void MlSldaState::InitializeAssignments(bool random_init) {
InitializeResponse();
InitializeLength();
LdawnState::InitializeAssignments(random_init);
if (FLAGS_num_seed_docs > 0) {
const gsl_vector* y = static_cast<lib_corpora::ReviewCorpus*>
(corpus_.get())->train_ratings();
boost::shared_ptr<gsl_permutation> sorted(gsl_permutation_alloc(y->size),
gsl_permutation_free);
boost::shared_ptr<gsl_permutation> rank(gsl_permutation_alloc(y->size),
gsl_permutation_free);
std::vector< std::vector<int> > num_seeds_used;
num_seeds_used.resize(corpus_->num_languages());
for (int ii = 0; ii < corpus_->num_languages(); ++ii) {
num_seeds_used[ii].resize(num_topics_);
}
gsl_sort_vector_index(sorted.get(), y);
gsl_permutation_inverse(rank.get(), sorted.get());
// We add one for padding so we don't try to set a document to be equal to
// the number of topics.
double num_train = corpus_->num_train() + 1.0;
int train_seen = 0;
int num_docs = corpus_->num_docs();
for (int dd = 0; dd < num_docs; ++dd) {
MlSeqDoc* doc = corpus_->seq_doc(dd);
int lang = doc->language();
if (!corpus_->doc(dd)->is_test()) {
// We don't assign to topic zero, so it can be stopwordy
int val = (int) floor((num_topics_ - 1) *
rank->data[train_seen] / num_train) + 1;
// Stop once we've used our limit of seed docs (too many leads to an
// overfit initial state)
if (num_seeds_used[lang][val] < FLAGS_num_seed_docs) {
cout << "Initializing doc " << lang << " " << dd << " to " << val <<
" score=" << truth_[dd] << endl;
for (int jj = 0; jj < (int)topic_assignments_[dd].size(); ++jj) {
int term = (*doc)[jj];
const topicmod_projects_ldawn::WordPaths word =
wordnet_->word(lang, term);
int num_paths = word.size();
if (num_paths > 0) {
ChangePath(dd, jj, val, rand() % num_paths);
} else {
if (use_aux_topics())
ChangeTopic(dd, jj, val);
}
}
++num_seeds_used[lang][val];
}
++train_seen;
}
}
}
}
Real MultipleCurve3<Real>::GetLength (Real t0, Real t1) const
{
assertion(mTMin <= t0 && t0 <= mTMax, "Invalid input\n");
assertion(mTMin <= t1 && t1 <= mTMax, "Invalid input\n");
assertion(t0 <= t1, "Invalid input\n");
if (!mLengths)
{
InitializeLength();
}
int key0, key1;
Real dt0, dt1;
GetKeyInfo(t0, key0, dt0);
GetKeyInfo(t1, key1, dt1);
Real length;
if (key0 < key1)
{
// Accumulate full-segment lengths.
length = (Real)0;
for (int i = key0 + 1; i < key1; ++i)
{
length += mLengths[i];
}
// Add on partial first segment.
length += GetLengthKey(key0, dt0, mTimes[key0 + 1] - mTimes[key0]);
// Add on partial last segment.
length += GetLengthKey(key1, (Real)0, dt1);
}
else
{
length = GetLengthKey(key0, dt0, dt1);
}
return length;
}
Real MultipleCurve3<Real>::GetTime (Real length, int iterations,
Real tolerance) const
{
if (!mLengths)
{
InitializeLength();
}
if (length <= (Real)0)
{
return mTMin;
}
if (length >= mAccumLengths[mNumSegments - 1])
{
return mTMax;
}
int key;
for (key = 0; key < mNumSegments; ++key)
{
if (length < mAccumLengths[key])
{
break;
}
}
if (key >= mNumSegments)
{
return mTimes[mNumSegments];
}
Real len0, len1;
if (key == 0)
{
len0 = length;
len1 = mAccumLengths[0];
}
else
{
len0 = length - mAccumLengths[key - 1];
len1 = mAccumLengths[key] - mAccumLengths[key - 1];
}
// If L(t) is the length function for t in [tmin,tmax], the derivative is
// L'(t) = |x'(t)| >= 0 (the magnitude of speed). Therefore, L(t) is a
// nondecreasing function (and it is assumed that x'(t) is zero only at
// isolated points; that is, no degenerate curves allowed). The second
// derivative is L"(t). If L"(t) >= 0 for all t, L(t) is a convex
// function and Newton's method for root finding is guaranteed to
// converge. However, L"(t) can be negative, which can lead to Newton
// iterates outside the domain [tmin,tmax]. The algorithm here avoids
// this problem by using a hybrid of Newton's method and bisection.
// Initial guess for Newton's method is dt0.
Real dt1 = mTimes[key + 1] - mTimes[key];
Real dt0 = dt1*len0/len1;
// Initial root-bounding interval for bisection.
Real lower = (Real)0, upper = dt1;
for (int i = 0; i < iterations; ++i)
{
Real difference = GetLengthKey(key, (Real)0, dt0) - len0;
if (Math<Real>::FAbs(difference) <= tolerance)
{
// |L(mTimes[key]+dt0)-length| is close enough to zero, report
// mTimes[key]+dt0 as the time at which 'length' is attained.
return mTimes[key] + dt0;
}
// Generate a candidate for Newton's method.
Real dt0Candidate = dt0 - difference/GetSpeedKey(key, dt0);
// Update the root-bounding interval and test for containment of the
// candidate.
if (difference > (Real)0)
{
upper = dt0;
if (dt0Candidate <= lower)
{
// Candidate is outside the root-bounding interval. Use
// bisection instead.
dt0 = ((Real)0.5)*(upper + lower);
}
else
{
// There is no need to compare to 'upper' because the tangent
// line has positive slope, guaranteeing that the t-axis
// intercept is smaller than 'upper'.
dt0 = dt0Candidate;
}
}
else
{
lower = dt0;
if (dt0Candidate >= upper)
{
// Candidate is outside the root-bounding interval. Use
// bisection instead.
dt0 = ((Real)0.5)*(upper + lower);
}
else
{
// There is no need to compare to 'lower' because the tangent
// line has positive slope, guaranteeing that the t-axis
// intercept is larger than 'lower'.
dt0 = dt0Candidate;
}
}
}
// A root was not found according to the specified number of iterations
// and tolerance. You might want to increase iterations or tolerance or
// integration accuracy. However, in this application it is likely that
// the time values are oscillating, due to the limited numerical
// precision of 32-bit floats. It is safe to use the last computed time.
return mTimes[key] + dt0;
}
