int RandomColor::generate( ColorList colors, Luminosity luminosity )
{
// Each color will have probability = color.hRange.size() / totalRangeSize,
// so calculate totalRangeSize
double totalRangeSize = 0;
for (Color color : colors) {
totalRangeSize += colorMap[color].hRange.size();
}
// Generate a number within [0; 1) and select a color according with the
// cumulative distribution function f(i) = f(i - 1) + colorProbability(i)
std::uniform_real_distribution<double> probability(0, 1);
double p = probability(randomEngine);
double f = 0;
for (Color color : colors) {
const double colorProbability = colorMap[color].hRange.size() / totalRangeSize;
f += colorProbability;
if (f >= p) {
return generate(color, luminosity);
}
}
// This place can be reached due to rounding (if p ~ 1 and f < p even
// for the last color). Then return the last color.
Color lastColor = *(colors.end() - 1);
return generate(lastColor, luminosity);
}
void Ft_feature_model::update(const arma::colvec& x){
std::cout<< arma::norm(x,2) << std::endl;
probability(air) = Decision_functions::bound_mv_gaussian_step_function(x,zero,0.5,3.0,1.0);
probability(up) = Decision_functions::step_function(x(0),1.5); //Decision_functions::step_function(-x(2),1.5);
probability(down) = Decision_functions::step_function(-x(0),1.5);
probability(left) = Decision_functions::step_function(x(1),1.5);
probability(right) = Decision_functions::step_function(-x(1),1.5);
/* if( arma::norm(x - zero,2) < 3){
probability(0) = 1;
}else{
probability(0) = 0;
}*/
//for(std::size_t i = 1; i < gmms.size();i++){
// probability(i) = gmms[i].gmm.Probability(x);
// probability(i) = likelihood(i);// Decision_functions::gaussian_step_function<float>(likelihood(i),desc_mu,desc_beta);
// }
}
/**
* calculates the average number for the distribution;
* note that this average is larger than the expectation value,
* since curves are limited to the left by x-value 1
*/
double GaussianDistribution::getAverage() {
double sum1=0;
double sum2=0;
for (unsigned i=x_limit_left;i<=x_limit_right;i++) {
sum1+=i*probability(i);
sum2+=probability(i);
}
return sum1/sum2;
}
DocClass classifyDocument(Document doc){
float p1 = getPdc(LESS_THAN_FIVE)*(1/3)/probability(doc);
float p2 = getPdc(GREATHER_THAN_FIVE)*(1/3)/probability(doc);
float p3 = getPdc(MIXED_VALUES)*(1/3)/probability(doc);
DocClass result;
if(p1 > std::max<float>(p2, p3))
result = LESS_THAN_FIVE;
else if(p2 > std::max<float>(p1, p3))
result = GREATHER_THAN_FIVE;
else
result = MIXED_VALUES;
return result;
}
double probability(int color,int total,int remain){
if(remain>color || remain>total || remain<0)
return 0.0;
if((total+remain)%2==1)
return 0.0;
if(total==0 || total==1)
return 1.0;
if(answerMemory[total][remain]==UNDEF)
answerMemory[total][remain]=probability(color,total-1,remain+1)*(remain+1.0)/color+
probability(color,total-1,remain-1)*(1.0-(remain-1.0)/color);
#ifdef DEBUG
printf("# probability(%d,%d,%d) = %f\n",color,total,remain,answerMemory[total][remain]);
#endif
return answerMemory[total][remain];
}
int main(){
float list[MAX_PROB];
memset(list, 0, sizeof(float) * MAX_PROB);
FILE *fp = fopen("introduction_to_probability_input.txt", "r");
check(fp != NULL, "Problem opening input file");
int i = 0;
while(!feof(fp)){
if(fscanf(fp, "%f", &list[i])) { i++; }
}
fclose(fp);
int length = i;
for (i = 0; i < length-1; i++) {
printf("%0.6f ", probability(list[i]));
}
printf("\n");
return 0;
error:
return 1;
}
void probability(int number, int* probabilities)
{
for (int i = 1; i <= g_maxValue; i++)
{
probability(number, number, i, probabilities);
}
}
void printProbability(int number)
{
if (number < 1)
{
return ;
}
int maxSum = number * g_maxValue;
int* probabilities = new int[maxSum - number + 1];
for (int i = number; i <= maxSum; ++i)
{
probabilities[i - number] = 0;
}
probability(number, probabilities);
int total = pow((double)g_maxValue, number);
for (int i = number; i <= maxSum; ++i)
{
double ratio = (double)probabilities[i - number] / total;
printf("%d: %e\n", i, ratio);
}
delete[] probabilities;
}
Rate CreditDefaultSwap::impliedHazardRate(
Real targetNPV,
const Handle<YieldTermStructure>& discountCurve,
const DayCounter& dayCounter,
Real recoveryRate,
Real accuracy) const {
boost::shared_ptr<SimpleQuote> flatRate(new SimpleQuote(0.0));
Handle<DefaultProbabilityTermStructure> probability(
boost::shared_ptr<DefaultProbabilityTermStructure>(new
FlatHazardRate(0, WeekendsOnly(),
Handle<Quote>(flatRate), dayCounter)));
MidPointCdsEngine engine(probability, recoveryRate, discountCurve);
setupArguments(engine.getArguments());
const CreditDefaultSwap::results* results =
dynamic_cast<const CreditDefaultSwap::results*>(
engine.getResults());
ObjectiveFunction f(targetNPV, *flatRate, engine, results);
Rate guess = 0.001;
Rate step = guess*0.1;
return Brent().solve(f, accuracy, guess, step);
}
int main()
{
int number;
printf("input num of dices:");
scanf("%d",&number);
probability(number);
return 0;
}
main(){
setlocale(LC_ALL, "Russian");
int n, m;
float p=0.45, q=0.55;
printf("Введите количество детей: n, m\n");
scanf("%d%d", &n, &m);
probability(n, m, p, q);
getch();
return 0;
}
DataType BernoulliDistribution<DataType>::Sample() const
{
DataType sample = arma::randu<DataType>
(probability.n_rows, probability.n_cols);
for (size_t i = 0; i < sample.n_elem; i++)
sample(i) = sample(i) < probability(i);
return sample;
}
GaussianDistribution::GaussianDistribution(unsigned _x_limit_left,double _mu,
double _sigma) :
mu(_mu), sigma(_sigma), x_limit_left(_x_limit_left),
r(Randomnizer::getInstance()), RANGE(0) {
if (mu<x_limit_left) {
exit(4); // this is not allows for this curve type
}
double sum=0;
unsigned num=x_limit_left;
// sum<=0.5 makes sure that we do not break too early
while (sum<=0.5 || probability(num)>OFFSET_VALUE) {
sum+=probability(num++);
bucket.push_back(Functions::doubleToUnsigned(sum*ACCURACY));
}
sum+=probability(num);
bucket.push_back(Functions::doubleToUnsigned(sum*ACCURACY));
RANGE=bucket[bucket.size()-1];
// this is the right bucket limit
x_limit_right=num;
}
void probability(int original, int current, int sum, int* probabilities)
{
if (current == 1)
{
probabilities[sum - original]++;
}
else
{
for (int i = 1; i <= g_maxValue; i++)
{
probability(original, current - 1, i + sum, probabilities);
}
}
}
int main()
{
int m,n,t,p;
cin>> m >> n >> t >> p;
if( 0 < m && m <= 1000 && \
0 < n && n <= m && \
0 < t && t <= 100 && \
0 < p && p <= m )
probability(m,n,t,p);
return 0;
}
int main(){
int color,total,remain;
while(true){
scanf("%d",&color);
if(color==0)
return 0;
scanf("%d%d",&total,&remain);
if(total>1000)
total=1000+total%2;
for(int i=0;i<=total;i++)
for(int j=0;j<=total;j++)
answerMemory[i][j]=UNDEF;
printf("%.3f\n",probability(color,total,remain));
}
}
void crf::tagger::tag(sequence& seq)
{
auto trellis = scorer_.viterbi(seq);
auto lbls = util::range(label_id{0},
label_id(static_cast<uint32_t>(num_labels_ - 1)));
auto last_lbl = functional::argmax(
lbls.begin(), lbls.end(), [&](label_id lbl)
{
return trellis.probability(seq.size() - 1, lbl);
});
seq[seq.size() - 1].label(*last_lbl);
for (uint64_t t = seq.size() - 1; t > 0; t--)
seq[t - 1].label(trellis.previous_tag(t, seq[t].label()));
}
//--------------------------------------------------------------------------
vector<Real> LossDist::probabilityOfNEvents(vector<Real>& p) {
//--------------------------------------------------------------------------
Size n = p.size();
vector<Real> probability(n+1, 0.0);
vector<Real> prev;
probability[0] = 1.0;
for (Size j = 0; j < n; j++) {
prev = probability;
probability[0] = prev[0] * (1.0 - p[j]);
for (Size i = 1; i <= j; i++)
probability[i] = prev[i-1] * p[j] + prev[i] * (1.0 - p[j]);
probability[j+1] = prev[j] * p[j];
}
return probability;
}
/*
* search for (approximation of) smallest value v such that probability(v,sigma,mu) >= t
*/
static double search(double const t, double const sigma, double const mu)
{
double low = mu - 10*sigma, high = mu + 10*sigma;
while ( high - low > 1e-6 )
{
double const mid = (high+low)/2.0;
double const v = probability(mid,sigma,mu);
if ( v < t )
low = mid;
else
high = mid;
}
return low;
}
probability_t operator() (sigma_t sigma) {
state_t state = m_state;
probability_t probability(1);
for(ObservationIteratorT it(m_observation_begin); it != m_observation_end; ++it) {
edge_t /*backward_edge, edge = switch_setting(sigma, state),*/
observation = *it;
//backward_edge = state.e == GRAPH_0 ? edge : GRAPH_0;
probability *= observation == state.e ? (1 - p) : p / 2;
state = m_graph.previous(state, sigma);
}
return probability;
}
QVector<float> AntAlgorithm::calculateProbability(QList<int> path, int node)
{
QVector<float> probability(attractivenessM.count(), 0);
float denominator = 0.;
for (int j = 0; j < attractivenessM.count(); ++j)
if ((weightM.at(node).at(j) != 0.) && (!path.contains(j)))
denominator += pow(attractivenessM.at(node).at(j), alpha) + pow(1 / weightM.at(node).at(j), beta);
for (int j = 0; j < attractivenessM.count(); ++j)
if ((weightM.at(node).at(j) != 0) && (!path.contains(j)) && (denominator != 0))
probability[j] = (pow(attractivenessM.at(node).at(j), alpha) +
pow(1 / weightM.at(node).at(j), beta)) / denominator;
else
probability[j] = 0.;
emit strToLog(tr("Агент в узле %1").arg(node).append(tr(". Вероятности перехода:")));
QList< QVector<float> > list;
list.append(probability);
showM(list);
return probability;
}
Rate CreditDefaultSwap::conventionalSpread(
Real conventionalRecovery,
const Handle<YieldTermStructure>& discountCurve,
const DayCounter& dayCounter) const {
Rate flatHazardRate = impliedHazardRate(0.0,
discountCurve,
dayCounter,
conventionalRecovery);
Handle<DefaultProbabilityTermStructure> probability(
boost::shared_ptr<DefaultProbabilityTermStructure>(
new FlatHazardRate(0, WeekendsOnly(),
flatHazardRate, dayCounter)));
MidPointCdsEngine engine(probability, conventionalRecovery,
discountCurve, true);
setupArguments(engine.getArguments());
engine.calculate();
const CreditDefaultSwap::results* results =
dynamic_cast<const CreditDefaultSwap::results*>(
engine.getResults());
return results->fairSpread;
}
void File::simulatedAnnealing(vector<Process> processes_list) {
srand(time(NULL));
parallelTask(processes_list);
if (time_is_over == true) {
cout << "Time is over before first Parallel Task ordering\n";
cout << "No file saved\n";
return;
}
if (processes_list.size() <= 2) {
return;
}
averageCalculating(processes_list);
//this->saveToFile(alternative_solution, "PRL");
cout << "this->averageReadyTime = " << this->averageReadyTime << endl;
cout << "this->averageProcsAmount = " << this->averageProcsAmount << endl;
vector<Process> actual_solution = alternative_solution;
lastTaskTime = alternative_last_task_time;
vector<Process> old_good_solution = actual_solution;
old_last_task_time = lastTaskTime;
int temperature = TEMPERATURE;
int min_temperature = MIN_TEMPERATURE;
unsigned int counter_worse_solution = 0;
unsigned int counter_better_solution = 0;
unsigned int max_counter_better_solution = MAX_COUNTER_BETTER_SOLUTION;
unsigned int max_counter_worse_solution = MAX_COUNTER_WORSE_SOLUTION;
int minutes_amount = MINUTES_AMOUNT;
int i = 0;
while (temperature > min_temperature) {
//cout << "temperature " << temperature << endl;
if ((clock() - start2) / CLOCKS_PER_SEC > minutes_amount * 60) {
cout << "Time is over\n";
actual_solution = old_good_solution;
lastTaskTime = old_last_task_time;
this->saveToFile(actual_solution,"SA");
return;
}
if (counter_worse_solution >= max_counter_worse_solution) {
cout << "Counter_worse_solution is max\n";
counter_worse_solution = 0;
actual_solution = old_good_solution;
lastTaskTime = old_last_task_time;
}
// FIND ALTERNATIVE SOLUTION
this->findAlternativeSolution(actual_solution);
if (time_is_over) {
cout << "Time is over\n";
actual_solution = old_good_solution;
lastTaskTime = old_last_task_time;
this->saveToFile(actual_solution,"SA");
return;
}
//this->saveToFile(alternative_solution, "ALTER");
if (alternative_last_task_time < lastTaskTime) {
//cout << "Processing better scheduling\n";
actual_solution.clear();
if (old_last_task_time > alternative_last_task_time) {
cout << "*********************Found new better scheduling************************** \n";
old_good_solution.clear();
old_good_solution = alternative_solution;
old_last_task_time = alternative_last_task_time;
}
actual_solution = alternative_solution;
lastTaskTime = alternative_last_task_time;
counter_better_solution++;
counter_worse_solution = 0;
if (counter_better_solution == max_counter_better_solution) {
temperature = (int)temperature_reducing(temperature);
}
}
else if ((rand() % 100 + 0) < (probability(actual_solution.back().f_t, temperature))*100) {
//cout << "get worse solution\n";
actual_solution.clear();
actual_solution = alternative_solution;
lastTaskTime = alternative_last_task_time;
counter_worse_solution++;
counter_better_solution = 0;
temperature = (int)temperature_reducing(temperature);
}
else {
//cout <<"I've done nothing\n";
temperature = (int)temperature_reducing(temperature);
}
}
cout << "Actual temperature is lower than boundary\n" << temperature << endl;
actual_solution = old_good_solution;
lastTaskTime = old_last_task_time;
this->saveToFile(actual_solution,"SA");
}
void main()
{ int i,j,k;
int X[16][4]= {{0,0,0,0},{0,0,0,1},{0,0,1,0},{0,0,1,1},{0,1,0,0},{0,1,0,1},{0,1,1,0},{0,1,1,1},
{1,0,0,0},{1,0,0,1},{1,0,1,0},{1,0,1,1},{1,1,0,0},{1,1,0,1},{1,1,1,0},{1,1,1,1}};
int temp[1][4],temp1[4];
// calculating X star
for(i=0;i<16;i++) {
for(j=0;j<4;j++) {
array[a++]=delta_X[i][j];
}
xor(array,X,i);
a=0;
}
/*printf("\n\n\t\t----X star table-----\n");//printing X star table
for(i=0;i<16;i++) {
for(j=0;j<16;j++) {
for(k=0;k<4;k++) {
printf("%d ",X_star[i][j][k]);
}
printf("\n");
}
printf("\n\n");
}*/
// Calculating Y table
for(i=0;i<16;i++) {
for(j=0;j<4;j++) {
temp[0][j]=delta_X[i][j];
}
sbox_f(temp,1); //function call to sbox
}
/*printf("\t\t----Y table---\n"); // printing Y table
for(j=0;j<16;j++) {
for(k=0;k<4;k++) {
printf("%d ",Y[j][k]);
}
printf("\n");
}*/
//Calculating Y star table
for(i=0;i<16;i++) {
for(j=0;j<16;j++) {
for(k=0;k<4;k++) {
temp[0][k]=X_star[i][j][k];
}
sbox_f(temp,0); //function call to sbox
}
col1++;
row1=0;
}
/*printf("\n\n\t\t----Y star table-----\n");//printing Y star table
for(i=0;i<16;i++) {
for(j=0;j<16;j++) {
for(k=0;k<4;k++) {
printf("%d ",Y_star[i][j][k]);
}
printf("\n");
}
printf("\n\n");
}
*/ //Calculating Y dash
for(i=0;i<16;i++) {
for(j=0;j<16;j++) {
for(k=0;k<4;k++) {
Y_dash[i][j][k]=Y[j][k]^Y_star[i][j][k];
}
}
}
/*printf("\n\n\t\t----Y dash table-----\n"); //printing Y dash table
for(i=0;i<16;i++) {
for(j=0;j<16;j++) {
for(k=0;k<4;k++) {
printf("%d ",Y_dash[i][j][k]);
}
printf("\n");
}
printf("\n\n");
}*/
ddt(); // function call ddt
printf("\n\n\t\t------- DDT Table -------\n\n"); // printing DDT table
for(i=0;i<16;i++) {
for(j=0;j<16;j++) {
printf("%d ",table[i][j]);
}
printf("\n\n");
}
probability();
printf("\n\n\t\t------- Probability Table -------\n\n");
for(i=0;i<16;i++) {
for(j=0;j<16;j++) {
printf("%f ",probab[i][j]);
}
printf("\n\n");
}
}
__host__ __device__
probability ProbabilisticVoxel::updateOccupancy(const probability occupancy)
{
m_occupancy = probability(MIN(MAX(int32_t(m_occupancy + occupancy), int32_t(MIN_PROBABILITY)), int32_t(MAX_PROBABILITY)));
return m_occupancy;
}
bool randomBool() {
return probability(50) ? true : false;
}
int randomSign() {
return probability(50) ? -1 : 1;
}
capd::interval formal::evaluate_pha(int min_depth, int max_depth)
{
CLOG_IF(global_config.verbose, INFO, "algorithm") << "Obtaining partition of domain of continuous random parameters";
// getting partition of domain of continuous random variables
std::vector<box> init_rv_partition = measure::get_rv_partition();
// getting domain of continuous random variables
box rv_domain = measure::bounds::get_rv_domain();
// here we start with entire domain instead of partition
if (!global_config.partition_prob) {
init_rv_partition.clear();
init_rv_partition.push_back(rv_domain);
}
// getting partition of domain of discrete random variables
std::vector<box> dd_partition = measure::get_dd_partition();
if (dd_partition.empty()) {
dd_partition.push_back(box());
}
capd::interval probability(0, 1);
// checking if there are any continuous random variables
CLOG_IF(global_config.verbose, INFO, "algorithm") << "P = " << probability;
// generating all paths of lengths [min_depth, max_depth]
std::vector<std::vector<pdrh::mode *>> paths = pdrh::get_all_paths(min_depth, max_depth);
//resulting probability
capd::interval res_prob(0.0);
// evaluating boxes
//#pragma omp parallel for
for (box dd : dd_partition)
{
if (pdrh::rv_map.size() > 0) probability = capd::interval(0, 2 - measure::p_measure(rv_domain, global_config.precision_prob).leftBound());
vector<box> rv_partition = init_rv_partition;
std::vector<box> rv_stack;
while (capd::intervals::width(probability) > global_config.precision_prob)
{
// sorting boxes by probability value
if (global_config.sort_rv_flag)
{
CLOG_IF(global_config.verbose, INFO, "algorithm")
<< "Sorting the partition of domain of continuous random parameters";
sort(rv_partition.begin(), rv_partition.end(), measure::compare_boxes_by_p_measure);
}
//for(box rv : rv_partition)
#pragma omp parallel for
for (size_t i = 0; i < rv_partition.size(); i++)
{
box rv = rv_partition.at(i);
// calculating probability measure of the box
// initially p_box = [1.0, 1.0]
CLOG_IF(global_config.verbose, INFO, "algorithm") << "====================";
capd::interval p_box(1);
if (!dd.empty())
{
p_box *= measure::p_dd_measure(dd);
CLOG_IF(global_config.verbose, INFO, "algorithm") << "dd_box: " << dd;
}
if (!rv.empty())
{
p_box *= measure::p_measure(rv, global_config.precision_prob);
CLOG_IF(global_config.verbose, INFO, "algorithm") << "rv_box: " << rv;
}
CLOG_IF(global_config.verbose, INFO, "algorithm") << "p_box: " << p_box;
// evaluating boxes
std::vector<box> boxes{dd, rv};
// undetermined answers counter
int undet_counter = 0;
// unsat counter
int unsat_counter = 0;
// sat flag
bool sat_flag = false;
// evaluating all paths for all dd and rv
//cout << "Before evaluate loop " << omp_get_thread_num() << endl;
for (std::vector<pdrh::mode *> path : paths) {
std::string solver_opt;
std::stringstream p_stream;
for (pdrh::mode *m : path) {
p_stream << m->id << " ";
}
// removing trailing whitespace
CLOG_IF(global_config.verbose, INFO, "algorithm") << "Path: " << p_stream.str().substr(0,
p_stream.str().find_last_of(
" "));
std::stringstream s;
// changing solver precision
#pragma omp critical
{
solver_opt = global_config.solver_opt;
s << solver_opt << " --precision " <<
measure::volume(rv).leftBound() * global_config.solver_precision_ratio;
global_config.solver_opt = s.str();
}
int res = decision_procedure::evaluate(path, boxes, global_config.solver_bin, s.str());
// setting old precision
#pragma omp critical
{
global_config.solver_opt = solver_opt;
switch (res)
{
case decision_procedure::SAT:
if (p_box.leftBound() > 0)
{
probability = capd::interval(probability.leftBound() + p_box.leftBound(),
probability.rightBound());
}
CLOG_IF(global_config.verbose, INFO, "algorithm") << "SAT";
CLOG_IF(global_config.verbose, INFO, "algorithm") << "P = " << probability;
/*
if(capd::intervals::width(probability) <= global_config.precision_prob)
{
return probability;
}
*/
sat_flag = true;
break;
case decision_procedure::UNSAT:
CLOG_IF(global_config.verbose, INFO, "algorithm") << "UNSAT";
unsat_counter++;
break;
case decision_procedure::UNDET:
CLOG_IF(global_config.verbose, INFO, "algorithm") << "UNDET";
undet_counter++;
break;
case decision_procedure::ERROR:
CLOG(ERROR, "algorithm") << "Error occurred while calling the solver";
exit(EXIT_FAILURE);
default:
break;
}
}
// breaking out of the loop if a sat path was found
if (sat_flag) break;
}
#pragma omp critical
{
// checking if there are no sat answers
if (!sat_flag)
{
// if the box is undetermined on either path
if (undet_counter > 0)
{
CLOG_IF(global_config.verbose, INFO, "algorithm") << "Bisect " << rv;
std::vector<box> rv_bisect = box_factory::bisect(rv);
rv_stack.insert(rv_stack.end(), rv_bisect.begin(), rv_bisect.end());
}
// if the box is unsat for all paths
else if (unsat_counter == paths.size())
{
if (p_box.rightBound() > 0)
{
probability = capd::interval(probability.leftBound(),
probability.rightBound() - p_box.leftBound());
}
CLOG_IF(global_config.verbose, INFO, "algorithm") << "P = " << probability;
}
}
};
}
// copying the bisected boxes from the stack to partition
rv_partition = rv_stack;
rv_stack.clear();
// breaking out of the loop if there are no continuous random variables
if (pdrh::rv_map.size() == 0)
{
rv_partition.push_back(box());
break;
}
}
if (!dd.empty())
{
capd::interval dd_measure = measure::p_dd_measure(dd);
res_prob += probability * dd_measure;
CLOG_IF(global_config.verbose, INFO, "algorithm") << "P(" << dd << ") = " << probability * dd_measure;
}
else
{
res_prob = probability;
}
}
return res_prob;
}
virtual double QoS(int Q) {
return QoS_from_prob(probability(Q));
};
double random_prob() { return probability(engine); };