void FFM::initParams(void)
{
int k_aligned = (int)ceil((double)parameters.num_factors/kALIGN)*kALIGN;
parameters.num_factors = k_aligned;
try
{
W = malloc_aligned_float((long long)num_features*num_fields*k_aligned*2);
}
catch(std::bad_alloc const &e)
{
throw;
}
init_coeff = 0.5/sqrt(parameters.num_factors);
float *w = W;
std::default_random_engine generator;
std::uniform_real_distribution<float> distribution(0.0, 1.0);
for(int j = 0; j < num_features; j++)
{
for(int f = 0; f < num_fields; f++)
{
for(int d = 0; d < parameters.num_factors; d++, w++)
*w = init_coeff*distribution(generator);
for(int d = parameters.num_factors; d < k_aligned; d++, w++)
*w = 0;
for(int d = k_aligned; d < 2*k_aligned; d++, w++)
*w = 1;
}
}
}
ffm_model* init_model(ffm_int n, ffm_int m, ffm_parameter param)
{
ffm_int k_aligned = (ffm_int)ceil((ffm_double)param.k/kALIGN)*kALIGN;
ffm_model *model = new ffm_model;
model->n = n;
model->k = k_aligned;
model->m = m;
model->W = nullptr;
model->normalization = param.normalization;
try
{
model->W = malloc_aligned_float((ffm_long)n*m*k_aligned*2);
}
catch(bad_alloc const &e)
{
ffm_destroy_model(&model);
throw;
}
ffm_float coef = 0.5/sqrt(param.k);
ffm_float *w = model->W;
default_random_engine generator;
uniform_real_distribution<ffm_float> distribution(0.0, 1.0);
for(ffm_int j = 0; j < model->n; j++)
{
for(ffm_int f = 0; f < model->m; f++)
{
for(ffm_int d = 0; d < param.k; d++, w++)
*w = coef*distribution(generator);
for(ffm_int d = param.k; d < k_aligned; d++, w++)
*w = 0;
for(ffm_int d = k_aligned; d < 2*k_aligned; d++, w++)
*w = 1;
}
}
return model;
}