/*
Substraction. Throw exception if their dimensions don't match.
*/
Mat operator -(const Mat& rhs){
if (rhs.get_n()!=n || rhs.get_m()!=m){
throw invalid_argument("Matrix dimensions are not consistent when trying '+' two matrices");
return Mat();
}
Mat res = Mat(*this);
for (int i=0; i<res.get_n(); i++){
for (int j=0; j<res.get_m(); j++)
res[i][j] -= rhs.get(i, j);
}
return res;
}
/*
Objective function value calculator:
Takes Doc feature matrix and Word feature matrix, as well as ground truth counts,
return objective function value and a vector of error value for each case.
The objective function is
F = acc_error + doc_reg + word_reg
= sum_{(doc_id, word_id, cnt):train_vec} (cnt - D(doc_id)*W(word_id))^2 + 0.5*lambda*(|D|^2 + |W|^2)
*/
double CalcObj(const Mat &D, const Mat &W, const vector<Triplet> &train_vec, const int &begin_idx, const int &end_idx, const double &lambda, const double &mean_cnt, Mat &error, Mat &pred_out){
double acc_error = 0.0, doc_reg = 0.0, word_reg = 0.0;
error = Mat(end_idx-begin_idx+1, 1, 0); pred_out = Mat(end_idx-begin_idx+1, 1, 0);
for (int i = begin_idx; i <= end_idx; i++){
int doc_id = train_vec[i].doc_id, word_id = train_vec[i].word_id;
double cnt = train_vec[i].cnt;
// calculate the prediction: inner product of D(doc_id), W(word_id)
double predict = 0.0;
for (int j=0; j < D.get_m(); j++){
double dd = D.get(doc_id, j), ww = W.get(word_id, j);
predict += (dd*ww);
doc_reg += sqr(dd);
word_reg += sqr(ww);
}
pred_out[i-begin_idx][0] = predict + mean_cnt;
error[i - begin_idx ][0] = pred_out[i-begin_idx][0] - train_vec[i].cnt;
// calculate the error between predict and ground truth cnt
acc_error += sqr(pred_out[i-begin_idx][0] - train_vec[i].cnt);
}
// Objective function value
double F = acc_error + 0.5*lambda*(doc_reg + word_reg);
return F;
}
/*
Return a matrix which is the elementwise square.
*/
Mat Sqr(){
Mat res = Mat(*this);
for (int i=0; i<res.get_n(); i++)
for (int j=0; j<res.get_m(); j++)
res[i][j] = res[i][j]*res[i][j];
return res;
}
/*
Multiply by a number.
*/
Mat operator *(const double& mult){
Mat res = Mat(*this);
for (int i=0; i<res.get_n(); i++){
for (int j=0; j<res.get_m(); j++)
res[i][j] *= mult;
}
return res;
}
/*
Copy constructor. Deep copy.
*/
Mat(const Mat &cp){
InitMat(cp.get_n(),cp.get_m());
for (int i=0; i<n; i++)
for (int j=0; j<m; j++)
arr[i][j] = cp.get(i,j);
}
