void generateW(double * x, double mean, int len, double demand_jump_factor)
{
std::default_random_engine generator;
double sigma = sqrt(mean)*demand_jump_factor;
double sigmaJump = mean / 4;
std::normal_distribution<double> jumpdistribution(0, sigmaJump);
x[0] = mean;
double curP = probabilityDensity(x[0], mean, sigma);
for (int i = 1; i < len; i++)
{
x[i] = x[i - 1];
double nextX = x[i - 1];
if (getRandNum(100) <= demand_jump_factor)
nextX += uniondist(mean);
else
nextX +=jumpdistribution(generator);
double nextP = probabilityDensity(nextX, mean, sigma);
if (nextP >= curP)
{
curP = nextP;
x[i] = nextX;
}
else
{
double u = (0.0+abso(getRandNum(10000))) / 10000;
if (u < nextP / curP)
{
curP = nextP;
x[i] = nextX;
}
}
if (x[i] < 0)
{
x[i] = 0;
curP = probabilityDensity(0, mean, sigma);
}
if (isnan(x[i]))
printf("Nan...........\n");
}
}
vector<double>* GaussianEstimator::estimatedWeight_LessThan_EqualTo_GreaterThan_Value(double value) {
double equalToWeight = probabilityDensity(value) * this->m_weightSum;
double stdDev = getStdDev();
double lessThanWeight = stdDev > 0.0 ? normalProbability((value - getMean()) / stdDev)
* this->m_weightSum - equalToWeight
: (value < getMean() ? this->m_weightSum - equalToWeight : 0.0);
double greaterThanWeight = this->m_weightSum - equalToWeight
- lessThanWeight;
if (greaterThanWeight < 0.0) {
greaterThanWeight = 0.0;
}
vector<double>* retVec = new vector<double>{lessThanWeight, equalToWeight, greaterThanWeight};
return retVec;
}
