mcmc.h

This file implements Markov chain Monte Carlo inference for Dirichlet processes (DP) and hierarchical Dirichlet processes (HDP). More specifically, this file implements a Gibbs sampler for the Chinese restaurant representation. See hdp.h for a description of DPs, HDPs, and the Chinese restaurant representation.

The following is an example of an HDP mixture model where the base distribution is a symmetric Dirichlet distribution with parameter 1. The likelihood distribution is a categorical over the set of ASCII characters, which we encode as an unsigned int in `{1, 2, ..., 256}`

.

#include <hdp/mcmc.h> using namespace core; constexpr unsigned int depth = 2; template<typename BaseDistribution, typename Likelihood, typename K, typename V> void posterior_predictive_probability( const hdp_sampler<BaseDistribution, Likelihood, K, V>& sampler, const cache<BaseDistribution, Likelihood, K, V>& cache, const K& observation) { array<weighted_feature_set<V>> paths(32); auto root_probabilities = cache.compute_root_probabilities(sampler, observation); predict(sampler, observation, paths, root_probabilities); cleanup_root_probabilities(root_probabilities, sampler.posterior.length); sort(paths, feature_set_sorter(depth)); for (unsigned int i = 0; i < paths.length; i++) { unsigned int*& path = paths[i].features.features; print("probability of drawing '", stdout); print((char) observation, stdout); if (path[0] == IMPLICIT_NODE) { print("' from any other child node: ", stdout); } else { print("' from child node ", stdout); print(path[0], stdout); print(": ", stdout); } print(exp(paths[i].log_probability), stdout); print('\n', stdout); free(paths[i]); } } template<typename BaseDistribution, typename Likelihood, typename K, typename V> void do_inference(hdp<BaseDistribution, Likelihood, K, V>& h) { hdp_sampler<BaseDistribution, Likelihood, K, V> sampler(h); cache<BaseDistribution, Likelihood, K, V> cache(sampler); prepare_sampler(sampler, cache); for (unsigned int i = 0; i < 200; i++) sample_hdp<true>(sampler, cache); for (unsigned int i = 0; i < 800; i++) { sample_hdp<true>(sampler, cache); if (i % 5 == 0) sampler.add_sample(); } posterior_predictive_probability(sampler, cache, (unsigned int) '+'); posterior_predictive_probability(sampler, cache, (unsigned int) '-'); posterior_predictive_probability(sampler, cache, (unsigned int) 'a'); } int main() { double alpha[] = {1.0e6, 1.0e-2}; dirichlet<double> base_distribution(256, 1.0); hdp<dirichlet<double>, dense_categorical<double>, unsigned int, double> h(base_distribution, alpha, depth); unsigned int first_child[] = { 1 }; unsigned int second_child[] = { 2 }; for (unsigned int i = 0; i < 100; i++) { add(h, first_child, depth, (unsigned int) '+'); add(h, second_child, depth, (unsigned int) '-'); } do_inference(h); }In this example, we use the following model:

\[ \begin{align*} H &= \text{Dirichlet}([1, \ldots, 1]), \\ G^{\textbf{0}} &\sim DP(H, 10^6), \\ G^{\textbf{1}}, G^{\textbf{2}}, G^{\textbf{3}} &\sim DP(G_{\textbf{0}}, 10^{-2}), \\ x^{\textbf{1}}_1, \ldots, x^{\textbf{1}}_{100} &\sim G_{\textbf{1}}, \\ x^{\textbf{2}}_1, \ldots, x^{\textbf{2}}_{100} &\sim G_{\textbf{2}}, \\ y^{\textbf{1}}_i &\sim \text{Categorical}(x^{\textbf{1}}_i) \text{ for } i = 1, \ldots, 100, \\ y^{\textbf{2}}_i &\sim \text{Categorical}(x^{\textbf{2}}_i) \text{ for } i = 1, \ldots, 100. \end{align*} \]

The only variables we observe are: $ y^{\textbf{1}}_i = \texttt{`+'} $ and $ y^{\textbf{2}}_i = \texttt{`-'} $ for $ i = 1, \ldots, 100 $. We want to compute the probabilities of: \[ \begin{equation*} p(y^{\textbf{1}}_{new} | \boldsymbol{y}), p(y^{\textbf{2}}_{new} | \boldsymbol{y}), p(y^{\textbf{3}}_{new} | \boldsymbol{y}), \end{equation*} \]

where $ \boldsymbol{y} \triangleq \{y^{\textbf{1}}_1, \ldots, y^{\textbf{1}}_{100}, y^{\textbf{2}}_1, \ldots, y^{\textbf{2}}_{100}\} $ is the set of all observations, and each $ y^{\textbf{n}}_{new} $ is a new (previously unobserved) sample drawn from $ x^{\textbf{n}}_{new} $ which is in turn drawn from $ G^{\textbf{n}} $ (i.e. the posterior predictive distribution). The above code does exactly this, and the expected output is: probability of drawing '+' from child node 1: 0.283680 probability of drawing '+' from child node 2: 0.002809 probability of drawing '+' from any other child node: 0.003907 probability of drawing '-' from child node 1: 0.002809 probability of drawing '-' from child node 2: 0.283680 probability of drawing '-' from any other child node: 0.003907 probability of drawing 'a' from child node 1: 0.002809 probability of drawing 'a' from child node 2: 0.002809 probability of drawing 'a' from any other child node: 0.003906

The function `do_inference`

constructs the hdp_sampler and cache structures necessary to perform MCMC sampling. hdp_sampler (and node_sampler) stores the variables used by the sampling algorithm. cache is a structure that optimizes the sampling algorithm for particular choices of the base distribution and likelihood. Once constructed, the function then executes 200 "burn-in" iterations, to allow the MCMC to mix (converge to the true posterior). Then, it performs 800 more iterations, keeping every fifth sample (to minimize autocorrelation among samples, since we want them to be as independent as possible). Finally, the function `posterior_predictive_probability`

is called to compute the above probabilities.

A unit test is also available in mcmc.cpp, as another example.

Classes, functions, and variables in this file | |
---|---|

struct | node_sampler |

struct | hdp_sampler |

bool | init (hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, hdp< BaseDistribution, DataDistribution, K, V > & root, int initial_table_count = 1) |

bool | copy (const hdp_sampler< BaseDistribution, DataDistribution, K, V > & src, hdp_sampler< BaseDistribution, DataDistribution, K, V > & dst, hdp< BaseDistribution, DataDistribution, K, V > & new_root, const hash_map< const node< K, V > *, node< K, V > *> & node_map) |

bool | print (const NodeType & node, Stream & out, KeyPrinter & key_printer, AtomPrinter & atom_printer, unsigned int level = 0) |

bool | read (hdp_sampler< BaseDistribution, DataDistribution, K, V > & n, Stream & stream, hdp< BaseDistribution, DataDistribution, K, V > & h, KeyReader & key_reader) |

bool | write (const hdp_sampler< BaseDistribution, DataDistribution, K, V > & n, Stream & stream, KeyWriter & key_writer) |

void | prepare_sampler (hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, cache< BaseDistribution, DataDistribution, K, V > & cache) |

bool | add (hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, const unsigned int * path, unsigned int depth, const K & observation, Cache & cache) |

bool | remove (hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, const unsigned int * path, unsigned int depth, const K & observation, Cache & cache) |

void | sample_hdp (hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, cache< BaseDistribution, DataDistribution, K, V > & cache) |

void | sample_alpha_each_node (hdp_sampler< BaseDistribution, DataDistribution, K, V > & n, const V * a, const V * b) |

bool | sample_alpha_each_level (hdp_sampler< BaseDistribution, DataDistribution, K, V > & n, const V * a, const V * b) |

V | log_probability_each_level (const hdp_sampler< BaseDistribution, DataDistribution, K, V > & n, const V * a, const V * b) |

V ** | copy_root_probabilities (const hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, const V *const * src) |

V ** | copy_root_probabilities (const hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, const V *const * src, unsigned int observation_count) |

array< unsigned int > * | copy_root_probabilities (const hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, const array< unsigned int > * src, unsigned int observation_count) |

void | predict (const hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, const K & observation, const unsigned int * path, const unsigned int *const * excluded, const unsigned int * excluded_counts, array< FeatureSet > & x, const RootDistributionType & root_probabilities) |

void | predict (const hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, const K & observation, array< FeatureSet > & x, const RootDistributionType & root_probabilities) |

void | predict (const hdp_sampler< BaseDistribution, DataDistribution, K, V > & h, const unsigned int * path, const unsigned int *const * excluded, const unsigned int * excluded_counts, hash_map< FeatureSet, V *> & x, const K * observations, unsigned int observation_count, const RootDistributionType & root_probabilities) |

V | log_probability (const hdp_sampler< BaseDistribution, DataDistribution, K, V > & h) |

template<typename K, typename V>

This structure stores Gibbs sampling variables for a single HDP non-root node (i.e. a node object). These "sampler" structures form a tree parallel to the HDP hierarchy consisting of hdp and node objects.

Since the Gibbs sampler is derived using the Chinese restaurant representation, every node_sampler contains an infinite number of tables, but we only store the non-empty tables. The number of non-empty tables is given by node_sampler::table_count. However, during inference, the number of occupied tables may increase and decrease. As such, the node keeps a larger "capacity" of tables node_sampler::table_capacity to avoid reallocating memory at every sampling step. As long as `table_count <= table_capacity`

, the algorithm doesn't need to reallocate memory. Every observation drawn from this node is assigned to a table (just as a customer picks a table at which to sit). All tables in this node are, in turn, sampled from the distribution of the parent node. As such, the tables in this node are assigned a table in the parent node.

For an example of how to perform MCMC inference with DPs and HDPs, see the code example above.

Template parameters | |
---|---|

K |
the generic type of the observations drawn from this distribution. |

V |
the type of the probabilities. |

Public members | |
---|---|

node_sampler< K, V > * | children |

node_sampler< K, V > * | parent |

node< K, V > * | n |

unsigned int * | observation_assignments |

unsigned int * | table_sizes |

unsigned int * | table_assignments |

unsigned int * | root_assignments |

array_multiset< K > * | descendant_observations |

unsigned int | table_count |

unsigned int | table_capacity |

unsigned int | customer_count |

typedef | K atom_type |

typedef | V value_type |

typedef | node_sampler< K, V > child_type |

typedef | node< K, V > node_type |

node_sampler< K, V > * node_sampler::children

A native array containing the child nodes of this node in the sampler hierarchy. This array is parallel to the node::children and hdp::children array maps.

node_sampler< K, V > * node_sampler::parent

A pointer to the parent node in the sampler hierarchy.

node< K, V > * node_sampler::n

unsigned int * node_sampler::observation_assignments

An array parallel to node::observations that indicates which table in this node each observation is assigned to.

unsigned int * node_sampler::table_sizes

An array with length node_sampler::table_count (and capacity node_sampler::table_capacity) that keeps track of the number of customers sitting at each table.

unsigned int * node_sampler::table_assignments

Each table in this node is assigned to a table in the parent node. This array with length node_sampler::table_count (and capacity node_sampler::table_capacity) keeps track the assignments of tables in this node to tables in the parent node.

unsigned int * node_sampler::root_assignments

Since every table in every node in the sampler hierarchy is assigned to a table in the parent node, we can follow the chain of table assignments to tables in the root node. This array with length node_sampler::table_count (and capacity node_sampler::table_capacity) keeps track of the assignemnts of tables in this node to tables in the root node.

array_multiset< K > * node_sampler::descendant_observations

This array of array_multiset objects keeps track, for each table in this node, the set of observations assigned to this table, either directly or indirectly via descendant nodes in the hierarchy. This array has length node_sampler::table_count and capacity node_sampler::table_capacity.

unsigned int node_sampler::table_count

The number of occupied tables in the Chinese restaurant represented by this sampler node.

unsigned int node_sampler::table_capacity

The current capacity of tables in this sampler node.

unsigned int node_sampler::customer_count

The total number of customers sitting at all tables at this sampler node.

typedef K node_sampler::atom_type

The generic type of the observations drawn from this distribution.

typedef V node_sampler::value_type

The type of the probabilities.

typedef node_sampler< K, V > node_sampler::child_type

The type of a child node of this node in the sampler hierarchy.

typedef node< K, V > node_sampler::node_type

The "sampler" structures form a tree parallel to the HDP hierarchy which consists of hdp and node objects. This typedef returns the type of the object that this node_sampler represents in the HDP hierarchy.

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

This structure stores Gibbs sampling variables for an HDP root node (i.e. an hdp object). These "sampler" structures form a tree parallel to the HDP hierarchy consisting of hdp and node objects.

Since the Gibbs sampler is derived using the Chinese restaurant representation, every hdp_sampler contains an infinite number of tables, but we only store the non-empty tables. The number of non-empty tables is given by hdp_sampler::table_count. However, during inference, the number of occupied tables may increase and decrease. As such, the node keeps a larger "capacity" of tables hdp_sampler::table_capacity to avoid reallocating memory at every sampling step. As long as `table_count <= table_capacity`

, the algorithm doesn't need to reallocate memory. Every observation drawn from this node is assigned to a table (just as a customer picks a table at which to sit).

For an example of how to perform MCMC inference with DPs and HDPs, see the code example above.

Template parameters | |
---|---|

BaseDistribution |
the type of the base distribution (see hdp). |

DataDistribution |
the type of the likelihood (see hdp). |

K |
the generic type of the observations drawn from this distribution. |

V |
the type of the probabilities. |

Public members | |
---|---|

node_sampler< K, V > * | children |

node_type * | n |

unsigned int * | observation_assignments |

unsigned int * | table_sizes |

array_multiset< K > * | descendant_observations |

unsigned int | table_count |

unsigned int | table_capacity |

unsigned int | customer_count |

hdp_sampler (hdp< BaseDistribution, DataDistribution, K, V > & root) | |

typedef | K atom_type |

typedef | V value_type |

typedef | BaseDistribution base_distribution_type |

typedef | DataDistribution data_distribution_type |

typedef | node_sampler< K, V > child_type |

typedef | hdp< BaseDistribution, DataDistribution, K, V > node_type |

node_sampler< K, V > * hdp_sampler::children

A native array containing the child nodes of this node in the sampler hierarchy. This array is parallel to the node::children and hdp::children array maps.

node_type * hdp_sampler::n

unsigned int * hdp_sampler::observation_assignments

An array parallel to hdp::observations that indicates which table in this node each observation is assigned to.

unsigned int * hdp_sampler::table_sizes

An array with length node_sampler::table_count (and capacity node_sampler::table_capacity) that keeps track of the number of customers sitting at each table.

array_multiset< K > * hdp_sampler::descendant_observations

This array of array_multiset objects keeps track, for each table in this node, the set of observations assigned to this table, either directly or indirectly via descendant nodes in the hierarchy. This array has length node_sampler::table_count and capacity node_sampler::table_capacity.

unsigned int hdp_sampler::table_count

The number of occupied tables in the Chinese restaurant represented by this sampler root node.

unsigned int hdp_sampler::table_capacity

The current capacity of tables in this sampler root node.

unsigned int hdp_sampler::customer_count

The total number of customers sitting at all tables at this sampler root node.

hdp_sampler::hdp_sampler(

hdp< BaseDistribution, DataDistribution, K, V > & | root | ) |

Constructs this root sampler node with the given `root`

node from the HDP hierarchy. All existing observations are assigned a table in the hierarchy one-by-one, each time sampling the table assignment from the conditional distribution: given that all other table assignments are fixed, the assignment for each observation is sampled. This constructor also initializes all descendant sampler nodes, recursively, parallel to the HDP hierarchy.

typedef K hdp_sampler::atom_type

The generic type of the observations drawn from this distribution.

typedef V hdp_sampler::value_type

The type of the probabilities.

typedef BaseDistribution hdp_sampler::base_distribution_type

The type of the base distribution (see hdp).

typedef DataDistribution hdp_sampler::data_distribution_type

The type of the likelihood (see hdp).

typedef node_sampler< K, V > hdp_sampler::child_type

The type of a child node of this node in the sampler hierarchy.

typedef hdp< BaseDistribution, DataDistribution, K, V > hdp_sampler::node_type

The "sampler" structures form a tree parallel to the HDP hierarchy which consists of hdp and node objects. This typedef returns the type of the object that this hdp_sampler represents in the HDP hierarchy.

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

bool init(hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

hdp< BaseDistribution, DataDistribution, K, V > & | root, | |

int | initial_table_count = 1 | ) |

Initializes the given root sampler node `h`

with the given `root`

node from the HDP hierarchy. All existing observations are assigned a table in the hierarchy one-by-one, each time sampling the table assignment from the conditional distribution: given that all other table assignments are fixed, the assignment for each observation is sampled. This constructor also initializes all descendant sampler nodes, recursively, parallel to the HDP hierarchy.

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

bool copy(const hdp_sampler< BaseDistribution, DataDistribution, K, V > & | src, | |

hdp_sampler< BaseDistribution, DataDistribution, K, V > & | dst, | |

hdp< BaseDistribution, DataDistribution, K, V > & | new_root, | |

const hash_map< const node< K, V > *, node< K, V > *> & | node_map | ) |

Copies the HDP sampler hierarchy rooted at `src`

into a new sampler hierarchy rooted at `dst`

(which should be uninitialized). The new sampler hierarchy may be associated with a new HDP hierarchy, rooted at `new_root`

, in which case, `node_map`

should contain a map of pointers to nodes from the old hierarchy to pointers to nodes in the new hierarchy. The `copy`

function for hdp is one way to produce such a map.

template<typename NodeType, typename Stream, typename KeyPrinter, typename AtomPrinter, typename K = typename NodeType::atom_type>

bool print(const NodeType & | node, | |

Stream & | out, | |

KeyPrinter & | key_printer, | |

AtomPrinter & | atom_printer, | |

unsigned int | level = 0 | ) |

Prints the given sampler `node`

to `out`

.

Template parameters | |
---|---|

NodeType |
either node_sampler or hdp_sampler. |

KeyPrinter |
a scribe type for which the function |

AtomPrinter |
a scribe type for which the function |

template<typename BaseDistribution, typename DataDistribution, typename K, typename V, typename Stream, typename KeyReader>

bool read(hdp_sampler< BaseDistribution, DataDistribution, K, V > & | n, | |

Stream & | stream, | |

hdp< BaseDistribution, DataDistribution, K, V > & | h, | |

KeyReader & | key_reader | ) |

Reads the sampler hierarchy rooted at `n`

from `stream`

. This function recursively reads all descendant nodes in the sampler hierarchy.

Template parameters | |
---|---|

KeyReader |
a scribe type for which the function |

template<typename BaseDistribution, typename DataDistribution, typename K, typename V, typename Stream, typename KeyWriter>

bool write(const hdp_sampler< BaseDistribution, DataDistribution, K, V > & | n, | |

Stream & | stream, | |

KeyWriter & | key_writer | ) |

Writes the sampler hierarchy rooted at `n`

to `stream`

. This function recursively writes all descendant nodes in the sampler hierarchy.

Template parameters | |
---|---|

KeyWriter |
a scribe type for which the function |

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

void prepare_sampler(hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

cache< BaseDistribution, DataDistribution, K, V > & | cache | ) |

Performs the necessary operations to prepare the variables in the sampler hierarchy given by `h`

and the `cache`

for calls to sample_hdp. This function also recursively prepares all descendant sampler nodes.

template<typename BaseDistribution, typename DataDistribution, typename K, typename V, typename Cache>

bool add(hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

const unsigned int * | path, | |

unsigned int | depth, | |

const K & | observation, | |

Cache & | cache | ) |

Adds the given `observation`

to the HDP hierarchy and sampler hierarchy represented by `h`

. The node to which to add the observation is given by the `path`

, which provides the indices to follow from each node to its child. If a node does not exist, it is created, with its concentration parameter copied from the appropriate index in hdp::alpha. Thus, `depth`

cannot be larger than `hdp::depth`

of `h`

. If `depth == 1`

, the observation is added to the root node.

This function also assigns the observation to the table according to the conditional distribution: Conditioned on the fact that all other random variables are fixed (including the other seating assignments), we sample the seating assignment for this new observation.

template<typename BaseDistribution, typename DataDistribution, typename K, typename V, typename Cache>

bool remove(hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

const unsigned int * | path, | |

unsigned int | depth, | |

const K & | observation, | |

Cache & | cache | ) |

Removes the given `observation`

from the HDP hierarchy and sampler hierarchy represented by `h`

. The node from which to remove the observation is given by the `path`

, which provides the indices to follow from each node to its child. If a node does not exist, a warning is printed. `depth`

cannot be larger than `hdp::depth`

of `h`

. If `depth == 1`

, the observation is removed from the root node.

template<bool Collapsed, typename BaseDistribution, typename DataDistribution, typename K, typename V>

void sample_hdp(hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

cache< BaseDistribution, DataDistribution, K, V > & | cache | ) |

Runs a single Gibbs iteration on the sampler node `h`

and all descendant sampler nodes. This function assumes prepare_sampler was called beforehand. prepare_sampler does not need to be called again before the next iteration.

Template parameters | |
---|---|

Collapsed |
if |

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

void sample_alpha_each_node(hdp_sampler< BaseDistribution, DataDistribution, K, V > & | n, | |

const V * | a, | |

const V * | b | ) |

This function implements the auxiliary variable sampler for inferring the HDP concentration parameters. This function assumes that hdp::alpha is distributed according to a Gamma distribution with parameters `a[level]`

and `b[level]`

.

This function samples a distinct alpha at every node in the hierarchy. The function sample_alpha_each_level, on the other hand, samples a distinct alpha at every *level* in the hierarchy (it assumes that hdp::alpha is the same across all nodes in the same level).

Parameters | |
---|---|

a |
an array of shape parameters for the Gamma prior on hdp::alpha, with length hdp::depth, one element for every level in the hierarchy. |

b |
an array of rate parameters for the Gamma prior on hdp::alpha, with length hdp::depth, one element for every level in the hierarchy. |

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

bool sample_alpha_each_level(hdp_sampler< BaseDistribution, DataDistribution, K, V > & | n, | |

const V * | a, | |

const V * | b | ) |

This function implements the auxiliary variable sampler for inferring the HDP concentration parameters. This function assumes that hdp::alpha is distributed according to a Gamma distribution with parameters `a[level]`

and `b[level]`

.

This function samples a distinct alpha at every *level* in the hierarchy (it assumes that hdp::alpha is the same across all nodes in the same level). The function sample_alpha_each_node, on the other hand, does not make this assumption and samples a distinct hdp::alpha at every node in the hierarchy.

Parameters | |
---|---|

a |
an array of shape parameters for the Gamma prior on hdp::alpha, with length hdp::depth, one element for every level in the hierarchy. |

b |
an array of rate parameters for the Gamma prior on hdp::alpha, with length hdp::depth, one element for every level in the hierarchy. |

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

V log_probability_each_level(const hdp_sampler< BaseDistribution, DataDistribution, K, V > & | n, | |

const V * | a, | |

const V * | b | ) |

Returns the joint probability of the concentration parameters hdp::alpha and node::alpha under the assumption that alpha is distributed according to a Gamma distribution with parameters `a[level]`

and `b[level]`

. This function also assumes that alpha is the same across all nodes in the same level.

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

V ** copy_root_probabilities(const hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

const V *const * | src | ) |

Returns a copy of the root probabilities from `src`

, which this function assumes were computed from the sampler hierarchy rooted at `h`

using the function `V** compute_root_probabilities(const hdp_sampler<BaseDistribution, DataDistribution, K, V>&, const K&)`

in cache.

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

V ** copy_root_probabilities(const hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

const V *const * | src, | |

unsigned int | observation_count | ) |

Returns a copy of the root probabilities from `src`

, which this function assumes were computed from the sampler hierarchy rooted at `h`

using the function `V** compute_root_probabilities(const hdp_sampler<BaseDistribution, DataDistribution, K, V>&, const K*, unsigned int)`

in cache.

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

array< unsigned int > * copy_root_probabilities(const hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

const array< unsigned int > * | src, | |

unsigned int | observation_count | ) |

Returns a copy of the root probabilities from `src`

, which this function assumes were computed from the sampler hierarchy rooted at `h`

using the function `array<unsigned int>* compute_root_probabilities(const hdp_sampler<BaseDistribution, DataDistribution, K, V>&, const K*, unsigned int)`

in the specialization of cache.

template<typename BaseDistribution, typename DataDistribution, typename K, typename V, typename FeatureSet, typename RootDistributionType>

void predict(const hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

const K & | observation, | |

const unsigned int * | path, | |

const unsigned int *const * | excluded, | |

const unsigned int * | excluded_counts, | |

array< FeatureSet > & | x, | |

const RootDistributionType & | root_probabilities | ) |

Computes the posterior predictive probability of `observation`

being drawn from the nodes characterized by the path set. The path set is specified by the variables `path`

, `excluded`

, and `excluded_counts`

. Whenever an element of `path`

is IMPLICIT_NODE, this indicates that any child node at that level lies within the path set. Exclusions can be specified by the set of keys `excluded[i]`

which is a native array containing a *sorted* list of keys to be excluded from the path set, at that level. The length of `excluded[i]`

is given by `excluded_counts[i]`

.

This function uses the posterior samples stored in hdp_sampler::posterior and node_sampler::posterior to perform its computations.

The results are stored in the array `x`

of `FeatureSet`

objects.

Template parameters | |
---|---|

FeatureSet |
a "weighted feature set" type that can be initialized with the function |

RootDistributionType |
the return type of the function |

template<typename BaseDistribution, typename DataDistribution, typename K, typename V, typename FeatureSet, typename RootDistributionType>

void predict(const hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

const K & | observation, | |

array< FeatureSet > & | x, | |

const RootDistributionType & | root_probabilities | ) |

Computes the posterior predictive probability of `observation`

being drawn from the nodes characterized by the path set. The path set is specified by the variables `path`

, `excluded`

, and `excluded_counts`

. Whenever an element of `path`

is IMPLICIT_NODE, this indicates that any child node at that level lies within the path set.

This function uses the posterior samples stored in hdp_sampler::posterior and node_sampler::posterior to perform its computations.

The results are stored in the array `x`

of `FeatureSet`

objects.

Template parameters | |
---|---|

FeatureSet |
a "weighted feature set" type that can be initialized with the function |

RootDistributionType |
the return type of the function |

template<typename BaseDistribution, typename DataDistribution, typename K, typename V, typename FeatureSet, typename RootDistributionType>

void predict(const hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h, | |

const unsigned int * | path, | |

const unsigned int *const * | excluded, | |

const unsigned int * | excluded_counts, | |

hash_map< FeatureSet, V *> & | x, | |

const K * | observations, | |

unsigned int | observation_count, | |

const RootDistributionType & | root_probabilities | ) |

Computes the posterior predictive probability of each observation in `observations`

being drawn from the nodes characterized by the path set. The path set is specified by the variables `path`

, `excluded`

, and `excluded_counts`

. Whenever an element of `path`

is IMPLICIT_NODE, this indicates that any child node at that level lies within the path set. Exclusions can be specified by the set of keys `excluded[i]`

which is a native array containing a *sorted* list of keys to be excluded from the path set, at that level. The length of `excluded[i]`

is given by `excluded_counts[i]`

.

This function *does not* use the posterior samples stored in hdp_sampler::posterior and node_sampler::posterior to perform its computations. Rather, it uses the single point sample currently stored in the given sampler hierarchy (the state in hdp_sampler and node_sampler nodes).

The results are stored in the map `x`

from `FeatureSet`

objects to arrays of probabilities. Each probability array is parallel to `observations`

, where the probability at index `i`

is the posterior predictive probability of the observation `observations[i]`

.

Template parameters | |
---|---|

FeatureSet |
a "feature set" type that can be initialized with the function |

RootDistributionType |
the return type of the function |

template<typename BaseDistribution, typename DataDistribution, typename K, typename V>

V log_probability(const hdp_sampler< BaseDistribution, DataDistribution, K, V > & | h | ) |

Returns the joint log probability of the seating arrangement in the sampler root node `h`

and all descendant nodes in the hierarchy.