// computes gradient for the whole training set
static void gradientForWholeBatch(
DataSet training_set,
FeatureType* gradient) {
memset(gradient, 0, training_set.num_features * sizeof(FeatureType));
float* probabilities_of_positive = new float[training_set.num_data_points];
// computes logistc function for each data point in the training set
size_t idx = 0;
for (size_t i = 0; i < training_set.num_data_points; i++) {
idx = i * training_set.num_features;
probabilities_of_positive[i] = logisticFunction(
training_set.parameter_vector,
&training_set.data_points[idx],
training_set.num_features);
}
// computes difference between
// predicted probability and actual label: (PI - Y)
addVectors(probabilities_of_positive,
training_set.labels,
training_set.num_data_points,
-1);
// finishes computation of gradient: (1/n) * X^T * (PI(theta, X) - YI)
float factor = 1.0f / training_set.num_data_points;
matrixVectorMultiply(training_set.data_points,
probabilities_of_positive,
factor,
training_set.num_data_points,
training_set.num_features,
gradient);
delete[] probabilities_of_positive;
}
matrix tridiagonalize(matrix A){
/*
Tridiagonalize a symmetric matrix using Householder transformations.
Transformations are performed in place with Householder vectors and coefficients,
rather than explicitly forming matrices and explicitly computing their products.
Returns T = Q^t * A * Q
Source: Golub and Van Loan, "Matrix Computations" p. 415
Input:
Matrix A Matrix to tridiagonalize.
Output:
Matrix T (returned) Tridiagonal matrix similar to input matrix.
*/
double *v = (double *) malloc(A.n * sizeof(double)) ;
double *p = (double *) malloc(A.n * sizeof(double)) ;
double *w = (double *) malloc(A.n * sizeof(double)) ;
double beta ;
double temp ;
double t1, t2;
int currentVectorLength = A.n - 1 ;
int n = A.n ;
int iVector, jVector;
int i,j,k;
for(k=0; k < n-2; k++){
beta = householder(A, k+1, k, v) ;
matrixVectorMultiply(A, k+1, k+1, v, beta, p) ;
temp = - 0.5 * beta * innerProduct(p, v, currentVectorLength ) ;
vectorPlusConstantByVector(w, p, temp, v, currentVectorLength ) ;
temp = 0.0;
for(j=k+1; j<n; j++){
temp += A.values[j][k] * A.values[j][k] ;
}
temp = sqrt(temp) ;
A.values[k+1][k] = temp ;
A.values[k][k+1] = temp ;
for(i=k+1, iVector=0; i<n; i++, iVector++){
for(j=k+1, jVector=0; j<n; j++, jVector++){
t1 = v[i]*w[j] ;
t2 = v[j]*w[i] ;
A.values[i][j] -= (v[iVector]*w[jVector] + v[jVector]*w[iVector]) ;
}
}
currentVectorLength-- ;
}
// zero off diagonal multipliers
for(i=0; i<n-1; i++){
for(j=i+2; j<n; j++){
A.values[i][j] = 0.0 ;
A.values[j][i] = 0.0 ;
}
}
free(v) ;
free(p) ;
free(w) ;
return A;
}
