What are some cool things you can do with yield?

What are the interesting, cool, and unexpected things you can do with the generator (Generator) and yield?
Unlimited programming languages, such as Python, JavaScript, and more.

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One of the most likely uses of yield in JavaScript is to implement asynchronous operations such as Promise/thunk, such as the famous Tj/co GitHub , so it's not an "unexpected" thing anymore.
After understanding the characteristics of Generator, it is simple to implement a toy version of CO:

function async(generator) {  return new Promise(function(resolve, reject) {    var g = generator()    function next(val) {      var result = g.next(val)      var value = result.value      if (!result.done) {        value.then(next).catch(reject)      }      else {        resolve(value)      }    }    next()  })}

The most typical is not async/await?


Do not understand yield how to achieve async/await, use C # code to try to give an example:

IEnumerable> SomeAsyncMethod(){  //blabla  yield return await( asyncMethod, context );  //blabla  yield return await( asyncMethod, context );  //blabla}

Can do animation ah, effect

#-*-Coding:utf-8-*-Import NumPy  as NPImport Matplotlib.pyplot  as PLTImport matplotlib.animation  as AnimationImport Math, Random# Libraries to install: NumPy and matplotlib, recommended direct AnacondaFig, axes1 = PLT.Subplots()# Set the axis lengthaxes1.Set_ylim(0, 1.4)axes1.Set_xlim(0, 1*NP.Pi/0.01)# Set initial x, y array of valuesXData = NP.Arange(0, 2*NP.Pi, 0.01)Ydata = NP.Sin(XData)# Get Lines Line, = axes1.plot(XData)# burr magnification, starting from 0, the greater the size of the offset .Offset = 0.0#因为update的参数是调用函数data_gen, so the first default parameter cannot be a framenumdef Update(Data):    Global Offset     Line.Set_ydata(Data)    return  Line,# Generate 10 random data at a time# Every time you change the whole picture, yield a whole picture.def Data_gen():    Global Offset     while True:        length = float(Len(XData))         for I inch Range(Len(XData)):            Ydata[I]=Math.Sin(XData[I])+0.2            if I>length/18.0  and I<(length*2.7/6.0):                Ydata[I]+=Offset*(Random.Random()-0.5)        Offset += 0.05        #可以设置offset的最大值        if Offset>=0.5:           Offset=0.0        yield Ydata# Configuration complete, start playbackANI = Animation.funcanimation(Fig, Update, Data_gen, interval= -, Repeat=True)PLT.Show()

Simulation of discrete events, and a more concise and elegant way?

Overview-simpy 3.0.8 Documentation This is a question for me.

When someone claims to have achieved a sandbox in CPython, you can use yield to tease him, I was looking through the code and saw someone submitted this but didn ' t run it: ...

Cool to no work ... A Curious Course on Coroutines and Concurrency Can write a concurrent library
Generator Tricks for Systems programmers You can write a flow-processing framework see David Beazley several times Pycon PDF, I was shocked to read it. http://www. dabeaz.com Can be used to train neural networks.
Like Lasagne/lasagne. GitHub A sample code in:

def Train(Iter_funcs, DataSet, batch_size=batch_size):    "" "Train the Model with a ' dataset ' with Mini-batch training. eachMini-batch has ' batch_size ' recordings.    """    Num_batches_train = DataSet[' Num_examples_train '] // batch_size    Num_batches_valid = DataSet[' Num_examples_valid '] // batch_size     for Epoch inch Itertools.Count(1):        batch_train_losses = []         for b inch Range(Num_batches_train):            Batch_train_loss = Iter_funcs[' Train '](b)            batch_train_losses.Append(Batch_train_loss)        Avg_train_loss = NP.mean(batch_train_losses)        batch_valid_losses = []        batch_valid_accuracies = []         for b inch Range(Num_batches_valid):            Batch_valid_loss, batch_valid_accuracy = Iter_funcs[' valid '](b)            batch_valid_losses.Append(Batch_valid_loss)            batch_valid_accuracies.Append(batch_valid_accuracy)        Avg_valid_loss = NP.mean(batch_valid_losses)        avg_valid_accuracy = NP.mean(batch_valid_accuracies)        yield {            ' number ': Epoch,            ' Train_loss ': Avg_train_loss,            ' Valid_loss ': Avg_valid_loss,            ' Valid_accuracy ': avg_valid_accuracy,        }

Tornado is the use of the generator implementation of the co-process (Coroutine) model, coupled with the event loop to achieve high concurrency using iterators to traverse the binary tree.

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