initializer element is not con

时间: 2023-04-23 10:07:32 浏览: 55
"initializer element is not constant" 意思是初始化元素不是常量。这通常表示在定义静态变量时,初始值不是常量表达式。这可能是因为你在初始化器中使用了函数调用或其他非常量值。
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vue BMap is not defined

The error "BMap is not defined" usually occurs when the BMap object from the Baidu Maps JavaScript API is not properly imported or initialized in a Vue project. To resolve this error, make sure you have properly imported the Baidu Maps JavaScript API and initialized the BMap object before using it in your Vue component. Here are the steps you can follow: 1. Install the Baidu Maps JavaScript API package via npm or yarn: ``` npm install bmap-gl ``` or ``` yarn add bmap-gl ``` 2. In your Vue component, import the BMap object from the bmap-gl package: ```javascript import { BMapGL } from 'bmap-gl' ``` 3. Initialize the BMap object in the `mounted` or `created` lifecycle hook of your Vue component: ```javascript mounted() { // Initialize BMap object const BMap = window.BMapGL // or just window.BMap if you are using the previous version this.map = new BMap.Map('map-container') } ``` 4. Make sure you have a container element with the id `map-container` in your template where you want to render the map: ```html <template> <div id="map-container"></div> </template> ``` By following these steps, you should be able to fix the "BMap is not defined" error and use Baidu Maps in your Vue project successfully.

nuxt BScroll document is not defined

The error "document is not defined" typically occurs when you try to access the `document` object in a non-browser environment, such as when server-side rendering with Nuxt.js. To resolve this issue, you can use the `process.client` flag provided by Nuxt.js to conditionally load the code that requires the `document` object. Here's an example of how you can use it with BScroll: ```javascript if (process.client) { // Code that requires the document object // For example, initialize BScroll here const bs = new BScroll(document.getElementById('scroll-container')) } ``` By wrapping the code that depends on the `document` object inside the `process.client` check, it will only be executed on the client-side when `document` is defined. Make sure to replace `'scroll-container'` with the actual ID or selector of the element you want to apply BScroll to. I hope this helps! Let me know if you have any further questions.

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The starting configuration of this puzzle is a row of cells, with disks located on cells through . The goal is to move the disks to the end of the row using a constrained set of actions. At each step, a disk can only be moved to an adjacent empty cell, or to an empty cell two spaces away if another disk is located on the intervening square. Given these restrictions, it can be seen that in many cases, no movements will be possible for the majority of the disks. For example, from the starting position, the only two options are to move the last disk from cell to cell , or to move the second-to-last disk from cell to cell . 1. [15 points] Write a function solve_identical_disks(length, n) that returns an optimal solution to the above problem as a list of moves, where length is the number of cells in the row and n is the number of disks. Each move in the solution should be a twoelement tuple of the form (from, to) indicating a disk movement from the cell from to the cell to. As suggested by its name, this function should treat all disks as being identical. Your solver for this problem should be implemented using a breadth-first graph search. The exact solution produced is not important, as long as it is of minimal length. Unlike in the previous two sections, no requirement is made with regards to the manner in which puzzle configurations are represented. Before you begin, think carefully about which data structures might be best suited for the problem, as this choice may affect the efficiency of your search

代码We now want to always redraw all the points that have ever been drawn in the panel, not just the last point. To do this, we must save the coordinates of all these points so that we can redraw them all one by one in the paintComponent method every time this method is called. To save the coordinates of the various mouse positions we click, replace the x and y instance variables of the MyPanel class with a single private instance variable called points of type ArrayList. The Point class is provided to you by Swing. In the constructor of MyPanel, initialize the points instance variable with a new arraylist object of the same type. In the mouseClicked method of the mouse listener, use the getPoint method of the mouse event object to get a Point object representing the position of the mouse click (that Point object internally stores both the x and y coordinates of the mouse click event). Then add this Point object to the arraylist using the arraylist’s add method. Then, in the paintComponent method, add a loop to draw in the panel all the points of the arraylist. You can get the number of elements in the arraylist by using the size method of the arraylist; you can access a specific element of the arraylist at index i by using the get(i) method of the arraylist (element indexes start at zero in an arraylist). The Point class has getX and getY methods to get the coordinates of the point (these two methods return values of type double so you need to cast the returned values into the int type before you can use them to draw a point).

import numpy as np def sigmoid(x): # the sigmoid function return 1/(1+np.exp(-x)) class LogisticReg(object): def __init__(self, indim=1): # initialize the parameters with all zeros # w: shape of [d+1, 1] self.w = np.zeros((indim + 1, 1)) def set_param(self, weights, bias): # helper function to set the parameters # NOTE: you need to implement this to pass the autograde. # weights: vector of shape [d, ] # bias: scaler def get_param(self): # helper function to return the parameters # NOTE: you need to implement this to pass the autograde. # returns: # weights: vector of shape [d, ] # bias: scaler def compute_loss(self, X, t): # compute the loss # X: feature matrix of shape [N, d] # t: input label of shape [N, ] # NOTE: return the average of the log-likelihood, NOT the sum. # extend the input matrix # compute the loss and return the loss X_ext = np.concatenate((X, np.ones((X.shape[0], 1))), axis=1) # compute the log-likelihood def compute_grad(self, X, t): # X: feature matrix of shape [N, d] # grad: shape of [d, 1] # NOTE: return the average gradient, NOT the sum. def update(self, grad, lr=0.001): # update the weights # by the gradient descent rule def fit(self, X, t, lr=0.001, max_iters=1000, eps=1e-7): # implement the .fit() using the gradient descent method. # args: # X: input feature matrix of shape [N, d] # t: input label of shape [N, ] # lr: learning rate # max_iters: maximum number of iterations # eps: tolerance of the loss difference # TO NOTE: # extend the input features before fitting to it. # return the weight matrix of shape [indim+1, 1] def predict_prob(self, X): # implement the .predict_prob() using the parameters learned by .fit() # X: input feature matrix of shape [N, d] # NOTE: make sure you extend the feature matrix first, # the same way as what you did in .fit() method. # returns the prediction (likelihood) of shape [N, ] def predict(self, X, threshold=0.5): # implement the .predict() using the .predict_prob() method # X: input feature matrix of shape [N, d] # returns the prediction of shape [N, ], where each element is -1 or 1. # if the probability p>threshold, we determine t=1, otherwise t=-1

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