Examples of java.util.Random.nextFloat()

java.util.Random.nextFloat()
Returns the next pseudorandom, uniformly distributed {@code float}value between {@code 0.0} and {@code 1.0} from this randomnumber generator's sequence.
The general contract of {@code nextFloat} is that one{@code float} value, chosen (approximately) uniformly from therange {@code 0.0f} (inclusive) to {@code 1.0f} (exclusive), ispseudorandomly generated and returned. All 2²⁴ possible {@code float} valuesof the form m x 2^-24, where m is a positive integer less than 2²⁴ , are produced with (approximately) equal probability.
The method {@code nextFloat} is implemented by class {@code Random}as if by:
```
  {@code}public float nextFloat()  return next(24) / ((float)(1 << 24)); }}
```
The hedge "approximately" is used in the foregoing description only because the next method is only approximately an unbiased source of independently chosen bits. If it were a perfect source of randomly chosen bits, then the algorithm shown would choose {@code float}values from the stated range with perfect uniformity.
[In early versions of Java, the result was incorrectly calculated as:
```
  {@code return next(30) / ((float)(1 << 30));}
```
This might seem to be equivalent, if not better, but in fact it introduced a slight nonuniformity because of the bias in the rounding of floating-point numbers: it was slightly more likely that the low-order bit of the significand would be 0 than that it would be 1.] @return the next pseudorandom, uniformly distributed {@code float}value between {@code 0.0} and {@code 1.0} from thisrandom number generator's sequence

    tree.insert(new float[]{8.0f, 6.0f}, 4);


    Random rand = new Random();
    for (int i = 5; i < 5000; i++) {
      tree.insert(
          new float[]{(float) (rand.nextFloat() * 10 + 10.0), (float) (rand.nextFloat() * 10 + 10.0)},
          i
      );
    }


    ImmutableRTree searchTree = ImmutableRTree.newImmutableFromMutable(tree);

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    tree.insert(new float[]{8.0f, 6.0f}, 4);


    Random rand = new Random();
    for (int i = 5; i < 5000; i++) {
      tree.insert(
          new float[]{(float) (rand.nextFloat() * 10 + 10.0), (float) (rand.nextFloat() * 10 + 10.0)},
          i
      );
    }


    ImmutableRTree searchTree = ImmutableRTree.newImmutableFromMutable(tree);

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    tree.insert(new float[]{8.0f, 6.0f}, 4);


    Random rand = new Random();
    for (int i = 5; i < 5000; i++) {
      tree.insert(
          new float[]{(float) (rand.nextFloat() * 10 + 10.0), (float) (rand.nextFloat() * 10 + 10.0)},
          i
      );
    }


    ImmutableRTree searchTree = ImmutableRTree.newImmutableFromMutable(tree);

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    tree.insert(new float[]{8.0f, 6.0f}, 4);


    Random rand = new Random();
    for (int i = 5; i < 5000; i++) {
      tree.insert(
          new float[]{(float) (rand.nextFloat() * 10 + 10.0), (float) (rand.nextFloat() * 10 + 10.0)},
          i
      );
    }


    ImmutableRTree searchTree = ImmutableRTree.newImmutableFromMutable(tree);

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  {
    BitmapFactory bf = new ConciseBitmapFactory();
    RTree tree = new RTree(2, new LinearGutmanSplitStrategy(0, 50, bf), bf);
    Random rand = new Random();
    for (int i = 0; i < 100; i++) {
      tree.insert(new float[]{rand.nextFloat(), rand.nextFloat()}, i);
    }


    Assert.assertTrue(getNumPoints(tree.getRoot()) >= tree.getSize());
  }

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  {
    BitmapFactory bf = new ConciseBitmapFactory();
    RTree tree = new RTree(2, new LinearGutmanSplitStrategy(0, 50, bf), bf);
    Random rand = new Random();
    for (int i = 0; i < 100; i++) {
      tree.insert(new float[]{rand.nextFloat(), rand.nextFloat()}, i);
    }


    Assert.assertTrue(getNumPoints(tree.getRoot()) >= tree.getSize());
  }

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  {
    BitmapFactory bf = new RoaringBitmapFactory();
    RTree tree = new RTree(2, new LinearGutmanSplitStrategy(0, 50, bf), bf);
    Random rand = new Random();
    for (int i = 0; i < 100; i++) {
      tree.insert(new float[]{rand.nextFloat(), rand.nextFloat()}, i);
    }


    Assert.assertTrue(getNumPoints(tree.getRoot()) >= tree.getSize());
  }

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  {
    BitmapFactory bf = new RoaringBitmapFactory();
    RTree tree = new RTree(2, new LinearGutmanSplitStrategy(0, 50, bf), bf);
    Random rand = new Random();
    for (int i = 0; i < 100; i++) {
      tree.insert(new float[]{rand.nextFloat(), rand.nextFloat()}, i);
    }


    Assert.assertTrue(getNumPoints(tree.getRoot()) >= tree.getSize());
  }

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  @Test
  public void testSplitOccurs()
  {
    Random rand = new Random();
    for (int i = 0; i < 100; i++) {
      tree.insert(new float[]{rand.nextFloat(), rand.nextFloat()}, i);
    }


    Assert.assertTrue(tree.getRoot().getChildren().size() > 1);
  }

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  @Test
  public void testSplitOccurs()
  {
    Random rand = new Random();
    for (int i = 0; i < 100; i++) {
      tree.insert(new float[]{rand.nextFloat(), rand.nextFloat()}, i);
    }


    Assert.assertTrue(tree.getRoot().getChildren().size() > 1);
  }

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