In Python, How to put constraits on four random numbers making two negative and two positive and equating to 0?
How do i make two random negative numbers and two positive random numbers equal zero.For example,
array([-.7,-.2,.8,.10]) = 0. [-.3,.2,-.5,.6] = 0.
The numbers are constrained between -1 and 1. Size is 4 with 2 neg,2 pos. I dont want np.uniform answers I need 4 random nums in arr,arr,arr,arr = 0
equate_to_zero = np.random.rand(4) equate_to_zero = np.random.randint(-1,1,4)
You can do the following:
import random numbers = [random.random(), random.random()] third = 1 while third < -1 or sum(numbers) + third > 1: third = -random.random() * sum(numbers) numbers.append(third) numbers.append(-sum(numbers)) print(numbers)
8.6. random — Generate pseudo-random numbers — Python v3.2.6 , Almost all module functions depend on the basic function random(), which generates a It produces 53-bit precision floats and has a period of 2**19937-1. Return a k length list of unique elements chosen from the population sequence or set. infinity if lambd is positive, and from negative infinity to 0 if lambd is negative. To generate a random number in python, you can import random module and use the function randInt(). randInt() takes two integers as arguments for deciding the range from which random number has to be picked.
9.6. random — Generate pseudo-random numbers — Python v3.1.5 , For sequences, uniform selection of a random element, a function to generate a random permutation of a list in-place, and a function for random sampling without replacement. It produces 53-bit precision floats and has a period of 2**19937-1 . infinity if lambd is positive, and from negative infinity to 0 if lambd is negative. The random() method in random module generates a float number between 0 and 1. Example import random n = random.random() print(n) Output. Running the above code gives us the following result − 0.2112200 Generating Number in a Range. The randint() method generates a integer between a given range of numbers. Example import random n = random
You can use
import random x=random.uniform(-1,1) y=random.uniform(-1,1) output = [x,-x,y,-y] print(output,sum(output))
Output of this program is:
[-0.9694091718268465, 0.9694091718268465, -0.6982058977316767, 0.6982058977316767] 0.0
random — Generate pseudo-random numbers — Python 3.8.5 , Almost all module functions depend on the basic function random() , which generates a It produces 53-bit precision floats and has a period of 2**19937-1. weights before making selections, so supplying the cumulative weights saves work. infinity if lambd is positive, and from negative infinity to 0 if lambd is negative. Generate Random Numbers using Python. In this article, I will explain the usage of the random module in Python. As the name implies it allows you to generate random numbers. This random module contains pseudo-random number generators for various distributions. The function random() is one of them, it generates a number between 0 and 1.
pick 1 random negative
from random import uniform neg1 = uniform(-1,0)
pick one random positive
pos1 = uniform(0,1)
your second negative must meet the condition of n1 + n2 < p1 - 1.0
neg2 = uniform(min(pos1+1-abs(neg1),1)*-1,0)
second positive MUST be the difference
pos2 = abs(neg1) + abs(neg2) - pos1 numbers = [neg1,neg2,pos1,pos2]
9.6. random — Generate pseudo-random numbers — Python 2.7.18 , It produces 53-bit precision floats and has a period of 2**19937-1. each thread, and using the jumpahead() method to make it likely that the uses the system function os.urandom() to generate random numbers 0 to positive infinity if lambd is positive, and from negative infinity to 0 if lambd is negative. Using the random module, we can generate pseudo-random numbers. The function random() generates a random number between zero and one [0, 0.1 .. 1]. Numbers generated with this module are not truly random but they are enough random for most purposes. Related Course: Python Programming Bootcamp: Go from zero to hero Random number between 0 and 1.
Since there's no solution without fiddling here or there, that'd be my approach: Pick two random negative numbers, pick two random positive numbers, and subtract the mean of the four values. Both negative numbers will stay negative, both positive numbers will stay positive. The only possible unwanted behavior I can think of, is that one of the negative values becomes
< -1 resp. one of the positive values becomes
> 1 after subtracting the mean. In that case, just pick four new values.
import numpy as np while True: numbers = np.hstack((np.random.rand(2), -np.random.rand(2))) numbers -= np.mean(numbers) print(numbers) print(np.sum(numbers)) if np.all(np.abs(numbers) <= 1): break
Output for the described case with unwanted behavior:
[ 0.75018366 0.82691508 -0.55406187 -1.02303687] 2.220446049250313e-16 [ 0.57616847 0.6875385 -0.29527166 -0.9684353 ] -2.220446049250313e-16
Gladly, this case doesn't occur that often, especially for more than two consecutive occurrences.
Hope that helps!
Python random randrange() & randint() to get Random Integer, Python random randrange() and randint() to generate random integer Create a list of random integers; Generate a secure random integer 20, 2) will return any random number between 2 to 20, such as 2, 4, 6, …18. import random randomList =  # Set a length of the list to 10 for i in range(0, 10): # any� random.shuffle (x [, random]) ¶ Shuffle the sequence x in place. The optional argument random is a 0-argument function returning a random float in [0.0, 1.0); by default, this is the function random(). To shuffle an immutable sequence and return a new shuffled list, use sample(x, k=len(x)) instead.
Perhaps the most important thing is that it allows you to generate random numbers. When to use it? We want the computer to pick a random number in a given range Pick a random element from a list, pick a random card from a deck, flip a coin etc. When making your password database more secure or powering a random page feature of your website.
The use of randomness is an important part of the configuration and evaluation of machine learning algorithms. From the random initialization of weights in an artificial neural network, to the splitting of data into random train and test sets, to the random shuffling of a training dataset in stochastic gradient descent, generating random numbers and harnessing randomness is a required skill.
A random number from list is : 4 A random number from range is : 41 3. random():- This number is used to generate a float random number less than 1 and greater or equal to 0. 4. seed():- This function maps a particular random number with the seed argument mentioned. All random numbers called after the seeded value returns the mapped number.
- Interesting. They won't be random but the examples in the OP posted are not random either.
- Nothing in this world is random my friend!
- @Solen'ya good answer but as stated I need random numbers not uniform. Even though I showed uniform in my post.
- There is no guarantee that the second positive will be between 0 and 1.
pos1 = random.uniform(max(0, sum_of_negatives - 1), min(1, sum_of_negatives))seems like a good bet.
- lol yeah i think theres no way to ensure
n1+n2 < p1-1unless you pick
p1see final answer ...
- how could I change this code to equate to 1 with 2 random neg and 2 pos?