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Python implementation: def streaming_algorithm(A, epsilon, delta): # Initialize parameters p = 1 X = set() thresh = math.ceil((12 / epsilon ** 2) * math.log(8 * len(A) / delta)) # Process the stream for ai in A: if ai in X: X.remove(ai) if random.random() = 0.5} p /= 2 if len(X) == thresh: return '⊥' return len(X) / p # Example usage A = [1, 2, 3, 1, 2, 3] epsilon = 0.1 delta = 0.01 output = streaming_algorithm(A, ep…
return '⊥' what's this?
Ah, so Thanos was just conducting a census.
Python implementation: def streaming_algorithm(A, epsilon, delta): # Initialize parameters p = 1 X = set() thresh = math.ceil((12 / epsilon ** 2) * math.log(8 * len(A) / delta)) # Process the stream for ai in A: if ai in X: X.remove(ai) if random.random() = 0.5} p /= 2 if len(X) == thresh: return '⊥' return len(X) / p # Example usage A = [1, 2, 3, 1, 2, 3] epsilon = 0.1 delta = 0.01 output = streaming_algorithm(A, ep…
Python implementation: def streaming_algorithm(A, epsilon, delta): # Initialize parameters p = 1 X = set() thresh = math.ceil((12 / epsilon ** 2) * math.log(8 * len(A) / delta)) # Process the stream for ai in A: if ai in X: X.remove(ai) if random.random() = 0.5} p /= 2 if len(X) == thresh: return '⊥' return len(X) / p # Example usage A = [1, 2, 3, 1, 2, 3] epsilon = 0.1 delta = 0.01 output = streaming_algorithm(A, ep…
Is this ChatGPT? Also, I feel like this would be more useful if it included import statements.
import math
import randomEarlier quoted context omitted.
return '⊥' what's this?
In some symbolic logic classes, that character "bottom" represents "false" ad flipped "top" means true. Don't know what they're getting at in the code, though.
Python implementation: def streaming_algorithm(A, epsilon, delta): # Initialize parameters p = 1 X = set() thresh = math.ceil((12 / epsilon ** 2) * math.log(8 * len(A) / delta)) # Process the stream for ai in A: if ai in X: X.remove(ai) if random.random() = 0.5} p /= 2 if len(X) == thresh: return '⊥' return len(X) / p # Example usage A = [1, 2, 3, 1, 2, 3] epsilon = 0.1 delta = 0.01 output = streaming_algorithm(A, ep…
Earlier quoted context omitted.
return '⊥' what's this?
In some symbolic logic classes, that character "bottom" represents "false" ad flipped "top" means true. Don't know what they're getting at in the code, though.
That's unfortunate, because in the study of computer programming languages, it means "undefined" (raise an error).
I don't know a word or phrase for this, but I really enjoy any examples of "thinking outside the box" like this because it's something I struggle with in my professional career. Learning not only the right ways to solve problems, but figuring out the questions to ask that make solving the problems you have easier or even in some cases possible. In this case, it's hey, we don't need exact numbers if we can define a pr…
If you were paid by a big company to sit at a whiteboard all day with a team of equally intelligent engineers, I'm sure you'd be come up with SOMETHING that would look like an "outside the box" solution to the rest of the world.
However, most of us are paid to work the JIRA factory line instead, which limits the amount of time we can spend experimenting on just one single problem.
Earlier quoted context omitted.
For someone who's pretty well-versed in English, but not a math-oriented computer scientist, this seems like a distinction without a difference. Please remedy my ignorance.
My GP was wrong, but the words are different. Eatimation is a procedure the generates an estimate, which is a kind of approximation, while approximation is a result value. They are different "types", as a computer scientist would say. An approximation is any value that is justifiably considered to be nearly exact. ("prox" means "near". See also "proximate" and "proxy".) Estimation is one way to generate an approximat…
Estimation is the process of estimating. It produces an estimate.
Approximation is the process of approximating. It produces an approximation.
You can also derive adjectives from the verbs as well.
An estimate is an estimated value.
An approximation is an approximate value.
But you’re right that the ‘approximate’ terms make claims about the result - that it is in some way near to the correct value - while the ‘estimate’ derived terms all make a claim about the process that produced the result (ie that it was based on data that is known to be incomplete, uncertain, or approximate)