test_internal_functions: Test C++ functions

Description Usage Arguments Value

View source: R/test_internal_functions.R

Description

Allows the user to interface with the individual C++ functions.

Usage

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test_internal_functions(Test_Log_Space_Multinomial_Sampler = FALSE,
  Test_Edge_Probability = FALSE, Test_Sum_Over_T_Edge_Probs = FALSE,
  Test_Prior_Pobability_Of_I_P_Params = FALSE,
  Test_Sample_New_I_P_Parameters = FALSE, Test_LSM_Contribution = FALSE,
  Test_LDA_Contribution = FALSE,
  Test_Update_Single_Token_Topic_Assignment = FALSE,
  Test_Update_All_Token_Topic_Assignments = FALSE,
  Test_Update_Interaction_Pattern_Parameters = FALSE,
  Test_Update_Topic_Interaction_Pattern_Assignments = FALSE,
  Test_Adaptive_Metropolis = FALSE, Test_Inference = FALSE,
  Test_RDirichlet = FALSE,
  Test_Sample_Token_Topics_From_Generative_Process = FALSE,
  Getting_It_Right = FALSE, Schien_Testing = FALSE, seed = NULL,
  distribution = NULL, intercepts = NULL, coefficients = NULL,
  latent_positions = NULL, document_sender = NULL,
  document_recipient = NULL, current_covariates = NULL,
  interaction_pattern = NULL, using_coefficients = NULL,
  edge_probabilities = NULL, tokens_in_document = NULL,
  current_token_topic_assignment = NULL,
  current_document_topic_counts = NULL, leave_out_current_token = NULL,
  topic_interaction_patterns = NULL, leave_out_topic = NULL,
  intercept_prior_mean = NULL, intercept_prior_standard_deviation = NULL,
  intercept_proposal_standard_deviations = NULL,
  coefficient_prior_mean = NULL,
  coefficient_prior_standard_deviation = NULL,
  coefficient_proposal_standard_deviations = NULL,
  latent_position_prior_mean = NULL,
  latent_position_prior_standard_deviation = NULL,
  latent_position_proposal_standard_deviations = NULL, topic = NULL,
  document_edge_values = NULL, word_type_topic_counts = NULL,
  topic_token_counts = NULL, current_word_type = NULL, alpha_m = NULL,
  beta_n = NULL, beta = NULL, rand_num = NULL, author_indexes = NULL,
  document_edge_matrix = NULL, document_topic_counts = NULL,
  token_topic_assignments = NULL, token_word_types = NULL,
  covariates = NULL, random_numbers = NULL, accept_rates = NULL,
  target_accept_rate = NULL, tollerance = NULL, update_size = NULL,
  iterations = NULL, metropolis_iterations = NULL,
  total_number_of_tokens = NULL, iterations_before_t_i_p_updates = NULL,
  update_t_i_p_every_x_iterations = NULL,
  perform_adaptive_metropolis = NULL, resample_word_types = NULL,
  slice_sample_alpha_m = NULL, slice_sample_step_size = NULL,
  parallel = NULL, use_cached_token_topic_distribution = NULL,
  cached_token_topic_distribution = NULL, u = NULL, num_documents = NULL,
  words_per_doc = NULL, num_topics = NULL, num_terms = NULL,
  num_actors = NULL, num_ip = NULL, num_ld = NULL, GiR_samples = NULL,
  forward_sample = TRUE, use_collapsed_topic_sampling = FALSE,
  verbose = TRUE)

Arguments

Test_Log_Space_Multinomial_Sampler

Defualts to FALSE. If TRUE, then optional arguments distribution and seed must be provided.

Test_Edge_Probability

Defaults to FALSE. If TRUE, then optional arguments intercepts, coefficients, latent_pos, document_sender, document_recipient, current_covariates, interaction_pattern, and using_coefficients must be provided.

Test_Sum_Over_T_Edge_Probs

Defaults to FALSE. If TRUE, then optional arguments edge_probs, tokens_in_document, current_token_topic_assignment, current_document_topic_counts, leave_out_current_token, topic_interaction_patterns, document_sender,document_recipient, leave_out_topic must be provided.

Test_Prior_Pobability_Of_I_P_Params

Defaults to FALSE. If TRUE, then optional arguments intercepts, coefficients, latent_pos, intercept_prior_mean, intercept_prior_standard_deviation, coefficient_prior_mean, coefficient_prior_standard_deviation, latent_position_prior_mean, latent_position_prior_standard_deviation, using_coefficients must be provided.

Test_Sample_New_I_P_Parameters

Defaults to FALSE. If TRUE, then optional arguments intercepts, coefficients, latent_pos, intercept_proposal_standard_deviations, coefficient_proposal_standard_deviations, latent_position_proposal_standard_deviations, (all of which must be of length = number of interaction patterns, and should all be equal) using_coefficients must be provided.

Test_LSM_Contribution

Defaults to FALSE. If TRUE, then optional arguments edge_probs, tokens_in_document, topic, current_token_topic_assignment, current_document_topic_counts, document_edge_values, topic_interaction_patterns, document_sender must be provided.

Test_LDA_Contribution

Defaults to FALSE. If TRUE, then optional arguments tokens_in_document, current_token_topic_assignment, current_document_topic_counts, word_type_topic_counts, topic_token_counts, topic, current_word_type, alpha_m, beta_n, beta must be provided.

Test_Update_Single_Token_Topic_Assignment

Defaults to FALSE. If TRUE, then optional arguments edge_probs, tokens_in_document, current_token_topic_assignment, current_document_topic_counts, word_type_topic_counts, topic_token_counts, current_word_type, alpha_m, beta_n, document_edge_values, topic_interaction_patterns, document_sender, rand_num must be provided.

Test_Update_All_Token_Topic_Assignments

Defaults to FALSE. If TRUE, then optional arguments author_indexes, document_edge_matrix, topic_interaction_patterns, document_topic_counts, word_type_topic_counts, topic_token_counts, token_topic_assignments, token_word_types, intercepts, coefficients, latent_pos, covars, alpha_m, beta_n, random_numbers, using_coefficients must be provided. Make sure that random_numbers has length equal to the total number of tokens in the corpus.

Test_Update_Interaction_Pattern_Parameters

Defaults to FALSE. If TRUE, then optional arguments author_indexes, document_edge_matrix, document_topic_counts, topic_interaction_patterns, intercepts, coefficients, latent_pos, covars, using_coefficients, intercept_prior_mean, intercept_prior_standard_deviation, intercept_proposal_standard_deviations, coefficient_prior_mean, coefficient_prior_standard_deviation, coefficient_proposal_standard_deviations, latent_position_prior_mean, latent_position_prior_standard_deviation, latent_position_proposal_standard_deviations, rand_num, edge_probs must be provided.

Test_Update_Topic_Interaction_Pattern_Assignments

Defaults to FALSE. If TRUE, then optional arguments author_indexes, document_edge_matrix, document_topic_counts, topic_interaction_patterns, intercepts, coefficients, latent_pos, covars, using_coefficients, random_numbers, edge_probs must be provided.

Test_Adaptive_Metropolis

Defaults to FALSE. If TRUE, then optional arguments intercept_proposal_standard_deviations, coefficient_proposal_standard_deviations, latent_position_proposal_standard_deviations, accept_rates, target_accept_rate, tollerance, update_size must be provided.

Test_Inference

If TRUE, then optional arguments author_indexes, document_edge_matrix, document_topic_counts, topic_interaction_patterns, word_type_topic_counts, topic_token_counts, token_topic_assignments, token_word_types, intercepts, coefficients, latent_pos, covars, alpha_m, beta_n, using_coefficients, intercept_prior_mean, intercept_prior_standard_deviation, intercept_proposal_standard_deviations, coefficient_prior_mean, coefficient_prior_standard_deviation, coefficient_proposal_standard_deviations, latent_position_prior_mean, latent_position_prior_standard_deviation, latent_position_proposal_standard_deviations, target_accept_rate, tollerance, update_size, seed, iterations, metropolis_iterations, total_number_of_tokens, iterations_before_t_i_p_updates, update_t_i_p_every_x_iterations, perform_adaptive_metropolis must be provided.

Test_RDirichlet

If TRUE, then alpha_m must be provided.

Test_Sample_Token_Topics_From_Generative_Process

If TRUE, then optional arguments author_indexes, token_topic_assignments, token_word_types, alpha_m, beta_n, random_numbers, resample_word_types and use_collapsed_topic_sampling must be provided.

Getting_It_Right

If TRUE, then Geweke's getting it right test is performed

Schien_Testing

If TRUE, the Schien testing is performed. Defaults to FALSE.

seed

An integerg.

distribution

A log-space vector

intercepts

A vector.

coefficients

A matrix.

latent_positions

An array.

document_sender

An integer.

document_recipient

An integer.

current_covariates

A vector

interaction_pattern

An integer

using_coefficients

A boolean.

edge_probabilities

An array

tokens_in_document

An integer

current_token_topic_assignment

An integer

current_document_topic_counts

A vector.

leave_out_current_token

A logical

topic_interaction_patterns

a vector

leave_out_topic

An integer.

intercept_prior_mean

A double.

intercept_prior_standard_deviation

A double.

intercept_proposal_standard_deviations

A vector.

coefficient_prior_mean

A double.

coefficient_prior_standard_deviation

A double.

coefficient_proposal_standard_deviations

A vector.

latent_position_prior_mean

A double.

latent_position_prior_standard_deviation

A double.

latent_position_proposal_standard_deviations

A vector.

topic

An integer.

document_edge_values

A vector.

word_type_topic_counts

A matrix

topic_token_counts

A matrix.

current_word_type

An integer.

alpha_m

A vector

beta_n

A vector

beta

Is equal to sum(beta_n)

rand_num

An integer

author_indexes

A vector

document_edge_matrix

A matrix

document_topic_counts

A matrix

token_topic_assignments

A List

token_word_types

A List

covariates

An array

random_numbers

A vector

accept_rates

A vector.

target_accept_rate

A double.

tollerance

A dobule.

update_size

A double.

iterations

An integer.

metropolis_iterations

An integer.

total_number_of_tokens

An integer.

iterations_before_t_i_p_updates

An integer.

update_t_i_p_every_x_iterations

An integer.

perform_adaptive_metropolis

A boolean.

resample_word_types

A boolean.

slice_sample_alpha_m

An integer (normally a boolean but you can set the value directly (normally 5, set to a negative number to turn off)),

slice_sample_step_size

An integer.

parallel

A boolean.

use_cached_token_topic_distribution

A boolean.

cached_token_topic_distribution

A vector with legth equal to the number of topics.

u

A numeric of length 1 between 0 and 1.

num_documents

An integer.

words_per_doc

An integer.

num_topics

An integer.

num_terms

An integer.

num_actors

An integer.

num_ip

An integer.

num_ld

An integer.

GiR_samples

An integer.

forward_sample

A logical. If TRUE, the forward GiR samples are generated. If FALSE, then backwards GiR samples are generated.

use_collapsed_topic_sampling

Defaults to FALSE. IF TRUE, then collapsed topic sampling is done. This only matters if you are using the Test_Sample_Token_Topics_From_Generative_Process option.

verbose

Defaults to TRUE, if FALSE, then no output is printed to the screen by the inference code.

Value

Whatever is returned by the internal function being tested


matthewjdenny/CCAS documentation built on May 21, 2019, 1:01 p.m.