You can use numpy’s built-in function var: import numpy as np results = [-14.82381293, -0.29423447, -13.56067979, -1.6288903, -0.31632439, 0.53459687, -1.34069996, -1.61042692, -4.03220519, -0.24332097] print(np.var(results)) This gives you 28.822364260579157 If – for whatever reason – you cannot use numpy and/or you don’t want to use a built-in function for it, you can also calculate it “by … Read more
The problem is that GenericTraversableTemplate is used twice: once for mutable collections (where its type parameter should be invariant), and once for immutable collections (where covariance is invariably king). GenericTraversableTemplate’s typechecks assuming either covariance or invariance for the A type parameter. However, when we inherit it in a mutable trait, we have to pick invariance. … Read more
It’s certainly related to polymorphism. I wouldn’t say they’re just “another word” for polymorphism though – they’re about very specific situations, where you can treat one type as if it were another type in a certain context. For instance, with normal polymorphism you can treat any reference to a Banana as a reference to a … Read more
Q[A <: B] means that class Q can take any class A that is a subclass of B. Q[+B] means that Q can take any class, but if A is a subclass of B, then Q[A] is considered to be a subclass of Q[B]. Q[+A <: B] means that class Q can only take subclasses … Read more
I’ve run across this problem as well. There are some great posts out there in computing the running cumulative variance such as John Cooke’s Accurately computing running variance post and the post from Digital explorations, Python code for computing sample and population variances, covariance and correlation coefficient. Just could not find any that were adapted … Read more