Numpy random - Specify Multiple Bounds & Steps

Question

I need to generate the initial population of a genetic algorithm. Consider the following vector:

[20, 2, 20, 1.5, 5, 20, 5, 0.5, -0.5, 5, 20, 5, 3, 14, 70, 30, 10, 5, 5, 20, 8, 20, 2.5]

I would do this:

new_population = numpy.random.uniform(low=0.1, high=50.0, size=pop_size)

The problem is, some of the chromosomes in the problem space have different steps and different maximum values. Element 0 should be 1-100 with a step of 1 (So int). Element 3 should be 0.1-10 with a step of 0.1 (Float). What is the easiest way to do this randomization?

snus74 · Accepted Answer · 2019-04-08 12:17:29Z

1

Since it seems that the ranges for your chromosomes are hard-coded, I suggest you generate all the numbers with only one numpy.random.uniform() with the smallest range you need i.e 0.1-10 in your example and then you multiply this obtained number by the following ratio:

wanted_range/base_range

In your example you would multiply by 10. ( note that the ratios between the steps and ranges has to be the same for this method)

answered Apr 8, 2019 at 12:17

snus74

4793 silver badges9 bronze badges

Sign up to request clarification or add additional context in comments.

2 Comments

user3053452 Over a year ago

this way you might encounter problems where ratio of step vs bounds will not match.

snus74 Over a year ago

you're right, I was still finishing to edit my answer

user3053452 · Accepted Answer · 2019-04-08 12:30:54Z

1

You didnt give enough data to see any pattern for shorter code.

However you could do the following: Make a list of lists where each sublist in composed of the following elements: bounds = [[low, high, step], ...]

Then initialize an empty numpy array, i.e. new_population = np.empty(23) And after that you can just loop through bounds with for loop and generate each element:

for i, value in enumerate(bounds):
    new_population[i] = np.random.uniform((low=value[0], high=value[1], size=value[2])

answered Apr 8, 2019 at 12:30

user3053452

6741 gold badge14 silver badges39 bronze badges

Comments

Simply Beautiful Art · Accepted Answer · 2021-04-25 00:04:16Z

0

The numpy.vectorize decorator allows you to easily define functions which act over arrays of values, one element at a time. You can define your specific case as

@np.vectorize
def vectorized_random(low, high, step):
    # whatever kind of random value you want

which can be directly used over arrays of inputs.

>>> vectorized_random([1, 1, 0.1], [100, 10, 10], [1, 1, 0.1])
array([...])

answered Apr 25, 2021 at 0:04

Simply Beautiful Art

1,88721 silver badges18 bronze badges

Collectives™ on Stack Overflow

Numpy random - Specify Multiple Bounds & Steps

3 Answers 3

2 Comments

Comments

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

3 Answers 3

2 Comments

Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Related