generator

问题 Is there any way to generate a unique random number that has 8 bytes size in python language? I used the UUID library but it has 16 bytes which are not aligned with my requirement. Any help would be much appreciated. Thanks in advance 回答1: Well, you could use Linear Congruential Generator which, with proper selection of parameters, produce perfect mapping from u64 to u64. In other words, if you have access to previous 8bytes UUID, you could generate reasonable random next 8bytes UUID WITHOUT

问题 Consider a generator in Julia that if collected will take a lot of memory g=(x^2 for x=1:9999999999999999) I want to take a random small subsample (Say 1%) of it, but I do not want to collect() the object because will take a lot of memory Until now the trick I was using was this temp=collect((( rand()>0.01 ? nothing : x ) for x in g)) random_sample= temp[temp.!=nothing] But this is not efficient for generators with a lot of elements, collecting something with so many nothing elements doesnt

问题 I am using the scholarly module in python to search for a keyword. I am getting back a generator object as follows: import pandas as pd import numpy as np import scholarly search_query = scholarly.search_keyword('Python') print(next(search_query)) {'_filled': False, 'affiliation': 'Juelich Center for Neutron Science', 'citedby': 75900, 'email': '@fz-juelich.de', 'id': 'zWxqzzAAAAAJ', 'interests': ['Physics', 'C++', 'Python'], 'name': 'Gennady Pospelov', 'url_picture': 'https://scholar.google

问题 I want this generator to yield the cosine of each successive value from a list, but am getting the same value each time. import math angles = range(0,361,3) # calculate x coords: def calc_x(angle_list): for a in angle_list: yield round(radius * cos(radians(a)), 3) Yields the same value with each call: Why is this and how do I fix it? >>>calc_x(angles).next() 5.0 >>>calc_x(angles).next() 5.0 >>>calc_x(angles).next() 5.0 回答1: Every time you call calc_x you create a new generator. What you need

问题 I'm doing an assignment and here is what the class looks like: class GameStateNode: ''' A tree of possible states for a two-player, sequential move, zero-sum, perfect-information game. value: GameState -- the game state at the root of this tree children: list -- all possible game states that can be reached from this game state via one legal move in the game. children is None until grow is called. ''' def __init__(self, game_state): ''' (GameStateNode, GameState) -> NoneType Initialize a new

问题 I'm doing an assignment and here is what the class looks like: class GameStateNode: ''' A tree of possible states for a two-player, sequential move, zero-sum, perfect-information game. value: GameState -- the game state at the root of this tree children: list -- all possible game states that can be reached from this game state via one legal move in the game. children is None until grow is called. ''' def __init__(self, game_state): ''' (GameStateNode, GameState) -> NoneType Initialize a new

问题 Working with redux-saga, I wrote some tests. I tried to generate code coverage using istanbul.js. It works fine for most of the code but the result for the sagas is broken: seems like random information. Anyone knows how to solve this ? 回答1: I personally use Jest, which includes code coverage and it seems to work fine with my sagas. I also wrote a test utility to help testing sagas (redux-saga-testing), and in the repo, you'll find examples of tests written with Jest, Mocha and AVA. Both Jest

问题 Does Java SecureRandom.nextLong() return all possible values given it inherits from Random which uses only 48 bits? If not, can I still do it in Java maybe by modifying the Random class and how to do it? I just want to use an all random long number generator where all possible long values can be returned, if possible. 回答1: While SecureRandom inherits from Random, it doesn't use the same maths or have the same limitation. It will produce all possible 64-bit values eventually. This class

问题 I have a context decorator that has side effects when it's done. I've noticed that the side effects don't occur if I use a dict comprehension. from contextlib import contextmanager import traceback import sys accumulated = [] @contextmanager def accumulate(s): try: yield finally: print("Appending %r to accumulated" % s) accumulated.append(s) def iterate_and_accumulate(iterable): for item in iterable: with accumulate(item): yield item def boom_unless_zero(i): if i > 0: raise RuntimeError("Boom

问题 Context: I am currently working on time series prediction using Keras with Tensorflow backend and, therefore, studied the tutorial provided here. Following this tutorial, I came to the point where the generator for the fit_generator() method is described. The output this generator generates is as follows (left sample, right target): [[[10. 15.] [20. 25.]]] => [[30. 35.]] -> Batch no. 1: 2 Samples | 1 Target --------------------------------------------- [[[20. 25.] [30. 35.]]] => [[40. 45.]] -