"""returns a random int between 1 and 6""" ed(0) # Fixed seed, always the same.īefore looking at what is the probability of an event, we define a function that simulates the roll of a six-faced die: If the seed is fixed, the number generated “randomly” will be always the same.ĭeterministically generates and returns an even number We can use this property to generate deterministic numbers. This is very useful for debugging purpose but means also that one needs to be very careful when choosing the seed (for example, choosing atmospheric signals or other noises). Generally, in applications such as in security applications, hardware generators are generally preferred over software algorithm.Ī pseudo-random algorithms, like the Python random library above, is called pseudo because is not really unpredictable: the sequence of random numbers generated depends on the initial seed: using the same number as seed will generate the same sequence. Stochastically generates and returns a uniformly distributed even Returns a random even number x, where 0 <= x < 100 We generate a (pseudo) random number in Python using the random library: As usual they are also available in a notebook. Let’s see some practical examples with Python. Individual random events are by definition unpredictable, but in many cases the frequency of different outcomes over a large number of events is predictable.Īnd this is what is interesting for us: if I throw a die with six faces thousands of times, how many times in percent shall I expect to see the face number six? A random sequence of events therefore has no order and does not follow an intelligible combination. Randomly generated is a big area by itself, for our scope is enough to say that randomness is the lack of pattern or predictability in events. On the other side, deterministic means that the outcome – given the same input – will always be the same. It originally came from Greek στόχος (stokhos), meaning ‘aim, guess’. The English word stochastic is an adjective describing something that was randomly determined. To see examples in Python we need first to introduce the concept of random numbers. To make it more interesting, I am mixing in it some MonteCarlo simulation ideas too! This is a post to introduce a couple of probability concepts that are useful for machine learning.
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