Function in loop and closure

本文探讨了JavaScript中常见的闭包与循环问题,解释了为何循环内的匿名函数会在闭包环境中导致意料之外的行为,并提供了多种解决方法。

This article describe the famious issue “function in loop and closure” in JavaScript.

The root cause is loop statements (such as for, while) don’t have their own scope.

Let’s see an example first:

    <ul>
        <li>Item1</li>
        <li>Item2</li>
        <li>Item3</li>
    </ul>
    var liNodes = document.getElementsByTagName("li");
    for (var i = 0; i < liNodes.length; i++) {
        liNodes[i].onclick = function() {
            alert("You click item " + i);
        };
    }

Now, if you click each of the list, all will produce a “You click item 3″ alert
box.

The number 3 comes out of the end execution of the loop (0, 1, 2 and out of the
loop i === 3).

Obviously, the result is not expected.

If you use JSLint to validate this piece of code, you will get the following warning:

Be careful when making functions within a loop. Consider putting the function in
a closure.

According to JSLint’s suggest, we have the first solution:

    // GOOD - 0
    function clickNode(liNode, i) {
        liNode.onclick = function() {
            alert("You click item " + i);
        };
    }  

    var liNodes = document.getElementsByTagName("li");
    for (var i = 0; i < liNodes.length; i++) {
        clickNode(liNodes[i], i);
    }

If you don’t want to create another function, consider using anonymous funtion:

    // GOOD - 1
    var liNodes = document.getElementsByTagName("li");
    for (var i = 0; i < liNodes.length; i++) {
        (function(i) {
            liNodes[i].onclick = function() {
                // You click item 0
                // You click item 1
                // You click item 2
                alert("You click item " + i);
            };
        })(i);
    }

Notice: The self-executing function create a context scope which contains a local
variable i.

When the click event occurs, the variable i is coming from the closure which is
just the self-executing function scope.

There are many ways to solve this problem, following are another three ways:

    // GOOD - 2
    var liNodes = document.getElementsByTagName("li");
    $.each(liNodes, function(i, item) {
        $(item).click(function() {
            // You click item 0
            // You click item 1
            // You click item 2
            alert("You click item " + i);
        });
    });  

    // GOOD - 3
    $("li").each(function(i, item) {
        $(item).click(function() {
            // You click item 0
            // You click item 1
            // You click item 2
            alert("You click item " + i);
        });
    });  

    // PREFERED - 4
    var liNodes = $("li").click(function(event) {
        var i = liNodes.index(this);
        // You click item 0
        // You click item 1
        // You click item 2
        alert("You click item " + i);
    });
这是yaml文件%YAML:1.0 #common parameters #support: 1 imu 1 cam; 1 imu 2 cam: 2 cam; imu: 1 num_of_cam: 1 imu_topic: "/mavros/imu/data_raw" #"/imu0" image0_topic: "/cam0/image_raw_pod" #"/cam0/image_raw" #image1_topic: "/cam1/image_raw" output_path: "~/output/" cam0_calib: "cam0_mei.yaml" #cam1_calib: "cam1_mei.yaml" image_width: 752 image_height: 480 # Extrinsic parameter between IMU and Camera. estimate_extrinsic: 1 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it. # 1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess. body_T_cam0: !!opencv-matrix rows: 4 cols: 4 dt: d data: [0.0148655429818, -0.999880929698, 0.00414029679422, -0.0216401454975, 0.999557249008, 0.0149672133247, 0.025715529948, -0.064676986768, -0.0257744366974, 0.00375618835797, 0.999660727178, 0.00981073058949, 0, 0, 0, 1] #Multiple thread support multiple_thread: 1 #feature traker paprameters max_cnt: 150 # max feature number in feature tracking min_dist: 30 # min distance between two features freq: 10 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image F_threshold: 1.0 # ransac threshold (pixel) show_track: 1 # publish tracking image as topic flow_back: 1 # perform forward and backward optical flow to improve feature tracking accuracy #optimization parameters max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time max_num_iterations: 8 # max solver itrations, to guarantee real time keyframe_parallax: 10.0 # keyframe selection threshold (pixel) #imu parameters The more accurate parameters you provide, the better performance acc_n: 0.22 #0.1 # accelerometer measurement noise standard deviation. gyr_n: 0.031 #0.01 # gyroscope measurement noise standard deviation. acc_w: 0.0064 #0.001 # accelerometer bias random work noise standard deviation. gyr_w: 0.00054 #0.0001 # gyroscope bias random work noise standard deviation. g_norm: #9.81007 # gravity magnitude #unsynchronization parameters estimate_td: 0 # online estimate time offset between camera and imu td: 0.0 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock) #loop closure parameters load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path' pose_graph_save_path: "~/output/pose_graph/" # save and load path save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0
03-08
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