// The constructor uses the appropriate settings in the config file to
// properly set up the sensor model for the specified "phase" of the LGMD
// input signal.
SensorModel::SensorModel(const std::string& lgmd_phase)
: m_sigma(0.0f), m_name(lgmd_phase)
{
const range<float> lgmd_range =
get_conf(locust_model(), "spike_range", make_range(0.0f, 800.0f)) ;
// Get the LGMD ranges for the columns of the sensor model
m_lgmd_ranges = string_to_deque<float>(
conf<std::string>(lgmd_phase + "_lgmd_ranges", "0 800")) ;
if (m_lgmd_ranges.size() < 2) { // crappy configuration!
m_lgmd_ranges.clear() ;
m_lgmd_ranges.push_back(lgmd_range.min()) ;
m_lgmd_ranges.push_back(lgmd_range.max()) ;
}
sort(m_lgmd_ranges.begin(), m_lgmd_ranges.end()) ;
if (m_lgmd_ranges.front() > lgmd_range.min())
m_lgmd_ranges.push_front(lgmd_range.min()) ;
if (m_lgmd_ranges.back() < lgmd_range.max())
m_lgmd_ranges.push_back(lgmd_range.max()) ;
// Figure out how many rows and columns the sensor model's probability
// table has and allocate space for the required number of elements.
// Initialize the probability table using a uniform distribution.
const int C = m_lgmd_ranges.size() - 1 ;
const int R = column_size() ;
const int N = R * C ;
m_prob.reserve(N) ;
std::fill_n(std::back_inserter(m_prob), N, 1.0f/N) ;
// Apply Gabbiani model to obtain causal probabilities and Gaussian
// blur neighbouring bins in each row.
update(clamp(conf(lgmd_phase + "_sigma", 1.0f),
0.1f, static_cast<float>(row_size()))) ;
}
// Quick helper to return a label for the current range of probability
// values used for scaling the texels used to represent the sensor
// model's probabilities.
static std::string prob_label(const range<float>& prob_range)
{
std::ostringstream str ;
str << "P-range: ["
<< prob_range.min() << ", " << prob_range.max() << ']' ;
return str.str() ;
}
// This method regenerates the sensor model's probabilities using the
// Gabbiani LGMD model and the given standard deviation for the Gaussian
// blurring operation for bins near the ones actually "pointed to" by the
// [TTI, LGMD] pairs returned by the Gabbiani model.
//
// DEVNOTE: The sigma provided to this function is actually added to the
// m_sigma member variable. This allows client behaviours to increment or
// decrement the current sigma value rather than provide an actual sigma.
// The very first sigma will be read from the config file (see
// constructor).
void SensorModel::update(float dsigma)
{
AutoMutex M(m_mutex) ;
// Record new standard deviation
const float R = row_size() ;
m_sigma = clamp(m_sigma + dsigma, 0.1f, R) ;
// Begin with a uniform distribution for each state
const int N = m_prob.size() ;
std::fill_n(m_prob.begin(), N, 1/R) ;
// Apply Gabbiani LGMD model to generate causal likelihoods
const float step = row_step()/4.0f ;
const range<float> tti = conf(m_name + "_tti_range", Params::tti_range()) ;
for (float t = tti.min(); t <= tti.max(); t += step)
update_row(t, GabbianiModel::spike_rate(t), m_sigma) ;
}