549 lines
11 KiB
JavaScript
549 lines
11 KiB
JavaScript
/*
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Language: Stan
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Description: The Stan probabilistic programming language
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Author: Jeffrey B. Arnold <jeffrey.arnold@gmail.com>
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Website: http://mc-stan.org/
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Category: scientific
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*/
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function stan(hljs) {
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// variable names cannot conflict with block identifiers
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const BLOCKS = [
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'functions',
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'model',
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'data',
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'parameters',
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'quantities',
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'transformed',
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'generated'
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];
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const STATEMENTS = [
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'for',
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'in',
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'if',
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'else',
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'while',
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'break',
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'continue',
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'return'
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];
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const SPECIAL_FUNCTIONS = [
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'print',
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'reject',
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'increment_log_prob|10',
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'integrate_ode|10',
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'integrate_ode_rk45|10',
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'integrate_ode_bdf|10',
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'algebra_solver'
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];
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const VAR_TYPES = [
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'int',
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'real',
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'vector',
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'ordered',
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'positive_ordered',
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'simplex',
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'unit_vector',
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'row_vector',
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'matrix',
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'cholesky_factor_corr|10',
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'cholesky_factor_cov|10',
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'corr_matrix|10',
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'cov_matrix|10',
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'void'
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];
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const FUNCTIONS = [
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'Phi',
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'Phi_approx',
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'abs',
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'acos',
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'acosh',
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'algebra_solver',
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'append_array',
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'append_col',
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'append_row',
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'asin',
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'asinh',
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'atan',
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'atan2',
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'atanh',
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'bernoulli_cdf',
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'bernoulli_lccdf',
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'bernoulli_lcdf',
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'bernoulli_logit_lpmf',
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'bernoulli_logit_rng',
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'bernoulli_lpmf',
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'bernoulli_rng',
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'bessel_first_kind',
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'bessel_second_kind',
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'beta_binomial_cdf',
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'beta_binomial_lccdf',
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'beta_binomial_lcdf',
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'beta_binomial_lpmf',
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'beta_binomial_rng',
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'beta_cdf',
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'beta_lccdf',
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'beta_lcdf',
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'beta_lpdf',
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'beta_rng',
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'binary_log_loss',
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'binomial_cdf',
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'binomial_coefficient_log',
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'binomial_lccdf',
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'binomial_lcdf',
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'binomial_logit_lpmf',
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'binomial_lpmf',
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'binomial_rng',
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'block',
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'categorical_logit_lpmf',
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'categorical_logit_rng',
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'categorical_lpmf',
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'categorical_rng',
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'cauchy_cdf',
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'cauchy_lccdf',
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'cauchy_lcdf',
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'cauchy_lpdf',
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'cauchy_rng',
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'cbrt',
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'ceil',
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'chi_square_cdf',
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'chi_square_lccdf',
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'chi_square_lcdf',
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'chi_square_lpdf',
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'chi_square_rng',
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'cholesky_decompose',
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'choose',
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'col',
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'cols',
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'columns_dot_product',
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'columns_dot_self',
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'cos',
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'cosh',
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'cov_exp_quad',
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'crossprod',
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'csr_extract_u',
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'csr_extract_v',
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'csr_extract_w',
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'csr_matrix_times_vector',
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'csr_to_dense_matrix',
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'cumulative_sum',
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'determinant',
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'diag_matrix',
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'diag_post_multiply',
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'diag_pre_multiply',
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'diagonal',
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'digamma',
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'dims',
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'dirichlet_lpdf',
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'dirichlet_rng',
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'distance',
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'dot_product',
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'dot_self',
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'double_exponential_cdf',
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'double_exponential_lccdf',
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'double_exponential_lcdf',
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'double_exponential_lpdf',
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'double_exponential_rng',
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'e',
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'eigenvalues_sym',
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'eigenvectors_sym',
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'erf',
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'erfc',
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'exp',
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'exp2',
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'exp_mod_normal_cdf',
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'exp_mod_normal_lccdf',
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'exp_mod_normal_lcdf',
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'exp_mod_normal_lpdf',
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'exp_mod_normal_rng',
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'expm1',
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'exponential_cdf',
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'exponential_lccdf',
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'exponential_lcdf',
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'exponential_lpdf',
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'exponential_rng',
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'fabs',
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'falling_factorial',
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'fdim',
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'floor',
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'fma',
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'fmax',
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'fmin',
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'fmod',
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'frechet_cdf',
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'frechet_lccdf',
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'frechet_lcdf',
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'frechet_lpdf',
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'frechet_rng',
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'gamma_cdf',
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'gamma_lccdf',
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'gamma_lcdf',
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'gamma_lpdf',
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'gamma_p',
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'gamma_q',
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'gamma_rng',
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'gaussian_dlm_obs_lpdf',
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'get_lp',
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'gumbel_cdf',
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'gumbel_lccdf',
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'gumbel_lcdf',
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'gumbel_lpdf',
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'gumbel_rng',
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'head',
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'hypergeometric_lpmf',
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'hypergeometric_rng',
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'hypot',
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'inc_beta',
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'int_step',
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'integrate_ode',
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'integrate_ode_bdf',
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'integrate_ode_rk45',
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'inv',
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'inv_Phi',
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'inv_chi_square_cdf',
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'inv_chi_square_lccdf',
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'inv_chi_square_lcdf',
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'inv_chi_square_lpdf',
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'inv_chi_square_rng',
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'inv_cloglog',
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'inv_gamma_cdf',
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'inv_gamma_lccdf',
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'inv_gamma_lcdf',
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'inv_gamma_lpdf',
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'inv_gamma_rng',
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'inv_logit',
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'inv_sqrt',
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'inv_square',
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'inv_wishart_lpdf',
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'inv_wishart_rng',
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'inverse',
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'inverse_spd',
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'is_inf',
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'is_nan',
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'lbeta',
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'lchoose',
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'lgamma',
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'lkj_corr_cholesky_lpdf',
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'lkj_corr_cholesky_rng',
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'lkj_corr_lpdf',
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'lkj_corr_rng',
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'lmgamma',
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'lmultiply',
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'log',
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'log10',
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'log1m',
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'log1m_exp',
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'log1m_inv_logit',
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'log1p',
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'log1p_exp',
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'log2',
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'log_determinant',
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'log_diff_exp',
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'log_falling_factorial',
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'log_inv_logit',
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'log_mix',
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'log_rising_factorial',
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'log_softmax',
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'log_sum_exp',
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'logistic_cdf',
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'logistic_lccdf',
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'logistic_lcdf',
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'logistic_lpdf',
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'logistic_rng',
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'logit',
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'lognormal_cdf',
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'lognormal_lccdf',
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'lognormal_lcdf',
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'lognormal_lpdf',
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'lognormal_rng',
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'machine_precision',
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'matrix_exp',
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'max',
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'mdivide_left_spd',
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'mdivide_left_tri_low',
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'mdivide_right_spd',
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'mdivide_right_tri_low',
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'mean',
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'min',
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'modified_bessel_first_kind',
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'modified_bessel_second_kind',
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'multi_gp_cholesky_lpdf',
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'multi_gp_lpdf',
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'multi_normal_cholesky_lpdf',
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'multi_normal_cholesky_rng',
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'multi_normal_lpdf',
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'multi_normal_prec_lpdf',
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'multi_normal_rng',
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'multi_student_t_lpdf',
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'multi_student_t_rng',
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'multinomial_lpmf',
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'multinomial_rng',
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'multiply_log',
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'multiply_lower_tri_self_transpose',
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'neg_binomial_2_cdf',
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'neg_binomial_2_lccdf',
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'neg_binomial_2_lcdf',
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'neg_binomial_2_log_lpmf',
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'neg_binomial_2_log_rng',
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'neg_binomial_2_lpmf',
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'neg_binomial_2_rng',
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'neg_binomial_cdf',
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'neg_binomial_lccdf',
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'neg_binomial_lcdf',
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'neg_binomial_lpmf',
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'neg_binomial_rng',
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'negative_infinity',
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'normal_cdf',
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'normal_lccdf',
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'normal_lcdf',
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'normal_lpdf',
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'normal_rng',
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'not_a_number',
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'num_elements',
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'ordered_logistic_lpmf',
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'ordered_logistic_rng',
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'owens_t',
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'pareto_cdf',
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'pareto_lccdf',
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'pareto_lcdf',
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'pareto_lpdf',
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'pareto_rng',
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'pareto_type_2_cdf',
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'pareto_type_2_lccdf',
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'pareto_type_2_lcdf',
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'pareto_type_2_lpdf',
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'pareto_type_2_rng',
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'pi',
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'poisson_cdf',
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'poisson_lccdf',
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'poisson_lcdf',
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'poisson_log_lpmf',
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'poisson_log_rng',
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'poisson_lpmf',
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'poisson_rng',
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'positive_infinity',
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'pow',
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'print',
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'prod',
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'qr_Q',
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'qr_R',
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'quad_form',
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'quad_form_diag',
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'quad_form_sym',
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'rank',
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'rayleigh_cdf',
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'rayleigh_lccdf',
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'rayleigh_lcdf',
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'rayleigh_lpdf',
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'rayleigh_rng',
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'reject',
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'rep_array',
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'rep_matrix',
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'rep_row_vector',
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'rep_vector',
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'rising_factorial',
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'round',
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'row',
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'rows',
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'rows_dot_product',
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'rows_dot_self',
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'scaled_inv_chi_square_cdf',
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'scaled_inv_chi_square_lccdf',
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'scaled_inv_chi_square_lcdf',
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'scaled_inv_chi_square_lpdf',
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'scaled_inv_chi_square_rng',
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'sd',
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'segment',
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'sin',
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'singular_values',
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'sinh',
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'size',
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'skew_normal_cdf',
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'skew_normal_lccdf',
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'skew_normal_lcdf',
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'skew_normal_lpdf',
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'skew_normal_rng',
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'softmax',
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'sort_asc',
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'sort_desc',
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'sort_indices_asc',
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'sort_indices_desc',
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'sqrt',
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'sqrt2',
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'square',
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'squared_distance',
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'step',
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'student_t_cdf',
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'student_t_lccdf',
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'student_t_lcdf',
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'student_t_lpdf',
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'student_t_rng',
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'sub_col',
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'sub_row',
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'sum',
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'tail',
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'tan',
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'tanh',
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'target',
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'tcrossprod',
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'tgamma',
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'to_array_1d',
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'to_array_2d',
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'to_matrix',
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'to_row_vector',
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'to_vector',
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'trace',
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'trace_gen_quad_form',
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'trace_quad_form',
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'trigamma',
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'trunc',
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'uniform_cdf',
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'uniform_lccdf',
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'uniform_lcdf',
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'uniform_lpdf',
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'uniform_rng',
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'variance',
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'von_mises_lpdf',
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'von_mises_rng',
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'weibull_cdf',
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'weibull_lccdf',
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'weibull_lcdf',
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'weibull_lpdf',
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'weibull_rng',
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'wiener_lpdf',
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'wishart_lpdf',
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'wishart_rng'
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];
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const DISTRIBUTIONS = [
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'bernoulli',
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'bernoulli_logit',
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'beta',
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'beta_binomial',
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'binomial',
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'binomial_logit',
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'categorical',
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'categorical_logit',
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'cauchy',
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'chi_square',
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'dirichlet',
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'double_exponential',
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'exp_mod_normal',
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'exponential',
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'frechet',
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'gamma',
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'gaussian_dlm_obs',
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'gumbel',
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'hypergeometric',
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'inv_chi_square',
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'inv_gamma',
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'inv_wishart',
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'lkj_corr',
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'lkj_corr_cholesky',
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'logistic',
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'lognormal',
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'multi_gp',
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'multi_gp_cholesky',
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'multi_normal',
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'multi_normal_cholesky',
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'multi_normal_prec',
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'multi_student_t',
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'multinomial',
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'neg_binomial',
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'neg_binomial_2',
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'neg_binomial_2_log',
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'normal',
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'ordered_logistic',
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'pareto',
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'pareto_type_2',
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'poisson',
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'poisson_log',
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'rayleigh',
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'scaled_inv_chi_square',
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'skew_normal',
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'student_t',
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'uniform',
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'von_mises',
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'weibull',
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'wiener',
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'wishart'
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];
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return {
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name: 'Stan',
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aliases: [ 'stanfuncs' ],
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keywords: {
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$pattern: hljs.IDENT_RE,
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title: BLOCKS,
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keyword: STATEMENTS.concat(VAR_TYPES).concat(SPECIAL_FUNCTIONS),
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built_in: FUNCTIONS
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},
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contains: [
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hljs.C_LINE_COMMENT_MODE,
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hljs.COMMENT(
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/#/,
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/$/,
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{
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relevance: 0,
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keywords: {
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'meta-keyword': 'include'
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}
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}
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),
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hljs.COMMENT(
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/\/\*/,
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/\*\//,
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{
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relevance: 0,
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// highlight doc strings mentioned in Stan reference
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contains: [
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{
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className: 'doctag',
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begin: /@(return|param)/
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}
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]
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}
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),
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{
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// hack: in range constraints, lower must follow "<"
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begin: /<\s*lower\s*=/,
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keywords: 'lower'
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},
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{
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// hack: in range constraints, upper must follow either , or <
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// <lower = ..., upper = ...> or <upper = ...>
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begin: /[<,]\s*upper\s*=/,
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keywords: 'upper'
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},
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{
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className: 'keyword',
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begin: /\btarget\s*\+=/,
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relevance: 10
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},
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|
{
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begin: '~\\s*(' + hljs.IDENT_RE + ')\\s*\\(',
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keywords: DISTRIBUTIONS
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},
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|
{
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|
className: 'number',
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|
variants: [
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{
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begin: /\b\d+(?:\.\d*)?(?:[eE][+-]?\d+)?/
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},
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|
{
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|
begin: /\.\d+(?:[eE][+-]?\d+)?\b/
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}
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],
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|
relevance: 0
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|
},
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|
{
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className: 'string',
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begin: '"',
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end: '"',
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relevance: 0
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}
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]
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};
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}
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module.exports = stan;
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