/* removes the most recently observed symbol from the context tree */
/*
* void revert(UInt64 offset) {
*
* m_cts[offset].revert();
* for (size_t i=0; i < m_cts.size(); i++) {
* if (i != offset) m_cts[i].m_history.pop_back();
* }
* }*/
// removes the most recently observed symbol from the context tree
public void revert()
{
// TODONE: implement
if (m_history.size() == 0)
{
return;
}
// 1. remove the most recent symbol from the context buffer
symbol_t sym = m_history.back();
m_history.pop_back();
// compute the current context
symbol_list_t context = new symbol_list_t(0); //m_depth);
// context.reserve((int)m_depth);
getContext(context);
// no need to undo a context tree update if there was
// not enough context to begin with
if (context.size() < m_depth)
{
return;
}
// 2. determine the path to the leaf nodes
Stack path = new Stack();
path.Push(m_root);
// add the path to the leaf nodes
CTNode ctn = m_root;
for (UInt64 i = 0; i < context.size() && ctn != null; i++)
{
ctn = ctn.m_child[context.ibits(i)];
path.Push(ctn);
}
// 3. update the probability estimates from the leaf node back up to the root,
// deleting any superfluous nodes as we go
for (; path.Count != 0; path.Pop())
{
ctn = (CTNode)path.Peek(); //top();
if (ctn == null)
{
break;
}
// undo the previous KT estimate update
ctn.m_count[sym? 1:0]--;
double log_kt_mul = ctn.logKTMul(sym);
ctn.m_log_prob_est -= log_kt_mul;
// reclaim memory for any children nodes that now have seen no data
for (UInt64 i = 0; i < 2; i++)
{
// bool sym = symbols[i];
bool my_sym = (i == 1);
if (ctn.m_child[my_sym ? 1 : 0] != null && ctn.m_child[my_sym ? 1 : 0].visits() == 0)
{
//m_ctnode_pool.free(ctn.m_child[sym]);
ctn.m_child[my_sym ? 1 : 0] = null;
}
}
// update the weighted probabilities
if (path.Count == (int)m_depth + 1)
{
ctn.m_log_prob_weighted = ctn.logProbEstimated();
}
else
{
// computes P_w = log{0.5 * [P_kt + P_w0*P_w1]}
double log_prob_on = ctn.child(true) != null?ctn.child(true).logProbWeighted() : 0.0;
double log_prob_off = ctn.child(false) != null?ctn.child(false).logProbWeighted() : 0.0;
double log_one_plus_exp = log_prob_off + log_prob_on - ctn.logProbEstimated();
// NOTE: no need to compute the log(1+e^x) if x is large, plus it avoids overflows
if (log_one_plus_exp < 100.0)
{
log_one_plus_exp = Math.Log(1.0 + Math.Exp(log_one_plus_exp));
}
ctn.m_log_prob_weighted = log_point_five + ctn.logProbEstimated() + log_one_plus_exp;
}
}
}
/* updates the context tree with a single symbol */
void update(symbol_t sym)
{
// TODONE: implement
// compute the current context
symbol_list_t context = new symbol_list_t(0); //m_depth);
//context.reserve((int) m_depth);
getContext(context);
// if we have not seen enough context, append the symbol
// to the history buffer and skip updating the context tree
if (context.size() < m_depth)
{
m_history.push_back(sym);
return;
}
// 1. create new nodes in the context tree (if necessary)
createNodesInCurrentContext(context);
// 2. walk down the tree to the relevant leaf context, saving the path as we go
Stack path = new Stack();
path.Push(m_root); // add the empty context
// add the path to the leaf nodes
CTNode ctn = m_root;
for (UInt64 i = 0; i < context.size(); i++)
{
ctn = ctn.m_child[context.ibits((int)i)];
path.Push(ctn);
}
// 3. update the probability estimates from the leaf node back up to the root
for (; path.Count != 0; path.Pop())
{
CTNode n = (CTNode)path.Peek(); // .Top();
// update the KT estimate and counts
double log_kt_mul = n.logKTMul(sym);
n.m_log_prob_est += log_kt_mul;
n.m_count[(sym ? 1 : 0)]++;
// update the weighted probabilities
if (path.Count == (int)m_depth + 1)
{
n.m_log_prob_weighted = n.logProbEstimated();
}
else
{
// computes P_w = log{0.5 * [P_kt + P_w0*P_w1]}
double log_prob_on = n.child(true) != null?n.child(true).logProbWeighted() : 0.0;
double log_prob_off = n.child(false) != null?n.child(false).logProbWeighted() : 0.0;
double log_one_plus_exp = log_prob_off + log_prob_on - n.logProbEstimated();
// NOTE: no need to compute the log(1+e^x) if x is large, plus it avoids overflows
if (log_one_plus_exp < 100.0)
{
log_one_plus_exp = Math.Log(1.0 + Math.Exp(log_one_plus_exp));
}
n.m_log_prob_weighted = log_point_five + n.logProbEstimated() + log_one_plus_exp;
}
}
// 4. save the new symbol to the context buffer
m_history.push_back(sym);
}