## Does my code produce a pseudorandom or random number?

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```import time

point1 = time.time()

point2 = time.time()

point3 = time.time()

random_number = str((point1 + point2 - point3) * point1)

print(random_number[7])
print(random_number[8])
print(random_number[9])
print(random_number[10])
```

It was my homework to design an algorithm that prodeces pseudorandom numbers. Would the above code be concidered to be pseudorandom? I am assuming it could be called that because the number outcome is unpredictable but i just wanted to make sure. Also, how do i print all the numbers on the same line? When i do `print(random_number[10][7][8][9])`, i get an error. I dont want to print out all the numbers because the first few are almost always the same. Thanks for helping.

i would definetly call those pseudorandom, there is no randomizing involved, because you are just using a clock, which is definetly predictable, although it's hard to predict the microseconds on execution.

Depending on the time your last digits might be 0 and get stripped, that's the reason why you are running into an `IndexError` exception.

Pseudorandom number generator, A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers� When you use Pseudorandom numbers in statistics, the only thing you care about is the sequence of numbers you get from generator passes the bunch of statistical test and whether it’s distributed like random numbers. In Java there are 3 ways to generate the pseudorandom Number as below – Math.random() util.Random.nextInt()

The simple answer is "no": you have not produced either random or pseudo-random numbers. Search on line for "random number generation" ... which you should have done before writing your program, let alone posting here.

The three "point" variables are highly dependent on one another; for all practical purposes, you've taken the present time in seconds, cubed it, and extracted four consecutive digits from the product. Since the digits of cubes are not "nicely" distributed, you should find that your do not have a uniform distribution over the ten digits, let alone a pseudo-random sequence.

Random number generation, True" vs. pseudo-random numbers There are two principal methods used to generate random numbers. The The speed at which entropy can be harvested from natural� Linear Congruential Generator is most common and oldest algorithm for generating pseudo-randomized numbers. The generator is defined by the recurrence relation: X n+1 = (aX n + c) mod m where X is the sequence of pseudo-random values m, 0 < m - modulus a, 0 < a < m - multiplier c, 0 ≤ c < m - increment x 0, 0 ≤ x 0 < m - the seed or start value

Since the code does not read from any physical source of randomness (a reverse-biased diode, Geiger counter, weather patterns, a lava lamp), it is the result of a deterministic process, and therefore is at best pseudo-random. Because the process you chose has no mathematical foundation to explain its distribution or other properties, it is likely to be not pseudo-random either, or very weakly pseudo-random.

random — Generate pseudo-random numbers — Python 3.8.5 , Source code: Lib/random.py. This module implements pseudo-random number generators for various distributions. For integers The Mersenne Twister is one of the most extensively tested random number generators in existence. However � A random number generator helps to generate a sequence of digits that can be saved as a function to be used later in operations. Random number generator doesn’t actually produce random values as it requires an initial value called SEED. Random number generation can be controlled with SET.SEED() functions.

How can a totally logical computer generate a random number , I read the section in the GPS article about "pseudo-random numbers," and I have heard about computers generating random numbers before. How can a totally� There are two categories of random numbers — “true” random numbers and pseudorandom numbers — and the difference is important for the security of encryption systems. Computers can generate truly random numbers by observing some outside data, like mouse movements or fan noise, which is not predictable, and creating data from it.

Random numbers are not Random!. T he truth is that no body tolds , The random number classes or libraries that we use in our code are not a pseudo-random number generator typically produces an integer on� For example, consider binomial random numbers. A binomial random number is the number of heads in N tosses of a coin with probability p of a heads on any single toss. If you generate N uniform random numbers on the interval (0,1) and count the number less than p, then the count is a binomial random number with parameters N and p.

Guide on a Random Number Generator C++: The Use of C++ Srand, The random number generator in C++ is a program that generates seemingly random numbers. The following example of the C++ code shows how the roll dice feature lets you Thus, the selection is referred to as pseudo-random. Learning how to generate random numbers in C++ is not difficult (but you� Pseudorandom Numbers vs True Random Numbers Pseudorandom numbers depend on a random factor known as a seed to improve their randomness. In many cases, these are taken from the physical world. For example, recent touchscreen input or the state of a physical device such as a hard drive may be used.

• `print(random_number[7], random_number[8], random_number[9], random_number[10])` or `print(random_number[7:11])`
• Pseudorandom because, according to Wikipedia "A pseudorandom process produces predictable outcomes given information which is typically difficult to acquire; absent such information, pseudorandom sequences of numbers exhibit statistical randomness." If you want to print what's in your `random_number` variable just print it.
• The `IndexError` is because you've tried to access a 4D structure, which you do not have.