void createVectorWithIndexPair(Vector *vetA, Vector *vetB) {
vetB->numberOfElements = returnNumberColOfIndexPair(vetB);
int i, col = 0;
if(allocateVector(vetB)) {
for(i=0; i< vetA->numberOfElements; i++) {
if(i%2 == 0) {
*(vetB->ptr + col) = *(vetA->ptr + i);
col++;
}
}
} else {
printf("Não conseguiu alocar memória pra VetorB.\n");
}
}
void createVector(Vector *vetA, Vector *vetB) {
vetB->numberOfElements = returnNumberOfCol(vetA);
int i, col = 0;
if(allocateVector(vetB)) {
for(i=0; i< vetA->numberOfElements; i++) {
if(*(vetA->ptr+i) > 10 && *(vetA->ptr+i) < 40) {
*(vetB->ptr+col) = *(vetA->ptr+i);
col++;
}
}
} else {
printf("Não conseguiu alocar memória para VetorB.\n");
}
}
void createVectorStartingTwoVector(Vector *vetA, Vector *vetB, Vector *vetC) {
vetB->numberOfElements = returnNumberColOfIndexPair(vetB);
int i;
if(allocateVector(vetB)) {
for (i = 0; i< vetA->numberOfElements; i++) {
if(i%2==0) {
*(vetC->ptr + i)= *(vetB->ptr + i);
} else {
*(vetC->ptr + i)= *(vetA->ptr + i);
}
}
} else {
printf("Não conseguiu alocar o VetorC. \n");
}
}
/**
* Compute the objective function value (the mean log-likelihood on training
* data for CRFs). Gradient is also calculated if required.
*
* @param F d x 1 feature vector for D training data sequences
*
* @param Ms transition matrices
*
* @param calcGrad if gradient required
*
* @param Grad gradient to be assigned in place if required
*
* @param W model parameters
*
* @return objective function value
*
*/
double CRF::computeObjectiveFunctionValue(Vector& F, Matrix** Ms, bool calcGrad, Vector& Grad, Vector& W) {
double fval = 0;
// int[] Y = null;
/*
* d x 1 array conditional expectation of feature functions
* for D training data sequences, i.e.,
* EF = sum_k E_{P_{\bf \lambda}(Y|x_k)}[F(Y, x_k)]
*/
Vector* EF = null;
double* EFArr = allocateVector(d);
/*
* Compute global feature vector for all training data sequences,
* i.e., sum_k F(y_k, x_k)
*/
int n_x = 0;
Matrix*** Fs_k = null;
Matrix* f_j_x_i = null;
for (int k = 0; k < D; k++) {
Fs_k = Fs[k];
n_x = lengths[k];
// Ms = new Matrix[n_x];
/*
* Compute transition matrix set
*/
for (int i = 0; i < n_x; i++) {
// Ms[i] = new DenseMatrix(numStates, numStates, 0);
Ms[i]->clear();
for (int j = 0; j < d; j++) {
f_j_x_i = Fs_k[i][j];
if (j == 0)
// Ms[i] = times(W.get(j), f_j_x_i);
times(*Ms[i], W.get(j), *f_j_x_i);
else
// Ms[i] = plus(Ms[i], times(W.get(j), f_j_x_i));
plusAssign(*Ms[i], W.get(j), *f_j_x_i);
}
expAssign(*Ms[i]);
/*for (int s = 0; s < numStates; s++) {
if (sum(Ms[i].getRow(s)) == 0) {
Ms[i].setRow(s, allocateVector(numStates, 1e-10));
}
}*/
}
/*
* Forward recursion with scaling
*/
/*Matrix Alpha_hat = new BlockRealMatrix(numStates, n_x);
Matrix Alpha_hat_0 = new BlockRealMatrix(numStates, 1);
RealVector e_start = new ArrayRealVector(numStates);
e_start.setEntry(startIdx, 1);*/
Vector** Alpha_hat = new Vector*[n_x];
/*for (int i = 0; i < n_x; i++) {
Alpha_hat[i] = new DenseVector(numStates);
}*/
// Vector* Alpha_hat_0 = null;
Vector& e_start = *new SparseVector(numStates);
e_start.set(startIdx, 1);
double* c = allocateVector(n_x);
// Alpha_hat_0.setColumnVector(0, e_start);
Vector& Alpha_hat_0 = e_start;
for (int i = 0; i < n_x; i++) {
if (i == 0) {
// Alpha_hat.setColumnMatrix(i, Ms[i].transpose().multiply(Alpha_hat_0));
Alpha_hat[i] = &Alpha_hat_0.operate(*Ms[i]);
} else {
// Alpha_hat.setColumnMatrix(i, Ms[i].transpose().multiply(Alpha_hat.getColumnMatrix(i - 1)));
Alpha_hat[i] = &Alpha_hat[i - 1]->operate(*Ms[i]);
}
// c[i] = 1.0 / sum(Alpha_hat.getColumnVector(i));
c[i] = 1.0 / sum(*Alpha_hat[i]);
/*if (Double.isInfinite(c[i])) {
int a = 1;
a = a + 1;
}*/
// Alpha_hat.setColumnMatrix(i, times(c[i], Alpha_hat.getColumnMatrix(i)));
timesAssign(*Alpha_hat[i], c[i]);
}
/*
* Backward recursion with scaling
*/
// Matrix Beta_hat = new BlockRealMatrix(numStates, n_x);
Vector** Beta_hat = new Vector*[n_x];
for (int i = n_x - 1; i >= 0; i--) {
if ( i == n_x - 1) {
// Beta_hat.setColumnMatrix(i, ones(numStates, 1));
Beta_hat[i] = new DenseVector(numStates, 1);
} else {
// Beta_hat.setColumnMatrix(i, mtimes(Ms[i + 1], Beta_hat.getColumnMatrix(i + 1)));
Beta_hat[i] = &Ms[i + 1]->operate(*Beta_hat[i + 1]);
}
// Beta_hat.setColumnMatrix(i, times(c[i], Beta_hat.getColumnMatrix(i)));
timesAssign(*Beta_hat[i], c[i]);
}
/*
* Accumulate the negative conditional log-likelihood on the
* D training data sequences
*/
for (int i = 0; i < n_x; i++) {
fval -= log(c[i]);
}
/*if (Double.isNaN(fval)) {
int a = 1;
a = a + 1;
}*/
if (!calcGrad)
continue;
/*
* Compute E_{P_{\bf \lambda}(Y|x_k)}[F(Y, x_k)]
*/
for (int j = 0; j < d; j++) {
/*
* Compute E_{P_{\bf \lambda}(Y|x_k)}[F_{j}(Y, x_k)]
*/
for (int i = 0; i < n_x; i++) {
if (i == 0) {
// EFArr[j] += Alpha_hat_0.transpose().multiply(times(Ms[i], f_j_x_i)).multiply(Beta_hat.getColumnMatrix(i)).getEntry(0, 0);
EFArr[j] += innerProduct(Alpha_hat_0, Ms[i]->times(*f_j_x_i).operate(*Beta_hat[i]));
} else {
// EFArr[j] += Alpha_hat.getColumnMatrix(i - 1).transpose().multiply(times(Ms[i], f_j_x_i)).multiply(Beta_hat.getColumnMatrix(i)).getEntry(0, 0);
EFArr[j] += innerProduct(*Alpha_hat[i - 1], Ms[i]->times(*f_j_x_i).operate(*Beta_hat[i]));
}
}
}
}
/*
* Calculate the eventual negative conditional log-likelihood
*/
fval -= innerProduct(W, F);
fval += sigma * innerProduct(W, W);
fval /= D;
if (!calcGrad) {
return fval;
}
/*
* Calculate the gradient of negative conditional log-likelihood
* w.r.t. W. on the D training data sequences
*/
// EF.setColumn(0, EFArr);
EF = new DenseVector(EFArr, d);
times(Grad, 1.0 / D, plus(minus(*EF, F), W.times(2 * sigma)));
return fval;
}
/**
* Predict the single best label sequence given the features for an
* observation sequence by Viterbi algorithm.
*
* @param Fs a 2D {@code Matrix} array, where F[i][j] is the sparse
* feature matrix for the j-th feature of the observation sequence
* at position i, i.e., f_{j}^{{\bf x}, i}
*
* @return the single best label sequence for an observation sequence
*
*/
int* CRF::predict(Matrix*** Fs, int length) {
Matrix** Ms = computeTransitionMatrix(Fs, length);
/*
* Alternative backward recursion with scaling for the Viterbi
* algorithm
*/
int n_x = length;
double* b = allocateVector(n_x);
// Matrix Beta_tilta = new BlockRealMatrix(numStates, n_x);
Vector** Beta_tilta = new Vector*[n_x];
for (int i = n_x - 1; i >= 0; i--) {
if ( i == n_x - 1) {
// Beta_tilta.setColumnMatrix(i, ones(numStates, 1));
Beta_tilta[i] = new DenseVector(numStates, 1);
} else {
// Beta_tilta.setColumnMatrix(i, mtimes(Ms[i + 1], Beta_tilta.getColumnMatrix(i + 1)));
Beta_tilta[i] = &Ms[i + 1]->operate(*Beta_tilta[i + 1]);
}
b[i] = 1.0 / sum(*Beta_tilta[i]);
// Beta_tilta.setColumnMatrix(i, times(b[i], Beta_tilta.getColumnMatrix(i)));
timesAssign(*Beta_tilta[i], b[i]);
}
/*fprintf("Beta:\n");
display(Beta_tilta);*/
/*
* Gammas[i](y_{i-1}, y_[i]) is P(y_i|y_{i-1}, Lambda), thus each row of
* Gammas[i] should be sum to one.
*/
double** Gamma_i = allocate2DArray(numStates, numStates, 0);
double** Phi = allocate2DArray(n_x, numStates, 0);
double** Psi = allocate2DArray(n_x, numStates, 0);
double** M_i = null;
double* M_i_Row = null;
double* Gamma_i_Row = null;
double* Beta_tilta_i = null;
double* Phi_i = null;
double* Phi_im1 = null;
double** maxResult = null;
for (int i = 0; i < n_x; i++) {
M_i = ((DenseMatrix*) Ms[i])->getData();
Beta_tilta_i = ((DenseVector*) Beta_tilta[i])->getPr();
for (int y_im1 = 0; y_im1 < numStates; y_im1++) {
M_i_Row = M_i[y_im1];
Gamma_i_Row = Gamma_i[y_im1];
assign(Gamma_i_Row, M_i_Row, numStates);
timesAssign(Gamma_i_Row, Beta_tilta_i, numStates);
sum2one(Gamma_i_Row, numStates);
}
Phi_i = Phi[i];
if (i == 0) { // Initialization
log(Phi_i, Gamma_i[startIdx], numStates);
} else {
Phi_im1 = Phi[i - 1];
for (int y_im1 = 0; y_im1 < numStates; y_im1++) {
Gamma_i_Row = Gamma_i[y_im1];
logAssign(Gamma_i_Row, numStates);
plusAssign(Gamma_i_Row, Phi_im1[y_im1], numStates);
}
maxResult = max(Gamma_i, numStates, numStates, 1);
Phi[i] = maxResult[0];
Psi[i] = maxResult[1];
}
}
/*
* Predict the single best label sequence.
*/
// double[] phi_n_x = Phi.getRow(n_x - 1);
double* phi_n_x = Phi[n_x - 1];
int* YPred = allocateIntegerVector(n_x);
for (int i = n_x - 1; i >= 0; i--) {
if (i == n_x - 1) {
YPred[i] = argmax(phi_n_x, numStates);
} else {
// YPred[i] = (int)Psi.getEntry(i + 1, YPred[i + 1]);
YPred[i] = (int) Psi[i + 1][YPred[i + 1]];
}
}
/*display(Phi);
display(Psi);*/
/*
* Predict the optimal conditional probability: P*(y|x)
*/
double p = exp(phi_n_x[YPred[n_x - 1]]);
fprintf("P*(YPred|x) = %g\n", p);
return YPred;
}
