evaluation

Edit Ok, since there seems to be a lot of confusion, I'm going to simplify the question a little. You can try to answer the original question below, or you can tackle this version instead and ignore everything below the line. My goal is to take an arbitrary expression and evaluate it in an extremely restricted environment. This environment will contain only variables with the following types of values: Numeric vectors Pure functions that take one or more numeric vectors and return numeric vectors (i.e. arithmetic operators) In addition, the expression would necessarily be able to use any

I have heard of deferred evaluation in python (for example here ), is it just referring to how lambdas are evaluated by the interpreter only when they are used? Or is this the proper term for describing how, due to python's dynamic design, it will not catch many errors until runtime? Or am I missing something entirely? Dietrich's answer is a good one, but I just want to add that the simplest form of deferred evaluation is the if statement: if True: x = 5 else: x = y # huh? what is y? This code parses and runs correctly, although the else clause makes no sense - y is undefined. The else clause

I describe this question by using an example from a book. In Simon Thompson's book "HASKELL the craft of functional programming" on page 82 (see images below) are shown the evaluation steps for fac 4 . QUESTION: Is it possible to use some tool or some "Haskell debugger" that would write out the evaluation steps that GHCi uses when it would evaluate the value of fac 4 ? Preferably in a human readable format, for educational and learning purposes. It would be also good to have some automatic explanation for each evaluation step, for example which equation was used in the rewriting step. My main

As far as I know, eager evaluation/applicative order evaluates all arguments to a function before applying it, on the other hand, lazy evaluation/normal order evaluates the arguments only when needed. So, what are the differences between the pair of terms eager evaluation and applicative order , and lazy evaluation and normal order ? Thanks. Lazy evaluation evaluates a term at most once, while normal order would evaluate it as often as it appears. So for example if you have f(x) = x+x and you call it as f(g(42)) then g(42) is called once under lazy evaluation or applicative order, but twice

I just created a model using scikit-learn which estimates the probability of how likely a client will respond to some offer. Now I'm trying to evaluate my model. For that I want to plot the lift chart. I understand the concept of lift, but I'm struggling to understand how to actually implement it in python. Lift/cumulative gains charts aren't a good way to evaluate a model (as it cannot be used for comparison between models), and are instead a means of evaluating the results where your resources are finite. Either because there's a cost to action each result (in a marketing scenario) or you

It is generally accepted that setting measurable objectives for software developers doesn't work , as too much focus on the objectives can lead to behaviour counter to the organisational goals (so-called " measurement dysfunction "). However, in my company, we are required to set objectives for all staff, and are encouraged by Human Resources to make them SMART . In the past, my fellow first-level managers (team leads) and I have tried a number of approaches: Set measurable objectives that are additional to the normal job, like "Do training on technology X", "Create documentation for piece of