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mtwist
calling conventionsAll of the functions in this library simulate a random draw from a distribution. There is a choice of distribution statistics depending on the function used.
bern
takes a floating point number p between 0 and 1, encoded as in
math
, and returns a boolean value, either (nil,nil)
for
true or nil
for false. A true value is returned only if a
random draw from a uniform distribution ranging from 0 to 1 is less
than p. This function therefore simulates a draw from a
Bernoulli distribution. A nil
value of p is treated as
1/2.
u_cont
takes a floating point number x as an
argument, and returns a random draw from a continuous uniform
distribution ranging from 0 to x. A nil
value of x
is treated as unity.
u_disc
simulates a draw from a uniform discrete distribution whose domain is the set of natural numbers from 0 to n - 1. The number n is given as a parameter to this function, and the retuned value is the draw.
u_path
takes a pair of natural numbers (n,m)
and returns a
randomly chosen tree (Raw Material) with 1 leaf and n
non-leaves each having either a left or a right descendent but not
both. The number m constrains the result to fall within the
first m - 1 trees of this form enumerated by exhausting all
possibilities at lower levels before admitting a right descendent at a
higher level. Within these criteria, all possible results are equally
probable. Both numbers are masked to 64 bits, but if m is zero,
it is treated as 2^n.
u_enum
simulates a random draw from a uniform discrete distribution whose domain is enumerated. The argument to the function is a non-empty list, and the result is an item selected from the list, with all choices being equally probable.
w_disc
simulates a random draw from a non-uniform, or “weighted” discrete distribution whose domain is a set of consecutive natural numbers starting from zero. The argument to the function is a list giving the probability of each outcome starting from zero as a floating point number. Probabilities must be non-negative but needn’t be normalized.
w_enum
simulates a random draw from a non-uniform, or “weighted” discrete
distribution with an arbitrary domain enumerated in the argument. The
argument is a list of pairs <(x,p)..>
, where
x is a possible outcome and p is its probability. The
result returned is one of the values of x from the input list
chosen at random according to the associated
probability. Probabilities must be non-negative but needn’t be
normalized.
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