1,0,7.48008,0.15 --6.86262,0.893077,0.932037,2,3,0,9.38827,0.15 --6.74929,0.996117,0.932037,1,3,0,6.86347,0.15 --6.74964,0.943201,0.932037,2,3,0,7.03592,0.15 --6.9015,0.968295,0.932037,2,3,0,6.96204,0.15 --7.81106,0.864491,0.932037,1,3,0,7.86554,0.15 --8.616,0.90424,0.932037,1,1,0,8.63659,0.15 --8.16761,1,0.932037,1,1,0,8.67561,0.15 --6.96545,1,0.932037,1,3,0,8.01828,0.15 --6.75813,1,0.932037,1,3,0,6.92669,0.15 --6.75424,1,0.932037,1,1,0,6.75812,0.15 --7.76056,0.503853,0.932037,2,3,0,10.2889,0.15 --7.12518,1,0.932037,1,1,0,7.64631,0.15 --7.67249,0.905419,0.932037,1,1,0,7.67491,0.15 --7.63881,1,0.932037,1,1,0,7.83222,0.15 --7.3844,0.889767,0.932037,1,3,0,8.80917,0.15 --6.76719,1,0.932037,1,3,0,7.348,0.15 --7.47953,0.852161,0.932037,1,3,0,7.59716,0.15 --7.42386,1,0.932037,2,3,0,7.59422,0.15 --7.91668,0.975432,0.932037,2,3,0,7.94672,0.15 --6.83066,1,0.932037,2,3,0,7.62924,0.15 --6.96269,0.956934,0.932037,1,1,0,6.97473,0.15 --6.74847,0.963031,0.932037,2,3,0,7.15706,0.15 --6.80708,0.988647,0.932037,2,3,0,6.82289,0.15 --6.91792,0.9732,0.932037,1,1,0,6.91947,0.15 --6.84983,1,0.932037,2,3,0,6.91467,0.15 --6.88905,0.990653,0.932037,1,1,0,6.90679,0.15 --7.15463,0.975663,0.932037,2,3,0,7.16,0.15 --6.9586,1,0.932037,1,1,0,7.13267,0.15 --7.56708,0.803851,0.932037,1,1,0,7.59178,0.15 --6.76736,1,0.932037,1,3,0,7.31987,0.15 --6.80123,0.959699,0.932037,2,3,0,6.95431,0.15 --6.80123,0.759564,0.932037,1,3,0,7.84546,0.15 --6.80123,0.902778,0.932037,1,3,0,7.20675,0.15 --6.80123,0.96482,0.932037,1,1,0,6.88076,0.15 --6.78299,0.988689,0.932037,2,3,0,6.88032,0.15 --7.1107,0.936673,0.932037,1,3,0,7.12632,0.15 --7.31567,0.961429,0.932037,1,1,0,7.35195,0.15 --7.07455,0.968694,0.932037,2,3,0,7.7776,0.15 --6.74831,0.993779,0.932037,1,3,0,7.05357,0.15 --6.84249,0.975275,0.932037,1,3,0,6.85756,0.15 --6.88224,0.995647,0.932037,2,3,0,6.90622,0.15 --8.68021,0.668585,0.932037,2,3,0,8.68611,0.15 --7.80603,1,0.932037,1,1,0,8.5851,0.15 --7.27572,1,0.932037,1,1,0,7.73044,0.15 --8.91337,0.902662,0.932037,2,3,0,9.15324,0.15 --7.22471,0.840347,0.932037,1,3,0,9.85532,0.15 --7.57214,0.980256,0.932037,2,3,0,7.60142,0.15 --7.46188,1,0.932037,1,1,0,7.66831,0.15 --7.21293,1,0.932037,1,1,0,7.46083,0.15 --7.27196,0.911127,0.932037,1,3,0,8.06683,0.15 --6.99285,1,0.932037,1,1,0,7.22442,0.15 --7.64686,0.789204,0.932037,1,1,0,7.67915,0.15 --7.64686,0.588674,0.932037,1,1,0,8.70948,0.15 --6.76317,0.84811,0.932037,2,3,0,8.40171,0.15 --6.75852,0.995961,0.932037,2,3,0,6.79457,0.15 --6.96185,0.956175,0.932037,2,3,0,7.06538,0.15 --6.7597,0.95957,0.932037,2,3,0,7.23201,0.15 --6.95356,0.940807,0.932037,1,3,0,7.05195,0.15 --6.74802,0.995879,0.932037,1,3,0,6.94105,0.15 --6.80409,0.985416,0.932037,1,3,0,6.81492,0.15 --6.78431,0.93291,0.932037,2,3,0,7.30965,0.15 --6.85124,0.98309,0.932037,1,1,0,6.85256,0.15 --6.91874,0.970098,0.932037,1,3,0,7.11707,0.15 --6.91874,0.434093,0.932037,1,3,0,9.39656,0.15 --6.92998,0.940657,0.932037,2,3,0,7.25758,0.15 --6.7616,0.987015,0.932037,1,3,0,6.96899,0.15 --6.77385,0.996249,0.932037,1,1,0,6.77617,0.15 --7.11956,0.891801,0.932037,1,3,0,7.32561,0.15 --7.10361,0.938678,0.932037,1,3,0,7.72046,0.15 --7.10361,0.642361,0.932037,1,1,0,7.89438,0.15 --8.5001,0.593919,0.932037,1,1,0,8.53813,0.15 --7.1692,1,0.932037,2,3,0,8.39313,0.15 --6.99964,1,0.932037,2,3,0,7.15856,0.15 --6.82605,1,0.932037,1,1,0,6.95904,0.15 --6.80069,1,0.932037,1,1,0,6.8286,0.15 --6.80069,0.826864,0.932037,1,3,0,7.56839,0.15 --6.80069,0.712989,0.932037,1,3,0,8.08135,0.15 --6.79864,1,0.932037,1,1,0,6.81323,0.15 --7.21975,0.922333,0.932037,1,3,0,7.24006,0.15 --6.75061,0.978686,0.932037,1,3,0,7.23991,0.15 --8.50882,0.710206,0.932037,2,3,0,8.58833,0.15 --6.98994,1,0.932037,1,3,0,8.37388,0.15 --8.68191,0.796805,0.932037,2,3,0,9.47091,0.15 --7.31609,1,0.932037,2,3,0,8.2644,0.15 --7.14381,1,0.932037,1,1,0,7.36712,0.15 --7.14381,0.475525,0.932037,1,1,0,8.41075,0.15 --7.29505,0.813437,0.932037,2,3,0,8.20133,0.15 --7.97276,0.895575,0.932037,1,1,0,7.97663,0.15 --9.00874,0.888587,0.932037,1,1,0,9.0167,0.15 --8.5484,1,0.932037,1,1,0,9.10361,0.15 --7.88735,1,0.932037,1,1,0,8.51142,0.15 --7.36782,1,0.932037,1,1,0,7.8262,0.15 --7.34887,1,0.932037,1,1,0,7.48713,0.15 --6.81839,0.987631,0.932037,2,3,0,7.46646,0.15 --6.81839,0.712592,0.932037,1,3,0,8.16085,0.15 --6.74807,0.998893,0.932037,1,3,0,6.81316,0.15 --6.87013,0.959268,0.932037,2,3,0,6.97825,0.15 --6.76585,1,0.932037,1,1,0,6.83725,0.15 --6.7566,0.994396,0.932037,2,3,0,6.80045,0.15 --6.7566,0.628726,0.932037,1,3,0,8.7002,0.15 --6.82277,0.986161,0.932037,1,3,0,6.82387,0.15 --7.3013,0.877627,0.932037,1,3,0,7.5333,0.15 --8.78447,0.79108,0.932037,2,3,0,8.87542,0.15 --7.50584,1,0.932037,1,1,0,8.46306,0.15 --7.15669,1,0.932037,1,1,0,7.48033,0.15 --7.43591,0.967588,0.932037,2,3,0,7.54437,0.15 --6.96987,0.913229,0.932037,1,3,0,7.88228,0.15 --6.89773,1,0.932037,1,1,0,6.97541,0.15 --7.36158,0.930716,0.932037,1,3,0,7.36402,0.15 --7.16589,1,0.932037,1,1,0,7.37174,0.15 --6.95869,1,0.932037,1,1,0,7.13528,0.15 --7.16168,0.929564,0.932037,1,3,0,7.57434,0.15 --8.02154,0.725795,0.932037,1,1,0,8.09574,0.15 --8.63423,0.930003,0.932037,2,3,0,9.03662,0.15 --6.93887,1,0.932037,1,1,0,8.0172,0.15 --6.87271,1,0.932037,1,1,0,6.94734,0.15 --6.77339,1,0.932037,2,3,0,6.85408,0.15 --6.98298,0.942095,0.932037,1,3,0,7.07789,0.15 --6.75868,1,0.932037,1,3,0,6.91623,0.15 --6.7906,0.973381,0.932037,2,3,0,6.88716,0.15 --7.56742,0.820209,0.932037,1,3,0,7.57193,0.15 --6.75108,1,0.932037,1,3,0,7.35001,0.15 --6.80655,0.983396,0.932037,1,3,0,6.83092,0.15 --6.91728,0.973208,0.932037,1,1,0,6.91879,0.15 --7.7691,0.915431,0.932037,2,3,0,7.98664,0.15 --6.765,1,0.932037,1,3,0,7.46164,0.15 --7.37311,0.856184,0.932037,1,3,0,7.3856,0.15 --7.3305,1,0.932037,1,1,0,7.55404,0.15 --7.3305,0.663523,0.932037,1,1,0,8.12143,0.15 --7.12567,0.837343,0.932037,1,3,0,8.09517,0.15 --7.15885,0.957841,0.932037,2,3,0,7.71194,0.15 --6.75049,0.994782,0.932037,1,3,0,7.12461,0.15 --6.81461,0.982255,0.932037,1,3,0,6.83366,0.15 --6.75614,0.996516,0.932037,1,3,0,6.83027,0.15 --6.74853,0.904663,0.932037,2,3,0,7.2911,0.15 --7.01677,0.911602,0.932037,2,3,0,7.26139,0.15 --6.92929,0.642216,0.932037,2,3,0,8.77716,0.15 --7.65529,0.771289,0.932037,1,1,0,7.66932,0.15 --7.01834,1,0.932037,1,1,0,7.46402,0.15 --6.74897,1,0.932037,1,3,0,6.95557,0.15 --9.81129,0.348606,0.932037,1,3,0,11.6845,0.15 --10.4379,0.964322,0.932037,1,1,0,10.6078,0.15 --12.3737,0.923455,0.932037,1,1,0,12.3737,0.15 --8.6914,0.906628,0.932037,1,3,0,12.5921,0.15 --7.8205,1,0.932037,1,1,0,8.59832,0.15 --6.76573,0.925015,0.932037,1,3,0,8.00716,0.15 --7.18424,0.803585,0.932037,2,3,0,7.86457,0.15 --6.74867,0.98391,0.932037,1,3,0,7.18696,0.15 --6.74867,0.809678,0.932037,1,3,0,7.5541,0.15 --6.78928,0.864392,0.932037,2,3,0,7.45892,0.15 --7.73008,0.857073,0.932037,2,3,0,7.95215,0.15 --6.90924,0.999548,0.932037,1,3,0,7.59486,0.15 --6.83204,1,0.932037,1,1,0,6.89889,0.15 --6.92095,0.978894,0.932037,1,1,0,6.92784,0.15 --6.84946,1,0.932037,1,1,0,6.91633,0.15 --6.87329,0.97872,0.932037,1,3,0,7.05429,0.15 --7.77797,0.726161,0.932037,1,1,0,7.77978,0.15 --7.91451,0.983052,0.932037,2,3,0,8.2695,0.15 --6.99541,0.905965,0.932037,1,3,0,8.38798,0.15 --6.76541,0.9819,0.932037,1,3,0,7.04888,0.15 --6.75969,1,0.932037,1,1,0,6.76622,0.15 --6.88511,0.980043,0.932037,2,3,0,6.89657,0.15 --6.77682,0.92494,0.932037,2,3,0,7.55206,0.15 --7.22693,0.885941,0.932037,1,3,0,7.36359,0.15 --7.08301,1,0.932037,2,3,0,7.27,0.15 --6.74807,1,0.932037,1,3,0,7.01313,0.15 --6.78184,0.991048,0.932037,1,3,0,6.7889,0.15 --7.7414,0.782413,0.932037,1,3,0,7.94246,0.15 --8.34868,0.79224,0.932037,1,1,0,8.64598,0.15 --6.96656,1,0.932037,1,1,0,7.85206,0.15 --8.55062,0.559382,0.932037,1,1,0,8.55696,0.15 --8.71259,0.939683,0.932037,1,1,0,9.36157,0.15 --7.02011,1,0.932037,1,1,0,8.0992,0.15 --7.73032,0.771978,0.932037,1,1,0,7.7681,0.15 --7.58699,1,0.932037,1,1,0,7.95753,0.15 --10.1664,0.37457,0.932037,1,1,0,10.2962,0.15 --9.05941,1,0.932037,2,3,0,10.4928,0.15 --6.95163,0.994678,0.932037,1,3,0,9.25144,0.15 --6.991,0.991344,0.932037,1,1,0,7.03106,0.15 --6.93665,1,0.932037,1,1,0,7.01246,0.15 --7.53693,0.951931,0.932037,2,3,0,7.60421,0.15 --7.35042,1,0.932037,1,1,0,7.65542,0.15 --6.74833,1,0.932037,1,3,0,7.21002,0.15 --7.30096,0.866757,0.932037,1,3,0,7.34306,0.15 --7.33424,0.995938,0.932037,2,3,0,7.52117,0.15 --7.00834,1,0.932037,1,1,0,7.27119,0.15 --7.00834,0.750927,0.932037,1,3,0,7.93882,0.15 --6.85276,0.717415,0.932037,2,3,0,8.2801,0.15 --6.88149,0.915088,0.932037,2,3,0,7.26489,0.15 --7.01531,0.954346,0.932037,2,3,0,7.30842,0.15 --8.49716,0.821021,0.932037,2,3,0,8.90444,0.15 --7.75198,1,0.932037,2,3,0,8.42455,0.15 --6.78374,0.995713,0.932037,1,3,0,7.67855,0.15 --6.78319,0.955918,0.932037,2,3,0,7.05067,0.15 --6.76877,0.993356,0.932037,2,3,0,6.83116,0.15 --6.76604,1,0.932037,2,3,0,6.77242,0.15 --6.80257,0.828664,0.932037,2,3,0,7.81548,0.15 --6.7921,0.928548,0.932037,2,3,0,7.34502,0.15 --6.7921,0.869785,0.932037,1,3,0,7.3846,0.15 --6.78247,1,0.932037,1,1,0,6.7969,0.15 --6.77338,0.944315,0.932037,2,3,0,7.11056,0.15 --6.88767,0.984579,0.932037,2,3,0,6.89149,0.15 --6.87758,1,0.932037,1,1,0,6.91573,0.15 --6.98427,0.975828,0.932037,1,1,0,6.997,0.15 --8.16006,0.847199,0.932037,1,3,0,8.23533,0.15 --6.92926,1,0.932037,1,3,0,8.02086,0.15 --6.75334,1,0.932037,1,3,0,6.89875,0.15 --6.76157,0.984481,0.932037,2,3,0,6.84166,0.15 --6.77437,0.916298,0.932037,2,3,0,7.16943,0.15 --6.89883,0.966797,0.932037,2,3,0,7.01941,0.15 --7.61554,0.929243,0.932037,2,3,0,7.77465,0.15 --6.74865,1,0.932037,1,3,0,7.4327,0.15 --7.90889,0.736401,0.932037,1,3,0,8.30046,0.15 --7.36433,1,0.932037,1,1,0,7.84078,0.15 --6.7517,1,0.932037,2,3,0,7.32628,0.15 --7.47731,0.835796,0.932037,1,3,0,7.65004,0.15 --6.82682,1,0.932037,1,3,0,7.36972,0.15 --6.75257,1,0.932037,1,3,0,6.81023,0.15 --6.74814,0.924039,0.932037,2,3,0,7.16556,0.15 --6.77547,0.992637,0.932037,1,3,0,6.7818,0.15 --6.77883,0.96017,0.932037,2,3,0,7.01271,0.15 --6.83214,0.92718,0.932037,2,3,0,7.10706,0.15 --6.74855,0.999857,0.932037,1,3,0,6.82323,0.15 --6.75208,0.991977,0.932037,2,3,0,6.79074,0.15 --6.74875,1,0.932037,2,3,0,6.7511,0.15 --6.76331,0.987877,0.932037,2,3,0,6.81164,0.15 --7.11253,0.925924,0.932037,1,3,0,7.14728,0.15 --7.33832,0.934594,0.932037,2,3,0,7.89183,0.15 --7.40317,0.891695,0.932037,1,3,0,8.40906,0.15 --6.83199,1,0.932037,2,3,0,7.43757,0.15 --6.83199,0.774019,0.932037,1,3,0,8.04658,0.15 --6.99888,0.961352,0.932037,1,1,0,7.00012,0.15 --6.76802,0.991205,0.932037,2,3,0,7.06627,0.15 --7.1899,0.912501,0.932037,1,3,0,7.23327,0.15 --7.0344,0.951025,0.932037,1,3,0,7.71962,0.15 --6.74861,0.964191,0.932037,2,3,0,7.23266,0.15 --6.9356,0.954146,0.932037,1,3,0,6.96807,0.15 --6.8766,1,0.932037,1,1,0,6.94182,0.15 --6.86303,0.963467,0.932037,2,3,0,7.17361,0.15 --6.89691,0.996253,0.932037,2,3,0,6.92839,0.15 --6.74898,0.989445,0.932037,2,3,0,6.95643,0.15 --6.75192,0.906807,0.932037,2,3,0,7.25324,0.15 --6.89356,0.962004,0.932037,1,3,0,6.92873,0.15 --6.8915,1,0.932037,1,1,0,6.93856,0.15 --8.04782,0.661095,0.932037,1,1,0,8.04944,0.15 --6.96778,1,0.932037,2,3,0,8.10818,0.15 --6.94531,0.967319,0.932037,1,3,0,7.30665,0.15 --6.79303,1,0.932037,1,1,0,6.89785,0.15 --6.75073,0.998473,0.932037,1,3,0,6.79703,0.15 --6.74807,0.922346,0.932037,2,3,0,7.17097,0.15 --6.79664,0.986959,0.932037,2,3,0,6.82676,0.15 --7.42084,0.890966,0.932037,2,3,0,7.44601,0.15 --7.07938,1,0.932037,1,1,0,7.36895,0.15 --6.76996,0.975074,0.932037,1,3,0,7.1524,0.15 --6.97097,0.781354,0.932037,2,3,0,7.8694,0.15 --7.45672,0.874497,0.932037,1,3,0,7.94747,0.15 --7.73982,0.898709,0.932037,1,1,0,7.95818,0.15 --6.75761,1,0.932037,1,3,0,7.45331,0.15 --6.75128,1,0.932037,2,3,0,6.75597,0.15 --6.74857,0.969275,0.932037,2,3,0,6.90378,0.15 --6.78162,0.964009,0.932037,2,3,0,6.94604,0.15 --6.94452,0.969782,0.932037,1,3,0,6.94577,0.15 --6.83464,1,0.932037,1,1,0,6.92359,0.15 --6.85125,0.978801,0.932037,2,3,0,7.03988,0.15 --8.70862,0.498506,0.932037,2,3,0,10.892,0.15 --7.14422,0.988771,0.932037,1,3,0,8.54862,0.15 --8.22719,0.86569,0.932037,1,3,0,8.25029,0.15 --7.98302,1,0.932037,1,1,0,8.34174,0.15 --7.66766,1,0.932037,1,1,0,8.02784,0.15 --8.24143,0.924458,0.932037,1,1,0,8.28161,0.15 --7.67998,1,0.932037,1,1,0,8.20801,0.15 --7.87681,0.972029,0.932037,1,1,0,7.99803,0.15 --6.74849,0.941373,0.932037,1,3,0,7.96721,0.15 --7.11836,0.837369,0.932037,2,3,0,7.62397,0.15 --6.79142,1,0.932037,2,3,0,7.01021,0.15 --6.77524,1,0.932037,1,1,0,6.79217,0.15 --6.79636,0.997801,0.932037,2,3,0,6.8016,0.15 --6.79636,0.948581,0.932037,1,1,0,6.90729,0.15 --6.87559,0.872276,0.932037,2,3,0,7.36427,0.15 --7.91757,0.674512,0.932037,1,3,0,8.87065,0.15 --6.74935,0.935438,0.932037,1,3,0,8.03013,0.15 --6.74803,0.97594,0.932037,2,3,0,6.86976,0.15 --6.81513,0.982667,0.932037,1,3,0,6.82306,0.15 --7.49872,0.879887,0.932037,2,3,0,7.51782,0.15 --7.49872,0.948913,0.932037,1,1,0,7.85236,0.15 --8.04293,0.922216,0.932037,1,1,0,8.07282,0.15 --7.35293,1,0.932037,2,3,0,7.93996,0.15 --7.35293,0.883408,0.932037,1,1,0,8.02395,0.15 --6.80493,1,0.932037,1,3,0,7.25923,0.15 --6.82698,0.998151,0.932037,2,3,0,6.83752,0.15 --6.88114,0.986899,0.932037,1,1,0,6.89144,0.15 --6.74827,0.964042,0.932037,2,3,0,7.17919,0.15 --6.93633,0.882605,0.932037,2,3,0,7.40536,0.15 --8.02922,0.766313,0.932037,1,3,0,8.54794,0.15 --6.88064,1,0.932037,1,1,0,7.62039,0.15 --6.99856,0.960668,0.932037,1,1,0,7.02674,0.15 --6.9205,1,0.932037,1,1,0,7.0171,0.15 --7.23497,0.895629,0.932037,1,1,0,7.26191,0.15 --6.81255,0.65863,0.932037,2,3,0,8.81442,0.15 --6.8557,0.986062,0.932037,1,1,0,6.86976,0.15 --6.8557,0.724177,0.932037,1,3,0,7.66358,0.15 --6.75301,0.997497,0.932037,1,3,0,6.85969,0.15 --9.62741,0.500859,0.932037,1,3,0,9.855,0.15 --7.2064,1,0.932037,2,3,0,8.74926,0.15 --6.75431,0.815869,0.932037,2,3,0,7.99167,0.15 --6.77251,0.990864,0.932037,2,3,0,6.80395,0.15 --7.81008,0.844756,0.932037,2,3,0,8.00316,0.15 --8.11847,0.959711,0.932037,1,1,0,8.22643,0.15 --9.24757,0.890376,0.932037,2,3,0,10.2168,0.15 --6.88942,0.799493,0.932037,1,3,0,10.3695,0.15 --6.88942,0.261019,0.932037,1,3,0,10.9724,0.15 --6.80124,0.870892,0.932037,2,3,0,7.43049,0.15 --6.74978,0.998863,0.932037,1,3,0,6.80164,0.15 --7.17245,0.854684,0.932037,2,3,0,7.6,0.15 --8.81972,0.538088,0.932037,1,1,0,8.86828,0.15 --7.40622,1,0.932037,2,3,0,8.5393,0.15 --7.26318,0.961918,0.932037,2,3,0,8.05474,0.15 --7.34728,0.984906,0.932037,1,1,0,7.43596,0.15 --6.86243,0.997387,0.932037,1,3,0,7.24798,0.15 --6.89308,0.975225,0.932037,1,3,0,7.09809,0.15 --7.32886,0.939094,0.932037,2,3,0,7.34307,0.15 --7.57152,0.913466,0.932037,1,1,0,7.74604,0.15 --6.94728,1,0.932037,1,1,0,7.36923,0.15 --6.81842,1,0.932037,1,1,0,6.91356,0.15 --6.76216,1,0.932037,2,3,0,6.80728,0.15 --6.85768,0.974601,0.932037,2,3,0,6.94204,0.15 --6.77062,0.988638,0.932037,1,3,0,6.90934,0.15 --6.91451,0.978923,0.932037,2,3,0,6.91452,0.15 --6.78227,1,0.932037,1,1,0,6.87294,0.15 --7.62346,0.806356,0.932037,1,3,0,7.63204,0.15 --6.83206,1,0.932037,1,1,0,7.34914,0.15 --7.29941,0.815839,0.932037,2,3,0,7.843,0.15 --7.31671,0.996885,0.932037,1,1,0,7.4311,0.15 --6.78343,0.991818,0.932037,2,3,0,7.37529,0.15 --6.86006,0.980687,0.932037,1,1,0,6.8608,0.15 --6.77605,1,0.932037,1,1,0,6.83853,0.15 --6.86042,0.973286,0.932037,1,3,0,6.93518,0.15 --6.75338,0.997358,0.932037,1,3,0,6.86494,0.15 --6.83799,0.976298,0.932037,1,3,0,6.868,0.15 --7.00374,0.713633,0.932037,2,3,0,8.19304,0.15 --6.75259,0.98344,0.932037,2,3,0,7.10169,0.15 --6.79496,0.987555,0.932037,1,3,0,6.81495,0.15 --6.9391,0.981259,0.932037,2,3,0,6.94195,0.15 --7.22517,0.904285,0.932037,1,1,0,7.25937,0.15 --7.21052,1,0.932037,1,1,0,7.37602,0.15 --6.75985,0.787087,0.932037,2,3,0,8.11836,0.15 --6.86031,0.946513,0.932037,2,3,0,7.03793,0.15 --8.19403,0.780681,0.932037,1,3,0,8.38612,0.15 --6.7609,1,0.932037,2,3,0,7.87703,0.15 --6.95309,0.78127,0.932037,2,3,0,7.88077,0.15 --9.56705,0.565191,0.932037,1,3,0,10.2818,0.15 --6.75776,1,0.932037,1,3,0,8.93023,0.15 --6.77632,0.994978,0.932037,1,1,0,6.77679,0.15 --6.82265,0.996019,0.932037,2,3,0,6.82416,0.15 --6.89139,0.983404,0.932037,1,1,0,6.89889,0.15 --6.89139,0.777655,0.932037,1,3,0,8.12126,0.15 --7.13161,0.926877,0.932037,1,3,0,7.44089,0.15 --8.35546,0.633109,0.932037,1,1,0,8.40627,0.15 --7.06834,0.886614,0.932037,1,3,0,8.96147,0.15 --7.28399,0.958522,0.932037,1,1,0,7.31204,0.15 --7.28327,1,0.932037,1,1,0,7.40052,0.15 --7.93284,0.898826,0.932037,1,1,0,7.93762,0.15 --7.81694,1,0.932037,1,1,0,8.08024,0.15 --7.6919,1,0.932037,1,1,0,7.94232,0.15 --6.76979,0.932315,0.932037,1,3,0,7.86435,0.15 --6.97696,0.966811,0.932037,2,3,0,6.99094,0.15 --7.49857,0.902721,0.932037,2,3,0,7.93254,0.15 --7.50324,0.999237,0.932037,1,1,0,7.65578,0.15 --6.91611,1,0.932037,2,3,0,7.33316,0.15 --6.74956,1,0.932037,1,3,0,6.89861,0.15 --8.35635,0.667972,0.932037,1,3,0,8.48862,0.15 --8.35709,0.999715,0.932037,1,1,0,8.94945,0.15 --7.01492,1,0.932037,2,3,0,8.49553,0.15 --7.08433,0.985549,0.932037,1,1,0,7.13054,0.15 --7.29556,0.986569,0.932037,2,3,0,7.32674,0.15 --6.82002,1,0.932037,2,3,0,7.16863,0.15 --6.82002,0.827599,0.932037,1,1,0,7.18527,0.15 --6.96415,0.766705,0.932037,2,3,0,7.8936,0.15 --7.10108,0.953204,0.932037,1,1,0,7.15511,0.15 --7.08567,1,0.932037,1,1,0,7.2067,0.15 --8.18465,0.665208,0.932037,1,1,0,8.22692,0.15 --6.98816,1,0.932037,2,3,0,8.27915,0.15 --6.9991,0.951104,0.932037,2,3,0,7.40622,0.15 --6.87003,0.959893,0.932037,1,3,0,7.2399,0.15 --8.86597,0.67165,0.932037,1,3,0,9.3173,0.15 --9.22117,0.969943,0.932037,1,1,0,9.41531,0.15 --10.3294,0.926924,0.932037,1,1,0,10.3727,0.15 --8.32325,0.989504,0.932037,2,3,0,10.9313,0.15 --8.85098,0.946649,0.932037,1,1,0,8.95519,0.15 --9.31959,0.960963,0.932037,1,1,0,9.47897,0.15 --10.3022,0.93649,0.932037,1,1,0,10.3691,0.15 --10.3363,0.998194,0.932037,1,1,0,10.7444,0.15 --7.06812,1,0.932037,2,3,0,10.0104,0.15 --7.09449,0.998201,0.932037,2,3,0,7.16215,0.15 --7.41044,0.941678,0.932037,1,1,0,7.42835,0.15 --6.74954,0.897451,0.932037,2,3,0,8.358,0.15 --7.01483,0.952044,0.932037,2,3,0,7.06471,0.15 --7.04361,0.949288,0.932037,1,3,0,7.50082,0.15 --6.84344,1,0.932037,1,1,0,6.98754,0.15 --6.76346,1,0.932037,1,1,0,6.81845,0.15 --6.77171,0.981093,0.932037,2,3,0,6.85292,0.15 --6.84864,0.975946,0.932037,1,1,0,6.84963,0.15 --6.79009,1,0.932037,1,1,0,6.8359,0.15 --7.3603,0.823525,0.932037,1,3,0,7.7309,0.15 --7.81047,0.929962,0.932037,1,1,0,7.84015,0.15 --7.78154,1,0.932037,1,1,0,7.99363,0.15 --6.7517,1,0.932037,2,3,0,7.65292,0.15 --6.78798,0.848042,0.932037,2,3,0,7.61324,0.15 --6.76,0.921142,0.932037,2,3,0,7.25211,0.15 --6.79557,0.983288,0.932037,2,3,0,6.85013,0.15 --7.19183,0.926349,0.932037,1,3,0,7.21025,0.15 --7.38792,0.988215,0.932037,2,3,0,7.43851,0.15 --6.96601,1,0.932037,2,3,0,7.30212,0.15 --7.05786,0.980406,0.932037,1,1,0,7.08973,0.15 --7.16196,0.993052,0.932037,2,3,0,7.20877,0.15 --6.77281,0.912405,0.932037,2,3,0,8.28922,0.15 --6.75974,1,0.932037,1,1,0,6.77054,0.15 --6.74904,0.896827,0.932037,2,3,0,7.34903,0.15 --6.99017,0.938166,0.932037,1,3,0,7.02613,0.15 --9.18272,0.444151,0.932037,1,1,0,9.18621,0.15 --7.27998,1,0.932037,1,1,0,8.53072,0.15 --6.81588,0.976625,0.932037,1,3,0,7.39388,0.15 --6.85158,0.981914,0.932037,1,3,0,6.98055,0.15 --7.61781,0.765141,0.932037,1,1,0,7.61874,0.15 --6.79258,0.997137,0.932037,1,3,0,7.61795,0.15 --8.09444,0.722711,0.932037,1,3,0,8.35089,0.15 --8.09444,0.731058,0.932037,1,1,0,9.00859,0.15 --8.96537,0.715603,0.932037,1,1,0,9.37304,0.15 --8.78035,1,0.932037,1,1,0,9.62889,0.15 --7.21874,0.836764,0.932037,1,3,0,9.71962,0.15 --6.75061,0.911636,0.932037,2,3,0,8.14176,0.15 --7.65204,0.639147,0.932037,2,3,0,8.88575,0.15 --6.95151,1,0.932037,1,1,0,7.42019,0.15 --7.03245,0.89047,0.932037,2,3,0,7.4925,0.15 --6.93323,1,0.932037,1,1,0,7.03482,0.15 --6.93323,0.839701,0.932037,1,3,0,7.95662,0.15 --6.8181,1,0.932037,1,1,0,6.90794,0.15 --6.96817,0.964576,0.932037,1,1,0,6.96901,0.15 --6.75177,1,0.932037,2,3,0,6.94088,0.15 --6.75748,0.994596,0.932037,2,3,0,6.77947,0.15 --6.75598,1,0.932037,2,3,0,6.75911,0.15 --7.35596,0.838068,0.932037,1,3,0,7.60728,0.15 --6.75779,0.906448,0.932037,2,3,0,8.09794,0.15 --6.83162,0.952059,0.932037,2,3,0,6.9908,0.15 --7.67816,0.806003,0.932037,1,3,0,7.67819,0.15 --6.92603,0.932427,0.932037,1,3,0,8.01057,0.15 --7.07054,0.950192,0.932037,2,3,0,7.4224,0.15 --6.7484,0.994215,0.932037,1,3,0,7.0485,0.15 --7.08258,0.918347,0.932037,1,3,0,7.15481,0.15 --7.08258,0.737878,0.932037,1,3,0,9.18161,0.15 --7.35202,0.949338,0.932037,1,1,0,7.3743,0.15 --6.74881,0.983413,0.932037,1,3,0,7.33477,0.15 --6.95539,0.949695,0.932037,1,3,0,6.9911,0.15 --7.15608,0.958335,0.932037,1,1,0,7.16954,0.15 --6.83348,0.99773,0.932037,1,3,0,7.08351,0.15 --7.24483,0.931935,0.932037,1,3,0,7.25275,0.15 --6.98535,1,0.932037,1,1,0,7.20252,0.15 --6.79714,0.986131,0.932037,2,3,0,7.11242,0.15 --6.75975,0.994219,0.932037,2,3,0,6.84154,0.15 --7.85764,0.768175,0.932037,1,3,0,8.13554,0.15 --7.75607,1,0.932037,2,3,0,8.00291,0.15 --7.45436,1,0.932037,1,1,0,7.77479,0.15 --6.75812,0.956844,0.932037,1,3,0,7.53861,0.15 --6.78653,0.996723,0.932037,2,3,0,6.78699,0.15 --6.78653,0.689753,0.932037,1,3,0,7.7558,0.15 --7.13767,0.941965,0.932037,2,3,0,7.15428,0.15 --6.8166,0.824152,0.932037,2,3,0,8.71226,0.15 --6.79612,1,0.932037,1,1,0,6.82009,0.15 --7.16066,0.926371,0.932037,2,3,0,7.36744,0.15 --6.7485,0.867631,0.932037,2,3,0,8.25218,0.15 --6.7485,0.374064,0.932037,1,3,0,9.69677,0.15 --6.906,0.899506,0.932037,2,3,0,7.30301,0.15 --6.97495,0.960716,0.932037,1,3,0,7.26467,0.15 --6.87784,0.957976,0.932037,1,3,0,7.22871,0.15 --6.76764,1,0.932037,1,3,0,6.85023,0.15 --7.06045,0.939987,0.932037,2,3,0,7.20313,0.15 --7.56678,0.90837,0.932037,1,1,0,7.56807,0.15 --8.21909,0.9114,0.932037,1,1,0,8.24106,0.15 --7.1694,0.972439,0.932037,1,3,0,8.05615,0.15 --7.51659,0.939017,0.932037,1,1,0,7.53941,0.15 --6.80441,1,0.932037,2,3,0,7.33714,0.15 --6.84769,0.986098,0.932037,1,1,0,6.85924,0.15 --6.75447,1,0.932037,1,3,0,6.82024,0.15 --6.75447,0.941471,0.932037,1,3,0,6.92636,0.15 --7.31897,0.848317,0.932037,1,3,0,7.54592,0.15 --7.51256,0.936742,0.932037,2,3,0,8.23518,0.15 --7.00844,1,0.932037,1,1,0,7.41354,0.15 --6.81721,1,0.932037,2,3,0,6.95395,0.15 --7.45194,0.852204,0.932037,1,3,0,7.45194,0.15 --6.87259,1,0.932037,2,3,0,7.42285,0.15 --6.75616,1,0.932037,1,3,0,6.8471,0.15 --6.89502,0.961898,0.932037,1,3,0,6.94092,0.15 --6.77836,1,0.932037,2,3,0,6.87334,0.15 --6.79421,0.982898,0.932037,2,3,0,6.9004,0.15 --7.26621,0.798873,0.932037,2,3,0,7.90837,0.15 --6.86681,0.892468,0.932037,2,3,0,9.22078,0.15 --6.79361,1,0.932037,1,1,0,6.85058,0.15 --6.77748,1,0.932037,2,3,0,6.79435,0.15 --6.77887,0.999632,0.932037,1,1,0,6.78659,0.15 --6.78375,0.998717,0.932037,1,1,0,6.79108,0.15 --6.74954,0.987641,0.932037,2,3,0,6.86737,0.15 --7.83854,0.756676,0.932037,1,3,0,7.90005,0.15 --7.50039,1,0.932037,1,1,0,7.94015,0.15 --6.7491,1,0.932037,1,3,0,7.35194,0.15 --6.7636,0.996702,0.932037,1,3,0,6.76405,0.15 --6.78205,0.845782,0.932037,2,3,0,7.69714,0.15 --6.75014,0.997559,0.932037,2,3,0,6.79806,0.15 --8.34122,0.670679,0.932037,1,3,0,8.47733,0.15 --7.99963,1,0.932037,1,1,0,8.62888,0.15 --8.76385,0.745363,0.932037,1,1,0,9.14503,0.15 --7.04418,1,0.932037,1,1,0,8.14217,0.15 --7.43305,0.868749,0.932037,1,1,0,7.49441,0.15 --7.43305,0.297658,0.932037,1,1,0,9.48457,0.15 --6.83865,0.968461,0.932037,1,3,0,7.58424,0.15 --7.53033,0.883514,0.932037,1,3,0,7.57348,0.15 --6.96261,0.979104,0.932037,2,3,0,7.81614,0.15 --7.0999,0.990364,0.932037,2,3,0,7.12317,0.15 --8.07438,0.928426,0.932037,2,3,0,8.16995,0.15 --7.08332,1,0.932037,1,1,0,7.75278,0.15 --6.82918,1,0.932037,1,1,0,7.0036,0.15 --7.53288,0.784396,0.932037,1,1,0,7.53294,0.15 --7.05091,1,0.932037,2,3,0,7.39902,0.15 --7.40251,0.977978,0.932037,2,3,0,7.41207,0.15 --6.82898,1,0.932037,2,3,0,7.24836,0.15 --6.79153,1,0.932037,1,1,0,6.8253,0.15 --6.79153,0.459704,0.932037,1,3,0,9.85444,0.15 --6.76756,1,0.932037,1,1,0,6.7869,0.15 --6.97728,0.932763,0.932037,1,3,0,7.10469,0.15 --6.95157,1,0.932037,1,1,0,7.01491,0.15 --6.75905,0.960947,0.932037,2,3,0,7.21354,0.15 --6.7615,0.936647,0.932037,2,3,0,7.06828,0.15 --6.89331,0.972573,0.932037,1,3,0,6.89762,0.15 --6.77722,0.906489,0.932037,2,3,0,7.52048,0.15 --7.05175,0.910169,0.932037,1,3,0,7.23443,0.15 --6.92365,1,0.932037,1,1,0,7.04143,0.15 --6.92285,1,0.932037,2,3,0,6.9668,0.15 --7.13379,0.955304,0.932037,1,1,0,7.14184,0.15 --7.4804,0.884188,0.932037,1,3,0,8.21421,0.15 --7.40948,1,0.932037,1,1,0,7.67663,0.15 --6.7729,0.94969,0.932037,2,3,0,7.7319,0.15 --6.88124,0.967448,0.932037,1,3,0,6.95576,0.15 --8.24949,0.746027,0.932037,2,3,0,8.25756,0.15 --6.96905,1,0.932037,1,3,0,8.10239,0.15 --7.27306,0.939109,0.932037,1,1,0,7.27863,0.15 --7.00308,0.956818,0.932037,1,3,0,7.78354,0.15 --6.75472,0.998187,0.932037,1,3,0,6.9917,0.15 --6.75494,0.999941,0.932037,1,1,0,6.75678,0.15 --6.87358,0.96725,0.932037,1,3,0,6.91321,0.15 --6.88529,0.996099,0.932037,1,1,0,6.92345,0.15 --8.16803,0.810902,0.932037,2,3,0,8.16856,0.15 --8.16803,0.500501,0.932037,1,1,0,9.65653,0.15 --6.84099,0.909134,0.932037,2,3,0,8.86386,0.15 --6.7482,0.97014,0.932037,2,3,0,7.07304,0.15 --6.88126,0.965042,0.932037,1,3,0,6.91312,0.15 --6.91537,0.971395,0.932037,1,3,0,7.15288,0.15 --6.74803,1,0.932037,1,3,0,6.88372,0.15 --6.78862,0.991449,0.932037,2,3,0,6.8033,0.15 --6.75166,1,0.932037,1,3,0,6.77932,0.15 --7.64177,0.652534,0.932037,2,3,0,8.81793,0.15 --7.39307,1,0.932037,1,1,0,7.74629,0.15 --7.39307,0.385651,0.932037,1,1,0,9.0256,0.15 --6.79731,0.988358,0.932037,1,3,0,7.44468,0.15 --7.26742,0.912562,0.932037,1,3,0,7.29563,0.15 --8.03606,0.881597,0.932037,1,1,0,8.03636,0.15 --7.18684,0.969203,0.932037,1,3,0,7.89297,0.15 --7.82185,0.895632,0.932037,1,1,0,7.82342,0.15 --6.9258,0.999365,0.932037,1,3,0,7.68077,0.15 --6.84345,1,0.932037,1,1,0,6.91592,0.15 --8.02819,0.799375,0.932037,1,3,0,8.18865,0.15 --9.74173,0.805241,0.932037,1,3,0,9.76127,0.15 --8.33143,1,0.932037,1,1,0,9.60755,0.15 --7.18582,0.972127,0.932037,1,3,0,8.16309,0.15 --6.92783,1,0.932037,1,1,0,7.13538,0.15 --7.11005,0.961217,0.932037,1,1,0,7.12168,0.15 --6.79373,1,0.932037,1,3,0,7.04271,0.15 --7.11956,0.955726,0.932037,2,3,0,7.20271,0.15 --7.48398,0.958399,0.932037,2,3,0,7.57261,0.15 --7.05344,1,0.932037,2,3,0,7.38948,0.15 --6.89526,1,0.932037,1,1,0,7.02854,0.15 --6.75297,0.996542,0.932037,2,3,0,6.92061,0.15 --6.83977,0.975809,0.932037,1,3,0,6.86918,0.15 --6.8251,1,0.932037,2,3,0,6.85672,0.15 --6.76932,1,0.932037,1,1,0,6.8089,0.15 --7.2411,0.887441,0.932037,1,3,0,7.24713,0.15 --6.77198,0.978158,0.932037,2,3,0,7.39681,0.15 --6.91344,0.959133,0.932037,1,3,0,6.99309,0.15 --7.04881,0.85015,0.932037,2,3,0,7.62026,0.15 --7.60525,0.863371,0.932037,1,3,0,8.23873,0.15 --6.78473,0.999819,0.932037,1,3,0,7.58373,0.15 --6.78473,0.898858,0.932037,1,3,0,7.22409,0.15 --7.78377,0.579188,0.932037,2,3,0,9.7066,0.15 --7.70743,1,0.932037,1,1,0,7.93357,0.15 --7.96247,0.96471,0.932037,1,1,0,8.07171,0.15 --7.12674,0.974601,0.932037,1,3,0,7.81844,0.15 --6.75681,1,0.932037,1,3,0,7.07784,0.15 --6.74929,0.899259,0.932037,2,3,0,7.32696,0.15 --6.74929,0.646445,0.932037,1,3,0,8.08205,0.15 --7.15628,0.857788,0.932037,2,3,0,7.57467,0.15 --7.75903,0.923317,0.932037,2,3,0,7.84476,0.15 --6.85106,0.987384,0.932037,2,3,0,7.98227,0.15 --8.0294,0.746944,0.932037,1,3,0,8.39902,0.15 --6.8022,0.859301,0.932037,2,3,0,8.84948,0.15 --6.77032,1,0.932037,1,1,0,6.79542,0.15 --7.64151,0.740592,0.932037,2,3,0,8.46249,0.15 --8.37811,0.917274,0.932037,2,3,0,8.62816,0.15 --7.49482,1,0.932037,1,1,0,8.22793,0.15 --9.18827,0.523755,0.932037,1,1,0,9.33046,0.15 --7.76093,1,0.932037,1,1,0,8.88732,0.15 --6.97798,1,0.932037,1,1,0,7.50122,0.15 --7.35575,0.873861,0.932037,1,1,0,7.39654,0.15 --7.35575,0.110444,0.932037,1,1,0,11.0745,0.15 --7.35575,0.0800688,0.932037,1,3,0,15.4327,0.15 --7.48223,0.672997,0.932037,1,3,0,8.8362,0.15 --6.78001,0.901552,0.932037,2,3,0,8.42084,0.15 --7.27496,0.91456,0.932037,2,3,0,7.304,0.15 --7.27496,0.889573,0.932037,1,1,0,7.87313,0.15 --6.74818,0.981145,0.932037,1,3,0,7.27579,0.15 --6.74818,0.540736,0.932037,1,3,0,8.56126,0.15 --6.84577,0.975084,0.932037,1,3,0,6.8552,0.15 --6.84577,0.460221,0.932037,1,3,0,8.80117,0.15 --7.22623,0.87774,0.932037,1,1,0,7.23183,0.15 --6.86085,0.957571,0.932037,1,3,0,7.44679,0.15 --6.84216,1,0.932037,1,1,0,6.8764,0.15 --6.82726,0.902432,0.932037,2,3,0,7.67881,0.15 --7.01244,0.957121,0.932037,1,1,0,7.01282,0.15 --6.753,0.947388,0.932037,2,3,0,7.40574,0.15 --6.75612,0.999688,0.932037,2,3,0,6.75709,0.15 --7.10487,0.901325,0.932037,1,3,0,7.25063,0.15 --6.75288,0.934052,0.932037,2,3,0,7.61355,0.15 --6.75911,0.998131,0.932037,1,1,0,6.75973,0.15 --6.97448,0.935151,0.932037,1,3,0,7.07933,0.15 --8.33687,0.727783,0.932037,1,3,0,8.95629,0.15 --7.19311,1,0.932037,1,1,0,7.97929,0.15 --6.86239,0.957299,0.932037,1,3,0,7.41859,0.15 --8.81489,0.675307,0.932037,1,3,0,9.25628,0.15 --9.08174,0.976561,0.932037,1,1,0,9.30033,0.15 --8.72465,1,0.932037,2,3,0,9.23184,0.15 --9.02058,0.973248,0.932037,1,1,0,9.2222,0.15 --6.75096,0.87105,0.932037,1,3,0,9.3739,0.15 --6.77061,0.8673,0.932037,2,3,0,7.49474,0.15 --7.18049,0.687335,0.932037,2,3,0,8.77168,0.15 --6.83677,0.997719,0.932037,1,3,0,7.10412,0.15 --7.0242,0.942244,0.932037,1,3,0,7.2293,0.15 --6.75027,1,0.932037,1,3,0,6.95554,0.15 --7.19098,0.922423,0.932037,2,3,0,7.25741,0.15 --6.75095,0.998409,0.932037,2,3,0,7.18042,0.15 --6.87936,0.965588,0.932037,1,3,0,6.90968,0.15 --6.78928,1,0.932037,2,3,0,6.85581,0.15 --7.24713,0.912602,0.932037,1,3,0,7.27833,0.15 --7.64349,0.933948,0.932037,1,1,0,7.6686,0.15 --7.89818,0.963732,0.932037,1,1,0,7.99945,0.15 --9.96894,0.866816,0.932037,2,3,0,10.0037,0.15 --8.27408,1,0.932037,1,1,0,9.73188,0.15 --8.97796,0.763178,0.932037,1,1,0,9.46742,0.15 --7.07994,1,0.932037,2,3,0,9.38837,0.15 --6.75736,0.879611,0.932037,2,3,0,8.01228,0.15 --6.7519,1,0.932037,1,1,0,6.75617,0.15 --6.78524,0.992274,0.932037,1,3,0,6.78539,0.15 --7.86462,0.758008,0.932037,1,3,0,7.87664,0.15 --7.97349,0.959152,0.932037,1,1,0,8.36464,0.15 --7.11236,1,0.932037,2,3,0,7.71314,0.15 --7.71805,0.799983,0.932037,1,1,0,7.78908,0.15 --9.81575,0.716945,0.932037,2,3,0,10.0088,0.15 --7.32169,1,0.932037,1,1,0,8.93058,0.15 --6.74807,0.833534,0.932037,2,3,0,8.05557,0.15 --6.74803,1,0.932037,2,3,0,6.74806,0.15 --6.75538,0.991246,0.932037,2,3,0,6.79373,0.15 --6.93792,0.962649,0.932037,2,3,0,7.01983,0.15 --7.2862,0.88434,0.932037,1,1,0,7.31645,0.15 --6.99084,1,0.932037,2,3,0,7.19845,0.15 --7.65042,0.905856,0.932037,2,3,0,8.20379,0.15 --7.41337,1,0.932037,1,1,0,7.7679,0.15 --8.85702,0.576942,0.932037,1,1,0,8.98493,0.15 --6.78589,1,0.932037,1,3,0,8.17164,0.15 --6.90223,0.954007,0.932037,1,3,0,7.03131,0.15 --8.14191,0.811993,0.932037,1,3,0,8.27248,0.15 --8.10592,1,0.932037,1,1,0,8.36491,0.15 --8.2689,0.993522,0.932037,2,3,0,8.4528,0.15 --9.32894,0.899169,0.932037,1,1,0,9.34484,0.15 --8.02809,1,0.932037,1,1,0,9.18688,0.15 --6.99166,0.993898,0.932037,1,3,0,7.87392,0.15 --8.06441,0.911089,0.932037,2,3,0,8.31617,0.15 --8.7506,0.922675,0.932037,2,3,0,9.16274,0.15 --7.8978,1,0.932037,1,1,0,8.75888,0.15 --6.94446,1,0.932037,1,1,0,7.56554,0.15 --8.54285,0.771096,0.932037,2,3,0,8.54633,0.15 --6.78594,1,0.932037,1,3,0,7.9627,0.15 --6.78594,0.906519,0.932037,1,3,0,7.16707,0.15 --6.92112,0.942672,0.932037,2,3,0,7.08892,0.15 --6.87261,0.875781,0.932037,2,3,0,8.17854,0.15 --7.08975,0.952205,0.932037,1,1,0,7.09201,0.15 --7.02149,1,0.932037,2,3,0,7.12326,0.15 --7.04584,0.998287,0.932037,2,3,0,7.10444,0.15 --7.04584,0.672299,0.932037,1,3,0,9.55749,0.15 --7.77921,0.832639,0.932037,1,3,0,8.43718,0.15 --6.93132,0.932106,0.932037,1,3,0,8.11383,0.15 --7.37155,0.932331,0.932037,1,3,0,7.37171,0.15 --6.74802,0.977866,0.932037,1,3,0,7.37247,0.15 --6.90501,0.947815,0.932037,2,3,0,7.04469,0.15 --6.90501,0.515929,0.932037,1,1,0,8.06608,0.15 --7.26517,0.747153,0.932037,2,3,0,8.10285,0.15 --6.77759,0.959776,0.932037,1,3,0,7.38193,0.15 --6.75799,1,0.932037,1,1,0,6.77244,0.15 --6.79277,0.99575,0.932037,2,3,0,6.79381,0.15 --6.76109,1,0.932037,1,1,0,6.78457,0.15 --6.7596,0.969448,0.932037,2,3,0,6.95304,0.15 --6.79156,0.988014,0.932037,2,3,0,6.84381,0.15 --6.78312,1,0.932037,1,1,0,6.79707,0.15 --6.77762,0.949288,0.932037,2,3,0,7.08484,0.15 --7.09494,0.924565,0.932037,1,3,0,7.09566,0.15 --6.95426,1,0.932037,2,3,0,7.09613,0.15 --7.28442,0.889685,0.932037,1,1,0,7.32064,0.15 --6.79484,0.997398,0.932037,2,3,0,7.36362,0.15 --6.81648,0.99449,0.932037,1,1,0,6.8247,0.15 --6.84974,0.991789,0.932037,1,1,0,6.86103,0.15 --6.74879,0.865097,0.932037,2,3,0,7.74614,0.15 --6.88211,0.967449,0.932037,1,3,0,6.902,0.15 --6.74845,0.957844,0.932037,2,3,0,7.24214,0.15 --7.20685,0.887254,0.932037,1,3,0,7.25766,0.15 --7.20685,0.564767,0.932037,1,3,0,8.81898,0.15 --7.20685,0.320306,0.932037,1,3,0,12.13,0.15 --6.97752,0.935114,0.932037,2,3,0,7.66742,0.15 --6.87049,1,0.932037,1,1,0,6.96427,0.15 --6.74837,0.966424,0.932037,2,3,0,7.14605,0.15 --6.98253,0.907073,0.932037,2,3,0,7.2529,0.15 --7.79349,0.745081,0.932037,1,1,0,7.81771,0.15 --6.77388,1,0.932037,1,3,0,7.46942,0.15 --6.90086,0.954561,0.932037,1,3,0,7.00827,0.15 --6.79914,1,0.932037,2,3,0,6.8716,0.15 --8.11178,0.713466,0.932037,1,3,0,8.12201,0.15 --8.11178,0.190125,0.932037,1,1,0,11.1441,0.15 --7.24841,1,0.932037,1,1,0,7.88964,0.15 --7.23081,1,0.932037,1,1,0,7.40517,0.15 --6.80377,1,0.932037,2,3,0,7.08741,0.15 --6.85418,0.994603,0.932037,2,3,0,6.86482,0.15 --6.74965,0.98222,0.932037,2,3,0,6.95136,0.15 --7.03844,0.928412,0.932037,1,3,0,7.0566,0.15 --7.22621,0.808036,0.932037,1,3,0,7.93488,0.15 --6.77583,0.927823,0.932037,2,3,0,7.69178,0.15 --6.86535,0.971878,0.932037,1,1,0,6.86662,0.15 --6.77928,0.990027,0.932037,1,3,0,6.92787,0.15 --6.78451,0.998625,0.932037,1,1,0,6.79187,0.15 --6.78451,0.773207,0.932037,1,3,0,7.90069,0.15 --6.75189,0.967957,0.932037,2,3,0,6.99818,0.15 --6.88731,0.96889,0.932037,1,3,0,6.89993,0.15 --6.83332,1,0.932037,1,1,0,6.8859,0.15 --6.96445,0.969353,0.932037,1,1,0,6.9678,0.15 --6.74853,0.993691,0.932037,1,3,0,6.95955,0.15 --6.79227,0.98474,0.932037,2,3,0,6.83336,0.15 --6.78141,0.965461,0.932037,2,3,0,6.96936,0.15 --6.75837,0.995995,0.932037,1,3,0,6.80286,0.15 --6.80841,0.982527,0.932037,1,3,0,6.84862,0.15 --6.92145,0.989324,0.932037,2,3,0,6.92312,0.15 --6.94393,0.992369,0.932037,1,1,0,6.99666,0.15 --8.0719,0.66438,0.932037,1,1,0,8.0808,0.15 --8.30789,0.913191,0.932037,1,1,0,8.7652,0.15 --6.88395,1,0.932037,2,3,0,7.79691,0.15 --6.88395,0.525809,0.932037,1,3,0,8.46396,0.15 --7.66074,0.862122,0.932037,2,3,0,7.66587,0.15 --7.48718,1,0.932037,1,1,0,7.73391,0.15 --7.24771,1,0.932037,1,1,0,7.49536,0.15 --6.75294,0.907734,0.932037,2,3,0,8.2612,0.15 --6.75294,0.882023,0.932037,1,3,0,7.23222,0.15 --6.75294,0.648763,0.932037,1,3,0,8.1161,0.15 --6.75671,0.998954,0.932037,1,1,0,6.75747,0.15 --7.51733,0.821118,0.932037,1,3,0,7.63136,0.15 --7.23718,1,0.932037,2,3,0,7.55107,0.15 --7.23718,0.584164,0.932037,1,1,0,8.1976,0.15 --6.75151,1,0.932037,1,3,0,7.10952,0.15 --6.76119,0.993068,0.932037,2,3,0,6.78713,0.15 --6.79508,0.984853,0.932037,2,3,0,6.84415,0.15 --8.30022,0.789495,0.932037,2,3,0,8.51335,0.15 --7.08882,0.985416,0.932037,1,3,0,8.13433,0.15 --6.74804,0.991804,0.932037,1,3,0,7.07332,0.15 --6.76797,0.994746,0.932037,1,3,0,6.77114,0.15 --6.87611,0.984786,0.932037,2,3,0,6.88112,0.15 --7.10363,0.930507,0.932037,1,3,0,7.38443,0.15 --7.1931,0.968366,0.932037,1,1,0,7.30124,0.15 --7.02744,1,0.932037,1,1,0,7.20716,0.15 --7.47087,0.716122,0.932037,1,3,0,8.36114,0.15 --7.77445,0.953734,0.932037,1,1,0,7.84235,0.15 --7.10232,0.976364,0.932037,1,3,0,7.65215,0.15 --6.89247,0.977052,0.932037,2,3,0,7.36801,0.15 --6.88406,1,0.932037,2,3,0,6.92287,0.15 --7.00739,0.972361,0.932037,1,1,0,7.01912,0.15 --7.4581,0.959878,0.932037,2,3,0,7.46539,0.15 --7.4581,0.358894,0.932037,1,1,0,9.21803,0.15 --8.39486,0.699937,0.932037,1,1,0,8.5651,0.15 --6.98009,0.931934,0.932037,1,3,0,8.77344,0.15 --7.75622,0.895607,0.932037,2,3,0,8.30908,0.15 --7.45294,1,0.932037,1,1,0,7.85686,0.15 --7.81191,0.87317,0.932037,1,1,0,8.02199,0.15 --7.07889,1,0.932037,2,3,0,7.65163,0.15 --8.02018,0.93056,0.932037,2,3,0,8.11738,0.15 --7.32245,1,0.932037,1,1,0,7.89443,0.15 --7.11924,1,0.932037,2,3,0,7.35057,0.15 --7.13426,0.994637,0.932037,1,1,0,7.25741,0.15 --6.81324,1,0.932037,1,1,0,7.02809,0.15 --7.90962,0.554652,0.932037,2,3,0,9.26763,0.15 --10.1284,0.790216,0.932037,1,3,0,10.2416,0.15 --8.90062,1,0.932037,1,1,0,10.0537,0.15 --6.80723,1,0.932037,2,3,0,8.62687,0.15 --6.86729,0.973895,0.932037,2,3,0,7.02735,0.15 --6.79392,1,0.932037,1,1,0,6.85106,0.15 --7.40012,0.888235,0.932037,2,3,0,7.70807,0.15 --6.93792,1,0.932037,2,3,0,7.29993,0.15 --7.00949,0.984312,0.932037,1,1,0,7.03975,0.15 --7.13808,0.973678,0.932037,1,1,0,7.17115,0.15 --6.78692,0.965669,0.932037,1,3,0,7.25761,0.15 --6.87345,0.885758,0.932037,2,3,0,7.29989,0.15 --7.7385,0.869841,0.932037,2,3,0,7.74068,0.15 --7.7385,0.102215,0.932037,1,1,0,11.7215,0.15 --8.03266,0.893586,0.932037,1,1,0,8.35655,0.15 --8.22194,0.92979,0.932037,1,1,0,8.66902,0.15 --7.3561,1,0.932037,2,3,0,7.97443,0.15 --6.78127,1,0.932037,1,3,0,7.27452,0.15 --6.82275,0.984055,0.932037,1,3,0,6.89491,0.15 --7.6571,0.742126,0.932037,1,3,0,8.29259,0.15 --8.03026,0.829375,0.932037,1,3,0,9.64909,0.15 --7.37223,1,0.932037,1,1,0,7.93907,0.15 --6.79515,0.988926,0.932037,1,3,0,7.42116,0.15 --6.82487,0.992469,0.932037,1,1,0,6.83189,0.15 --7.12496,0.969767,0.932037,2,3,0,7.16248,0.15 --8.26834,0.652897,0.932037,1,1,0,8.32004,0.15 --8.67193,0.856184,0.932037,1,1,0,9.18766,0.15 --6.90442,1,0.932037,1,1,0,8.03082,0.15 --7.94108,0.68975,0.932037,1,1,0,7.94519,0.15 --7.78516,0.528351,0.932037,1,3,0,10.1916,0.15 --7.48177,1,0.932037,1,1,0,7.8074,0.15 --6.80338,0.90422,0.932037,2,3,0,8.07863,0.15 --6.89878,0.96934,0.932037,1,1,0,6.90583,0.15 --7.30937,0.866085,0.932037,1,1,0,7.32592,0.15 --6.85274,1,0.932037,1,1,0,7.15684,0.15 --6.85274,0.743543,0.932037,1,3,0,7.59752,0.15 --6.85274,0.563681,0.932037,1,3,0,9.12334,0.15 --7.14252,0.905701,0.932037,1,1,0,7.15244,0.15 --6.88606,1,0.932037,1,1,0,7.07414,0.15 --6.81352,1,0.932037,1,1,0,6.87385,0.15 --7.22426,0.603012,0.932037,2,3,0,8.88007,0.15 --6.75466,1,0.932037,1,3,0,7.17215,0.15 --6.77207,0.962029,0.932037,2,3,0,6.97984,0.15 --7.20122,0.9126,0.932037,1,3,0,7.2415,0.15 --7.97419,0.889846,0.932037,1,3,0,7.97427,0.15 --7.1089,0.976552,0.932037,1,3,0,7.82637,0.15 --7.1089,0.884111,0.932037,1,3,0,7.82698,0.15 --6.98201,0.835391,0.932037,2,3,0,9.32581,0.15 --7.4823,0.923908,0.932037,1,3,0,7.48233,0.15 --8.13382,0.907974,0.932037,1,1,0,8.15008,0.15 --7.0191,0.992287,0.932037,1,3,0,7.97523,0.15 --7.38884,0.929413,0.932037,1,1,0,7.39423,0.15 --7.05345,0.945247,0.932037,1,3,0,8.00914,0.15 --7.88022,0.88235,0.932037,2,3,0,7.92302,0.15 --7.47896,1,0.932037,1,1,0,7.94438,0.15 --8.26848,0.902685,0.932037,2,3,0,8.45552,0.15 --8.36145,0.964836,0.932037,1,1,0,8.90958,0.15 --6.80918,1,0.932037,1,3,0,7.82987,0.15 --7.23868,0.600861,0.932037,2,3,0,8.89375,0.15 --6.89006,1,0.932037,1,1,0,7.13419,0.15 --6.8044,1,0.932037,2,3,0,6.86543,0.15 --6.86009,0.995405,0.932037,2,3,0,6.86567,0.15 --6.86009,0.799308,0.932037,1,3,0,7.97123,0.15 --8.99856,0.728419,0.932037,2,3,0,9.23129,0.15 --9.09468,0.991837,0.932037,1,1,0,9.38997,0.15 --8.73756,1,0.932037,1,1,0,9.24597,0.15 --7.55986,0.937761,0.932037,1,3,0,8.57866,0.15 --7.99912,0.937675,0.932037,1,1,0,8.05117,0.15 --7.76055,1,0.932037,1,1,0,8.08641,0.15 --8.28732,0.977606,0.932037,2,3,0,8.34339,0.15 --6.82679,0.992827,0.932037,1,3,0,8.22618,0.15 --6.87274,0.996281,0.932037,2,3,0,6.88424,0.15 --6.78865,0.91873,0.932037,2,3,0,7.41091,0.15 --6.76058,1,0.932037,2,3,0,6.78115,0.15 --6.76058,0.44465,0.932037,2,3,0,10.1097,0.15 --8.19963,0.701948,0.932037,1,3,0,8.62984,0.15 --6.87827,1,0.932037,1,3,0,8.08649,0.15 --6.90011,0.99488,0.932037,1,1,0,6.92793,0.15 --7.58846,0.835161,0.932037,1,3,0,7.98997,0.15 --6.84168,1,0.932037,2,3,0,7.32922,0.15 --6.79898,0.907476,0.932037,2,3,0,7.23342,0.15 --7.18968,0.940405,0.932037,2,3,0,7.1897,0.15 --7.30081,0.811278,0.932037,2,3,0,8.27114,0.15 --7.44395,0.975262,0.932037,1,1,0,7.52376,0.15 --7.10913,1,0.932037,1,1,0,7.3984,0.15 --6.74902,0.994264,0.932037,1,3,0,7.08219,0.15 --7.95169,0.736282,0.932037,1,3,0,8.05657,0.15 -# -# Elapsed Time: 0.005 seconds (Warm-up) -# 0.012 seconds (Sampling) -# 0.017 seconds (Total) -# diff --git a/src/test/unit/mcmc/test_csv_files/bernoulli_const_2.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_const_2.csv deleted file mode 100644 index b4a727ec85..0000000000 --- a/src/test/unit/mcmc/test_csv_files/bernoulli_const_2.csv +++ /dev/null @@ -1,1057 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 35 -# stan_version_patch = 0 -# model = bernoulli_model -# start_datetime = 2024-07-20 18:56:30 UTC -# method = sample (Default) -# sample -# num_samples = 1000 (Default) -# num_warmup = 1000 (Default) -# save_warmup = false (Default) -# thin = 1 (Default) -# adapt -# engaged = true (Default) -# gamma = 0.05 (Default) -# delta = 0.8 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# save_metric = false (Default) -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# num_chains = 1 (Default) -# id = 1 (Default) -# data -# file = examples/bernoulli/bernoulli.data.json -# init = 2 (Default) -# random -# seed = 3635036313 (Default) -# output -# file = bernoulli_out.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -# sig_figs = -1 (Default) -# profile_file = profile.csv (Default) -# save_cmdstan_config = false (Default) -# num_threads = 1 (Default) -# stanc_version = stanc3 v2.35.0-25-gbb9ce42 -# stancflags = -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,zeta -# Adaptation terminated -# Step size = 0.932037 -# Diagonal elements of inverse mass matrix: -# 0.591014 --7.81355,0.761218,0.932037,1,3,0,8.13669,0.15 --8.71049,0.894869,0.932037,1,1,0,8.72268,0.15 --7.3531,0.957519,0.932037,1,3,0,8.53469,0.15 --7.02007,1,0.932037,1,1,0,7.29505,0.15 --8.49033,0.716959,0.932037,1,3,0,9.20407,0.15 --7.18724,1,0.932037,1,1,0,8.06459,0.15 --10.0621,0.636916,0.932037,2,3,0,10.0852,0.15 --8.11851,1,0.932037,1,1,0,9.65805,0.15 --6.86936,0.948809,0.932037,2,3,0,8.60097,0.15 --7.13756,0.935492,0.932037,2,3,0,7.43717,0.15 --7.09122,1,0.932037,1,1,0,7.19518,0.15 --6.91669,1,0.932037,1,1,0,7.06383,0.15 --6.76371,0.840542,0.932037,2,3,0,8.08838,0.15 --6.84805,0.983351,0.932037,1,3,0,6.84861,0.15 --8.3727,0.653843,0.932037,2,3,0,9.53079,0.15 --7.25928,0.806553,0.932037,1,3,0,9.43963,0.15 --6.95987,0.853051,0.932037,2,3,0,9.71446,0.15 --6.95334,1,0.932037,1,1,0,7.00708,0.15 --6.98417,0.993206,0.932037,1,1,0,7.02663,0.15 --8.9535,0.727406,0.932037,1,3,0,9.27425,0.15 --7.08162,0.995936,0.932037,2,3,0,9.00617,0.15 --7.16691,0.98306,0.932037,1,1,0,7.22238,0.15 --6.8898,1,0.932037,1,1,0,7.10684,0.15 --7.8902,0.846547,0.932037,1,3,0,7.96857,0.15 --7.24239,1,0.932037,1,1,0,7.78446,0.15 --7.94748,0.888836,0.932037,1,1,0,7.94857,0.15 --6.83694,0.864212,0.932037,2,3,0,8.91495,0.15 --6.83694,0.425574,0.932037,1,3,0,9.01249,0.15 --6.80131,0.985852,0.932037,1,3,0,6.93894,0.15 --6.74824,0.978446,0.932037,2,3,0,6.95179,0.15 --6.74832,0.945401,0.932037,2,3,0,7.0277,0.15 --6.85442,0.972221,0.932037,1,3,0,6.87104,0.15 --6.86406,0.996818,0.932037,1,1,0,6.89618,0.15 --7.11238,0.918504,0.932037,1,1,0,7.12695,0.15 --7.11238,0.431368,0.932037,1,1,0,8.53302,0.15 --6.79843,1,0.932037,1,1,0,7.00834,0.15 --6.74804,0.996585,0.932037,2,3,0,6.81732,0.15 --9.50952,0.509265,0.932037,1,3,0,9.67564,0.15 --8.27778,1,0.932037,1,1,0,9.48419,0.15 --7.2527,1,0.932037,2,3,0,8.04703,0.15 --6.75205,0.907555,0.932037,2,3,0,8.25548,0.15 --7.3411,0.857468,0.932037,1,3,0,7.42326,0.15 --7.07094,1,0.932037,1,1,0,7.32247,0.15 --7.04767,0.556206,0.932037,2,3,0,9.56585,0.15 --6.87625,1,0.932037,2,3,0,6.9975,0.15 --7.12535,0.945777,0.932037,1,1,0,7.12652,0.15 --7.12535,0.913353,0.932037,1,1,0,7.53634,0.15 --6.78755,1,0.932037,2,3,0,7.04258,0.15 --7.7545,0.780448,0.932037,1,3,0,7.76329,0.15 --6.77502,0.859809,0.932037,2,3,0,8.49845,0.15 --7.19668,0.726665,0.932037,2,3,0,8.64609,0.15 --9.64819,0.775506,0.932037,2,3,0,10.0305,0.15 --6.86272,0.7808,0.932037,1,3,0,10.9403,0.15 --6.99329,0.696273,0.932037,2,3,0,8.30815,0.15 --6.99329,0.700745,0.932037,1,1,0,7.63428,0.15 --7.11558,0.957857,0.932037,1,1,0,7.18108,0.15 --6.80487,1,0.932037,2,3,0,7.09376,0.15 --6.97334,0.955154,0.932037,2,3,0,7.16146,0.15 --7.2664,0.98044,0.932037,2,3,0,7.27318,0.15 --7.06819,1,0.932037,2,3,0,7.25815,0.15 --6.74803,0.991749,0.932037,1,3,0,7.05576,0.15 --7.05542,0.823118,0.932037,2,3,0,7.78068,0.15 --6.89251,1,0.932037,1,1,0,7.02757,0.15 --6.76584,0.932575,0.932037,2,3,0,7.49343,0.15 --6.75064,0.997924,0.932037,2,3,0,6.78013,0.15 --6.91371,0.96173,0.932037,1,3,0,6.93351,0.15 --6.74953,0.994131,0.932037,1,3,0,6.91533,0.15 --8.33302,0.635332,0.932037,1,3,0,9.05105,0.15 --8.04729,1,0.932037,1,1,0,8.43889,0.15 --8.42015,0.956552,0.932037,1,1,0,8.53727,0.15 --7.91154,1,0.932037,1,1,0,8.4304,0.15 --7.65791,1,0.932037,1,1,0,7.97836,0.15 --7.97869,0.955317,0.932037,1,1,0,8.06587,0.15 --6.81845,1,0.932037,1,3,0,7.88771,0.15 --6.76575,0.914099,0.932037,2,3,0,7.34829,0.15 --6.74964,0.996733,0.932037,2,3,0,6.78491,0.15 --6.77106,0.99494,0.932037,1,3,0,6.77141,0.15 --6.77106,0.945747,0.932037,1,3,0,6.92649,0.15 --6.77106,0.856062,0.932037,1,3,0,7.35372,0.15 --6.88028,0.948865,0.932037,2,3,0,7.03829,0.15 --7.33958,0.635769,0.932037,2,3,0,9.861,0.15 --8.06974,0.890942,0.932037,1,1,0,8.07292,0.15 --6.97186,0.998838,0.932037,1,3,0,7.91764,0.15 --6.76939,0.981956,0.932037,1,3,0,7.03238,0.15 --7.44177,0.80944,0.932037,1,3,0,7.79154,0.15 --8.01591,0.972061,0.932037,2,3,0,8.03736,0.15 --9.92045,0.865258,0.932037,2,3,0,9.92397,0.15 --7.31963,1,0.932037,2,3,0,10.3209,0.15 --6.9789,1,0.932037,1,1,0,7.25409,0.15 --7.21981,0.951312,0.932037,1,1,0,7.23197,0.15 --7.47612,0.930523,0.932037,2,3,0,8.12075,0.15 --6.81131,0.990375,0.932037,2,3,0,7.55719,0.15 --6.80278,0.920389,0.932037,2,3,0,7.43635,0.15 --6.88803,0.979103,0.932037,1,1,0,6.89063,0.15 --7.02902,0.795987,0.932037,2,3,0,8.6993,0.15 --6.74805,0.993012,0.932037,1,3,0,7.01716,0.15 --6.93322,0.952905,0.932037,1,3,0,6.95228,0.15 --6.76781,0.904559,0.932037,2,3,0,7.33629,0.15 --6.76232,0.985564,0.932037,2,3,0,6.83656,0.15 --6.87137,0.974516,0.932037,1,3,0,6.87148,0.15 --7.21556,0.953187,0.932037,2,3,0,7.22785,0.15 --6.76367,0.998041,0.932037,1,3,0,7.1984,0.15 --6.82308,0.981349,0.932037,1,3,0,6.86896,0.15 --6.82308,0.352125,0.932037,1,3,0,10.8402,0.15 --6.99474,0.738309,0.932037,2,3,0,8.0486,0.15 --6.99474,0.963463,0.932037,1,1,0,7.1311,0.15 --6.8637,0.713515,0.932037,2,3,0,8.29356,0.15 --6.94178,0.974091,0.932037,1,1,0,6.96832,0.15 --6.80481,1,0.932037,2,3,0,6.9081,0.15 --6.92327,0.971351,0.932037,1,1,0,6.92425,0.15 --7.1233,0.957499,0.932037,1,1,0,7.13244,0.15 --6.76778,0.973133,0.932037,1,3,0,7.19571,0.15 --6.90939,0.888041,0.932037,2,3,0,7.29419,0.15 --7.3757,0.927908,0.932037,2,3,0,7.37586,0.15 --7.33194,1,0.932037,1,1,0,7.48008,0.15 --6.86262,0.893077,0.932037,2,3,0,9.38827,0.15 --6.74929,0.996117,0.932037,1,3,0,6.86347,0.15 --6.74964,0.943201,0.932037,2,3,0,7.03592,0.15 --6.9015,0.968295,0.932037,2,3,0,6.96204,0.15 --7.81106,0.864491,0.932037,1,3,0,7.86554,0.15 --8.616,0.90424,0.932037,1,1,0,8.63659,0.15 --8.16761,1,0.932037,1,1,0,8.67561,0.15 --6.96545,1,0.932037,1,3,0,8.01828,0.15 --6.75813,1,0.932037,1,3,0,6.92669,0.15 --6.75424,1,0.932037,1,1,0,6.75812,0.15 --7.76056,0.503853,0.932037,2,3,0,10.2889,0.15 --7.12518,1,0.932037,1,1,0,7.64631,0.15 --7.67249,0.905419,0.932037,1,1,0,7.67491,0.15 --7.63881,1,0.932037,1,1,0,7.83222,0.15 --7.3844,0.889767,0.932037,1,3,0,8.80917,0.15 --6.76719,1,0.932037,1,3,0,7.348,0.15 --7.47953,0.852161,0.932037,1,3,0,7.59716,0.15 --7.42386,1,0.932037,2,3,0,7.59422,0.15 --7.91668,0.975432,0.932037,2,3,0,7.94672,0.15 --6.83066,1,0.932037,2,3,0,7.62924,0.15 --6.96269,0.956934,0.932037,1,1,0,6.97473,0.15 --6.74847,0.963031,0.932037,2,3,0,7.15706,0.15 --6.80708,0.988647,0.932037,2,3,0,6.82289,0.15 --6.91792,0.9732,0.932037,1,1,0,6.91947,0.15 --6.84983,1,0.932037,2,3,0,6.91467,0.15 --6.88905,0.990653,0.932037,1,1,0,6.90679,0.15 --7.15463,0.975663,0.932037,2,3,0,7.16,0.15 --6.9586,1,0.932037,1,1,0,7.13267,0.15 --7.56708,0.803851,0.932037,1,1,0,7.59178,0.15 --6.76736,1,0.932037,1,3,0,7.31987,0.15 --6.80123,0.959699,0.932037,2,3,0,6.95431,0.15 --6.80123,0.759564,0.932037,1,3,0,7.84546,0.15 --6.80123,0.902778,0.932037,1,3,0,7.20675,0.15 --6.80123,0.96482,0.932037,1,1,0,6.88076,0.15 --6.78299,0.988689,0.932037,2,3,0,6.88032,0.15 --7.1107,0.936673,0.932037,1,3,0,7.12632,0.15 --7.31567,0.961429,0.932037,1,1,0,7.35195,0.15 --7.07455,0.968694,0.932037,2,3,0,7.7776,0.15 --6.74831,0.993779,0.932037,1,3,0,7.05357,0.15 --6.84249,0.975275,0.932037,1,3,0,6.85756,0.15 --6.88224,0.995647,0.932037,2,3,0,6.90622,0.15 --8.68021,0.668585,0.932037,2,3,0,8.68611,0.15 --7.80603,1,0.932037,1,1,0,8.5851,0.15 --7.27572,1,0.932037,1,1,0,7.73044,0.15 --8.91337,0.902662,0.932037,2,3,0,9.15324,0.15 --7.22471,0.840347,0.932037,1,3,0,9.85532,0.15 --7.57214,0.980256,0.932037,2,3,0,7.60142,0.15 --7.46188,1,0.932037,1,1,0,7.66831,0.15 --7.21293,1,0.932037,1,1,0,7.46083,0.15 --7.27196,0.911127,0.932037,1,3,0,8.06683,0.15 --6.99285,1,0.932037,1,1,0,7.22442,0.15 --7.64686,0.789204,0.932037,1,1,0,7.67915,0.15 --7.64686,0.588674,0.932037,1,1,0,8.70948,0.15 --6.76317,0.84811,0.932037,2,3,0,8.40171,0.15 --6.75852,0.995961,0.932037,2,3,0,6.79457,0.15 --6.96185,0.956175,0.932037,2,3,0,7.06538,0.15 --6.7597,0.95957,0.932037,2,3,0,7.23201,0.15 --6.95356,0.940807,0.932037,1,3,0,7.05195,0.15 --6.74802,0.995879,0.932037,1,3,0,6.94105,0.15 --6.80409,0.985416,0.932037,1,3,0,6.81492,0.15 --6.78431,0.93291,0.932037,2,3,0,7.30965,0.15 --6.85124,0.98309,0.932037,1,1,0,6.85256,0.15 --6.91874,0.970098,0.932037,1,3,0,7.11707,0.15 --6.91874,0.434093,0.932037,1,3,0,9.39656,0.15 --6.92998,0.940657,0.932037,2,3,0,7.25758,0.15 --6.7616,0.987015,0.932037,1,3,0,6.96899,0.15 --6.77385,0.996249,0.932037,1,1,0,6.77617,0.15 --7.11956,0.891801,0.932037,1,3,0,7.32561,0.15 --7.10361,0.938678,0.932037,1,3,0,7.72046,0.15 --7.10361,0.642361,0.932037,1,1,0,7.89438,0.15 --8.5001,0.593919,0.932037,1,1,0,8.53813,0.15 --7.1692,1,0.932037,2,3,0,8.39313,0.15 --6.99964,1,0.932037,2,3,0,7.15856,0.15 --6.82605,1,0.932037,1,1,0,6.95904,0.15 --6.80069,1,0.932037,1,1,0,6.8286,0.15 --6.80069,0.826864,0.932037,1,3,0,7.56839,0.15 --6.80069,0.712989,0.932037,1,3,0,8.08135,0.15 --6.79864,1,0.932037,1,1,0,6.81323,0.15 --7.21975,0.922333,0.932037,1,3,0,7.24006,0.15 --6.75061,0.978686,0.932037,1,3,0,7.23991,0.15 --8.50882,0.710206,0.932037,2,3,0,8.58833,0.15 --6.98994,1,0.932037,1,3,0,8.37388,0.15 --8.68191,0.796805,0.932037,2,3,0,9.47091,0.15 --7.31609,1,0.932037,2,3,0,8.2644,0.15 --7.14381,1,0.932037,1,1,0,7.36712,0.15 --7.14381,0.475525,0.932037,1,1,0,8.41075,0.15 --7.29505,0.813437,0.932037,2,3,0,8.20133,0.15 --7.97276,0.895575,0.932037,1,1,0,7.97663,0.15 --9.00874,0.888587,0.932037,1,1,0,9.0167,0.15 --8.5484,1,0.932037,1,1,0,9.10361,0.15 --7.88735,1,0.932037,1,1,0,8.51142,0.15 --7.36782,1,0.932037,1,1,0,7.8262,0.15 --7.34887,1,0.932037,1,1,0,7.48713,0.15 --6.81839,0.987631,0.932037,2,3,0,7.46646,0.15 --6.81839,0.712592,0.932037,1,3,0,8.16085,0.15 --6.74807,0.998893,0.932037,1,3,0,6.81316,0.15 --6.87013,0.959268,0.932037,2,3,0,6.97825,0.15 --6.76585,1,0.932037,1,1,0,6.83725,0.15 --6.7566,0.994396,0.932037,2,3,0,6.80045,0.15 --6.7566,0.628726,0.932037,1,3,0,8.7002,0.15 --6.82277,0.986161,0.932037,1,3,0,6.82387,0.15 --7.3013,0.877627,0.932037,1,3,0,7.5333,0.15 --8.78447,0.79108,0.932037,2,3,0,8.87542,0.15 --7.50584,1,0.932037,1,1,0,8.46306,0.15 --7.15669,1,0.932037,1,1,0,7.48033,0.15 --7.43591,0.967588,0.932037,2,3,0,7.54437,0.15 --6.96987,0.913229,0.932037,1,3,0,7.88228,0.15 --6.89773,1,0.932037,1,1,0,6.97541,0.15 --7.36158,0.930716,0.932037,1,3,0,7.36402,0.15 --7.16589,1,0.932037,1,1,0,7.37174,0.15 --6.95869,1,0.932037,1,1,0,7.13528,0.15 --7.16168,0.929564,0.932037,1,3,0,7.57434,0.15 --8.02154,0.725795,0.932037,1,1,0,8.09574,0.15 --8.63423,0.930003,0.932037,2,3,0,9.03662,0.15 --6.93887,1,0.932037,1,1,0,8.0172,0.15 --6.87271,1,0.932037,1,1,0,6.94734,0.15 --6.77339,1,0.932037,2,3,0,6.85408,0.15 --6.98298,0.942095,0.932037,1,3,0,7.07789,0.15 --6.75868,1,0.932037,1,3,0,6.91623,0.15 --6.7906,0.973381,0.932037,2,3,0,6.88716,0.15 --7.56742,0.820209,0.932037,1,3,0,7.57193,0.15 --6.75108,1,0.932037,1,3,0,7.35001,0.15 --6.80655,0.983396,0.932037,1,3,0,6.83092,0.15 --6.91728,0.973208,0.932037,1,1,0,6.91879,0.15 --7.7691,0.915431,0.932037,2,3,0,7.98664,0.15 --6.765,1,0.932037,1,3,0,7.46164,0.15 --7.37311,0.856184,0.932037,1,3,0,7.3856,0.15 --7.3305,1,0.932037,1,1,0,7.55404,0.15 --7.3305,0.663523,0.932037,1,1,0,8.12143,0.15 --7.12567,0.837343,0.932037,1,3,0,8.09517,0.15 --7.15885,0.957841,0.932037,2,3,0,7.71194,0.15 --6.75049,0.994782,0.932037,1,3,0,7.12461,0.15 --6.81461,0.982255,0.932037,1,3,0,6.83366,0.15 --6.75614,0.996516,0.932037,1,3,0,6.83027,0.15 --6.74853,0.904663,0.932037,2,3,0,7.2911,0.15 --7.01677,0.911602,0.932037,2,3,0,7.26139,0.15 --6.92929,0.642216,0.932037,2,3,0,8.77716,0.15 --7.65529,0.771289,0.932037,1,1,0,7.66932,0.15 --7.01834,1,0.932037,1,1,0,7.46402,0.15 --6.74897,1,0.932037,1,3,0,6.95557,0.15 --9.81129,0.348606,0.932037,1,3,0,11.6845,0.15 --10.4379,0.964322,0.932037,1,1,0,10.6078,0.15 --12.3737,0.923455,0.932037,1,1,0,12.3737,0.15 --8.6914,0.906628,0.932037,1,3,0,12.5921,0.15 --7.8205,1,0.932037,1,1,0,8.59832,0.15 --6.76573,0.925015,0.932037,1,3,0,8.00716,0.15 --7.18424,0.803585,0.932037,2,3,0,7.86457,0.15 --6.74867,0.98391,0.932037,1,3,0,7.18696,0.15 --6.74867,0.809678,0.932037,1,3,0,7.5541,0.15 --6.78928,0.864392,0.932037,2,3,0,7.45892,0.15 --7.73008,0.857073,0.932037,2,3,0,7.95215,0.15 --6.90924,0.999548,0.932037,1,3,0,7.59486,0.15 --6.83204,1,0.932037,1,1,0,6.89889,0.15 --6.92095,0.978894,0.932037,1,1,0,6.92784,0.15 --6.84946,1,0.932037,1,1,0,6.91633,0.15 --6.87329,0.97872,0.932037,1,3,0,7.05429,0.15 --7.77797,0.726161,0.932037,1,1,0,7.77978,0.15 --7.91451,0.983052,0.932037,2,3,0,8.2695,0.15 --6.99541,0.905965,0.932037,1,3,0,8.38798,0.15 --6.76541,0.9819,0.932037,1,3,0,7.04888,0.15 --6.75969,1,0.932037,1,1,0,6.76622,0.15 --6.88511,0.980043,0.932037,2,3,0,6.89657,0.15 --6.77682,0.92494,0.932037,2,3,0,7.55206,0.15 --7.22693,0.885941,0.932037,1,3,0,7.36359,0.15 --7.08301,1,0.932037,2,3,0,7.27,0.15 --6.74807,1,0.932037,1,3,0,7.01313,0.15 --6.78184,0.991048,0.932037,1,3,0,6.7889,0.15 --7.7414,0.782413,0.932037,1,3,0,7.94246,0.15 --8.34868,0.79224,0.932037,1,1,0,8.64598,0.15 --6.96656,1,0.932037,1,1,0,7.85206,0.15 --8.55062,0.559382,0.932037,1,1,0,8.55696,0.15 --8.71259,0.939683,0.932037,1,1,0,9.36157,0.15 --7.02011,1,0.932037,1,1,0,8.0992,0.15 --7.73032,0.771978,0.932037,1,1,0,7.7681,0.15 --7.58699,1,0.932037,1,1,0,7.95753,0.15 --10.1664,0.37457,0.932037,1,1,0,10.2962,0.15 --9.05941,1,0.932037,2,3,0,10.4928,0.15 --6.95163,0.994678,0.932037,1,3,0,9.25144,0.15 --6.991,0.991344,0.932037,1,1,0,7.03106,0.15 --6.93665,1,0.932037,1,1,0,7.01246,0.15 --7.53693,0.951931,0.932037,2,3,0,7.60421,0.15 --7.35042,1,0.932037,1,1,0,7.65542,0.15 --6.74833,1,0.932037,1,3,0,7.21002,0.15 --7.30096,0.866757,0.932037,1,3,0,7.34306,0.15 --7.33424,0.995938,0.932037,2,3,0,7.52117,0.15 --7.00834,1,0.932037,1,1,0,7.27119,0.15 --7.00834,0.750927,0.932037,1,3,0,7.93882,0.15 --6.85276,0.717415,0.932037,2,3,0,8.2801,0.15 --6.88149,0.915088,0.932037,2,3,0,7.26489,0.15 --7.01531,0.954346,0.932037,2,3,0,7.30842,0.15 --8.49716,0.821021,0.932037,2,3,0,8.90444,0.15 --7.75198,1,0.932037,2,3,0,8.42455,0.15 --6.78374,0.995713,0.932037,1,3,0,7.67855,0.15 --6.78319,0.955918,0.932037,2,3,0,7.05067,0.15 --6.76877,0.993356,0.932037,2,3,0,6.83116,0.15 --6.76604,1,0.932037,2,3,0,6.77242,0.15 --6.80257,0.828664,0.932037,2,3,0,7.81548,0.15 --6.7921,0.928548,0.932037,2,3,0,7.34502,0.15 --6.7921,0.869785,0.932037,1,3,0,7.3846,0.15 --6.78247,1,0.932037,1,1,0,6.7969,0.15 --6.77338,0.944315,0.932037,2,3,0,7.11056,0.15 --6.88767,0.984579,0.932037,2,3,0,6.89149,0.15 --6.87758,1,0.932037,1,1,0,6.91573,0.15 --6.98427,0.975828,0.932037,1,1,0,6.997,0.15 --8.16006,0.847199,0.932037,1,3,0,8.23533,0.15 --6.92926,1,0.932037,1,3,0,8.02086,0.15 --6.75334,1,0.932037,1,3,0,6.89875,0.15 --6.76157,0.984481,0.932037,2,3,0,6.84166,0.15 --6.77437,0.916298,0.932037,2,3,0,7.16943,0.15 --6.89883,0.966797,0.932037,2,3,0,7.01941,0.15 --7.61554,0.929243,0.932037,2,3,0,7.77465,0.15 --6.74865,1,0.932037,1,3,0,7.4327,0.15 --7.90889,0.736401,0.932037,1,3,0,8.30046,0.15 --7.36433,1,0.932037,1,1,0,7.84078,0.15 --6.7517,1,0.932037,2,3,0,7.32628,0.15 --7.47731,0.835796,0.932037,1,3,0,7.65004,0.15 --6.82682,1,0.932037,1,3,0,7.36972,0.15 --6.75257,1,0.932037,1,3,0,6.81023,0.15 --6.74814,0.924039,0.932037,2,3,0,7.16556,0.15 --6.77547,0.992637,0.932037,1,3,0,6.7818,0.15 --6.77883,0.96017,0.932037,2,3,0,7.01271,0.15 --6.83214,0.92718,0.932037,2,3,0,7.10706,0.15 --6.74855,0.999857,0.932037,1,3,0,6.82323,0.15 --6.75208,0.991977,0.932037,2,3,0,6.79074,0.15 --6.74875,1,0.932037,2,3,0,6.7511,0.15 --6.76331,0.987877,0.932037,2,3,0,6.81164,0.15 --7.11253,0.925924,0.932037,1,3,0,7.14728,0.15 --7.33832,0.934594,0.932037,2,3,0,7.89183,0.15 --7.40317,0.891695,0.932037,1,3,0,8.40906,0.15 --6.83199,1,0.932037,2,3,0,7.43757,0.15 --6.83199,0.774019,0.932037,1,3,0,8.04658,0.15 --6.99888,0.961352,0.932037,1,1,0,7.00012,0.15 --6.76802,0.991205,0.932037,2,3,0,7.06627,0.15 --7.1899,0.912501,0.932037,1,3,0,7.23327,0.15 --7.0344,0.951025,0.932037,1,3,0,7.71962,0.15 --6.74861,0.964191,0.932037,2,3,0,7.23266,0.15 --6.9356,0.954146,0.932037,1,3,0,6.96807,0.15 --6.8766,1,0.932037,1,1,0,6.94182,0.15 --6.86303,0.963467,0.932037,2,3,0,7.17361,0.15 --6.89691,0.996253,0.932037,2,3,0,6.92839,0.15 --6.74898,0.989445,0.932037,2,3,0,6.95643,0.15 --6.75192,0.906807,0.932037,2,3,0,7.25324,0.15 --6.89356,0.962004,0.932037,1,3,0,6.92873,0.15 --6.8915,1,0.932037,1,1,0,6.93856,0.15 --8.04782,0.661095,0.932037,1,1,0,8.04944,0.15 --6.96778,1,0.932037,2,3,0,8.10818,0.15 --6.94531,0.967319,0.932037,1,3,0,7.30665,0.15 --6.79303,1,0.932037,1,1,0,6.89785,0.15 --6.75073,0.998473,0.932037,1,3,0,6.79703,0.15 --6.74807,0.922346,0.932037,2,3,0,7.17097,0.15 --6.79664,0.986959,0.932037,2,3,0,6.82676,0.15 --7.42084,0.890966,0.932037,2,3,0,7.44601,0.15 --7.07938,1,0.932037,1,1,0,7.36895,0.15 --6.76996,0.975074,0.932037,1,3,0,7.1524,0.15 --6.97097,0.781354,0.932037,2,3,0,7.8694,0.15 --7.45672,0.874497,0.932037,1,3,0,7.94747,0.15 --7.73982,0.898709,0.932037,1,1,0,7.95818,0.15 --6.75761,1,0.932037,1,3,0,7.45331,0.15 --6.75128,1,0.932037,2,3,0,6.75597,0.15 --6.74857,0.969275,0.932037,2,3,0,6.90378,0.15 --6.78162,0.964009,0.932037,2,3,0,6.94604,0.15 --6.94452,0.969782,0.932037,1,3,0,6.94577,0.15 --6.83464,1,0.932037,1,1,0,6.92359,0.15 --6.85125,0.978801,0.932037,2,3,0,7.03988,0.15 --8.70862,0.498506,0.932037,2,3,0,10.892,0.15 --7.14422,0.988771,0.932037,1,3,0,8.54862,0.15 --8.22719,0.86569,0.932037,1,3,0,8.25029,0.15 --7.98302,1,0.932037,1,1,0,8.34174,0.15 --7.66766,1,0.932037,1,1,0,8.02784,0.15 --8.24143,0.924458,0.932037,1,1,0,8.28161,0.15 --7.67998,1,0.932037,1,1,0,8.20801,0.15 --7.87681,0.972029,0.932037,1,1,0,7.99803,0.15 --6.74849,0.941373,0.932037,1,3,0,7.96721,0.15 --7.11836,0.837369,0.932037,2,3,0,7.62397,0.15 --6.79142,1,0.932037,2,3,0,7.01021,0.15 --6.77524,1,0.932037,1,1,0,6.79217,0.15 --6.79636,0.997801,0.932037,2,3,0,6.8016,0.15 --6.79636,0.948581,0.932037,1,1,0,6.90729,0.15 --6.87559,0.872276,0.932037,2,3,0,7.36427,0.15 --7.91757,0.674512,0.932037,1,3,0,8.87065,0.15 --6.74935,0.935438,0.932037,1,3,0,8.03013,0.15 --6.74803,0.97594,0.932037,2,3,0,6.86976,0.15 --6.81513,0.982667,0.932037,1,3,0,6.82306,0.15 --7.49872,0.879887,0.932037,2,3,0,7.51782,0.15 --7.49872,0.948913,0.932037,1,1,0,7.85236,0.15 --8.04293,0.922216,0.932037,1,1,0,8.07282,0.15 --7.35293,1,0.932037,2,3,0,7.93996,0.15 --7.35293,0.883408,0.932037,1,1,0,8.02395,0.15 --6.80493,1,0.932037,1,3,0,7.25923,0.15 --6.82698,0.998151,0.932037,2,3,0,6.83752,0.15 --6.88114,0.986899,0.932037,1,1,0,6.89144,0.15 --6.74827,0.964042,0.932037,2,3,0,7.17919,0.15 --6.93633,0.882605,0.932037,2,3,0,7.40536,0.15 --8.02922,0.766313,0.932037,1,3,0,8.54794,0.15 --6.88064,1,0.932037,1,1,0,7.62039,0.15 --6.99856,0.960668,0.932037,1,1,0,7.02674,0.15 --6.9205,1,0.932037,1,1,0,7.0171,0.15 --7.23497,0.895629,0.932037,1,1,0,7.26191,0.15 --6.81255,0.65863,0.932037,2,3,0,8.81442,0.15 --6.8557,0.986062,0.932037,1,1,0,6.86976,0.15 --6.8557,0.724177,0.932037,1,3,0,7.66358,0.15 --6.75301,0.997497,0.932037,1,3,0,6.85969,0.15 --9.62741,0.500859,0.932037,1,3,0,9.855,0.15 --7.2064,1,0.932037,2,3,0,8.74926,0.15 --6.75431,0.815869,0.932037,2,3,0,7.99167,0.15 --6.77251,0.990864,0.932037,2,3,0,6.80395,0.15 --7.81008,0.844756,0.932037,2,3,0,8.00316,0.15 --8.11847,0.959711,0.932037,1,1,0,8.22643,0.15 --9.24757,0.890376,0.932037,2,3,0,10.2168,0.15 --6.88942,0.799493,0.932037,1,3,0,10.3695,0.15 --6.88942,0.261019,0.932037,1,3,0,10.9724,0.15 --6.80124,0.870892,0.932037,2,3,0,7.43049,0.15 --6.74978,0.998863,0.932037,1,3,0,6.80164,0.15 --7.17245,0.854684,0.932037,2,3,0,7.6,0.15 --8.81972,0.538088,0.932037,1,1,0,8.86828,0.15 --7.40622,1,0.932037,2,3,0,8.5393,0.15 --7.26318,0.961918,0.932037,2,3,0,8.05474,0.15 --7.34728,0.984906,0.932037,1,1,0,7.43596,0.15 --6.86243,0.997387,0.932037,1,3,0,7.24798,0.15 --6.89308,0.975225,0.932037,1,3,0,7.09809,0.15 --7.32886,0.939094,0.932037,2,3,0,7.34307,0.15 --7.57152,0.913466,0.932037,1,1,0,7.74604,0.15 --6.94728,1,0.932037,1,1,0,7.36923,0.15 --6.81842,1,0.932037,1,1,0,6.91356,0.15 --6.76216,1,0.932037,2,3,0,6.80728,0.15 --6.85768,0.974601,0.932037,2,3,0,6.94204,0.15 --6.77062,0.988638,0.932037,1,3,0,6.90934,0.15 --6.91451,0.978923,0.932037,2,3,0,6.91452,0.15 --6.78227,1,0.932037,1,1,0,6.87294,0.15 --7.62346,0.806356,0.932037,1,3,0,7.63204,0.15 --6.83206,1,0.932037,1,1,0,7.34914,0.15 --7.29941,0.815839,0.932037,2,3,0,7.843,0.15 --7.31671,0.996885,0.932037,1,1,0,7.4311,0.15 --6.78343,0.991818,0.932037,2,3,0,7.37529,0.15 --6.86006,0.980687,0.932037,1,1,0,6.8608,0.15 --6.77605,1,0.932037,1,1,0,6.83853,0.15 --6.86042,0.973286,0.932037,1,3,0,6.93518,0.15 --6.75338,0.997358,0.932037,1,3,0,6.86494,0.15 --6.83799,0.976298,0.932037,1,3,0,6.868,0.15 --7.00374,0.713633,0.932037,2,3,0,8.19304,0.15 --6.75259,0.98344,0.932037,2,3,0,7.10169,0.15 --6.79496,0.987555,0.932037,1,3,0,6.81495,0.15 --6.9391,0.981259,0.932037,2,3,0,6.94195,0.15 --7.22517,0.904285,0.932037,1,1,0,7.25937,0.15 --7.21052,1,0.932037,1,1,0,7.37602,0.15 --6.75985,0.787087,0.932037,2,3,0,8.11836,0.15 --6.86031,0.946513,0.932037,2,3,0,7.03793,0.15 --8.19403,0.780681,0.932037,1,3,0,8.38612,0.15 --6.7609,1,0.932037,2,3,0,7.87703,0.15 --6.95309,0.78127,0.932037,2,3,0,7.88077,0.15 --9.56705,0.565191,0.932037,1,3,0,10.2818,0.15 --6.75776,1,0.932037,1,3,0,8.93023,0.15 --6.77632,0.994978,0.932037,1,1,0,6.77679,0.15 --6.82265,0.996019,0.932037,2,3,0,6.82416,0.15 --6.89139,0.983404,0.932037,1,1,0,6.89889,0.15 --6.89139,0.777655,0.932037,1,3,0,8.12126,0.15 --7.13161,0.926877,0.932037,1,3,0,7.44089,0.15 --8.35546,0.633109,0.932037,1,1,0,8.40627,0.15 --7.06834,0.886614,0.932037,1,3,0,8.96147,0.15 --7.28399,0.958522,0.932037,1,1,0,7.31204,0.15 --7.28327,1,0.932037,1,1,0,7.40052,0.15 --7.93284,0.898826,0.932037,1,1,0,7.93762,0.15 --7.81694,1,0.932037,1,1,0,8.08024,0.15 --7.6919,1,0.932037,1,1,0,7.94232,0.15 --6.76979,0.932315,0.932037,1,3,0,7.86435,0.15 --6.97696,0.966811,0.932037,2,3,0,6.99094,0.15 --7.49857,0.902721,0.932037,2,3,0,7.93254,0.15 --7.50324,0.999237,0.932037,1,1,0,7.65578,0.15 --6.91611,1,0.932037,2,3,0,7.33316,0.15 --6.74956,1,0.932037,1,3,0,6.89861,0.15 --8.35635,0.667972,0.932037,1,3,0,8.48862,0.15 --8.35709,0.999715,0.932037,1,1,0,8.94945,0.15 --7.01492,1,0.932037,2,3,0,8.49553,0.15 --7.08433,0.985549,0.932037,1,1,0,7.13054,0.15 --7.29556,0.986569,0.932037,2,3,0,7.32674,0.15 --6.82002,1,0.932037,2,3,0,7.16863,0.15 --6.82002,0.827599,0.932037,1,1,0,7.18527,0.15 --6.96415,0.766705,0.932037,2,3,0,7.8936,0.15 --7.10108,0.953204,0.932037,1,1,0,7.15511,0.15 --7.08567,1,0.932037,1,1,0,7.2067,0.15 --8.18465,0.665208,0.932037,1,1,0,8.22692,0.15 --6.98816,1,0.932037,2,3,0,8.27915,0.15 --6.9991,0.951104,0.932037,2,3,0,7.40622,0.15 --6.87003,0.959893,0.932037,1,3,0,7.2399,0.15 --8.86597,0.67165,0.932037,1,3,0,9.3173,0.15 --9.22117,0.969943,0.932037,1,1,0,9.41531,0.15 --10.3294,0.926924,0.932037,1,1,0,10.3727,0.15 --8.32325,0.989504,0.932037,2,3,0,10.9313,0.15 --8.85098,0.946649,0.932037,1,1,0,8.95519,0.15 --9.31959,0.960963,0.932037,1,1,0,9.47897,0.15 --10.3022,0.93649,0.932037,1,1,0,10.3691,0.15 --10.3363,0.998194,0.932037,1,1,0,10.7444,0.15 --7.06812,1,0.932037,2,3,0,10.0104,0.15 --7.09449,0.998201,0.932037,2,3,0,7.16215,0.15 --7.41044,0.941678,0.932037,1,1,0,7.42835,0.15 --6.74954,0.897451,0.932037,2,3,0,8.358,0.15 --7.01483,0.952044,0.932037,2,3,0,7.06471,0.15 --7.04361,0.949288,0.932037,1,3,0,7.50082,0.15 --6.84344,1,0.932037,1,1,0,6.98754,0.15 --6.76346,1,0.932037,1,1,0,6.81845,0.15 --6.77171,0.981093,0.932037,2,3,0,6.85292,0.15 --6.84864,0.975946,0.932037,1,1,0,6.84963,0.15 --6.79009,1,0.932037,1,1,0,6.8359,0.15 --7.3603,0.823525,0.932037,1,3,0,7.7309,0.15 --7.81047,0.929962,0.932037,1,1,0,7.84015,0.15 --7.78154,1,0.932037,1,1,0,7.99363,0.15 --6.7517,1,0.932037,2,3,0,7.65292,0.15 --6.78798,0.848042,0.932037,2,3,0,7.61324,0.15 --6.76,0.921142,0.932037,2,3,0,7.25211,0.15 --6.79557,0.983288,0.932037,2,3,0,6.85013,0.15 --7.19183,0.926349,0.932037,1,3,0,7.21025,0.15 --7.38792,0.988215,0.932037,2,3,0,7.43851,0.15 --6.96601,1,0.932037,2,3,0,7.30212,0.15 --7.05786,0.980406,0.932037,1,1,0,7.08973,0.15 --7.16196,0.993052,0.932037,2,3,0,7.20877,0.15 --6.77281,0.912405,0.932037,2,3,0,8.28922,0.15 --6.75974,1,0.932037,1,1,0,6.77054,0.15 --6.74904,0.896827,0.932037,2,3,0,7.34903,0.15 --6.99017,0.938166,0.932037,1,3,0,7.02613,0.15 --9.18272,0.444151,0.932037,1,1,0,9.18621,0.15 --7.27998,1,0.932037,1,1,0,8.53072,0.15 --6.81588,0.976625,0.932037,1,3,0,7.39388,0.15 --6.85158,0.981914,0.932037,1,3,0,6.98055,0.15 --7.61781,0.765141,0.932037,1,1,0,7.61874,0.15 --6.79258,0.997137,0.932037,1,3,0,7.61795,0.15 --8.09444,0.722711,0.932037,1,3,0,8.35089,0.15 --8.09444,0.731058,0.932037,1,1,0,9.00859,0.15 --8.96537,0.715603,0.932037,1,1,0,9.37304,0.15 --8.78035,1,0.932037,1,1,0,9.62889,0.15 --7.21874,0.836764,0.932037,1,3,0,9.71962,0.15 --6.75061,0.911636,0.932037,2,3,0,8.14176,0.15 --7.65204,0.639147,0.932037,2,3,0,8.88575,0.15 --6.95151,1,0.932037,1,1,0,7.42019,0.15 --7.03245,0.89047,0.932037,2,3,0,7.4925,0.15 --6.93323,1,0.932037,1,1,0,7.03482,0.15 --6.93323,0.839701,0.932037,1,3,0,7.95662,0.15 --6.8181,1,0.932037,1,1,0,6.90794,0.15 --6.96817,0.964576,0.932037,1,1,0,6.96901,0.15 --6.75177,1,0.932037,2,3,0,6.94088,0.15 --6.75748,0.994596,0.932037,2,3,0,6.77947,0.15 --6.75598,1,0.932037,2,3,0,6.75911,0.15 --7.35596,0.838068,0.932037,1,3,0,7.60728,0.15 --6.75779,0.906448,0.932037,2,3,0,8.09794,0.15 --6.83162,0.952059,0.932037,2,3,0,6.9908,0.15 --7.67816,0.806003,0.932037,1,3,0,7.67819,0.15 --6.92603,0.932427,0.932037,1,3,0,8.01057,0.15 --7.07054,0.950192,0.932037,2,3,0,7.4224,0.15 --6.7484,0.994215,0.932037,1,3,0,7.0485,0.15 --7.08258,0.918347,0.932037,1,3,0,7.15481,0.15 --7.08258,0.737878,0.932037,1,3,0,9.18161,0.15 --7.35202,0.949338,0.932037,1,1,0,7.3743,0.15 --6.74881,0.983413,0.932037,1,3,0,7.33477,0.15 --6.95539,0.949695,0.932037,1,3,0,6.9911,0.15 --7.15608,0.958335,0.932037,1,1,0,7.16954,0.15 --6.83348,0.99773,0.932037,1,3,0,7.08351,0.15 --7.24483,0.931935,0.932037,1,3,0,7.25275,0.15 --6.98535,1,0.932037,1,1,0,7.20252,0.15 --6.79714,0.986131,0.932037,2,3,0,7.11242,0.15 --6.75975,0.994219,0.932037,2,3,0,6.84154,0.15 --7.85764,0.768175,0.932037,1,3,0,8.13554,0.15 --7.75607,1,0.932037,2,3,0,8.00291,0.15 --7.45436,1,0.932037,1,1,0,7.77479,0.15 --6.75812,0.956844,0.932037,1,3,0,7.53861,0.15 --6.78653,0.996723,0.932037,2,3,0,6.78699,0.15 --6.78653,0.689753,0.932037,1,3,0,7.7558,0.15 --7.13767,0.941965,0.932037,2,3,0,7.15428,0.15 --6.8166,0.824152,0.932037,2,3,0,8.71226,0.15 --6.79612,1,0.932037,1,1,0,6.82009,0.15 --7.16066,0.926371,0.932037,2,3,0,7.36744,0.15 --6.7485,0.867631,0.932037,2,3,0,8.25218,0.15 --6.7485,0.374064,0.932037,1,3,0,9.69677,0.15 --6.906,0.899506,0.932037,2,3,0,7.30301,0.15 --6.97495,0.960716,0.932037,1,3,0,7.26467,0.15 --6.87784,0.957976,0.932037,1,3,0,7.22871,0.15 --6.76764,1,0.932037,1,3,0,6.85023,0.15 --7.06045,0.939987,0.932037,2,3,0,7.20313,0.15 --7.56678,0.90837,0.932037,1,1,0,7.56807,0.15 --8.21909,0.9114,0.932037,1,1,0,8.24106,0.15 --7.1694,0.972439,0.932037,1,3,0,8.05615,0.15 --7.51659,0.939017,0.932037,1,1,0,7.53941,0.15 --6.80441,1,0.932037,2,3,0,7.33714,0.15 --6.84769,0.986098,0.932037,1,1,0,6.85924,0.15 --6.75447,1,0.932037,1,3,0,6.82024,0.15 --6.75447,0.941471,0.932037,1,3,0,6.92636,0.15 --7.31897,0.848317,0.932037,1,3,0,7.54592,0.15 --7.51256,0.936742,0.932037,2,3,0,8.23518,0.15 --7.00844,1,0.932037,1,1,0,7.41354,0.15 --6.81721,1,0.932037,2,3,0,6.95395,0.15 --7.45194,0.852204,0.932037,1,3,0,7.45194,0.15 --6.87259,1,0.932037,2,3,0,7.42285,0.15 --6.75616,1,0.932037,1,3,0,6.8471,0.15 --6.89502,0.961898,0.932037,1,3,0,6.94092,0.15 --6.77836,1,0.932037,2,3,0,6.87334,0.15 --6.79421,0.982898,0.932037,2,3,0,6.9004,0.15 --7.26621,0.798873,0.932037,2,3,0,7.90837,0.15 --6.86681,0.892468,0.932037,2,3,0,9.22078,0.15 --6.79361,1,0.932037,1,1,0,6.85058,0.15 --6.77748,1,0.932037,2,3,0,6.79435,0.15 --6.77887,0.999632,0.932037,1,1,0,6.78659,0.15 --6.78375,0.998717,0.932037,1,1,0,6.79108,0.15 --6.74954,0.987641,0.932037,2,3,0,6.86737,0.15 --7.83854,0.756676,0.932037,1,3,0,7.90005,0.15 --7.50039,1,0.932037,1,1,0,7.94015,0.15 --6.7491,1,0.932037,1,3,0,7.35194,0.15 --6.7636,0.996702,0.932037,1,3,0,6.76405,0.15 --6.78205,0.845782,0.932037,2,3,0,7.69714,0.15 --6.75014,0.997559,0.932037,2,3,0,6.79806,0.15 --8.34122,0.670679,0.932037,1,3,0,8.47733,0.15 --7.99963,1,0.932037,1,1,0,8.62888,0.15 --8.76385,0.745363,0.932037,1,1,0,9.14503,0.15 --7.04418,1,0.932037,1,1,0,8.14217,0.15 --7.43305,0.868749,0.932037,1,1,0,7.49441,0.15 --7.43305,0.297658,0.932037,1,1,0,9.48457,0.15 --6.83865,0.968461,0.932037,1,3,0,7.58424,0.15 --7.53033,0.883514,0.932037,1,3,0,7.57348,0.15 --6.96261,0.979104,0.932037,2,3,0,7.81614,0.15 --7.0999,0.990364,0.932037,2,3,0,7.12317,0.15 --8.07438,0.928426,0.932037,2,3,0,8.16995,0.15 --7.08332,1,0.932037,1,1,0,7.75278,0.15 --6.82918,1,0.932037,1,1,0,7.0036,0.15 --7.53288,0.784396,0.932037,1,1,0,7.53294,0.15 --7.05091,1,0.932037,2,3,0,7.39902,0.15 --7.40251,0.977978,0.932037,2,3,0,7.41207,0.15 --6.82898,1,0.932037,2,3,0,7.24836,0.15 --6.79153,1,0.932037,1,1,0,6.8253,0.15 --6.79153,0.459704,0.932037,1,3,0,9.85444,0.15 --6.76756,1,0.932037,1,1,0,6.7869,0.15 --6.97728,0.932763,0.932037,1,3,0,7.10469,0.15 --6.95157,1,0.932037,1,1,0,7.01491,0.15 --6.75905,0.960947,0.932037,2,3,0,7.21354,0.15 --6.7615,0.936647,0.932037,2,3,0,7.06828,0.15 --6.89331,0.972573,0.932037,1,3,0,6.89762,0.15 --6.77722,0.906489,0.932037,2,3,0,7.52048,0.15 --7.05175,0.910169,0.932037,1,3,0,7.23443,0.15 --6.92365,1,0.932037,1,1,0,7.04143,0.15 --6.92285,1,0.932037,2,3,0,6.9668,0.15 --7.13379,0.955304,0.932037,1,1,0,7.14184,0.15 --7.4804,0.884188,0.932037,1,3,0,8.21421,0.15 --7.40948,1,0.932037,1,1,0,7.67663,0.15 --6.7729,0.94969,0.932037,2,3,0,7.7319,0.15 --6.88124,0.967448,0.932037,1,3,0,6.95576,0.15 --8.24949,0.746027,0.932037,2,3,0,8.25756,0.15 --6.96905,1,0.932037,1,3,0,8.10239,0.15 --7.27306,0.939109,0.932037,1,1,0,7.27863,0.15 --7.00308,0.956818,0.932037,1,3,0,7.78354,0.15 --6.75472,0.998187,0.932037,1,3,0,6.9917,0.15 --6.75494,0.999941,0.932037,1,1,0,6.75678,0.15 --6.87358,0.96725,0.932037,1,3,0,6.91321,0.15 --6.88529,0.996099,0.932037,1,1,0,6.92345,0.15 --8.16803,0.810902,0.932037,2,3,0,8.16856,0.15 --8.16803,0.500501,0.932037,1,1,0,9.65653,0.15 --6.84099,0.909134,0.932037,2,3,0,8.86386,0.15 --6.7482,0.97014,0.932037,2,3,0,7.07304,0.15 --6.88126,0.965042,0.932037,1,3,0,6.91312,0.15 --6.91537,0.971395,0.932037,1,3,0,7.15288,0.15 --6.74803,1,0.932037,1,3,0,6.88372,0.15 --6.78862,0.991449,0.932037,2,3,0,6.8033,0.15 --6.75166,1,0.932037,1,3,0,6.77932,0.15 --7.64177,0.652534,0.932037,2,3,0,8.81793,0.15 --7.39307,1,0.932037,1,1,0,7.74629,0.15 --7.39307,0.385651,0.932037,1,1,0,9.0256,0.15 --6.79731,0.988358,0.932037,1,3,0,7.44468,0.15 --7.26742,0.912562,0.932037,1,3,0,7.29563,0.15 --8.03606,0.881597,0.932037,1,1,0,8.03636,0.15 --7.18684,0.969203,0.932037,1,3,0,7.89297,0.15 --7.82185,0.895632,0.932037,1,1,0,7.82342,0.15 --6.9258,0.999365,0.932037,1,3,0,7.68077,0.15 --6.84345,1,0.932037,1,1,0,6.91592,0.15 --8.02819,0.799375,0.932037,1,3,0,8.18865,0.15 --9.74173,0.805241,0.932037,1,3,0,9.76127,0.15 --8.33143,1,0.932037,1,1,0,9.60755,0.15 --7.18582,0.972127,0.932037,1,3,0,8.16309,0.15 --6.92783,1,0.932037,1,1,0,7.13538,0.15 --7.11005,0.961217,0.932037,1,1,0,7.12168,0.15 --6.79373,1,0.932037,1,3,0,7.04271,0.15 --7.11956,0.955726,0.932037,2,3,0,7.20271,0.15 --7.48398,0.958399,0.932037,2,3,0,7.57261,0.15 --7.05344,1,0.932037,2,3,0,7.38948,0.15 --6.89526,1,0.932037,1,1,0,7.02854,0.15 --6.75297,0.996542,0.932037,2,3,0,6.92061,0.15 --6.83977,0.975809,0.932037,1,3,0,6.86918,0.15 --6.8251,1,0.932037,2,3,0,6.85672,0.15 --6.76932,1,0.932037,1,1,0,6.8089,0.15 --7.2411,0.887441,0.932037,1,3,0,7.24713,0.15 --6.77198,0.978158,0.932037,2,3,0,7.39681,0.15 --6.91344,0.959133,0.932037,1,3,0,6.99309,0.15 --7.04881,0.85015,0.932037,2,3,0,7.62026,0.15 --7.60525,0.863371,0.932037,1,3,0,8.23873,0.15 --6.78473,0.999819,0.932037,1,3,0,7.58373,0.15 --6.78473,0.898858,0.932037,1,3,0,7.22409,0.15 --7.78377,0.579188,0.932037,2,3,0,9.7066,0.15 --7.70743,1,0.932037,1,1,0,7.93357,0.15 --7.96247,0.96471,0.932037,1,1,0,8.07171,0.15 --7.12674,0.974601,0.932037,1,3,0,7.81844,0.15 --6.75681,1,0.932037,1,3,0,7.07784,0.15 --6.74929,0.899259,0.932037,2,3,0,7.32696,0.15 --6.74929,0.646445,0.932037,1,3,0,8.08205,0.15 --7.15628,0.857788,0.932037,2,3,0,7.57467,0.15 --7.75903,0.923317,0.932037,2,3,0,7.84476,0.15 --6.85106,0.987384,0.932037,2,3,0,7.98227,0.15 --8.0294,0.746944,0.932037,1,3,0,8.39902,0.15 --6.8022,0.859301,0.932037,2,3,0,8.84948,0.15 --6.77032,1,0.932037,1,1,0,6.79542,0.15 --7.64151,0.740592,0.932037,2,3,0,8.46249,0.15 --8.37811,0.917274,0.932037,2,3,0,8.62816,0.15 --7.49482,1,0.932037,1,1,0,8.22793,0.15 --9.18827,0.523755,0.932037,1,1,0,9.33046,0.15 --7.76093,1,0.932037,1,1,0,8.88732,0.15 --6.97798,1,0.932037,1,1,0,7.50122,0.15 --7.35575,0.873861,0.932037,1,1,0,7.39654,0.15 --7.35575,0.110444,0.932037,1,1,0,11.0745,0.15 --7.35575,0.0800688,0.932037,1,3,0,15.4327,0.15 --7.48223,0.672997,0.932037,1,3,0,8.8362,0.15 --6.78001,0.901552,0.932037,2,3,0,8.42084,0.15 --7.27496,0.91456,0.932037,2,3,0,7.304,0.15 --7.27496,0.889573,0.932037,1,1,0,7.87313,0.15 --6.74818,0.981145,0.932037,1,3,0,7.27579,0.15 --6.74818,0.540736,0.932037,1,3,0,8.56126,0.15 --6.84577,0.975084,0.932037,1,3,0,6.8552,0.15 --6.84577,0.460221,0.932037,1,3,0,8.80117,0.15 --7.22623,0.87774,0.932037,1,1,0,7.23183,0.15 --6.86085,0.957571,0.932037,1,3,0,7.44679,0.15 --6.84216,1,0.932037,1,1,0,6.8764,0.15 --6.82726,0.902432,0.932037,2,3,0,7.67881,0.15 --7.01244,0.957121,0.932037,1,1,0,7.01282,0.15 --6.753,0.947388,0.932037,2,3,0,7.40574,0.15 --6.75612,0.999688,0.932037,2,3,0,6.75709,0.15 --7.10487,0.901325,0.932037,1,3,0,7.25063,0.15 --6.75288,0.934052,0.932037,2,3,0,7.61355,0.15 --6.75911,0.998131,0.932037,1,1,0,6.75973,0.15 --6.97448,0.935151,0.932037,1,3,0,7.07933,0.15 --8.33687,0.727783,0.932037,1,3,0,8.95629,0.15 --7.19311,1,0.932037,1,1,0,7.97929,0.15 --6.86239,0.957299,0.932037,1,3,0,7.41859,0.15 --8.81489,0.675307,0.932037,1,3,0,9.25628,0.15 --9.08174,0.976561,0.932037,1,1,0,9.30033,0.15 --8.72465,1,0.932037,2,3,0,9.23184,0.15 --9.02058,0.973248,0.932037,1,1,0,9.2222,0.15 --6.75096,0.87105,0.932037,1,3,0,9.3739,0.15 --6.77061,0.8673,0.932037,2,3,0,7.49474,0.15 --7.18049,0.687335,0.932037,2,3,0,8.77168,0.15 --6.83677,0.997719,0.932037,1,3,0,7.10412,0.15 --7.0242,0.942244,0.932037,1,3,0,7.2293,0.15 --6.75027,1,0.932037,1,3,0,6.95554,0.15 --7.19098,0.922423,0.932037,2,3,0,7.25741,0.15 --6.75095,0.998409,0.932037,2,3,0,7.18042,0.15 --6.87936,0.965588,0.932037,1,3,0,6.90968,0.15 --6.78928,1,0.932037,2,3,0,6.85581,0.15 --7.24713,0.912602,0.932037,1,3,0,7.27833,0.15 --7.64349,0.933948,0.932037,1,1,0,7.6686,0.15 --7.89818,0.963732,0.932037,1,1,0,7.99945,0.15 --9.96894,0.866816,0.932037,2,3,0,10.0037,0.15 --8.27408,1,0.932037,1,1,0,9.73188,0.15 --8.97796,0.763178,0.932037,1,1,0,9.46742,0.15 --7.07994,1,0.932037,2,3,0,9.38837,0.15 --6.75736,0.879611,0.932037,2,3,0,8.01228,0.15 --6.7519,1,0.932037,1,1,0,6.75617,0.15 --6.78524,0.992274,0.932037,1,3,0,6.78539,0.15 --7.86462,0.758008,0.932037,1,3,0,7.87664,0.15 --7.97349,0.959152,0.932037,1,1,0,8.36464,0.15 --7.11236,1,0.932037,2,3,0,7.71314,0.15 --7.71805,0.799983,0.932037,1,1,0,7.78908,0.15 --9.81575,0.716945,0.932037,2,3,0,10.0088,0.15 --7.32169,1,0.932037,1,1,0,8.93058,0.15 --6.74807,0.833534,0.932037,2,3,0,8.05557,0.15 --6.74803,1,0.932037,2,3,0,6.74806,0.15 --6.75538,0.991246,0.932037,2,3,0,6.79373,0.15 --6.93792,0.962649,0.932037,2,3,0,7.01983,0.15 --7.2862,0.88434,0.932037,1,1,0,7.31645,0.15 --6.99084,1,0.932037,2,3,0,7.19845,0.15 --7.65042,0.905856,0.932037,2,3,0,8.20379,0.15 --7.41337,1,0.932037,1,1,0,7.7679,0.15 --8.85702,0.576942,0.932037,1,1,0,8.98493,0.15 --6.78589,1,0.932037,1,3,0,8.17164,0.15 --6.90223,0.954007,0.932037,1,3,0,7.03131,0.15 --8.14191,0.811993,0.932037,1,3,0,8.27248,0.15 --8.10592,1,0.932037,1,1,0,8.36491,0.15 --8.2689,0.993522,0.932037,2,3,0,8.4528,0.15 --9.32894,0.899169,0.932037,1,1,0,9.34484,0.15 --8.02809,1,0.932037,1,1,0,9.18688,0.15 --6.99166,0.993898,0.932037,1,3,0,7.87392,0.15 --8.06441,0.911089,0.932037,2,3,0,8.31617,0.15 --8.7506,0.922675,0.932037,2,3,0,9.16274,0.15 --7.8978,1,0.932037,1,1,0,8.75888,0.15 --6.94446,1,0.932037,1,1,0,7.56554,0.15 --8.54285,0.771096,0.932037,2,3,0,8.54633,0.15 --6.78594,1,0.932037,1,3,0,7.9627,0.15 --6.78594,0.906519,0.932037,1,3,0,7.16707,0.15 --6.92112,0.942672,0.932037,2,3,0,7.08892,0.15 --6.87261,0.875781,0.932037,2,3,0,8.17854,0.15 --7.08975,0.952205,0.932037,1,1,0,7.09201,0.15 --7.02149,1,0.932037,2,3,0,7.12326,0.15 --7.04584,0.998287,0.932037,2,3,0,7.10444,0.15 --7.04584,0.672299,0.932037,1,3,0,9.55749,0.15 --7.77921,0.832639,0.932037,1,3,0,8.43718,0.15 --6.93132,0.932106,0.932037,1,3,0,8.11383,0.15 --7.37155,0.932331,0.932037,1,3,0,7.37171,0.15 --6.74802,0.977866,0.932037,1,3,0,7.37247,0.15 --6.90501,0.947815,0.932037,2,3,0,7.04469,0.15 --6.90501,0.515929,0.932037,1,1,0,8.06608,0.15 --7.26517,0.747153,0.932037,2,3,0,8.10285,0.15 --6.77759,0.959776,0.932037,1,3,0,7.38193,0.15 --6.75799,1,0.932037,1,1,0,6.77244,0.15 --6.79277,0.99575,0.932037,2,3,0,6.79381,0.15 --6.76109,1,0.932037,1,1,0,6.78457,0.15 --6.7596,0.969448,0.932037,2,3,0,6.95304,0.15 --6.79156,0.988014,0.932037,2,3,0,6.84381,0.15 --6.78312,1,0.932037,1,1,0,6.79707,0.15 --6.77762,0.949288,0.932037,2,3,0,7.08484,0.15 --7.09494,0.924565,0.932037,1,3,0,7.09566,0.15 --6.95426,1,0.932037,2,3,0,7.09613,0.15 --7.28442,0.889685,0.932037,1,1,0,7.32064,0.15 --6.79484,0.997398,0.932037,2,3,0,7.36362,0.15 --6.81648,0.99449,0.932037,1,1,0,6.8247,0.15 --6.84974,0.991789,0.932037,1,1,0,6.86103,0.15 --6.74879,0.865097,0.932037,2,3,0,7.74614,0.15 --6.88211,0.967449,0.932037,1,3,0,6.902,0.15 --6.74845,0.957844,0.932037,2,3,0,7.24214,0.15 --7.20685,0.887254,0.932037,1,3,0,7.25766,0.15 --7.20685,0.564767,0.932037,1,3,0,8.81898,0.15 --7.20685,0.320306,0.932037,1,3,0,12.13,0.15 --6.97752,0.935114,0.932037,2,3,0,7.66742,0.15 --6.87049,1,0.932037,1,1,0,6.96427,0.15 --6.74837,0.966424,0.932037,2,3,0,7.14605,0.15 --6.98253,0.907073,0.932037,2,3,0,7.2529,0.15 --7.79349,0.745081,0.932037,1,1,0,7.81771,0.15 --6.77388,1,0.932037,1,3,0,7.46942,0.15 --6.90086,0.954561,0.932037,1,3,0,7.00827,0.15 --6.79914,1,0.932037,2,3,0,6.8716,0.15 --8.11178,0.713466,0.932037,1,3,0,8.12201,0.15 --8.11178,0.190125,0.932037,1,1,0,11.1441,0.15 --7.24841,1,0.932037,1,1,0,7.88964,0.15 --7.23081,1,0.932037,1,1,0,7.40517,0.15 --6.80377,1,0.932037,2,3,0,7.08741,0.15 --6.85418,0.994603,0.932037,2,3,0,6.86482,0.15 --6.74965,0.98222,0.932037,2,3,0,6.95136,0.15 --7.03844,0.928412,0.932037,1,3,0,7.0566,0.15 --7.22621,0.808036,0.932037,1,3,0,7.93488,0.15 --6.77583,0.927823,0.932037,2,3,0,7.69178,0.15 --6.86535,0.971878,0.932037,1,1,0,6.86662,0.15 --6.77928,0.990027,0.932037,1,3,0,6.92787,0.15 --6.78451,0.998625,0.932037,1,1,0,6.79187,0.15 --6.78451,0.773207,0.932037,1,3,0,7.90069,0.15 --6.75189,0.967957,0.932037,2,3,0,6.99818,0.15 --6.88731,0.96889,0.932037,1,3,0,6.89993,0.15 --6.83332,1,0.932037,1,1,0,6.8859,0.15 --6.96445,0.969353,0.932037,1,1,0,6.9678,0.15 --6.74853,0.993691,0.932037,1,3,0,6.95955,0.15 --6.79227,0.98474,0.932037,2,3,0,6.83336,0.15 --6.78141,0.965461,0.932037,2,3,0,6.96936,0.15 --6.75837,0.995995,0.932037,1,3,0,6.80286,0.15 --6.80841,0.982527,0.932037,1,3,0,6.84862,0.15 --6.92145,0.989324,0.932037,2,3,0,6.92312,0.15 --6.94393,0.992369,0.932037,1,1,0,6.99666,0.15 --8.0719,0.66438,0.932037,1,1,0,8.0808,0.15 --8.30789,0.913191,0.932037,1,1,0,8.7652,0.15 --6.88395,1,0.932037,2,3,0,7.79691,0.15 --6.88395,0.525809,0.932037,1,3,0,8.46396,0.15 --7.66074,0.862122,0.932037,2,3,0,7.66587,0.15 --7.48718,1,0.932037,1,1,0,7.73391,0.15 --7.24771,1,0.932037,1,1,0,7.49536,0.15 --6.75294,0.907734,0.932037,2,3,0,8.2612,0.15 --6.75294,0.882023,0.932037,1,3,0,7.23222,0.15 --6.75294,0.648763,0.932037,1,3,0,8.1161,0.15 --6.75671,0.998954,0.932037,1,1,0,6.75747,0.15 --7.51733,0.821118,0.932037,1,3,0,7.63136,0.15 --7.23718,1,0.932037,2,3,0,7.55107,0.15 --7.23718,0.584164,0.932037,1,1,0,8.1976,0.15 --6.75151,1,0.932037,1,3,0,7.10952,0.15 --6.76119,0.993068,0.932037,2,3,0,6.78713,0.15 --6.79508,0.984853,0.932037,2,3,0,6.84415,0.15 --8.30022,0.789495,0.932037,2,3,0,8.51335,0.15 --7.08882,0.985416,0.932037,1,3,0,8.13433,0.15 --6.74804,0.991804,0.932037,1,3,0,7.07332,0.15 --6.76797,0.994746,0.932037,1,3,0,6.77114,0.15 --6.87611,0.984786,0.932037,2,3,0,6.88112,0.15 --7.10363,0.930507,0.932037,1,3,0,7.38443,0.15 --7.1931,0.968366,0.932037,1,1,0,7.30124,0.15 --7.02744,1,0.932037,1,1,0,7.20716,0.15 --7.47087,0.716122,0.932037,1,3,0,8.36114,0.15 --7.77445,0.953734,0.932037,1,1,0,7.84235,0.15 --7.10232,0.976364,0.932037,1,3,0,7.65215,0.15 --6.89247,0.977052,0.932037,2,3,0,7.36801,0.15 --6.88406,1,0.932037,2,3,0,6.92287,0.15 --7.00739,0.972361,0.932037,1,1,0,7.01912,0.15 --7.4581,0.959878,0.932037,2,3,0,7.46539,0.15 --7.4581,0.358894,0.932037,1,1,0,9.21803,0.15 --8.39486,0.699937,0.932037,1,1,0,8.5651,0.15 --6.98009,0.931934,0.932037,1,3,0,8.77344,0.15 --7.75622,0.895607,0.932037,2,3,0,8.30908,0.15 --7.45294,1,0.932037,1,1,0,7.85686,0.15 --7.81191,0.87317,0.932037,1,1,0,8.02199,0.15 --7.07889,1,0.932037,2,3,0,7.65163,0.15 --8.02018,0.93056,0.932037,2,3,0,8.11738,0.15 --7.32245,1,0.932037,1,1,0,7.89443,0.15 --7.11924,1,0.932037,2,3,0,7.35057,0.15 --7.13426,0.994637,0.932037,1,1,0,7.25741,0.15 --6.81324,1,0.932037,1,1,0,7.02809,0.15 --7.90962,0.554652,0.932037,2,3,0,9.26763,0.15 --10.1284,0.790216,0.932037,1,3,0,10.2416,0.15 --8.90062,1,0.932037,1,1,0,10.0537,0.15 --6.80723,1,0.932037,2,3,0,8.62687,0.15 --6.86729,0.973895,0.932037,2,3,0,7.02735,0.15 --6.79392,1,0.932037,1,1,0,6.85106,0.15 --7.40012,0.888235,0.932037,2,3,0,7.70807,0.15 --6.93792,1,0.932037,2,3,0,7.29993,0.15 --7.00949,0.984312,0.932037,1,1,0,7.03975,0.15 --7.13808,0.973678,0.932037,1,1,0,7.17115,0.15 --6.78692,0.965669,0.932037,1,3,0,7.25761,0.15 --6.87345,0.885758,0.932037,2,3,0,7.29989,0.15 --7.7385,0.869841,0.932037,2,3,0,7.74068,0.15 --7.7385,0.102215,0.932037,1,1,0,11.7215,0.15 --8.03266,0.893586,0.932037,1,1,0,8.35655,0.15 --8.22194,0.92979,0.932037,1,1,0,8.66902,0.15 --7.3561,1,0.932037,2,3,0,7.97443,0.15 --6.78127,1,0.932037,1,3,0,7.27452,0.15 --6.82275,0.984055,0.932037,1,3,0,6.89491,0.15 --7.6571,0.742126,0.932037,1,3,0,8.29259,0.15 --8.03026,0.829375,0.932037,1,3,0,9.64909,0.15 --7.37223,1,0.932037,1,1,0,7.93907,0.15 --6.79515,0.988926,0.932037,1,3,0,7.42116,0.15 --6.82487,0.992469,0.932037,1,1,0,6.83189,0.15 --7.12496,0.969767,0.932037,2,3,0,7.16248,0.15 --8.26834,0.652897,0.932037,1,1,0,8.32004,0.15 --8.67193,0.856184,0.932037,1,1,0,9.18766,0.15 --6.90442,1,0.932037,1,1,0,8.03082,0.15 --7.94108,0.68975,0.932037,1,1,0,7.94519,0.15 --7.78516,0.528351,0.932037,1,3,0,10.1916,0.15 --7.48177,1,0.932037,1,1,0,7.8074,0.15 --6.80338,0.90422,0.932037,2,3,0,8.07863,0.15 --6.89878,0.96934,0.932037,1,1,0,6.90583,0.15 --7.30937,0.866085,0.932037,1,1,0,7.32592,0.15 --6.85274,1,0.932037,1,1,0,7.15684,0.15 --6.85274,0.743543,0.932037,1,3,0,7.59752,0.15 --6.85274,0.563681,0.932037,1,3,0,9.12334,0.15 --7.14252,0.905701,0.932037,1,1,0,7.15244,0.15 --6.88606,1,0.932037,1,1,0,7.07414,0.15 --6.81352,1,0.932037,1,1,0,6.87385,0.15 --7.22426,0.603012,0.932037,2,3,0,8.88007,0.15 --6.75466,1,0.932037,1,3,0,7.17215,0.15 --6.77207,0.962029,0.932037,2,3,0,6.97984,0.15 --7.20122,0.9126,0.932037,1,3,0,7.2415,0.15 --7.97419,0.889846,0.932037,1,3,0,7.97427,0.15 --7.1089,0.976552,0.932037,1,3,0,7.82637,0.15 --7.1089,0.884111,0.932037,1,3,0,7.82698,0.15 --6.98201,0.835391,0.932037,2,3,0,9.32581,0.15 --7.4823,0.923908,0.932037,1,3,0,7.48233,0.15 --8.13382,0.907974,0.932037,1,1,0,8.15008,0.15 --7.0191,0.992287,0.932037,1,3,0,7.97523,0.15 --7.38884,0.929413,0.932037,1,1,0,7.39423,0.15 --7.05345,0.945247,0.932037,1,3,0,8.00914,0.15 --7.88022,0.88235,0.932037,2,3,0,7.92302,0.15 --7.47896,1,0.932037,1,1,0,7.94438,0.15 --8.26848,0.902685,0.932037,2,3,0,8.45552,0.15 --8.36145,0.964836,0.932037,1,1,0,8.90958,0.15 --6.80918,1,0.932037,1,3,0,7.82987,0.15 --7.23868,0.600861,0.932037,2,3,0,8.89375,0.15 --6.89006,1,0.932037,1,1,0,7.13419,0.15 --6.8044,1,0.932037,2,3,0,6.86543,0.15 --6.86009,0.995405,0.932037,2,3,0,6.86567,0.15 --6.86009,0.799308,0.932037,1,3,0,7.97123,0.15 --8.99856,0.728419,0.932037,2,3,0,9.23129,0.15 --9.09468,0.991837,0.932037,1,1,0,9.38997,0.15 --8.73756,1,0.932037,1,1,0,9.24597,0.15 --7.55986,0.937761,0.932037,1,3,0,8.57866,0.15 --7.99912,0.937675,0.932037,1,1,0,8.05117,0.15 --7.76055,1,0.932037,1,1,0,8.08641,0.15 --8.28732,0.977606,0.932037,2,3,0,8.34339,0.15 --6.82679,0.992827,0.932037,1,3,0,8.22618,0.15 --6.87274,0.996281,0.932037,2,3,0,6.88424,0.15 --6.78865,0.91873,0.932037,2,3,0,7.41091,0.15 --6.76058,1,0.932037,2,3,0,6.78115,0.15 --6.76058,0.44465,0.932037,2,3,0,10.1097,0.15 --8.19963,0.701948,0.932037,1,3,0,8.62984,0.15 --6.87827,1,0.932037,1,3,0,8.08649,0.15 --6.90011,0.99488,0.932037,1,1,0,6.92793,0.15 --7.58846,0.835161,0.932037,1,3,0,7.98997,0.15 --6.84168,1,0.932037,2,3,0,7.32922,0.15 --6.79898,0.907476,0.932037,2,3,0,7.23342,0.15 --7.18968,0.940405,0.932037,2,3,0,7.1897,0.15 --7.30081,0.811278,0.932037,2,3,0,8.27114,0.15 --7.44395,0.975262,0.932037,1,1,0,7.52376,0.15 --7.10913,1,0.932037,1,1,0,7.3984,0.15 --6.74902,0.994264,0.932037,1,3,0,7.08219,0.15 --7.95169,0.736282,0.932037,1,3,0,8.05657,0.15 -# -# Elapsed Time: 0.005 seconds (Warm-up) -# 0.012 seconds (Sampling) -# 0.017 seconds (Total) -# diff --git a/src/test/unit/mcmc/test_csv_files/bernoulli_default.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_default.csv new file mode 100644 index 0000000000..d45074864f --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_default.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-20 18:56:30 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3635036313 (Default) +# output +# file = bernoulli_out.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.932037 +# Diagonal elements of inverse mass matrix: +# 0.591014 +-7.81355,0.761218,0.932037,1,3,0,8.13669,0.102158 +-8.71049,0.894869,0.932037,1,1,0,8.72268,0.0676546 +-7.3531,0.957519,0.932037,1,3,0,8.53469,0.131664 +-7.02007,1,0.932037,1,1,0,7.29505,0.166128 +-8.49033,0.716959,0.932037,1,3,0,9.20407,0.513986 +-7.18724,1,0.932037,1,1,0,8.06459,0.377282 +-10.0621,0.636916,0.932037,2,3,0,10.0852,0.615706 +-8.11851,1,0.932037,1,1,0,9.65805,0.482811 +-6.86936,0.948809,0.932037,2,3,0,8.60097,0.192035 +-7.13756,0.935492,0.932037,2,3,0,7.43717,0.151774 +-7.09122,1,0.932037,1,1,0,7.19518,0.157048 +-6.91669,1,0.932037,1,1,0,7.06383,0.182475 +-6.76371,0.840542,0.932037,2,3,0,8.08838,0.228308 +-6.84805,0.983351,0.932037,1,3,0,6.84861,0.197051 +-8.3727,0.653843,0.932037,2,3,0,9.53079,0.504537 +-7.25928,0.806553,0.932037,1,3,0,9.43963,0.139678 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+-6.74902,0.994264,0.932037,1,3,0,7.08219,0.24445 +-7.95169,0.736282,0.932037,1,3,0,8.05657,0.467411 +# +# Elapsed Time: 0.005 seconds (Warm-up) +# 0.012 seconds (Sampling) +# 0.017 seconds (Total) +# diff --git a/src/test/unit/mcmc/test_csv_files/bernoulli_thin.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_thin.csv new file mode 100644 index 0000000000..b0874d8ead --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_thin.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-23 15:13:19 UTC +# method = sample (Default) +# sample +# num_samples = 2000 +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 2 +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3880845880 (Default) +# output +# file = bernoulli_thin.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.893542 +# Diagonal elements of inverse mass matrix: +# 0.483503 +-8.91945,1,0.893542,1,1,0,9.73374,0.0619604 +-8.90814,1,0.893542,1,1,0,9.95846,0.545008 +-7.21915,1,0.893542,1,1,0,7.56642,0.382112 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b/src/test/unit/mcmc/test_csv_files/bernoulli_warmup.csv new file mode 100644 index 0000000000..799f5c8260 --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_warmup.csv @@ -0,0 +1,2057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-22 12:26:39 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = true +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3784445287 (Default) +# output +# file = output.csv (Default) +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +-9.8506,1,0.5,1,1,0,11.6894,0.604056 +-9.8506,0,14.3855,0,1,1,9.9718,0.604056 +-9.8506,1.07054e-09,2.43117,1,1,0,10.1705,0.604056 +-8.24874,1,0.239791,2,3,0,9.78315,0.49418 +-8.54651,0.976787,0.324332,1,1,0,8.57154,0.518378 +-7.21218,0.977577,0.47329,2,3,0,8.85175,0.38107 +-6.74823,1,0.750786,1,3,0,7.08766,0.247464 +-6.74823,0.0568166,1.34037,1,1,0,7.21871,0.247464 +-6.88855,0.99524,0.127193,3,15,0,7.76928,0.319814 +-7.18954,0.980866,0.230706,3,9,0,7.91162,0.146333 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+-7.21715,0.677044,0.825931,1,1,0,8.31299,0.381813 +# Adaptation terminated +# Step size = 0.83124 +# Diagonal elements of inverse mass matrix: +# 0.525769 +-6.77384,0.964073,0.83124,1,3,0,7.46753,0.222338 +-6.863,0.968947,0.83124,1,3,0,7.01137,0.312874 +-6.83479,0.964185,0.83124,1,3,0,7.16339,0.200486 +-6.80509,0.978314,0.83124,1,3,0,7.03713,0.293726 +-6.79148,0.786013,0.83124,2,3,0,8.42285,0.214411 +-6.79943,0.998548,0.83124,1,1,0,6.80547,0.211417 +-6.85456,0.967137,0.83124,2,3,0,7.15794,0.310427 +-7.11014,0.963078,0.83124,2,7,0,7.14083,0.154842 +-6.75222,0.990723,0.83124,1,3,0,7.18664,0.238666 +-7.40454,0.858025,0.83124,1,3,0,7.7421,0.127645 +-7.17574,0.883441,0.83124,1,3,0,8.96348,0.375502 +-7.6387,0.88609,0.83124,1,1,0,7.67194,0.435344 +-7.41421,0.638614,0.83124,1,3,0,10.1123,0.126916 +-7.80019,0.957369,0.83124,1,1,0,7.80975,0.102852 +-7.88627,0.991536,0.83124,1,1,0,7.99586,0.0985041 +-6.75872,1,0.83124,2,3,0,7.70683,0.232017 +-7.11378,0.930982,0.83124,1,3,0,7.23357,0.154425 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+-6.74891,0.911224,0.83124,2,3,0,7.43307,0.244756 +-7.42023,0.912902,0.83124,2,3,0,7.43121,0.126465 +-7.34223,0.999701,0.83124,2,7,0,7.4227,0.399457 +-6.75652,0.992019,0.83124,2,3,0,7.41195,0.266528 +-6.76283,0.994113,0.83124,2,7,0,6.7964,0.228913 +-6.80605,0.983335,0.83124,1,3,0,6.88718,0.294104 +-6.97427,0.917773,0.83124,1,3,0,7.34947,0.172796 +-6.78479,0.97611,0.83124,1,3,0,7.23119,0.284872 +-6.7651,1,0.83124,1,1,0,6.78103,0.273564 +-6.74958,1,0.83124,2,3,0,6.76193,0.243075 +-7.02863,0.969077,0.83124,2,3,0,7.05541,0.3504 +-6.91736,0.953693,0.83124,2,3,0,7.44255,0.326972 +-6.918,0.618501,0.83124,2,3,0,9.85086,0.182235 +-6.84677,1,0.83124,1,1,0,6.90933,0.197372 +-6.96252,0.980836,0.83124,1,1,0,6.96279,0.174638 +# +# Elapsed Time: 0.01 seconds (Warm-up) +# 0.008 seconds (Sampling) +# 0.018 seconds (Total) +# diff --git a/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_1.csv b/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_1.csv deleted file mode 100644 index c210031c92..0000000000 --- a/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_1.csv +++ /dev/null @@ -1,62 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 35 -# stan_version_patch = 0 -# model = eight_schools_model -# start_datetime = 2024-08-02 00:41:06 UTC -# method = sample (Default) -# sample -# num_samples = 5 -# num_warmup = 1000 (Default) -# save_warmup = false (Default) -# thin = 1 (Default) -# adapt -# engaged = true (Default) -# gamma = 0.05 (Default) -# delta = 0.8 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# save_metric = true -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# num_chains = 1 (Default) -# id = 1 (Default) -# data -# file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/eight_schools.data.json -# init = 2 (Default) -# random -# seed = 1272150304 -# output -# file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/test_csv_files/eight_schools-202408012041-1-5c2994.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -# sig_figs = -1 (Default) -# profile_file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/test_csv_files/eight_schools-profile-202408012041-1-71b713.csv -# save_cmdstan_config = true -# num_threads = 1 (Default) -# stanc_version = stanc3 v2.35.0-25-gbb9ce42 -# stancflags = -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,tau -# Adaptation terminated -# Step size = 0.255122 -# Diagonal elements of inverse mass matrix: -# 29.0476, 61.52, 39.9696, 71.637, 55.2727, 46.9849, 47.056, 46.955, 63.8751, 0.452837 --20.7877,0.992501,0.255122,4,31,0,26.7532,15.418,22.6964,2.33549,-0.47199,10.617,0.327037,10.1927,16.3596,19.9623,9.72387 --20.3617,0.786426,0.255122,3,15,0,23.3769,15.4589,17.628,4.63432,10.408,11.706,2.19791,13.2514,13.176,24.6951,11.005 --20.0581,0.970637,0.255122,3,15,0,23.1533,10.6479,9.46195,13.6135,12.0037,13.4518,-0.981723,14.7449,10.6646,23.8779,6.28949 --27.7784,0.685455,0.255122,4,31,0,32.1245,18.1249,29.0012,16.4194,21.7475,18.8411,-5.54255,-0.590031,2.02352,1.58803,26.5075 --20.4323,0.976331,0.255122,4,15,0,31.0216,6.67189,0.393845,2.87533,-5.4399,-0.80737,7.71426,5.1214,17.8469,7.69329,9.05484 -# -# Elapsed Time: 0.03 seconds (Warm-up) -# 0 seconds (Sampling) -# 0.03 seconds (Total) -# diff --git a/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_2.csv b/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_2.csv deleted file mode 100644 index 5ac7176c6f..0000000000 --- a/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_2.csv +++ /dev/null @@ -1,62 +0,0 @@ -# stan_version_major = 2 -# stan_version_minor = 35 -# stan_version_patch = 0 -# model = eight_schools_model -# start_datetime = 2024-08-02 00:41:07 UTC -# method = sample (Default) -# sample -# num_samples = 5 -# num_warmup = 1000 (Default) -# save_warmup = false (Default) -# thin = 1 (Default) -# adapt -# engaged = true (Default) -# gamma = 0.05 (Default) -# delta = 0.8 (Default) -# kappa = 0.75 (Default) -# t0 = 10 (Default) -# init_buffer = 75 (Default) -# term_buffer = 50 (Default) -# window = 25 (Default) -# save_metric = true -# algorithm = hmc (Default) -# hmc -# engine = nuts (Default) -# nuts -# max_depth = 10 (Default) -# metric = diag_e (Default) -# metric_file = (Default) -# stepsize = 1 (Default) -# stepsize_jitter = 0 (Default) -# num_chains = 1 (Default) -# id = 2 -# data -# file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/eight_schools.data.json -# init = 2 (Default) -# random -# seed = 1272150304 -# output -# file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/test_csv_files/eight_schools-202408012041-2-5c2994.csv -# diagnostic_file = (Default) -# refresh = 100 (Default) -# sig_figs = -1 (Default) -# profile_file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/test_csv_files/eight_schools-profile-202408012041-2-71b713.csv -# save_cmdstan_config = true -# num_threads = 1 (Default) -# stanc_version = stanc3 v2.35.0-25-gbb9ce42 -# stancflags = -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,tau -# Adaptation terminated -# Step size = 0.212532 -# Diagonal elements of inverse mass matrix: -# 36.7915, 71.7469, 39.8211, 74.4468, 50.0889, 46.2225, 50.7702, 47.6696, 63.508, 0.719632 --25.6347,0.977811,0.212532,4,15,0,30.7373,3.48962,18.9434,-8.37292,-16.7421,0.568964,9.96071,17.3906,8.33767,4.11305,13.9073 --26.2724,0.957923,0.212532,5,31,0,33.4452,1.10426,19.5777,7.16892,-4.27551,1.20374,1.75746,15.5193,-6.81069,-11.3926,16.3893 --25.8267,0.989673,0.212532,4,15,0,28.8245,1.07736,17.5014,13.9165,0.358151,2.27947,-3.36194,14.716,-5.54053,-11.8808,8.61073 --29.6451,0.640928,0.212532,5,31,0,35.3758,8.49943,13.8662,0.409198,7.39161,8.57074,6.92426,24.1421,4.40711,-10.1655,31.0153 --21.8872,0.886503,0.212532,4,15,0,32.6965,7.28245,18.5208,12.251,6.33112,7.20102,3.02724,23.5692,10.1139,4.93785,5.93738 -# -# Elapsed Time: 0.03 seconds (Warm-up) -# 0 seconds (Sampling) -# 0.03 seconds (Total) -#