Modified random distribution

Given a random number generator, (rng), generating numbers in the range 0.0 .. 1.0 called rgen, for example; and a function modifier(x) taking an number in the same range and generating the probability that the input should be generated, in the same range 0..1; then implement the following algorithm for generating random numbers to the probability given by function modifier:

while True:
    random1 = rgen()
    random2 = rgen()
    if random2 < modifier(random1):
        answer = random1
        break
    endif
endwhile

Task

Create a modifier function that generates a 'V' shaped probability of number generation using something like, for example:

modifier(x) = 2*(0.5 - x) if x < 0.5 else 2*(x - 0.5)

Create a generator of random numbers with probabilities modified by the above function.
Generate >= 10,000 random numbers subject to the probability modification.
Output a histogram with from 11 to 21 bins showing the distribution of the random numbers generated.

Show your output here, on this page.

Python

<lang python>import random from typing import List, Callable, Optional

def modifier(x: float) -> float:

   """
   V-shaped, modifier(x) goes from 1 at 0 to 0 at 0.5 then back to 1 at 1.0 .

   Parameters
   ----------
   x : float
       Number, 0.0 .. 1.0 .

   Returns
   -------
   float
       Target probability for generating x; between 0 and 1.

   """
   return 2*(.5 - x) if x < 0.5 else 2*(x - .5)

def modified_random_distribution(modifier: Callable[[float], float],

                                n: int) -> List[float]:
   """
   Generate n random numbers between 0 and 1 subject to modifier.

   Parameters
   ----------
   modifier : Callable[[float], float]
       Target random number gen. 0 <= modifier(x) < 1.0 for 0 <= x < 1.0 .
   n : int
       number of random numbers generated.

   Returns
   -------
   List[float]
       n random numbers generated with given probability.

   """
   d: List[float] = []
   while len(d) < n:
       r1 = prob = random.random()
       if random.random() < modifier(prob):
           d.append(r1)
   return d

if __name__ == '__main__':

   from collections import Counter

   data = modified_random_distribution(modifier, 50_000)
   bins = 15
   counts = Counter(d // (1 / bins) for d in data)
   #
   mx = max(counts.values())
   print("   BIN, COUNTS, DELTA: HISTOGRAM\n")
   last: Optional[float] = None
   for b, count in sorted(counts.items()):
       delta = 'N/A' if last is None else str(count - last)
       print(f"  {b / bins:5.2f},  {count:4},  {delta:>4}: "
             f"{'#' * int(40 * count / mx)}")
       last = count</lang>

Output:

   BIN, COUNTS, DELTA: HISTOGRAM

   0.00,  6326,   N/A: ########################################
   0.07,  5327,  -999: #################################
   0.13,  4487,  -840: ############################
   0.20,  3495,  -992: ######################
   0.27,  2601,  -894: ################
   0.33,  1744,  -857: ###########
   0.40,   914,  -830: #####
   0.47,   225,  -689: #
   0.53,   899,   674: #####
   0.60,  1783,   884: ###########
   0.67,  2623,   840: ################
   0.73,  3566,   943: ######################
   0.80,  4383,   817: ###########################
   0.87,  5422,  1039: ##################################
   0.93,  6205,   783: #######################################