generator

问题 I want to override the default model file that's generated with rails generate model . I've created a template based on this file, but I can't figure out where to put it. Other answers seem to suggest /lib/templates/rails/model/model.rb or /lib/templates/rails/model/model_generator.rb , but neither of those do anything - I put the template in that location but when I run rails generate model ModelName it gets ignored. Am I going about this the right way? Where should I put the template? 回答1:

问题 I am trying to create a generator that returns numbers in a given range that pass a particular test given by a function foo . However I would like the numbers to be tested in a random order. The following code will achieve this: from random import shuffle def MyGenerator(foo, num): order = list(range(num)) shuffle(order) for i in order: if foo(i): yield i The Problem The problem with this solution is that sometimes the range will be quite large ( num might be of the order 10**8 and upwards).

问题 I am trying to create a generator that returns numbers in a given range that pass a particular test given by a function foo . However I would like the numbers to be tested in a random order. The following code will achieve this: from random import shuffle def MyGenerator(foo, num): order = list(range(num)) shuffle(order) for i in order: if foo(i): yield i The Problem The problem with this solution is that sometimes the range will be quite large ( num might be of the order 10**8 and upwards).

问题 How can I use multiprocessing to create a dictionary with generator objects as values? Here is my problem in greater detail, using basic examples: I have a large dictionary of lists whereby I am applying functions to compute on the dictionary values using ProcessPoolExecutor in concurrent.futures . (Note I am using ProcessPoolExecutor , not threads---there is no GIL contention here.) Here is an example dictionary of lists: example_dict1 = {'key1':[367, 30, 847, 482, 887, 654, 347, 504, 413,

问题 Is there any tool able to create an AVRO schema from a 'typical' JSON document. For example: { "records":[{"name":"X1","age":2},{"name":"X2","age":4}] } I found http://jsonschema.net/reboot/#/ which generates a ' json-schema ' { "$schema": "http://json-schema.org/draft-04/schema#", "id": "http://jsonschema.net#", "type": "object", "required": false, "properties": { "records": { "id": "#records", "type": "array", "required": false, "items": { "id": "#1", "type": "object", "required": false,

问题 I'm trying to train an LSTM model on daily fundamental and price data from ~4000 stocks, due to memory limits I cannot hold everything in memory after converting to sequences for the model. This leads me to using a generator instead like the TimeseriesGenerator from Keras / Tensorflow. Problem is that if I try using the generator on all of my data stacked it would create sequences of mixed stocks, see the example below with a sequence of 5, here Sequence 3 would include the last 4

问题 What's the simplest way I can use flow_from_directory in Keras while limiting the number of examples used in each subdirectory by some number N ? For context, I'd like to be able to use a small subset of the total images for testing purposes without having to create a separate top level directory for the smaller dataset, since I'm pulling this data from AWS S3 buckets during training. 回答1: Create keras.preprocessing.image.ImageDataGenerator with argument validation_split specified as float.

问题 I have the following code, where I try to parallelize: import numpy as np from joblib import Parallel, delayed lst = [[0.0, 1, 2], [3, 4, 5], [6, 7, 8]] arr = np.array(lst) w, v = np.linalg.eigh(arr) def proj_func(i): return np.dot(v[:,i].reshape(-1, 1), v[:,i].reshape(1, -1)) proj = Parallel(n_jobs=-1)(delayed(proj_func)(i) for i in range(len(w))) proj returns a really large list and it's causing memory issues. Is there a way I could work around this? I had thought about returning a

问题 I have the following code, where I try to parallelize: import numpy as np from joblib import Parallel, delayed lst = [[0.0, 1, 2], [3, 4, 5], [6, 7, 8]] arr = np.array(lst) w, v = np.linalg.eigh(arr) def proj_func(i): return np.dot(v[:,i].reshape(-1, 1), v[:,i].reshape(1, -1)) proj = Parallel(n_jobs=-1)(delayed(proj_func)(i) for i in range(len(w))) proj returns a really large list and it's causing memory issues. Is there a way I could work around this? I had thought about returning a

问题 I'm trying to create a random password generator that asks for the user input on size 8-128 characters and then confirms the use of uppercase, lowercase, symbols, and or numbers. I'm trying to get the password to generate and print in the text area and i know i'm missing something but I'm not exactly sure what. I apologize for the rough code. I'm just starting out. var plength = prompt("How many characters would you like your password to be?") if (plength < 8 || plength > 128){ alert("Length