/**
* @brief This function is the sfunc of an aggregator computing the
* perplexity.
* @param args[0] The current state
* @param args[1] The unique words in the documents
* @param args[2] The counts of each unique words
* @param args[3] The topic counts in the document
* @param args[4] The model (word topic counts and corpus topic
* counts)
* @param args[5] The Dirichlet parameter for per-document topic
* multinomial, i.e. alpha
* @param args[6] The Dirichlet parameter for per-topic word
* multinomial, i.e. beta
* @param args[7] The size of vocabulary
* @param args[8] The number of topics
* @return The updated state
**/
AnyType lda_perplexity_sfunc::run(AnyType & args){
ArrayHandle<int32_t> words = args[1].getAs<ArrayHandle<int32_t> >();
ArrayHandle<int32_t> counts = args[2].getAs<ArrayHandle<int32_t> >();
ArrayHandle<int32_t> topic_counts = args[3].getAs<ArrayHandle<int32_t> >();
double alpha = args[5].getAs<double>();
double beta = args[6].getAs<double>();
int32_t voc_size = args[7].getAs<int32_t>();
int32_t topic_num = args[8].getAs<int32_t>();
if(alpha <= 0)
throw std::invalid_argument("invalid argument - alpha");
if(beta <= 0)
throw std::invalid_argument("invalid argument - beta");
if(voc_size <= 0)
throw std::invalid_argument(
"invalid argument - voc_size");
if(topic_num <= 0)
throw std::invalid_argument(
"invalid argument - topic_num");
if(words.size() != counts.size())
throw std::invalid_argument(
"dimensions mismatch: words.size() != counts.size()");
if(__min(words) < 0 || __max(words) >= voc_size)
throw std::invalid_argument(
"invalid values in words");
if(__min(counts) <= 0)
throw std::invalid_argument(
"invalid values in counts");
if(topic_counts.size() != (size_t)(topic_num))
throw std::invalid_argument(
"invalid dimension - topic_counts.size() != topic_num");
if(__min(topic_counts, 0, topic_num) < 0)
throw std::invalid_argument("invalid values in topic_counts");
MutableArrayHandle<int64_t> state(NULL);
if(args[0].isNull()){
if(args[4].isNull())
throw std::invalid_argument("invalid argument - the model \
parameter should not be null for the first call");
ArrayHandle<int64_t> model = args[4].getAs<ArrayHandle<int64_t> >();
if(model.size() != (size_t)((voc_size + 1) * topic_num))
throw std::invalid_argument(
"invalid dimension - model.size() != (voc_size + 1) * topic_num");
if(__min(model) < 0)
throw std::invalid_argument("invalid topic counts in model");
state = madlib_construct_array(NULL,
static_cast<int>(model.size()) + 1,
INT8TI.oid,
INT8TI.len,
INT8TI.byval,
INT8TI.align);
memcpy(state.ptr(), model.ptr(), model.size() * sizeof(int64_t));
}else{
/**
* @brief This function is the finalfunc of an aggregator computing the
* perplexity.
* @param args[0] The global state
* @return The perplexity
**/
AnyType lda_perplexity_ffunc::run(AnyType & args){
ArrayHandle<int64_t> state = args[0].getAs<ArrayHandle<int64_t> >();
const double * perp = reinterpret_cast<const double *>(state.ptr() + state.size() - 1);
return *perp;
}
/**
* @brief This function is the sfunc of an aggregator computing the
* perplexity.
* @param args[0] The current state
* @param args[1] The unique words in the documents
* @param args[2] The counts of each unique words
* @param args[3] The topic counts in the document
* @param args[4] The model (word topic counts and corpus topic
* counts)
* @param args[5] The Dirichlet parameter for per-document topic
* multinomial, i.e. alpha
* @param args[6] The Dirichlet parameter for per-topic word
* multinomial, i.e. beta
* @param args[7] The size of vocabulary
* @param args[8] The number of topics
* @return The updated state
**/
AnyType lda_perplexity_sfunc::run(AnyType & args){
ArrayHandle<int32_t> words = args[1].getAs<ArrayHandle<int32_t> >();
ArrayHandle<int32_t> counts = args[2].getAs<ArrayHandle<int32_t> >();
ArrayHandle<int32_t> doc_topic_counts = args[3].getAs<ArrayHandle<int32_t> >();
double alpha = args[5].getAs<double>();
double beta = args[6].getAs<double>();
int32_t voc_size = args[7].getAs<int32_t>();
int32_t topic_num = args[8].getAs<int32_t>();
size_t model64_size = static_cast<size_t>(voc_size * (topic_num + 1) + 1) * sizeof(int32_t) / sizeof(int64_t);
if(alpha <= 0)
throw std::invalid_argument("invalid argument - alpha");
if(beta <= 0)
throw std::invalid_argument("invalid argument - beta");
if(voc_size <= 0)
throw std::invalid_argument(
"invalid argument - voc_size");
if(topic_num <= 0)
throw std::invalid_argument(
"invalid argument - topic_num");
if(words.size() != counts.size())
throw std::invalid_argument(
"dimensions mismatch: words.size() != counts.size()");
if(__min(words) < 0 || __max(words) >= voc_size)
throw std::invalid_argument(
"invalid values in words");
if(__min(counts) <= 0)
throw std::invalid_argument(
"invalid values in counts");
if(doc_topic_counts.size() != (size_t)(topic_num))
throw std::invalid_argument(
"invalid dimension - doc_topic_counts.size() != topic_num");
if(__min(doc_topic_counts, 0, topic_num) < 0)
throw std::invalid_argument("invalid values in doc_topic_counts");
MutableArrayHandle<int64_t> state(NULL);
if (args[0].isNull()) {
ArrayHandle<int64_t> model64 = args[4].getAs<ArrayHandle<int64_t> >();
if (model64.size() != model64_size) {
std::stringstream ss;
ss << "invalid dimension: model64.size() = " << model64.size();
throw std::invalid_argument(ss.str());
}
if(__min(model64) < 0) {
throw std::invalid_argument("invalid topic counts in model");
}
state = madlib_construct_array(NULL,
static_cast<int>(model64.size())
+ topic_num
+ sizeof(double) / sizeof(int64_t),
INT8TI.oid,
INT8TI.len,
INT8TI.byval,
INT8TI.align);
memcpy(state.ptr(), model64.ptr(), model64.size() * sizeof(int64_t));
int32_t *_model = reinterpret_cast<int32_t *>(state.ptr());
int64_t *_total_topic_counts = reinterpret_cast<int64_t *>(state.ptr() + model64.size());
for (int i = 0; i < voc_size; i ++) {
for (int j = 0; j < topic_num; j ++) {
_total_topic_counts[j] += _model[i * (topic_num + 1) + j];
}
}
} else {
state = args[0].getAs<MutableArrayHandle<int64_t> >();
}
int32_t *model = reinterpret_cast<int32_t *>(state.ptr());
int64_t *total_topic_counts = reinterpret_cast<int64_t *>(state.ptr() + model64_size);
double *perp = reinterpret_cast<double *>(state.ptr() + state.size() - 1);
int32_t n_d = 0;
for(size_t i = 0; i < words.size(); i++){
n_d += counts[i];
}
for(size_t i = 0; i < words.size(); i++){
int32_t w = words[i];
int32_t n_dw = counts[i];
double sum_p = 0.0;
for(int32_t z = 0; z < topic_num; z++){
int32_t n_dz = doc_topic_counts[z];
int32_t n_wz = model[w * (topic_num + 1) + z];
int64_t n_z = total_topic_counts[z];
sum_p += (static_cast<double>(n_wz) + beta) * (n_dz + alpha)
/ (static_cast<double>(n_z) + voc_size * beta);
}
sum_p /= (n_d + topic_num * alpha);
*perp += n_dw * log(sum_p);
}
return state;
}
/**
* @brief This function is the sfunc for the aggregator computing the topic
* counts. It scans the topic assignments in a document and updates the word
* topic counts.
* @param args[0] The state variable, current topic counts
* @param args[1] The unique words in the document
* @param args[2] The counts of each unique word in the document
* @param args[3] The topic assignments in the document
* @param args[4] The size of vocabulary
* @param args[5] The number of topics
* @return The updated state
**/
AnyType lda_count_topic_sfunc::run(AnyType & args)
{
if(args[4].isNull() || args[5].isNull())
throw std::invalid_argument("null parameter - voc_size and/or \
topic_num is null");
if(args[1].isNull() || args[2].isNull() || args[3].isNull())
return args[0];
int32_t voc_size = args[4].getAs<int32_t>();
int32_t topic_num = args[5].getAs<int32_t>();
if(voc_size <= 0)
throw std::invalid_argument(
"invalid argument - voc_size");
if(topic_num <= 0)
throw std::invalid_argument(
"invalid argument - topic_num");
ArrayHandle<int32_t> words = args[1].getAs<ArrayHandle<int32_t> >();
ArrayHandle<int32_t> counts = args[2].getAs<ArrayHandle<int32_t> >();
ArrayHandle<int32_t> topic_assignment = args[3].getAs<ArrayHandle<int32_t> >();
if(words.size() != counts.size())
throw std::invalid_argument(
"dimensions mismatch - words.size() != counts.size()");
if(__min(words) < 0 || __max(words) >= voc_size)
throw std::invalid_argument(
"invalid values in words");
if(__min(counts) <= 0)
throw std::invalid_argument(
"invalid values in counts");
if(__min(topic_assignment) < 0 || __max(topic_assignment) >= topic_num)
throw std::invalid_argument("invalid values in topics");
if((size_t)__sum(counts) != topic_assignment.size())
throw std::invalid_argument(
"dimension mismatch - sum(counts) != topic_assignment.size()");
MutableArrayHandle<int64_t> state(NULL);
int32_t *model;
if(args[0].isNull()) {
// to store a voc_size x (topic_num+1) integer matrix in
// bigint[] (the +1 is for a flag of ceiling the count),
// we need padding if the size is odd.
// 1. when voc_size * (topic_num + 1) is (2n+1), gives (n+1)
// 2. when voc_size * (topic_num + 1) is (2n), gives (n)
int dims[1] = {static_cast<int>( (voc_size * (topic_num + 1) + 1) * sizeof(int32_t) / sizeof(int64_t) )};
int lbs[1] = {1};
state = madlib_construct_md_array(
NULL, NULL, 1, dims, lbs, INT8TI.oid, INT8TI.len, INT8TI.byval,
INT8TI.align);
// the reason we use bigint[] because integer[] has limit on number of
// elements and thus cannot be larger than 500MB
model = reinterpret_cast<int32_t *>(state.ptr());
} else {
state = args[0].getAs<MutableArrayHandle<int64_t> >();
model = reinterpret_cast<int32_t *>(state.ptr());
}
int32_t unique_word_count = static_cast<int32_t>(words.size());
int32_t word_index = 0;
for(int32_t i = 0; i < unique_word_count; i++){
int32_t wordid = words[i];
for(int32_t j = 0; j < counts[i]; j++){
int32_t topic = topic_assignment[word_index];
if (model[wordid * (topic_num + 1) + topic] <= 2e9) {
model[wordid * (topic_num + 1) + topic]++;
} else {
model[wordid * (topic_num + 1) + topic_num] = 1;
}
word_index++;
}
}
return state;
}
/**
* @brief This function learns the topics of words in a document and is the
* main step of a Gibbs sampling iteration. The word topic counts and
* corpus topic counts are passed to this function in the first call and
* then transfered to the rest calls through args.mSysInfo->user_fctx for
* efficiency.
* @param args[0] The unique words in the documents
* @param args[1] The counts of each unique words
* @param args[2] The topic counts and topic assignments in the document
* @param args[3] The model (word topic counts and corpus topic
* counts)
* @param args[4] The Dirichlet parameter for per-document topic
* multinomial, i.e. alpha
* @param args[5] The Dirichlet parameter for per-topic word
* multinomial, i.e. beta
* @param args[6] The size of vocabulary
* @param args[7] The number of topics
* @param args[8] The number of iterations (=1:training, >1:prediction)
* @return The updated topic counts and topic assignments for
* the document
**/
AnyType lda_gibbs_sample::run(AnyType & args)
{
ArrayHandle<int32_t> words = args[0].getAs<ArrayHandle<int32_t> >();
ArrayHandle<int32_t> counts = args[1].getAs<ArrayHandle<int32_t> >();
MutableArrayHandle<int32_t> doc_topic = args[2].getAs<MutableArrayHandle<int32_t> >();
double alpha = args[4].getAs<double>();
double beta = args[5].getAs<double>();
int32_t voc_size = args[6].getAs<int32_t>();
int32_t topic_num = args[7].getAs<int32_t>();
int32_t iter_num = args[8].getAs<int32_t>();
size_t model64_size = static_cast<size_t>(voc_size * (topic_num + 1) + 1) * sizeof(int32_t) / sizeof(int64_t);
if(alpha <= 0)
throw std::invalid_argument("invalid argument - alpha");
if(beta <= 0)
throw std::invalid_argument("invalid argument - beta");
if(voc_size <= 0)
throw std::invalid_argument(
"invalid argument - voc_size");
if(topic_num <= 0)
throw std::invalid_argument(
"invalid argument - topic_num");
if(iter_num <= 0)
throw std::invalid_argument(
"invalid argument - iter_num");
if(words.size() != counts.size())
throw std::invalid_argument(
"dimensions mismatch: words.size() != counts.size()");
if(__min(words) < 0 || __max(words) >= voc_size)
throw std::invalid_argument(
"invalid values in words");
if(__min(counts) <= 0)
throw std::invalid_argument(
"invalid values in counts");
int32_t word_count = __sum(counts);
if(doc_topic.size() != (size_t)(word_count + topic_num))
throw std::invalid_argument(
"invalid dimension - doc_topic.size() != word_count + topic_num");
if(__min(doc_topic, 0, topic_num) < 0)
throw std::invalid_argument("invalid values in topic_count");
if(
__min(doc_topic, topic_num, word_count) < 0 ||
__max(doc_topic, topic_num, word_count) >= topic_num)
throw std::invalid_argument( "invalid values in topic_assignment");
if (!args.getUserFuncContext()) {
ArrayHandle<int64_t> model64 = args[3].getAs<ArrayHandle<int64_t> >();
if (model64.size() != model64_size) {
std::stringstream ss;
ss << "invalid dimension: model64.size() = " << model64.size();
throw std::invalid_argument(ss.str());
}
if (__min(model64) < 0) {
throw std::invalid_argument("invalid topic counts in model");
}
int32_t *context =
static_cast<int32_t *>(
MemoryContextAllocZero(
args.getCacheMemoryContext(),
model64.size() * sizeof(int64_t)
+ topic_num * sizeof(int64_t)));
memcpy(context, model64.ptr(), model64.size() * sizeof(int64_t));
int32_t *model = context;
int64_t *running_topic_counts = reinterpret_cast<int64_t *>(
context + model64_size * sizeof(int64_t) / sizeof(int32_t));
for (int i = 0; i < voc_size; i ++) {
for (int j = 0; j < topic_num; j ++) {
running_topic_counts[j] += model[i * (topic_num + 1) + j];
}
}
args.setUserFuncContext(context);
}
int32_t *context = static_cast<int32_t *>(args.getUserFuncContext());
if (context == NULL) {
throw std::runtime_error("args.mSysInfo->user_fctx is null");
}
int32_t *model = context;
int64_t *running_topic_counts = reinterpret_cast<int64_t *>(
context + model64_size * sizeof(int64_t) / sizeof(int32_t));
int32_t unique_word_count = static_cast<int32_t>(words.size());
for(int32_t it = 0; it < iter_num; it++){
int32_t word_index = topic_num;
for(int32_t i = 0; i < unique_word_count; i++) {
int32_t wordid = words[i];
for(int32_t j = 0; j < counts[i]; j++){
int32_t topic = doc_topic[word_index];
int32_t retopic = __lda_gibbs_sample(
topic_num, topic, doc_topic.ptr(),
model + wordid * (topic_num + 1),
running_topic_counts, alpha, beta);
doc_topic[word_index] = retopic;
doc_topic[topic]--;
doc_topic[retopic]++;
if(iter_num == 1) {
if (model[wordid * (topic_num + 1) + retopic] <= 2e9) {
running_topic_counts[topic] --;
running_topic_counts[retopic] ++;
model[wordid * (topic_num + 1) + topic]--;
model[wordid * (topic_num + 1) + retopic]++;
} else {
model[wordid * (topic_num + 1) + topic_num] = 1;
}
}
word_index++;
}
}
}
return doc_topic;
}
/**
* @brief Get the sum of an array - for parameter checking
* @return The sum
* @note The caller will ensure that ah is always non-null.
**/
static int32_t __sum(ArrayHandle<int32_t> ah){
const int32_t * array = ah.ptr();
size_t size = ah.size();
return std::accumulate(array, array + size, static_cast<int32_t>(0));
}
template<class T> static T __max(ArrayHandle<T> ah){
return __max(ah, 0, ah.size());
}
AnyType vcrf_top1_label::run(AnyType& args) {
ArrayHandle<double> mArray = args[0].getAs<ArrayHandle<double> >();
ArrayHandle<double> rArray = args[1].getAs<ArrayHandle<double> >();
const int32_t numLabels = args[2].getAs<int32_t>();
if (numLabels == 0)
throw std::invalid_argument("Number of labels cannot be zero");
int doc_len = static_cast<int>(rArray.size() / numLabels);
double* prev_top1_array = new double[numLabels];
double* curr_top1_array = new double[numLabels];
double* prev_norm_array = new double[numLabels];
double* curr_norm_array = new double[numLabels];
int* path = new int[doc_len*numLabels];
memset(prev_top1_array, 0, numLabels*sizeof(double));
memset(prev_norm_array, 0, numLabels*sizeof(double));
memset(path, 0, doc_len*numLabels*sizeof(int));
for(int start_pos = 0; start_pos < doc_len; start_pos++) {
memset(curr_top1_array, 0, numLabels*sizeof(double));
memset(curr_norm_array, 0, numLabels*sizeof(double));
if (start_pos == 0) {
for (int label = 0; label < numLabels; label++) {
curr_norm_array[label] = rArray[label] + mArray[label];
curr_top1_array[label] = rArray[label] + mArray[label];
}
} else {
for (int curr_label = 0; curr_label < numLabels; curr_label++) {
for (int prev_label = 0; prev_label < numLabels; prev_label++) {
double top1_new_score = prev_top1_array[prev_label]
+ rArray[start_pos*numLabels + curr_label]
+ mArray[(prev_label+1)*numLabels + curr_label];
if (start_pos == doc_len - 1)
top1_new_score += mArray[(numLabels+1)*numLabels + curr_label];
if (top1_new_score > curr_top1_array[curr_label]) {
curr_top1_array[curr_label] = top1_new_score;
path[start_pos*numLabels + curr_label] = prev_label;
}
/* calculate the probability of the best label sequence */
double norm_new_score = prev_norm_array[prev_label]
+ rArray[start_pos * numLabels + curr_label]
+ mArray[(prev_label+1)*numLabels + curr_label];
/* last token in a sentence, the end feature should be fired */
if (start_pos == doc_len - 1)
norm_new_score += mArray[(numLabels+1)*numLabels + curr_label];
/* The following wants to do z = log(exp(x)+exp(y)), the faster implementation is
* z=min(x,y) + log(exp(abs(x-y))+1)
* 0.5 is for rounding
*/
if (curr_norm_array[curr_label] == 0)
curr_norm_array[curr_label] = norm_new_score;
else {
double x = curr_norm_array[curr_label];
double y = norm_new_score;
curr_norm_array[curr_label] = std::min(x,y) +
static_cast<double>(log(std::exp(std::abs(y-x)/1000.0) +1)*1000.0 + 0.5);
}
}
}
}
for (int label = 0; label < numLabels; label++) {
prev_top1_array[label] = curr_top1_array[label];
prev_norm_array[label] = curr_norm_array[label];
}
}
/* find the label of the last token in a sentence */
double max_score = 0.0;
int top1_label = 0;
for(int label = 0; label < numLabels; label++) {
if(curr_top1_array[label] > max_score) {
max_score = curr_top1_array[label];
top1_label = label;
}
}
/* Define the result array with doc_len+1 elements, where the first doc_len
* elements are used to store the best labels and the last element is used
* to store the conditional probability of the sequence.
*/
MutableArrayHandle<int> result(
madlib_construct_array(
NULL, doc_len+1, INT4TI.oid,
INT4TI.len, INT4TI.byval, INT4TI.align));
/* trace back to get the labels for the rest tokens in a sentence */
result[doc_len - 1] = top1_label;
for (int pos = doc_len - 1; pos >= 1; pos--) {
top1_label = path[pos * numLabels + top1_label];
result[pos-1] = top1_label;
}
/* compute the sum_i of log(v1[i]/1000), return (e^sum)*1000
* used in the UDFs which needs marginalization e.g., normalization
* the following wants to do z=log(exp(x)+exp(y)), the faster implementation is
* z = min(x,y) + log(exp(abs(x-y))+1)
*/
double norm_factor = 0.0;
for (int i = 0; i < numLabels; i++) {
if (i==0)
norm_factor = curr_norm_array[0];
else {
double x = curr_norm_array[i];
double y = norm_factor;
norm_factor = std::min(x,y) + static_cast<double>(log(exp(std::abs(y-x)/1000.0) +1)*1000.0+0.5);
}
}
/* calculate the conditional probability.
* To convert the probability into integer, firstly,let it multiply 1000000, then later make the product divided by 1000000
* to get the real conditional probability
*/
result[doc_len] = static_cast<int>(std::exp((max_score - norm_factor)/1000.0)*1000000);
delete[] prev_top1_array;
delete[] curr_top1_array;
delete[] prev_norm_array;
delete[] curr_norm_array;
delete[] path;
return result;
}
/**
* @brief This function is the sfunc for the aggregator computing the topic
* counts. It scans the topic assignments in a document and updates the word
* topic counts.
* @param args[0] The state variable, current topic counts
* @param args[1] The unique words in the document
* @param args[2] The counts of each unique word in the document
* @param args[3] The topic assignments in the document
* @param args[4] The size of vocabulary
* @param args[5] The number of topics
* @return The updated state
**/
AnyType lda_count_topic_sfunc::run(AnyType & args)
{
if(args[4].isNull() || args[5].isNull())
throw std::invalid_argument("null parameter - voc_size and/or \
topic_num is null");
if(args[1].isNull() || args[2].isNull() || args[3].isNull())
return args[0];
int32_t voc_size = args[4].getAs<int32_t>();
int32_t topic_num = args[5].getAs<int32_t>();
if(voc_size <= 0)
throw std::invalid_argument(
"invalid argument - voc_size");
if(topic_num <= 0)
throw std::invalid_argument(
"invalid argument - topic_num");
ArrayHandle<int32_t> words = args[1].getAs<ArrayHandle<int32_t> >();
ArrayHandle<int32_t> counts = args[2].getAs<ArrayHandle<int32_t> >();
ArrayHandle<int32_t> topic_assignment = args[3].getAs<ArrayHandle<int32_t> >();
if(words.size() != counts.size())
throw std::invalid_argument(
"dimensions mismatch - words.size() != counts.size()");
if(__min(words) < 0 || __max(words) >= voc_size)
throw std::invalid_argument(
"invalid values in words");
if(__min(counts) <= 0)
throw std::invalid_argument(
"invalid values in counts");
if(__min(topic_assignment) < 0 || __max(topic_assignment) >= topic_num)
throw std::invalid_argument("invalid values in topics");
if((size_t)__sum(counts) != topic_assignment.size())
throw std::invalid_argument(
"dimension mismatch - sum(counts) != topic_assignment.size()");
MutableArrayHandle<int64_t> state(NULL);
if(args[0].isNull()){
int dims[2] = {voc_size + 1, topic_num};
int lbs[2] = {1, 1};
state = madlib_construct_md_array(
NULL, NULL, 2, dims, lbs, INT8TI.oid, INT8TI.len, INT8TI.byval,
INT8TI.align);
} else {
state = args[0].getAs<MutableArrayHandle<int64_t> >();
}
int32_t unique_word_count = static_cast<int32_t>(words.size());
int32_t word_index = 0;
for(int32_t i = 0; i < unique_word_count; i++){
int32_t wordid = words[i];
for(int32_t j = 0; j < counts[i]; j++){
int32_t topic = topic_assignment[word_index];
state[wordid * topic_num + topic]++;
state[voc_size * topic_num + topic]++;
word_index++;
}
}
return state;
}
/**
* @brief This function learns the topics of words in a document and is the
* main step of a Gibbs sampling iteration. The word topic counts and
* corpus topic counts are passed to this function in the first call and
* then transfered to the rest calls through args.mSysInfo->user_fctx for
* efficiency.
* @param args[0] The unique words in the documents
* @param args[1] The counts of each unique words
* @param args[2] The topic counts and topic assignments in the document
* @param args[3] The model (word topic counts and corpus topic
* counts)
* @param args[4] The Dirichlet parameter for per-document topic
* multinomial, i.e. alpha
* @param args[5] The Dirichlet parameter for per-topic word
* multinomial, i.e. beta
* @param args[6] The size of vocabulary
* @param args[7] The number of topics
* @param args[8] The number of iterations (=1:training, >1:prediction)
* @return The updated topic counts and topic assignments for
* the document
**/
AnyType lda_gibbs_sample::run(AnyType & args)
{
ArrayHandle<int32_t> words = args[0].getAs<ArrayHandle<int32_t> >();
ArrayHandle<int32_t> counts = args[1].getAs<ArrayHandle<int32_t> >();
MutableArrayHandle<int32_t> doc_topic = args[2].getAs<MutableArrayHandle<int32_t> >();
double alpha = args[4].getAs<double>();
double beta = args[5].getAs<double>();
int32_t voc_size = args[6].getAs<int32_t>();
int32_t topic_num = args[7].getAs<int32_t>();
int32_t iter_num = args[8].getAs<int32_t>();
if(alpha <= 0)
throw std::invalid_argument("invalid argument - alpha");
if(beta <= 0)
throw std::invalid_argument("invalid argument - beta");
if(voc_size <= 0)
throw std::invalid_argument(
"invalid argument - voc_size");
if(topic_num <= 0)
throw std::invalid_argument(
"invalid argument - topic_num");
if(iter_num <= 0)
throw std::invalid_argument(
"invalid argument - iter_num");
if(words.size() != counts.size())
throw std::invalid_argument(
"dimensions mismatch: words.size() != counts.size()");
if(__min(words) < 0 || __max(words) >= voc_size)
throw std::invalid_argument(
"invalid values in words");
if(__min(counts) <= 0)
throw std::invalid_argument(
"invalid values in counts");
int32_t word_count = __sum(counts);
if(doc_topic.size() != (size_t)(word_count + topic_num))
throw std::invalid_argument(
"invalid dimension - doc_topic.size() != word_count + topic_num");
if(__min(doc_topic, 0, topic_num) < 0)
throw std::invalid_argument("invalid values in topic_count");
if(
__min(doc_topic, topic_num, word_count) < 0 ||
__max(doc_topic, topic_num, word_count) >= topic_num)
throw std::invalid_argument( "invalid values in topic_assignment");
if (!args.getUserFuncContext())
{
if(args[3].isNull())
throw std::invalid_argument("invalid argument - the model \
parameter should not be null for the first call");
ArrayHandle<int64_t> model = args[3].getAs<ArrayHandle<int64_t> >();
if(model.size() != (size_t)((voc_size + 1) * topic_num))
throw std::invalid_argument(
"invalid dimension - model.size() != (voc_size + 1) * topic_num");
if(__min(model) < 0)
throw std::invalid_argument("invalid topic counts in model");
int64_t * state =
static_cast<int64_t *>(
MemoryContextAllocZero(
args.getCacheMemoryContext(),
model.size() * sizeof(int64_t)));
memcpy(state, model.ptr(), model.size() * sizeof(int64_t));
args.setUserFuncContext(state);
}
int64_t * state = static_cast<int64_t *>(args.getUserFuncContext());
if(NULL == state){
throw std::runtime_error("args.mSysInfo->user_fctx is null");
}
int32_t unique_word_count = static_cast<int32_t>(words.size());
for(int32_t it = 0; it < iter_num; it++){
int32_t word_index = topic_num;
for(int32_t i = 0; i < unique_word_count; i++) {
int32_t wordid = words[i];
for(int32_t j = 0; j < counts[i]; j++){
int32_t topic = doc_topic[word_index];
int32_t retopic = __lda_gibbs_sample(
topic_num, topic, doc_topic.ptr(),
state + wordid * topic_num,
state + voc_size * topic_num, alpha, beta);
doc_topic[word_index] = retopic;
doc_topic[topic]--;
doc_topic[retopic]++;
if(iter_num == 1){
state[voc_size * topic_num + topic]--;
state[voc_size * topic_num + retopic]++;
state[wordid * topic_num + topic]--;
state[wordid * topic_num + retopic]++;
}
word_index++;
}
}
}
return doc_topic;
}