void mlr(TreeNode* root, int level){
if(!root) return ;
if(level >= ans.size()){
vector<int> t;
ans.push_back(t);
}
ans[level].push_back(root->val);
mlr(root->left, level+1);
mlr(root->right, level+1);
}
bool isValidBST(TreeNode *root) {
// Start typing your C/C++ solution below
// DO NOT write int main() function
flag=true;int s,b;
mlr(root,s,b);
return flag;
}
void mlr(TreeNode*node,int&s,int&b)
{
if(!node||flag==false)
return;
if(!node->left&&!node->right)
{
s=b=node->val;
return;
}
else
{
int ls,lb;int rs,rb;
mlr(node->left,ls,lb);mlr(node->right,rs,rb);
if(flag==false)
return;
if(node->left&&node->right)
{
if(node->val<=lb||node->val>=rs)
{
flag=false;
return;
}
s=ls;b=rb;
}
else if(node->left)
{
if(node->val<=lb)
{
flag=false;
return;
}
s=ls;b=node->val;
}
else
{
if(node->val>=rs)
{
flag=false;
return;
}
s=node->val;b=rb;
}
}
}
void FastOnlineSupervisedMStep<Scalar>::m_step(
std::shared_ptr<parameters::Parameters> parameters
) {
// Check whether we should actually perform the m_step
if (docs_seen_so_far_ < minibatch_size_)
return;
docs_seen_so_far_ = 0;
// Extract the parameters from the struct
auto model = std::static_pointer_cast<parameters::SupervisedModelParameters<Scalar> >(parameters);
MatrixX & beta = model->beta;
MatrixX & eta = model->eta;
// update the topic distributions
// TODO: Change the update to something more formal like the online update
// of Hoffman et al.
beta.array() = (
beta_weight_ * beta.array() +
(1-beta_weight_) * (b_.array().colwise() / b_.array().rowwise().sum())
);
// update the eta
optimization::MultinomialLogisticRegression<Scalar> mlr(
expected_z_bar_,
y_,
regularization_penalty_
);
mlr.gradient(eta, eta_gradient_);
eta_velocity_ = eta_momentum_ * eta_velocity_ - eta_learning_rate_ * eta_gradient_;
eta += eta_velocity_;
this->get_event_dispatcher()->template dispatch<events::MaximizationProgressEvent<Scalar> >(
-mlr.value(eta) // minus the value to be minimized is the log likelihood
);
}
vector<vector<int>> levelOrderBottom(TreeNode* root) {
ans.clear();
mlr(root, 0);
reverse(ans.begin(), ans.end());
return ans;
}
