/*
* Generate a uniform distribution on the half-open interval [lower, upper).
*/
double rds_uniform(
mt_state * state, /* State of the MT PRNG to use */
double lower, /* Lower limit of distribution */
double upper) /* Upper limit of distribution */
{
return lower + mts_drand(state) * (upper - lower);
}
/*
* Generate a p-Erlang distribution with the given mean.
*/
double rds_erlang(
mt_state * state, /* State of the MT PRNG to use */
int p, /* Order of distribution to generate */
double mean) /* Mean of generated distribution */
{
int order; /* Order generated so far */
double random_value; /* Value generated so far */
do
{
if (p <= 1)
p = 1;
random_value = mts_drand(state);
for (order = 1; order < p; order++)
random_value *= mts_drand(state);
}
while (random_value == 0.0);
return -mean * log(random_value) / p;
}
/*
* Generate an exponential distribution with the given mean.
*/
double rds_exponential(
mt_state * state, /* State of the MT PRNG to use */
double mean) /* Mean of generated distribution */
{
double random_value; /* Random sample on [0,1) */
do
random_value = mts_drand(state);
while (random_value == 0.0);
return -mean * log(random_value);
}
/*
* Generate a Weibull distribution with the given shape and scale parameters.
*/
double rds_weibull(
mt_state * state, /* State of the MT PRNG to use */
double shape, /* Shape of the distribution */
double scale) /* Scale of the distribution */
{
double random_value; /* Random sample on [0,1) */
do
random_value = mts_drand(state);
while (random_value == 0.0);
return scale * exp(log(-log(random_value)) / shape);
}
/*
* Generate a normal distribution with the given mean and standard
* deviation. See Law and Kelton, p. 491.
*/
double rds_normal(
mt_state * state, /* State of the MT PRNG to use */
double mean, /* Mean of generated distribution */
double sigma) /* Standard deviation to generate */
{
double mag; /* Magnitude of (x,y) point */
double offset; /* Unscaled offset from mean */
double xranval; /* First random value on [-1,1) */
double yranval; /* Second random value on [-1,1) */
/*
* Generating a normal distribution is a bit tricky. We may need
* to make several attempts before we get a valid result. When we
* are done, we will have two normally distributed values, one of
* which we discard.
*/
do
{
xranval = 2.0 * mts_drand(state) - 1.0;
yranval = 2.0 * mts_drand(state) - 1.0;
mag = xranval * xranval + yranval * yranval;
}
while (mag > 1.0 || mag == 0.0);
offset = sqrt((-2.0 * log(mag)) / mag);
return mean + sigma * xranval * offset;
/*
* The second random variate is given by:
*
* mean + sigma * yranval * offset;
*
* If this were a C++ function, it could probably save that value
* somewhere and return it in the next subsequent call. But
* that's too hard to make bulletproof (and reentrant) in C.
*/
}
/*
* Generate a triangular distibution between given limits, with a
* given mode.
*/
double rds_triangular(
mt_state * state, /* State of the MT PRNG to use */
double lower, /* Lower limit of distribution */
double upper, /* Upper limit of distribution */
double mode) /* Highest point of distribution */
{
double ran_value; /* Value generated by PRNG */
double scaled_mode; /* Scaled version of mode */
scaled_mode = (mode - lower) / (upper - lower);
ran_value = mts_drand(state);
if (ran_value <= scaled_mode)
ran_value = sqrt(scaled_mode * ran_value);
else
ran_value = 1.0 - sqrt((1.0 - scaled_mode) * (1.0 - ran_value));
return lower + (upper - lower) * ran_value;
}
inline float urand(Sampler *s) {
return mts_drand(&s->mState);
}
real_t Sampler::urand(real_t lower, real_t upper) {
return mts_drand(&m_state) * (upper-lower)+lower; //rds_uniform(&m_state, lower, upper);
}
