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\[\d+\]$')