CAutoencoder::CAutoencoder(int32_t num_inputs, CNeuralLayer* hidden_layer,
CNeuralLayer* decoding_layer, float64_t sigma) : CNeuralNetwork()
{
init();
if (decoding_layer==NULL)
decoding_layer = new CNeuralLinearLayer(num_inputs);
CDynamicObjectArray* layers = new CDynamicObjectArray();
layers->append_element(new CNeuralInputLayer(num_inputs));
layers->append_element(hidden_layer);
layers->append_element(decoding_layer);
set_layers(layers);
quick_connect();
hidden_layer->autoencoder_position = NLAP_ENCODING;
decoding_layer->autoencoder_position = NLAP_DECODING;
initialize_neural_network(sigma);
}
CAutoencoder::CAutoencoder(
int32_t input_width, int32_t input_height, int32_t input_num_channels,
CNeuralConvolutionalLayer* hidden_layer,
CNeuralConvolutionalLayer* decoding_layer,
float64_t sigma)
: CNeuralNetwork()
{
init();
CDynamicObjectArray* layers = new CDynamicObjectArray();
layers->append_element(new CNeuralInputLayer(input_width, input_height, input_num_channels));
layers->append_element(hidden_layer);
layers->append_element(decoding_layer);
set_layers(layers);
quick_connect();
hidden_layer->autoencoder_position = NLAP_ENCODING;
decoding_layer->autoencoder_position = NLAP_DECODING;
initialize_neural_network(sigma);
}
void test_leaf_sets_multiplication()
{
SG_SPRINT("shogun\ntest_leaf_sets_multiplication()\n");
SGVector<float64_t> param_vector(6);
SGVector<float64_t>::range_fill_vector(param_vector.vector, param_vector.vlen);
CDynamicObjectArray sets;
CParameterCombination* new_root=new CParameterCombination();
SG_REF(new_root);
CDynamicObjectArray* current=new CDynamicObjectArray();
sets.append_element(current);
Parameter* p=new Parameter();
p->add(¶m_vector.vector[0], "0");
CParameterCombination* pc=new CParameterCombination(p);
current->append_element(pc);
p=new Parameter();
p->add(¶m_vector.vector[1], "1");
pc=new CParameterCombination(p);
current->append_element(pc);
/* first case: one element */
CDynamicObjectArray* result_simple=
CParameterCombination::leaf_sets_multiplication(sets, new_root);
SG_SPRINT("one set\n");
for (index_t i=0; i<result_simple->get_num_elements(); ++i)
{
CParameterCombination* tpc=(CParameterCombination*)
result_simple->get_element(i);
tpc->print_tree();
SG_UNREF(tpc);
}
SG_UNREF(result_simple);
/* now more elements are created */
current=new CDynamicObjectArray();
sets.append_element(current);
p=new Parameter();
p->add(¶m_vector.vector[2], "2");
pc=new CParameterCombination(p);
current->append_element(pc);
p=new Parameter();
p->add(¶m_vector.vector[3], "3");
pc=new CParameterCombination(p);
current->append_element(pc);
current=new CDynamicObjectArray();
sets.append_element(current);
p=new Parameter();
p->add(¶m_vector.vector[4], "4");
pc=new CParameterCombination(p);
current->append_element(pc);
p=new Parameter();
p->add(¶m_vector.vector[5], "5");
pc=new CParameterCombination(p);
current->append_element(pc);
/* second case: more element */
CDynamicObjectArray* result_complex=
CParameterCombination::leaf_sets_multiplication(sets, new_root);
SG_SPRINT("more sets\n");
for (index_t i=0; i<result_complex->get_num_elements(); ++i)
{
CParameterCombination* tpc=(CParameterCombination*)
result_complex->get_element(i);
tpc->print_tree();
SG_UNREF(tpc);
}
SG_UNREF(result_complex);
SG_UNREF(new_root);
}
SGMatrix<float64_t> CLogDetEstimator::sample_without_averaging(
index_t num_estimates)
{
SG_DEBUG("Entering...\n")
REQUIRE(m_operator_log, "Operator function is NULL\n");
// call the precompute of operator function to compute all prerequisites
m_operator_log->precompute();
REQUIRE(m_trace_sampler, "Trace sampler is NULL\n");
// call the precompute of the sampler
m_trace_sampler->precompute();
// for storing the aggregators that submit_jobs return
CDynamicObjectArray aggregators;
index_t num_trace_samples=m_trace_sampler->get_num_samples();
for (index_t i=0; i<num_estimates; ++i)
{
for (index_t j=0; j<num_trace_samples; ++j)
{
// get the trace sampler vector
SGVector<float64_t> s=m_trace_sampler->sample(j);
// create jobs with the sample vector and store the aggregator
CJobResultAggregator* agg=m_operator_log->submit_jobs(s);
aggregators.append_element(agg);
SG_UNREF(agg);
}
}
REQUIRE(m_computation_engine, "Computation engine is NULL\n");
// wait for all the jobs to be completed
m_computation_engine->wait_for_all();
// the samples matrix which stores the estimates without averaging
// dimension: number of trace samples x number of log-det estimates
SGMatrix<float64_t> samples(num_trace_samples, num_estimates);
// use the aggregators to find the final result
int32_t num_aggregates=aggregators.get_num_elements();
for (int32_t i=0; i<num_aggregates; ++i)
{
CJobResultAggregator* agg=dynamic_cast<CJobResultAggregator*>
(aggregators.get_element(i));
if (!agg)
SG_ERROR("Element is not CJobResultAggregator type!\n");
// call finalize on all the aggregators
agg->finalize();
CScalarResult<float64_t>* r=dynamic_cast<CScalarResult<float64_t>*>
(agg->get_final_result());
if (!r)
SG_ERROR("Result is not CScalarResult type!\n");
// its important that we don't just unref the result here
index_t idx_row=i%num_trace_samples;
index_t idx_col=i/num_trace_samples;
samples(idx_row, idx_col)=r->get_result();
SG_UNREF(agg);
}
// clear all aggregators
aggregators.clear_array();
SG_DEBUG("Leaving\n")
return samples;
}
