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使用事件委派实现事件处理是一种常见的优化技术，它可以帮助我们减少事件绑定的数量，提高性能并改善代码结构。在 jQuery 中，我们可以通过使用 .on() 方法和事件冒泡来实现事件委派。下面我将详细解释这个方法的用法，并提供相应的源代码示例。
事件委派是一种将事件处理程序绑定到一个父元素上，以代替将处理程序绑定到每个子元素的技术。这意味着我们可以利用事件冒泡的特性，通过在父元素上捕获事件并根据目标元素的选择器来处理事件。
首先，我们需要选择一个合适的父元素，它将成为我们委派事件的目标。可以选择文档中的任何元素，但最好选择最接近目标元素的共同父元素，以限定事件处理程序的范围并提高性能。
一旦选择了父元素，我们可以使用 .on() 方法来绑定事件处理程序。.on() 方法接受两个参数：事件类型和事件处理程序。但是，我们还可以传递一个可选的第三个参数，即选择器，它用于过滤事件目标。
下面是一个示例，演示如何使用事件委派在点击列表项时改变其背景颜色：
// HTML
<ul id="myList">
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plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; 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nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
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