Skip to main content

Custom cache with iterator does not work as intended

I got the following class, where:

iterable is the passed argument, like for example range(20), n_max is an optional value, which limits the numbers of elements the cache should have, iterator is a field that gets initiated with the iterable, cache is the list I am trying to fill and finished is a bool which signals if the iterator is "empty" or not. Here is an example input:

>>> iterable = range(20)
>>> cachedtuple = CachedTuple(iterable)
>>> print(cachedtuple[0])
0
>>> print(len(cachedtuple.cache))
1
>>> print(cachedtuple[10])
10
>>> print(len(cachedtuple.cache))
11
>>> print(len(cachedtuple))
20
>>> print(len(cachedtuple.cache))
20
>>> print(cachedtuple[25])


@dataclass
class CachedTuple:
    iterable: Iterable = field(init=True)
    n_max: Optional[int] = None
    iterator: Iterator = field(init=False)
    cache: list = field(default_factory=list)
    finished: bool = False

    def __post_init__(self):
        self.iterator = iter(self.iterable)

    def cache_next(self):
        
        if self.n_max and self.n_max <= len(self.cache):
            self.finished = True
        else:
            try:
                nxt = next(self.iterator)
                self.cache.append(nxt)

            except StopIteration:
                self.finished = True


    def __getitem__(self, item: int):

        match item:
            case item if type(item) != int:
                raise IndexError

            case item if item < 0:
                raise IndexError

            case item if self.finished or self.n_max and item > self.n_max:
                raise IndexError(f"Index {item} out of range")

            case item if item >= len(self.cache):
                while item - len(self.cache) >= 0:
                    self.cache_next()

                return self.__getitem__(item)

            case item if item < len(self.cache):
                return self.cache[item]


    def __len__(self):

        while not self.finished:
            self.cache_next()
        return len(self.cache)

Although this code is certainly not good, at least it works for almost every scenario, but using the range function of Python for example. If I use for example

cachedtuple = CachedTuple(range(20))
for element in cachedtuple:
    print(element)

I get the element until 19 and then the program loops infinitely. I think one problem might be that I have no raise StopIteration in my code. So I am kind of lost how to fix this mess.



source https://stackoverflow.com/questions/70793606/custom-cache-with-iterator-does-not-work-as-intended

Comments

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...