def _ext_identity(samples): """Extracts the values of all latent variables.""" res = collections.OrderedDict() res['avg_effect'] = util.get_columns(samples, r'^avg_effect$')[:, 0] res['log_stddev'] = util.get_columns(samples, r'^log_stddev$')[:, 0] res['school_effects'] = util.get_columns(samples, r'^school_effects\.\d+$') return res
def _ext_identity(samples): """Extracts the values of all latent variables.""" return { 'innovation_noise_scale': util.get_columns(samples, r'^innovation_noise_scale$')[:, 0], 'observation_noise_scale': util.get_columns(samples, r'^observation_noise_scale$')[:, 0], 'locs': util.get_columns(samples, r'^loc\.\d+$') }
def _ext_identity(samples): """Extracts the values of all latent variables.""" latents = util.get_columns(samples, r'^latents\.\d+\.\d+$') return { 'innovation_scale': util.get_columns(samples, r'^innovation_scale$')[:, 0], 'observation_scale': util.get_columns(samples, r'^observation_scale$')[:, 0], # Last two dimensions are swapped in Stan output. 'latents': latents.reshape((-1, 3, 30)).swapaxes(1, 2)}
def _ext_identity(samples): """Extracts the values of all latent variables.""" res = collections.OrderedDict() res['county_effect_mean'] = util.get_columns( samples, r'^county_effect_mean$')[:, 0] res['county_effect_scale'] = util.get_columns( samples, r'^county_effect_scale$')[:, 0] res['county_effect'] = util.get_columns(samples, r'^county_effect\[\d+\]$') res['weight'] = util.get_columns(samples, r'^weight\[\d+\]$') res['log_radon_scale'] = (util.get_columns(samples, r'^log_radon_scale$')[:, 0]) return res
def _ext_identity(samples): """Extracts the values of all latent variables.""" res = collections.OrderedDict() res['mean_log_volatility'] = util.get_columns( samples, r'^mean_log_volatility$')[:, 0] res['white_noise_shock_scale'] = util.get_columns( samples, r'^white_noise_shock_scale$')[:, 0] res['persistence_of_volatility'] = util.get_columns( samples, r'^persistence$')[:, 0] res['log_volatility'] = util.get_columns( samples, r'^log_volatilities\[\d+\]$', ) return res
def _ext_identity(samples): """Extracts all the parameters.""" res = collections.OrderedDict() res['mean_student_ability'] = util.get_columns( samples, r'^mean_student_ability$', )[:, 0] res['student_ability'] = util.get_columns( samples, r'^student_ability\[\d+\]$', ) res['question_difficulty'] = util.get_columns( samples, r'^question_difficulty\[\d+\]$', ) return res
def _ext_identity(samples): """Extract all the parameters.""" res = collections.OrderedDict() res['unscaled_weights'] = util.get_columns( samples, r'^unscaled_weights\[\d+\]$', ) res['local_scales'] = util.get_columns( samples, r'^local_scales\[\d+\]$', ) res['global_scale'] = util.get_columns( samples, r'^global_scale$', )[:, 0] return res
def _ext_identity(samples): """Extract all the parameters.""" res = collections.OrderedDict() res['amplitude'] = util.get_columns( samples, r'^amplitude$', )[:, 0] res['length_scale'] = util.get_columns( samples, r'^length_scale$', )[:, 0] res['log_intensity'] = util.get_columns( samples, r'^log_intensity\.\d+$', ) return res
def _ext_identity(samples): """Extracts the values of all latent variables.""" locs = util.get_columns(samples, r'^loc\.\d+$') return locs
def _ext_per_example_test_nll(samples): return util.get_columns(samples, r'^per_example_test_nll\[\d+\]$')
def _ext_test_nll(samples): return util.get_columns(samples, r'^test_nll$')[:, 0]
def _ext_identity(samples): """Extracts the values of all latent variables.""" latents = util.get_columns(samples, r'^latents\.\d+\.\d+$') # Last two dimensions are swapped in Stan output. return latents.reshape((-1, 3, 30)).swapaxes(1, 2)
def _ext_identity(samples): return util.get_columns(samples, r'^weights\[\d+\]$')