function $(param) { var domList = []; if (param.indexOf('#') == 0) { var id = param.substring(1); var dom = document.getElementById(id); domList[0] = dom; } else if (param.indexOf('.') == 0) { var name = param.substring(1); domList = document.getElementsByName(name); } else { domList = document.getElementsByTagName(param); } domList.prop = function () { if (arguments.length == 1) { var dom = domList[0]; return dom[arguments[0]]; } else if (arguments.length == 2) { for (let i = 0; i < domList.length; i++) { var dom = domList[i]; dom[arguments[0]] = arguments[1]; } } } return domList; }帮我解释一下这些代码

时间: 2024-03-07 21:52:53 浏览: 91
这段代码定义了一个名为 `$` 的函数,该函数可以根据参数返回一个或多个DOM节点(元素)。 函数接受一个参数,参数可以是标签名、类名或ID。如果参数以 `#` 开头,则返回ID匹配的DOM节点;如果参数以 `.` 开头,则返回类名匹配的DOM节点;否则,返回标签名匹配的DOM节点。 代码中定义了一个 `domList` 数组来存储匹配的DOM节点。如果参数为ID,则使用 `document.getElementById()` 方法获取DOM节点,并将其存储在 `domList` 数组的第一个元素中。如果参数为类名,则使用 `document.getElementsByName()` 方法获取DOM节点,并将其存储在 `domList` 数组中。如果参数为标签名,则使用 `document.getElementsByTagName()` 方法获取DOM节点,并将其存储在 `domList` 数组中。 此外,函数还定义了一个 `prop()` 方法来设置或获取DOM节点的属性。当 `prop()` 方法接收一个参数时,它将返回第一个DOM节点的指定属性的值。当 `prop()` 方法接收两个参数时,它将设置所有DOM节点的指定属性为指定的值。 最后,该函数返回 `domList` 数组,该数组包含匹配的DOM节点。可以链式调用该函数来对匹配的DOM节点进行操作。
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layui.define(["jquery"], function (exports) { var $ = layui.$; var miniTongji = { /** * 初始化 * @param options */ render: function (options) { options.specific = options.specific || false; options.domains = options.domains || []; var domain = window.location.hostname; if (options.specific === false || (options.specific === true && options.domains.indexOf(domain) >=0)) { miniTongji.listen(); } }, /** * 监听统计代码 */ listen: function () { var _hmt = _hmt || []; (function () { var hm = document.createElement("script"); hm.src = "https://hm.baidu.com/hm.js?d97abf6d61c21d773f97835defbdef4e"; var s = document.getElementsByTagName("script")[0]; s.parentNode.insertBefore(hm, s); })(); } }; exports("miniTongji", miniTongji); });

这段代码是一个使用了layui框架的JavaScript模块,其中定义了一个名为miniTongji的模块,该模块主要用于初始化和监听百度统计代码。在初始化时,可以传入一个options对象,其属性包括specific和domains,分别表示是否仅在特定域名下生效和特定域名列表。在listen函数中,通过创建一个script标签并设置src属性,来加载百度统计的JavaScript代码。最后,通过layui的exports函数将miniTongji模块导出。

优化此代码 const openKeys = ref<string[]>([]) const selectedKeys = ref<string[]>([]) const { currentMenu, currentMenuTree, currentMenuList } = storeToRefs( useLayoutStore(), ) const rootSubmenuKeys = currentMenuList.value.filter((v: any) => { if (v.type === 0) { return v.parentId } }) watch( () => currentMenu, () => { openKeys.value = [currentMenu.value?.parentId] selectedKeys.value = [currentMenu.value?.id] }, { immediate: true }, ) const router = useRouter() /** * 点击事件 * @param e 事件对象 */ const handleClick = (e: any) => { const item = currentMenuList.value.find((_) => _.id === e.key) if (item) { router.push(item.path) } } /** * SubMenu 展开/关闭的回调 * @param e 展开的openKeys */ const onOpenChange = (e: any) => { const latestOpenKey = e.find((key: any) => openKeys.value.indexOf(key) === -1) if (rootSubmenuKeys.indexOf(latestOpenKey) === -1) { openKeys.value = e } else { openKeys.value = latestOpenKey ? [latestOpenKey] : [] } }

There are a few optimizations that could be made to this code: 1. Instead of using `ref` for `openKeys` and `selectedKeys`, you can use `reactive` to make the code more concise: ``` const state = reactive({ openKeys: [], selectedKeys: [], }) ``` 2. Instead of using `storeToRefs` to convert the store state to refs, you can use the `toRefs` function, which is shorter and more concise: ``` const { currentMenu, currentMenuTree, currentMenuList } = toRefs(useLayoutStore()) ``` 3. Instead of using `watch` to watch the `currentMenu` state changes, you can use a computed property to update the `openKeys` and `selectedKeys` arrays: ``` const selectedMenu = computed(() => { const item = currentMenuList.value.find((_) => _.id === currentMenu.value?.id) return [item?.id] || [] }) const parentMenu = computed(() => { const item = currentMenuList.value.find((_) => _.id === currentMenu.value?.parentId) return [item?.id] || [] }) watch([selectedMenu, parentMenu], ([selected, parent]) => { state.selectedKeys = selected state.openKeys = parent }) ``` 4. Instead of using `router.push` in the `handleClick` function, you can use the `router.push` method directly in the template: ``` <Menu.Item :key="item.id" :to="item.path">{{ item.name }}</Menu.Item> ``` 5. Finally, instead of using `rootSubmenuKeys` to filter the list of menu items, you can use a computed property to filter the list of menu items based on their type: ``` const subMenuItems = computed(() => { return currentMenuList.value.filter((v: any) => v.type === 0 && v.parentId) }) ``` By applying these optimizations, the code can be simplified and made more concise.
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优化代码 def cluster_format(self, start_time, end_time, save_on=True, data_clean=False, data_name=None): """ local format function is to format data from beihang. :param start_time: :param end_time: :return: """ # 户用簇级数据清洗 if data_clean: unused_index_col = [i for i in self.df.columns if 'Unnamed' in i] self.df.drop(columns=unused_index_col, inplace=True) self.df.drop_duplicates(inplace=True, ignore_index=True) self.df.reset_index(drop=True, inplace=True) dupli_header_lines = np.where(self.df['sendtime'] == 'sendtime')[0] self.df.drop(index=dupli_header_lines, inplace=True) self.df = self.df.apply(pd.to_numeric, errors='ignore') self.df['sendtime'] = pd.to_datetime(self.df['sendtime']) self.df.sort_values(by='sendtime', inplace=True, ignore_index=True) self.df.to_csv(data_name, index=False) # 调用基本格式化处理 self.df = super().format(start_time, end_time) module_number_register = np.unique(self.df['bat_module_num']) # if registered m_num is 0 and not changed, there is no module data if not np.any(module_number_register): logger.logger.warning("No module data!") sys.exit() if 'bat_module_voltage_00' in self.df.columns: volt_ref = 'bat_module_voltage_00' elif 'bat_module_voltage_01' in self.df.columns: volt_ref = 'bat_module_voltage_01' elif 'bat_module_voltage_02' in self.df.columns: volt_ref = 'bat_module_voltage_02' else: logger.logger.warning("No module data!") sys.exit() self.df.dropna(axis=0, subset=[volt_ref], inplace=True) self.df.reset_index(drop=True, inplace=True) self.headers = list(self.df.columns) # time duration of a cluster self.length = len(self.df) if self.length == 0: logger.logger.warning("After cluster data clean, no effective data!") raise ValueError("No effective data after cluster data clean.") self.cluster_stats(save_on) for m in range(self.mod_num): print(self.clusterid, self.mod_num) self.module_list.append(np.unique(self.df[f'bat_module_sn_{str(m).zfill(2)}'].dropna())[0])

请解释: def GetPhase(self, index, Tstance, Tswing): """Retrieves the phase of an individual leg. NOTE modification from original paper: if ti < -Tswing: ti += Tstride This is to avoid a phase discontinuity if the user selects a Step Length and Velocity combination that causes Tstance > Tswing. :param index: the leg's index, used to identify the required phase lag :param Tstance: the current user-specified stance period :param Tswing: the swing period (constant, class member) :return: Leg Phase, and StanceSwing (bool) to indicate whether leg is in stance or swing mode """ StanceSwing = STANCE Sw_phase = 0.0 Tstride = Tstance + Tswing ti = self.Get_ti(index, Tstride) # NOTE: PAPER WAS MISSING THIS LOGIC!! if ti < -Tswing: ti += Tstride # STANCE if ti >= 0.0 and ti <= Tstance: StanceSwing = STANCE if Tstance == 0.0: Stnphase = 0.0 else: Stnphase = ti / float(Tstance) if index == self.ref_idx: # print("STANCE REF: {}".format(Stnphase)) self.StanceSwing = StanceSwing return Stnphase, StanceSwing # SWING elif ti >= -Tswing and ti < 0.0: StanceSwing = SWING Sw_phase = (ti + Tswing) / Tswing elif ti > Tstance and ti <= Tstride: StanceSwing = SWING Sw_phase = (ti - Tstance) / Tswing # Touchdown at End of Swing if Sw_phase >= 1.0: Sw_phase = 1.0 if index == self.ref_idx: # print("SWING REF: {}".format(Sw_phase)) self.StanceSwing = StanceSwing self.SwRef = Sw_phase # REF Touchdown at End of Swing if self.SwRef >= 0.999: self.TD = True # else: # self.TD = False return Sw_phase, StanceSwing

帮我看看nginx 的conf配置文件,看看文件有没有错误 ,我想要的效果是请求遇到v1就转发到别的网址。文件内容是:#user nobody; worker_processes 1; #error_log logs/error.log; #error_log logs/error.log notice; #error_log logs/error.log info; #pid logs/nginx.pid; events { worker_connections 1024; } http { include mime.types; default_type application/octet-stream; #log_format main '$remote_addr - $remote_user [$time_local] "$request" ' # '$status $body_bytes_sent "$http_referer" ' # '"$http_user_agent" "$http_x_forwarded_for"'; #access_log logs/access.log main; sendfile on; #tcp_nopush on; #keepalive_timeout 0; keepalive_timeout 65; #gzip on; server { listen 80; server_name localhost; #charset koi8-r; #access_log logs/host.access.log main; location ^~/v1 { proxy_pass https://u91298-ad38-3bb835ff.neimeng.seetacloud.com:6443/api/; } location / { root C:/Users/Administrator/Desktop/chat-cs/dist; index index.html index.htm; } #error_page 404 /404.html; # redirect server error pages to the static page /50x.html # error_page 500 502 503 504 /50x.html; location = /50x.html { root html; } # proxy the PHP scripts to Apache listening on 127.0.0.1:80 # #location ~ .php$ { # proxy_pass http://127.0.0.1; #} # pass the PHP scripts to FastCGI server listening on 127.0.0.1:9000 # #location ~ .php$ { # root html; # fastcgi_pass 127.0.0.1:9000; # fastcgi_index index.php; # fastcgi_param SCRIPT_FILENAME /scripts$fastcgi_script_name; # include fastcgi_params; #} # deny access to .htaccess files, if Apache's document root # concurs with nginx's one # #location ~ /.ht { # deny all; #} } # another virtual host using mix of IP-, name-, and port-based configuration # #server { # listen 8000; # listen somename:8080; # server_name somename alias another.alias; # location / { # root html; # index index.html index.htm; # } #} # HTTPS server # #server { # listen 443 ssl; # server_name localhost; # ssl_certificate cert.pem; # ssl_certificate_key cert.key; # ssl_session_cache shared:SSL:1m; # ssl_session_timeout 5m; # ssl_ciphers HIGH:!aNULL:!MD5; # ssl_prefer_server_ciphers on; # location / { # root html; # index index.html index.htm; # } #} }

java: 无法从静态上下文中引用非静态 方法 add(int,int)出现这个错误怎么解决package table; import java.util.Arrays; /** * @author 小蒲七七 * @date 2023/5/28 10:08 * @version 1.0 / public class ArrayList { public int[] elem;// NULL public int useSize;// 存储了多少个有效的数据 0 public static final int DEFAULT_SIZE = 10; public ArrayList() { this.elem = new int[DEFAULT_SIZE]; } // 打印 public void display() { for (int i = 0; i < this.useSize; i++) { System.out.println(this.elem[i] + " "); } System.out.println(); } // 获取长度 public int size() { return this.useSize; } // 判断是否包含某个元素 public boolean contains(int toFind) { for (int i = 0; i < this.useSize; i++) { if (this.elem[i] == toFind) { return true; } } return false; } // 查找某个元素对应的位置 public int indexOf(int toFind) { for (int i = 0; i < this.useSize; i++) { if (this.elem[i] == toFind) { return i; } } return -1;// 因为数组没有负数下标 } // 新增元素,默认在数组最后新增 public void add(int data) { if (this.isFull()) { this.elem = Arrays.copyOf(this.elem, 2this.elem.length); } this.elem[this.useSize] = data; this.useSize++; } /** * 扩容 / private void resize() { } /* * 判断是否为满 * @return / public boolean isFull() { /if(this.useSize == this.elem.length) { return true; } return false;/ return this.useSize == this.elem.length; } // 在pos 位置新增元素 public void add(int pos, int data) {// 重载 checkAddIndex(pos); if(isFull()){ this.elem = Arrays.copyOf(this.elem, 2this.elem.length); } for (int i = useSize - 1; i <= pos; i--) { elem[i + 1] = elem[i]; } elem[pos] = data; useSize++; } /** * 检查add数据时, pos是否合法 * @param */ private void checkAddIndex(int pos) { if(pos < 0 || pos > useSize) { throw new AddIndexOutOfException("add元素时,位置不合法,请检查合法性"); } } }

Create a function pixel_flip(lst, orig_lst, budget, results, i=0) that uses recursion to generate all possible new unique images from the input orig_lst, following these rules: • The input lst is the current list being processed. Initially, this will be the same as orig_lst which is the original flattened image. • The input budget represents the number of pixels that can still be flipped. When the budget reaches 0, no more pixels can be flipped. • The input results is a list of resulting flattened images with flipped pixels. Initially, this will be an empty list. • The input i represents the index of the pixel being processed, by default set to 0, which is used to drive the recursive function towards its base case (i.e., initially starting from i=0). At termination of the function, the argument results should contain all possibilities of the input orig_lst by only flipping pixels from 0 to 1 under both the budget and the adjacency constraints. fill code at #TODO def pixel_flip(lst: list[int], orig_lst: list[int], budget: int, results: list, i: int = 0) -> None: """ Uses recursion to generate all possibilities of flipped arrays where a pixel was a 0 and there was an adjacent pixel with the value of 1. :param lst: 1D list of integers representing a flattened image . :param orig_lst: 1D list of integers representing the original flattened image. :param budget: Integer representing the number of pixels that can be flipped . :param results: List of 1D lists of integers representing all possibilities of flipped arrays, initially empty. :param i: Integer representing the index of the pixel in question. :return: None. """ #TODO def check_adjacent_for_one(flat_image: list[int], flat_pixel: int) -> bool: """ Checks if a pixel has an adjacent pixel with the value of 1. :param flat_image: 1D list of integers representing a flattened image . :param flat_pixel: Integer representing the index of the pixel in question. :return: Boolean. """ #TODO

class PointnetFPModule(nn.Module): r"""Propigates the features of one set to another""" def __init__(self, *, mlp: List[int], bn: bool = True): """ :param mlp: list of int :param bn: whether to use batchnorm """ super().__init__() self.mlp = pt_utils.SharedMLP(mlp, bn=bn) def forward( self, unknown: torch.Tensor, known: torch.Tensor, unknow_feats: torch.Tensor, known_feats: torch.Tensor ) -> torch.Tensor: """ :param unknown: (B, n, 3) tensor of the xyz positions of the unknown features :param known: (B, m, 3) tensor of the xyz positions of the known features :param unknow_feats: (B, C1, n) tensor of the features to be propigated to :param known_feats: (B, C2, m) tensor of features to be propigated :return: new_features: (B, mlp[-1], n) tensor of the features of the unknown features """ if known is not None: dist, idx = pointnet2_utils.three_nn(unknown, known) dist_recip = 1.0 / (dist + 1e-8) norm = torch.sum(dist_recip, dim=2, keepdim=True) weight = dist_recip / norm interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight) else: interpolated_feats = known_feats.expand(*known_feats.size()[0:2], unknown.size(1)) if unknow_feats is not None: new_features = torch.cat([interpolated_feats, unknow_feats], dim=1) # (B, C2 + C1, n) else: new_features = interpolated_feats new_features = new_features.unsqueeze(-1) new_features = self.mlp(new_features) return new_features.squeeze(-1)运行时报错: File "/root/autodl-tmp/project/tools/../pointnet2_lib/pointnet2/pointnet2_modules.py", line 165, in forward new_features = torch.cat([interpolated_feats, unknow_feats], dim=1) # (B, C2 + C1, n) RuntimeError: Sizes of tensors must match except in dimension 2. Got 64 and 256 (The offending index is 0)

Create a function pixel_flip(lst, orig_lst, budget, results, i=0) that uses recursion to generate all possible new unique images from the input orig_lst, following these rules: • The input lst is the current list being processed. Initially, this will be the same as orig_lst which is the original flattened image. • The input budget represents the number of pixels that can still be flipped. When the budget reaches 0, no more pixels can be flipped. • The input results is a list of resulting flattened images with flipped pixels. Initially, this will be an empty list. • The input i represents the index of the pixel being processed, by default set to 0, which is used to drive the recursive function towards its base case (i.e., initially starting from i=0). At termination of the function, the argument results should contain all possibilities of the input orig_lst by only flipping pixels from 0 to 1 under both the budget and the adjacency constraints. fill code at #TODO def pixel_flip(lst: list[int], orig_lst: list[int], budget: int, results: list, i: int = 0) -> None: """ Uses recursion to generate all possibilities of flipped arrays where a pixel was a 0 and there was an adjacent pixel with the value of 1. :param lst: 1D list of integers representing a flattened image . :param orig_lst: 1D list of integers representing the original flattened image. :param budget: Integer representing the number of pixels that can be flipped . :param results: List of 1D lists of integers representing all possibilities of flipped arrays, initially empty. :param i: Integer representing the index of the pixel in question. :return: None. """ #TODO

根据以下代码,利用shap库写出绘制bar plot图的代码“def five_fold_train(x: pd.DataFrame, y: pd.DataFrame, model_class: type, super_parameters: dict = None, return_model=False): """ 5折交叉验证训练器 :param x: :param y: :param model_class: 学习方法类别,传入一个类型 :param super_parameters: 超参数 :param return_model: 是否返回每个模型 :return: list of [pred_y,val_y,auc,precision,recall] """ res = [] models = [] k_fold = KFold(5, random_state=456, shuffle=True) for train_index, val_index in k_fold.split(x, y): #即对数据进行位置索引,从而在数据表中提取出相应的数据 train_x, train_y, val_x, val_y = x.iloc[train_index], y.iloc[train_index], x.iloc[val_index], y.iloc[val_index] if super_parameters is None: super_parameters = {} model = model_class(**super_parameters).fit(train_x, train_y) pred_y = model.predict(val_x) auc = metrics.roc_auc_score(val_y, pred_y) precision = metrics.precision_score(val_y, (pred_y > 0.5) * 1) recall = metrics.recall_score(val_y, (pred_y > 0.5) * 1) res.append([pred_y, val_y, auc, precision, recall]) models.append(model) # print(f"fold: auc{auc} precision{precision} recall{recall}") if return_model: return res, models else: return res best_params = { "n_estimators": 500, "learning_rate": 0.05, "max_depth": 6, "colsample_bytree": 0.6, "min_child_weight": 1, "gamma": 0.7, "subsample": 0.6, "random_state": 456 } res, models = five_fold_train(x, y, XGBRegressor, super_parameters=best_params, return_model=True)”

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