TEST_F(TriangularSolveTest, BackwardSubstBandedMatchEigenComputedResults)
{
int n=6;
int m=3;
Eigen::MatrixXd U = Eigen::MatrixXd::Random(n,n);
Eigen::VectorXd b = Eigen::VectorXd::Random(n);
U.triangularView<Eigen::StrictlyLower>().setZero();
for (int i=0; i<n; i++) {
for (int j=i; j<n; j++) {
if (j-i>m) {
U(i,j) = 0;
}
}
}
Eigen::VectorXd x = backward_subst_banded(U, m, b);
Eigen::VectorXd y = U.triangularView<Eigen::Upper>().solve(b);
double tol = 1e-13;
EXPECT_NEAR((x-y).norm(), 0, tol);
EXPECT_NEAR((U*x-b).norm(), 0, tol);
// band storage solution
Eigen::ArrayXXd a = Eigen::ArrayXXd::Zero(n,m+1);
a.col(0) = U.diagonal();
for (int i=1; i<=m; i++) {
a.col(i).block(0,0,n-i,1) = U.diagonal(i);
}
Eigen::VectorXd z = backward_subst_banded(a,b);
tol = 1e-16;
EXPECT_NEAR((x-z).norm(), 0, tol);
}
TEST_F(TriangularSolveTest, ForwardSubstBandedMatchEigenComputedResults)
{
int n=6;
int m=3;
Eigen::MatrixXd L = Eigen::MatrixXd::Random(n,n);
Eigen::VectorXd b = Eigen::VectorXd::Random(n);
L.triangularView<Eigen::StrictlyUpper>().setZero();
for (int j=0; j<n; j++) {
for (int i=j; i<n; i++) {
if (i-j>m) {
L(i,j) = 0;
}
}
}
Eigen::VectorXd x = forward_subst_banded(L, m, b);
Eigen::VectorXd y = L.triangularView<Eigen::Lower>().solve(b);
double tol = 1e-13;
EXPECT_NEAR((x-y).norm(), 0, tol);
EXPECT_NEAR((L*x-b).norm(), 0, tol);
// band storage solution
Eigen::ArrayXXd a = Eigen::ArrayXXd::Zero(n,m+1);
a.col(m) = L.diagonal();
for (int i=1; i<=m; i++) {
a.col(m-i).block(i,0,n-i,1) = L.diagonal(-i);
}
Eigen::VectorXd z = forward_subst_banded(a,b);
tol = 1e-16;
EXPECT_NEAR((x-z).norm(), 0, tol);
}
Eigen::ArrayXXd band_from_dense(const Eigen::MatrixXd & A, int m1, int m2)
{
int n = A.rows();
assert(n == A.cols());
Eigen::ArrayXXd a = Eigen::ArrayXXd::Zero(n,m1+m2+1);
a.col(m1) = A.diagonal();
for (int i=1; i<=m1; i++) {
a.col(m1-i).block(i,0,n-i,1) = A.diagonal(-i);
}
for (int i=1; i<=m2; i++) {
a.col(m1+i).block(0,0,n-i,1) = A.diagonal(i);
}
return a;
}
Eigen::ArrayXXd mpFlow::math::circularPoints(double const radius, double const distance,
double const offset, bool const invertDirection, Eigen::Ref<Eigen::ArrayXd const> const midpoint) {
Eigen::ArrayXd const phi = arange(offset / radius, offset / radius + 2.0 * M_PI, distance / radius);
Eigen::ArrayXXd result = Eigen::ArrayXXd::Zero(phi.rows(), 2);
result.col(0) = midpoint(0) + radius * phi.cos();
result.col(1) = midpoint(1) + (invertDirection ? -1.0 : 1.0) * radius * phi.sin();
return result;
}
// project points outside of domain back to boundary
void distmesh::utils::projectPointsToBoundary(
Functional const& distanceFunction, double const initialPointDistance,
Eigen::Ref<Eigen::ArrayXXd> points) {
Eigen::ArrayXd distance = distanceFunction(points);
// check for points outside of boundary
Eigen::Array<bool, Eigen::Dynamic, 1> outside = distance > 0.0;
if (outside.any()) {
// calculate gradient
Eigen::ArrayXXd gradient(points.rows(), points.cols());
Eigen::ArrayXXd deltaX = Eigen::ArrayXXd::Zero(points.rows(), points.cols());
for (int dim = 0; dim < points.cols(); ++dim) {
deltaX.col(dim).fill(constants::deltaX * initialPointDistance);
gradient.col(dim) = (distanceFunction(points + deltaX) - distance) /
(constants::deltaX * initialPointDistance);
deltaX.col(dim).fill(0.0);
}
// project points back to boundary
points -= outside.replicate(1, points.cols()).select(
gradient.colwise() * distance / gradient.square().rowwise().sum(), 0.0);
}
}
/**
* Compute maximum dissolution and evaporation ratios at
* average hydrocarbon pressure.
*
* Uses the pressure value computed by averagePressure()
* and must therefore be called *after* that method.
*/
void
calcRmax()
{
Rmax_.setZero();
const PhaseUsage& pu = props_.phaseUsage();
if (Details::PhaseUsed::oil(pu) &&
Details::PhaseUsed::gas(pu))
{
const Eigen::ArrayXXd::Index
io = Details::PhasePos::oil(pu),
ig = Details::PhasePos::gas(pu);
// Note: Intentionally does not take capillary
// pressure into account. This facility uses the
// average *hydrocarbon* pressure rather than
// average phase pressure.
typedef BlackoilPropsAdInterface::ADB ADB;
Rmax_.col(io) = props_.rsSat(ADB::constant(p_avg_), ADB::constant(T_avg_), repcells_).value();
Rmax_.col(ig) = props_.rvSat(ADB::constant(p_avg_), ADB::constant(T_avg_), repcells_).value();
}
}
Eigen::ArrayXXd band_transpose(const Eigen::ArrayXXd & a, int m1, int m2)
{
int n = a.rows();
int m = m1+m2+1;
assert( a.cols() == m );
Eigen::ArrayXXd b = Eigen::ArrayXXd::Zero(n,m);
b.col(m2) = a.col(m1);
for (int i=1; i<=m1; i++) {
b.col(m2+i).block(0,0,n-i,1) = a.col(m1-i).block(i,0,n-i,1);
}
for (int i=1; i<=m2; i++) {
b.col(m2-i).block(i,0,n-i,1) = a.col(m1+i).block(0,0,n-i,1);
}
return b;
}
//[[Rcpp::export]]
List SPMBgraphsqrt(Eigen::Map<Eigen::MatrixXd> data, NumericVector &lambda, int nlambda, int d, NumericVector &x, IntegerVector &col_cnz, IntegerVector &row_idx)
{
Eigen::ArrayXd Xb, r, grad, w1, Y, XX, gr;
Eigen::ArrayXXd X;
Eigen::MatrixXd tmp_icov;
tmp_icov.resize(d, d);
tmp_icov.setZero();
std::vector<Eigen::MatrixXd > tmp_icov_p;
tmp_icov_p.clear();
for(int i = 0; i < nlambda; i++)
tmp_icov_p.push_back(tmp_icov);
int n = data.rows();
X = data;
XX.resize(d);
for (int j = 0; j < d; j++)
XX[j] = (X.col(j)*X.col(j)).sum()/n;
double prec = 1e-4;
int max_iter = 1000;
int num_relaxation_round = 3;
int cnz = 0;
for(int m=0;m<d;m++)
{
Xb.resize(n);
Xb.setZero();
grad.resize(d);
grad.setZero();
gr.resize(d);
gr.setZero();
w1.resize(d);
w1.setZero();
r.resize(n);
r.setZero();
Y = X.col(m);
Eigen::ArrayXd Xb_master(n);
Eigen::ArrayXd w1_master(n);
std::vector<int> actset_indcat(d, 0);
std::vector<int> actset_indcat_master(d, 0);
std::vector<int> actset_idx;
std::vector<double> old_coef(d, 0);
std::vector<double> grad(d, 0);
std::vector<double> grad_master(d, 0);
double a = 0, g = 0, L = 0, sum_r = 0, sum_r2 = 0;
double tmp_change = 0, local_change = 0;
r = Y - Xb;
sum_r = r.sum();
sum_r2 = r.matrix().dot(r.matrix());
L = sqrt(sum_r2 / n);
double dev_thr = fabs(L) * prec;
//cout<<dev_thr<<endl;
for(int i = 0; i < d; i++)
{
grad[i] = (r * X.col(i)).sum() / (n*L);
}
for(int i = 0; i < d; i++) gr[i] = abs(grad[i]);
w1_master = w1;
Xb_master = Xb;
for (int i = 0; i < d; i++) grad_master[i] = gr[i];
std::vector<double> stage_lambdas(d, 0);
for(int i=0;i<nlambda;i++)
{
double ilambda = lambda[i];
w1 = w1_master;
Xb = Xb_master;
for (int j = 0; j < d; j++)
{
gr[j] = grad_master[j];
actset_indcat[j] = actset_indcat_master[j];
}
// init the active set
double threshold;
if (i > 0)
threshold = 2 * lambda[i] - lambda[i - 1];
else
threshold = 2 * lambda[i];
for (int j = 0; j < m; ++j)
{
stage_lambdas[j] = lambda[i];
if (gr[j] > threshold) actset_indcat[j] = 1;
}
for (int j = m+1; j < d; ++j)
{
stage_lambdas[j] = lambda[i];
if (gr[j] > threshold) actset_indcat[j] = 1;
}
stage_lambdas[m] = lambda[i];
r = Y - Xb;
sum_r = r.sum();
sum_r2 = r.matrix().dot(r.matrix());
L = sqrt(sum_r2 / n);
// loop level 0: multistage convex relaxation
int loopcnt_level_0 = 0;
int idx;
double old_w1, updated_coord;
while (loopcnt_level_0 < num_relaxation_round)
{
loopcnt_level_0++;
// loop level 1: active set update
int loopcnt_level_1 = 0;
bool terminate_loop_level_1 = true;
while (loopcnt_level_1 < max_iter)
{
loopcnt_level_1++;
terminate_loop_level_1 = true;
for (int j = 0; j < d; j++) old_coef[j] = w1[j];
// initialize actset_idx
actset_idx.clear();
for (int j = 0; j < m; j++)
if (actset_indcat[j])
{
g = 0.0;
a = 0.0;
double tmp;
sum_r2 = r.matrix().dot(r.matrix());
L = sqrt(sum_r2 / n);
Eigen::ArrayXd wXX = (1 - r*r/sum_r2) * X.col(j) * X.col(j);
g = (wXX * w1[j] + r * X.col(j)).sum()/(n*L);
a = wXX.sum()/(n*L);
tmp = w1[j];
w1[j] = thresholdl1(g, stage_lambdas[j]) / a;
tmp = w1[j] - tmp;
// Xb += delta*X[idx*n]
Xb = Xb + tmp * X.col(j);
sum_r = 0.0;
sum_r2 = 0.0;
// r -= delta*X
r = r - tmp * X.col(j);
sum_r = r.sum();
sum_r2 = r.matrix().dot(r.matrix());
L = sqrt(sum_r2 / n);
updated_coord = w1[j];
if (fabs(updated_coord) > 0) actset_idx.push_back(j);
}
for (int j = m+1; j < d; j++)
if (actset_indcat[j])
{
g = 0.0;
a = 0.0;
double tmp;
sum_r2 = r.matrix().dot(r.matrix());
L = sqrt(sum_r2 / n);
Eigen::ArrayXd wXX = (1 - r*r/sum_r2) * X.col(j) * X.col(j);
g = (wXX * w1[j] + r * X.col(j)).sum()/(n*L);
a = wXX.sum()/(n*L);
tmp = w1[j];
w1[j] = thresholdl1(g, stage_lambdas[j]) / a;
tmp = w1[j] - tmp;
// Xb += delta*X[idx*n]
Xb = Xb + tmp * X.col(j);
sum_r = 0.0;
sum_r2 = 0.0;
// r -= delta*X
r = r - tmp * X.col(j);
sum_r = r.sum();
sum_r2 = r.matrix().dot(r.matrix());
L = sqrt(sum_r2 / n);
updated_coord = w1[j];
if (fabs(updated_coord) > 0) actset_idx.push_back(j);
}
// loop level 2: proximal newton on active set
int loopcnt_level_2 = 0;
bool terminate_loop_level_2 = true;
while (loopcnt_level_2 < max_iter)
{
loopcnt_level_2++;
terminate_loop_level_2 = true;
for (int k = 0; k < actset_idx.size(); k++)
{
idx = actset_idx[k];
old_w1 = w1[idx];
g = 0.0;
a = 0.0;
double tmp;
sum_r2 = r.matrix().dot(r.matrix());
L = sqrt(sum_r2 / n);
Eigen::ArrayXd wXX = (1 - r*r/sum_r2) * X.col(idx) * X.col(idx);
g = (wXX * w1[idx] + r * X.col(idx)).sum()/(n*L);
a = wXX.sum()/(n*L);
tmp = w1[idx];
w1[idx] = thresholdl1(g, stage_lambdas[idx]) / a;
tmp = w1[idx] - tmp;
// Xb += delta*X[idx*n]
Xb = Xb + tmp * X.col(idx);
sum_r = 0.0;
sum_r2 = 0.0;
// r -= delta*X
r = r - tmp * X.col(idx);
sum_r = r.sum();
sum_r2 = r.matrix().dot(r.matrix());
L = sqrt(sum_r2 / n);
updated_coord = w1[idx];
tmp_change = old_w1 - w1[idx];
double a = (X.col(idx) * X.col(idx) * (1 - r * r/(L*L*n))).sum()/(n*L);
local_change = a * tmp_change * tmp_change / (2 * L * n);
if (local_change > dev_thr)
terminate_loop_level_2 = false;
}
if (terminate_loop_level_2)
break;
}
terminate_loop_level_1 = true;
// check stopping criterion 1: fvalue change
for (int k = 0; k < actset_idx.size(); ++k)
{
idx = actset_idx[k];
tmp_change = old_w1 - w1[idx];
double a = (X.col(idx) * X.col(idx) * (1 - r * r/(L*L*n))).sum()/(n*L);
local_change = a * tmp_change * tmp_change / (2 * L * n);
if (local_change > dev_thr)
terminate_loop_level_1 = false;
}
r = Y - Xb;
sum_r = r.sum();
sum_r2 = r.matrix().dot(r.matrix());
L = sqrt(sum_r2 / n);
if (terminate_loop_level_1)
break;
// check stopping criterion 2: active set change
bool new_active_idx = false;
for (int k = 0; k < m; k++)
if (actset_indcat[k] == 0)
{
grad[idx] = (r * X.col(idx)).sum() / (n*L);
//cout<<grad[idx];
gr[k] = fabs(grad[k]);
if (gr[k] > stage_lambdas[k])
{
actset_indcat[k] = 1;
new_active_idx = true;
}
}
for (int k = m+1; k < d; k++)
if (actset_indcat[k] == 0)
{
grad[idx] = (r * X.col(idx)).sum() / (n*L);
//cout<<grad[idx]
gr[k] = fabs(grad[k]);
if (gr[k] > stage_lambdas[k])
{
actset_indcat[k] = 1;
new_active_idx = true;
}
}
if(!new_active_idx)
break;
}
if (loopcnt_level_0 == 1)
{
for (int j = 0; j < d; j++)
{
w1_master[j] = w1[j];
grad_master[j] = gr[j];
actset_indcat_master[j] = actset_indcat[j];
}
for (int j = 0; j < n; j++) Xb_master[j] = Xb[j];
}
}
for(int j=0;j<actset_idx.size();j++)
{
int w_idx = actset_idx[j];
x[cnz] = w1[w_idx];
row_idx[cnz] = i*d+w_idx;
cnz++;
//cout<<cnz<<" ";
}
double tal = 0;
Eigen::MatrixXd temp;
temp.resize(n, 1);
for(int j = 0; j < n; j++)
{
temp(j, 0) = 0;
for(int k = 0; k < d; k++)
temp(j, 0) += X.matrix()(j, k)*w1[k];
temp(j, 0) = Y[j] - temp(j, 0);
}
//temp = Y.matrix() - X.matrix().transpose()*w1.matrix();
for(int j = 0; j < n; j++)
tal += temp(j, 0)*temp(j, 0);
tal = sqrt(tal)/sqrt(n);
tmp_icov = tmp_icov_p[i];
tmp_icov(m, m) = pow(tal, -2);
for(int j = 0; j < m; j++)
tmp_icov(j, m) = -tmp_icov(m, m)*w1[j];
for(int j = m+1; j < d; j++)
tmp_icov(j, m) = -tmp_icov(m, m)*w1[j];
tmp_icov_p[i] = tmp_icov;
}
col_cnz[m+1]=cnz;
}
for(int i = 0; i < nlambda; i++)
tmp_icov_p[i] = (tmp_icov_p[i].transpose()+tmp_icov_p[i])/2;
return List::create(
_["col_cnz"] = col_cnz,
_["row_idx"] = row_idx,
_["x"] = x,
_["icov"] = tmp_icov_p
);
}
int main()
{
//initialisation
Eigen::ArrayXXd R = Eigen::ArrayXXd::Zero(FEATURE_SIZE, NO_OF_POSTAGS); //matrix R, dimension: Nf x Nw
std::default_random_engine generator;
std::normal_distribution<double> distr(0.0, 0.1);
//initialise the matrix R with small, random, non-zero numbers
for (int i = 0; i < R.rows(); i++) {
for (int j = 0; j < R.cols(); j++) {
double d = distr(generator);
//avoid having 0's as the weight, try to get all the coefficients initialised with small numbers
while (d == 0.0) {
d = distr(generator);
}
R(i, j) = d;
}
}
//initialise the matrix Q, of size feature_size x (no_of_postags * no_of_distance)
Eigen::ArrayXXd Q = Eigen::ArrayXXd::Zero(FEATURE_SIZE, NO_OF_POSTAGS);
for (int i = 0; i < Q.rows(); i++) {
for (int j = 0; j < Q.cols(); j++) {
double d = distr(generator);
//avoid having 0's as the weight, try to get all the coefficients initialised with small numbers
while (d == 0.0) {
d = distr(generator);
}
Q(i, j) = d;
}
}
//create an array of Ci (where each Ci is a matrix of Nf x Nf that governs the interaction between the ith feature and vn)
std::vector<Eigen::ArrayXXd> Ci;
for (int i = 0; i < 5; i++) {
Eigen::ArrayXXd Ci_temp = Eigen::ArrayXXd::Zero(FEATURE_SIZE, FEATURE_SIZE);
std::default_random_engine generator;
std::normal_distribution<double> distr(0.0, 0.1);
for (int j = 0; j < Ci_temp.rows(); j++) {
for (int k = 0; k < Ci_temp.cols(); k++) {
double d = distr(generator);
//avoid having 0's as the weight, try to get all the coefficients initialised with small numbers
while (d == 0.0) {
d = distr(generator);
}
Ci_temp(j, k) = d;
}
}
Ci.push_back(Ci_temp);
}
//initialise vi. We use a context of 5 (the postag of the head itself, the postag before the modifier, the postag after the modifier, the postag before the head, and the postag after the head)
//initialise vn. Both vi and vn are column vectors with all 0's and a single 1, which indicates the POS-tag we desire to get
//FOR NOW, we ignore the term br^transpose * R^transpose*vn, but find out what it is
//initialise the bias term bv
Eigen::ArrayXd bv;
//print the array of biases into an external file
std::ofstream final_postag_bias("final_postag_bias.txt");
if (!final_postag_bias.is_open()) {
std::cout << "fail to create final postag bias file" << std::endl;
}
/*std::map<std::string, int> big_pos_tag_map = get_postag_bias(bv, "distance_count.txt", "fine_pos_tags.txt");
std::cout << "size of big pos tag map = " << big_pos_tag_map.size() << std::endl;
for (std::map<std::string, int>::iterator it = big_pos_tag_map.begin(); it != big_pos_tag_map.end(); it++) {
final_postag_bias << "Pos tag " << it->first << " has bias value " << bv(it->second) << std::endl;
} */
/* create a pair between each "target pos tag" with a bias value (tested and correct)*/
//Eigen::ArrayXd word_bias_pair = Eigen::ArrayXd::Zero(NO_OF_POSTAGS_WITHOUT_DUMMY * NO_OF_DISTANCE);
int i_3 = -1;
std::ifstream fine_pos_tags("fine_pos_tags.txt");
std::string temp_string;
/*
while (getline(fine_pos_tags, temp_string)) {
for (int i = -MAX_DISTANCE; i <= MAX_DISTANCE; i++) {
if (i != 0) {
if (i == -49 || i == -45 || i == -40 || i == -35 || i == -30 || i == -25 || i == -20 || i == -15 || i == -10 || i == -5 || i == -4 || i == -3 || i == -2 || i == -1 || i == 1 || i == 2 || i == 3 || i == 4 || i == 5 || i == 6 || i == 11 || i == 16 || i == 21 || i == 26 || i == 31 || i == 36|| i == 41 || i == 46) {
i_3++;
std::stringstream sstm;
sstm << temp_string << i;
if (big_pos_tag_map.find(sstm.str()) == big_pos_tag_map.end()) {
std::cout << "ERROR" << std::endl;
} else {
int pos_tag_idx = big_pos_tag_map[sstm.str()];
//word_bias_pair(i_3) = bv(pos_tag_idx);
}
}
}
}
} */
/* end */
//main training part
std::map<std::string, int> mapping = postag_mapping("fine_pos_tags.txt");
fine_pos_tags.close();
//print out both the small and big mapping
//std::ofstream big_pos_tag_mapping("big_pos_tag_map.txt");
std::ofstream small_pos_tag_mapping("small_pos_tag_map.txt");
/*
for (std::map<std::string, int>::iterator it = big_pos_tag_map.begin(); it != big_pos_tag_map.end(); it++) {
big_pos_tag_mapping << it->first << " " << it->second << std::endl;
} */
for (std::map<std::string, int>::iterator it = mapping.begin(); it != mapping.end(); it++) {
small_pos_tag_mapping << it->first << " " << it->second << std::endl;
}
small_pos_tag_mapping.close();
//create the pairs
std::ifstream pairs ("real_pairs_medium.txt");
//initialise the gradient matrix
std::vector<Eigen::MatrixXd> dCi;
for (int i = 0; i < 5; i++) {
Eigen::MatrixXd temp = Eigen::MatrixXd::Zero(FEATURE_SIZE, FEATURE_SIZE);
dCi.push_back(temp);
}
Eigen::MatrixXd dR = Eigen::MatrixXd::Zero(FEATURE_SIZE, NO_OF_POSTAGS);
Eigen::MatrixXd dQ = Eigen::MatrixXd::Zero(FEATURE_SIZE, NO_OF_POSTAGS);
std::ofstream parameters ("parameters_each_epoch_small_trial_1_check_R.txt");
if (!parameters.is_open()) {
std::cout << "Error: output file not opening" << std::endl;
}
//initialise all the temporary variables outside the loop to save time on memory allocation on the heap (expensive)
//all these temporary variables are reset to zero using .setZero() method at the end of each loop
Eigen::MatrixXd tempC = Eigen::MatrixXd::Zero(FEATURE_SIZE, FEATURE_SIZE);
Eigen::MatrixXd temp_R_Model = Eigen::MatrixXd::Zero(FEATURE_SIZE, NO_OF_POSTAGS);
Eigen::MatrixXd temp_R_Data = Eigen::MatrixXd::Zero(FEATURE_SIZE, NO_OF_POSTAGS);
Eigen::MatrixXd temp_Q = Eigen::MatrixXd::Zero(FEATURE_SIZE, NO_OF_POSTAGS);
Eigen::MatrixXd temp_Q_Model = Eigen::MatrixXd::Zero(FEATURE_SIZE, NO_OF_POSTAGS);
Eigen::MatrixXd temp_Q_Data = Eigen::MatrixXd::Zero(FEATURE_SIZE, NO_OF_POSTAGS);
Eigen::VectorXd temp_Qw = Eigen::VectorXd::Zero(FEATURE_SIZE);
//Eigen::VectorXd dbias = Eigen::VectorXd::Zero(NO_OF_POSTAGS_WITHOUT_DUMMY * NO_OF_DISTANCE);
//Eigen::VectorXd temp_dbias = Eigen::VectorXd::Zero(NO_OF_POSTAGS_WITHOUT_DUMMY * NO_OF_DISTANCE);
Eigen::VectorXd tempCr = Eigen::VectorXd::Zero(FEATURE_SIZE);
Eigen::ArrayXd temp_e = Eigen::ArrayXd::Zero(NO_OF_POSTAGS);
/*
Eigen::MatrixXd temp_matrix = Eigen::MatrixXd::Zero(NO_OF_POSTAGS, FEATURE_SIZE);
Eigen::MatrixXd temp_matrix_2 = Eigen::MatrixXd::Zero(NO_OF_POSTAGS, FEATURE_SIZE); */
//calculate the normalising constant for the first time
std::ofstream gradient_check_R("gradient_check_R_big.txt");
Eigen::MatrixXd curr_dR = Eigen::MatrixXd::Zero(FEATURE_SIZE, NO_OF_POSTAGS); //DELETE THIS LATER
int no_of_iter = 0;
for (int i = 0; i < NO_OF_EPOCH; i++) {
if (!pairs.is_open()) {
std::cout << "Fail to open input file" << std::endl;
} else {
std::vector<std::string> temp;
std::string line;
while (getline(pairs, line)) {
if (line.size() > 1) {
no_of_iter++;
temp = split(line, ' ');
//initialise an array with the elements as the indices of: postag of the modifier, postag of the head, postag of before modifier, postag of after modifier, postag of before head, postag of after head
int arr_temp[6] = {mapping[temp[0]], mapping[temp[1]], mapping[temp[2]], mapping[temp[3]], mapping[temp[4]], mapping[temp[5]]};
if (no_of_iter % 10000 == 0) {
std::cout << no_of_iter << std::endl;
}
//calculate the normalising constant
double normalising_constant = 0.0;
for (int iter2 = 0; iter2 < 5; iter2++) {
tempCr += (Ci[iter2].matrix() * R.col(arr_temp[iter2+1]).matrix()); //prediction
}
for (int iter = 0; iter < Q.cols(); iter++) {
double temp = exp((tempCr.transpose() * Q.col(iter).matrix()));//dot product
temp_e(iter) = temp;
normalising_constant += temp;
}
//calculate all the gradient changes for each of the Ci, from dCi[0] to dCi[4]
//this operation is already efficient
for (int j = 0; j < 5; j++) {
for (int k = 0; k < Q.cols(); k++) {
tempC += (temp_e(k) * (Q.col(k).matrix() * R.col(arr_temp[j+1]).transpose().matrix()));
}
//normalise
tempC /= normalising_constant;
dCi[j] += (Q.col(arr_temp[0]).matrix() * R.col(arr_temp[j+1]).transpose().matrix() - tempC);
//reset the matrix tempC to prepare to calculate the next dCi[j]
tempC.setZero();
}
//calculate dQ VERIFY
for (int j = 0; j < 5; j++) {
temp_Qw += (Ci[j].matrix() * R.col(arr_temp[j+1]).matrix());
}
for (int j = 0; j < Q.cols(); j++) {
temp_Q_Model.col(j) += (temp_e(j) * temp_Qw);
}
temp_Q_Model /= normalising_constant;
temp_Q_Data.col(arr_temp[0]) = temp_Qw;
dQ += (temp_Q_Data - temp_Q_Model);
temp_Qw.setZero();
temp_Q_Model.setZero();
temp_Q_Data.setZero();
//calculate dR
//calculate dR with respect to data
for (int j = 0; j < 5; j++) {
temp_R_Data.col(arr_temp[j+1]) += Ci[j].transpose().matrix() * Q.col(arr_temp[0]).matrix();
}
//calculate dR with respect to model
for (int k = 0; k < Q.cols(); k++) {
for (int j = 0; j < 5; j++) {
temp_R_Model.col(arr_temp[j+1]) += (temp_e(k) * (Ci[j].transpose().matrix() * Q.col(k).matrix()));
}
}
temp_R_Model /= normalising_constant;
dR += (temp_R_Data - temp_R_Model);
if (no_of_iter % 1000 == 0) {
curr_dR = temp_R_Data - temp_R_Model;
}
temp_R_Data.setZero();
temp_R_Model.setZero();
//calculate dbias
//temp_dbias(arr_temp[0]) = 1.0;
//dbias += (temp_dbias - temp_e.matrix() / normalising_constant);
//temp_dbias(arr_temp[0]) = 0.0;
tempCr.setZero();
temp_e.setZero();
if (no_of_iter % 1000 == 0) {
Eigen::VectorXd temp = Eigen::VectorXd::Zero(FEATURE_SIZE);
gradient_check_R << "current data point = " << no_of_iter <<std::endl;
for (int j = 0; j < R.rows(); j++) {
for (int k = 0; k < R.cols(); k++) {
Eigen::ArrayXXd R_copy = R;
double result_1 = 0.0;
double result_2 = 0.0;
double temp_double = 0.0;
gradient_check_R << curr_dR(j, k) << " ";
R_copy(j, k) += EPSILON;
for (int iter2 = 0; iter2 < 5; iter2++) {
temp += (Ci[iter2].matrix() * R_copy.col(arr_temp[iter2+1]).matrix());
}
result_1 += temp.transpose() * Q.col(arr_temp[0]).matrix();
for (int l = 0; l < Q.cols(); l++) {
temp_double += exp(temp.transpose() * Q.col(l).matrix());
}
result_1 -= log(temp_double);
temp_double = 0.0;
//calculate result_2
R_copy(j, k) -= (2 * EPSILON);
temp.setZero();
for (int iter2 = 0; iter2 < 5; iter2++) {
temp += (Ci[iter2].matrix() * R_copy.col(arr_temp[iter2 + 1]).matrix());
}
result_2 += temp.transpose() * Q.col(arr_temp[0]).matrix();
for (int l = 0; l < Q.cols(); l++) {
temp_double += exp(temp.transpose() * Q.col(l).matrix());
}
result_2 -= log(temp_double);
temp.setZero();
gradient_check_R << (result_1 - result_2) / (2 * EPSILON) << std::endl;
}
}
curr_dR.setZero();
}
//update the gradient if no_of_iter mod mini batch size == 0, reset the gradient matrix to 0
if (no_of_iter % BATCH_SIZE == 0) {
double curr_learning_rate = LEARNING_RATE / (1 + no_of_iter * LEARNING_DECAY);
for (int j = 0; j < 5; j++) {
Ci[j] = Ci[j].matrix() + curr_learning_rate * dCi[j];
//reset the matrix dCi[j]
dCi[j].setZero();
}
//word_bias_pair = word_bias_pair + curr_learning_rate * dbias.array();
R = R.matrix() + curr_learning_rate * dR;
Q = Q.matrix() + curr_learning_rate * dQ;
dR.setZero();
dQ.setZero();
//dbias.setZero();
}
}
temp.clear();
}
pairs.close();
}
parameters << "Epoch number: " << i+1 << std::endl;
for (int j = 0; j < 5; j++) {
parameters << "Matrix C" << j+1 << std::endl;
parameters << Ci[j] << std::endl;
parameters << std::endl;
parameters << "End of matrix C" << j+1 << std::endl;
}
parameters << "Matrix R" << std::endl;
parameters << R << std::endl;
parameters << "End of matrix R" << std::endl;
parameters << "Matrix Q" << std::endl;
parameters << Q << std::endl;
parameters << "End of matrix Q" << std::endl;
//parameters << "Bias vector" << std::endl;
//parameters << word_bias_pair << std::endl;
//parameters << "End of bias vector" << std::endl;
pairs.open("real_pairs_medium.txt");
}
pairs.close();
parameters.close();
return 0;
}
