error: 'optimoptions' undefined near line 1, column 11 The 'optimoptions' function belongs to the optim package from Octave Forge, which you have installed but not loaded. Run 'pkg load optim' to use 'optimoptions'. Please read <https://www.octave.org/missing.html> to learn how you can contribute missing functionality. error: Invalid call to options. Correct usage is: -- Function File: OPT = options ('KEY1', VALUE1, 'KEY2', VALUE2, ...) Additional help for built-in functions and operators is available in the online version of the manual. Use the command 'doc <topic>' to search the manual index. Help and information about Octave is also available on the WWW at https://www.octave.org and via the help@octave.org mailing list. !!! OUT OF TIME !!!

Dba.getCluster: This function is not available through a session to a standalone instance (metadata exists, instance belongs to that metadata, but GR is not active) (MYSQLSH 51314)

这是一个关于 MySQL 数据库的错误信息，可能是在访问集群时出现的。需要检查数据库的配置和连接方式是否正确，以及是否存在 GR（Group Replication）集群。建议您检查相应的文档和日志，以确定具体的问题和解决方法...

error while loading shared libraries: /athrun/lib/librockchip_mpp.so.1: file too short

The error message "error while loading shared libraries: /athrun/lib/librockchip_mpp.so.1: file too short" typically indicates that the shared library file librockchip_mpp.so.1 is corrupted or ...

K-core decomposition The k-core is the maximal subgraph in which every node has degree of at least k. This also means that the (k+1)-core will be a subgraph of the k-core. The result here gives the coreness of each vertex in the network. A node has coreness D if it belongs to a D-core but not to (D+1)-core.帮我解释一下

这也意味着(K-1)-core将是K-core的子图。该算法的结果是给出了网络中每个节点的核心度。如果一个节点属于一个D-core，但不属于(D-1)-core，则该节点的核心度为D。简单来说，核心度就是一个节点在网络中的重要性，它...

We have learned how to find the strong components for a directed graph in the class. Now, you are asked to implement the algorithm we discussed in the class. Given the adjacency lists of a di-graph, your function should print the affiliation of each node to the root of traversal tree it belongs to. For example, given the adjacency lists of the example below as [[3],[0],[0,3,4],[1],[2]]. We get two traversal trees after the second run of DFS: T1 = {(0,3), (3,1)}, T2 = {(2,4)}. Then, the nodes 0,1,3 all belong the tree rooted at node 0, and nodes 2, 4 belong to the tree rooted at node 2. You need to print an array [0, 0, 2, 0, 2]. You can use any python built-in package to complete this exercise.

To use this function with the example adjacency list you gave, you can simply call assign_affiliations([[3],[0],[0,3,4],[1],[2]]). This will return the desired output: [0, 0, 2, 0, 2]. The basic ...

AttributeError: 'numpy.ndarray' object has no attribute 'columns'

To solve this error, you can convert the numpy.ndarray object to a Pandas DataFrame object using the pandas.DataFrame() function. Then, you can access the columns attribute. Example: ...

以上代码返回错误 Illuminate\Database\Eloquent\RelationNotFoundException: Call to undefined relationship [orderDetail] on model [App\Models\OrderBoxDetail]. in file

return $this->belongsTo(OrderDetail::class, 'orderBoxId', 'id'); } /** * 获取与 inbound_order_details 表的关联关系 */ public function inboundOrderDetail() { return $this->hasOne...

用JDEA编写下列题目：下面是计算英文句子中单词个数的方法头（假设字符串中的单词分割符连续出现次数小于或等于1，无中文出现）。请实现这个方法，方法头定义如下： public static int sumWord(String s) 提示：单词分隔符是指任何标点符号和空白字符，如s=”The world belongs to you and us,but ultimately it belongs to you.”返回13

如果是字母且当前不在单词中，我们将 inWord 设置为 true 并将 count 加 1，表示我们已经找到了一个新单词。如果当前不是字母，我们将 inWord 设置为 false，表示当前不在单词中。最后，我们返回单词的...

帮我简写Direct minimization of the classification loss may lead to overfitting. To avoid this, prototype loss is added as regularization to improve the model's generalization ability. The so-called prototype loss, that is, center loss centered on the centroid of the subclasses, is used to determine the class to which the input x belongs to. Then, its decision boundary is the location where the distances to the centers of the subclasses of two adjacent classes are equal.

Directly minimizing classification loss may cause overfitting. To prevent this, prototype loss is ... The decision boundary is located where the distances to the centers of adjacent subclasses are equal.

(b) The unique function in NumPy can be used to get the sorted unique elements of an array (or objects that can be converted to array). Write a function create_dummies which takes a list with T elements of strings (e.g. ['good', 'neutral','bad','neutral','good']), and returns a T by C array of indicator variables where C is the number of unique values of the elements (for the example of ['good', 'neutral','bad','neutral','good'], T=5, and C=3). Each column of the output is an indicator variable (0 or 1) for whether the element belongs to one of the categories. For example, if x = ['good', 'neutral','bad','neutral','good'], the output of create_dummies should be \begin{equation} \left[\begin{array}{ll} 0 & 1 & 0 \\ 0 & 0 & 1 \\ 1 & 0 & 0 \\ 0 & 0 & 1 \\ 0 & 1 & 0 \end{array}\right] \end{equation} where first column corresponds to bad, second to good and third to neural. The particular order of the columns does not matter ofr your solution.

As expected, the first column corresponds to 'bad', the second to 'good', and the third to 'neutral', and each row has a single 1 indicating the category to which the corresponding element belongs.

and $P_{i,k}$ represents the predicted probability of voxel $i$ belonging to category $k$.这段话润色一下

This statement could be rephrased as follows: "In this context, the symbol $P_{i,k}$ denotes the estimated likelihood that voxel $i$ belongs to category $k".

In your own words, explain, using a most easy example, how the this reference works. What is an advantage of using this reference?

The this reference in programming is used to refer to the current object instance that a method or property belongs to. This is especially useful when you have multiple objects of the same class and...

用java写这道题Given a 0-indexed 2D grid of size m x n, you should find the matrix answer of size m x n. The value of each cell (r, c) of the matrix answer is calculated in the following way: Let topLeft[r][c] be the number of distinct values in the top-left diagonal of the cell (r, c) in the matrix grid. Let bottomRight[r][c] be the number of distinct values in the bottom-right diagonal of the cell (r, c) in the matrix grid. Then answer[r][c] = |topLeft[r][c] - bottomRight[r][c]|. Return the matrix answer. A matrix diagonal is a diagonal line of cells starting from some cell in either the topmost row or leftmost column and going in the bottom-right direction until reaching the matrix's end. A cell (r1, c1) belongs to the top-left diagonal of the cell (r, c), if both belong to the same diagonal and r1 < r. Similarly is defined bottom-right diagonal.

for (int i = r-1, j = c-1; i >= 0 && j >= 0; i--, j--) { topLeft.add(grid[i][j]); } // Calculate bottom-right diagonal for (int i = r+1, j = c+1; i ; i++, j++) { bottomRight.add(grid[i][j]); ...

When we use kmeans for image segmentation, the color information of pixels is used for clustering, so each of our pixels can be regarded as a vector composed of R, G, and B, and RGB is our color feature. The specific process is similar to our example above, but the calculation object is changed from a scalar to a 3-dimensional vector. Please implement the kmean_color function in segmentation.py and call it to complete the segmentation of color images. (very similar to kmeans function)### Clustering Methods for colorful image def kmeans_color(features, k, num_iters=500): N=None # 像素个数 assignments = np.zeros(N, dtype=np.uint32) #Like the kmeans function above ### YOUR CODE HERE ### END YOUR CODE return assignments

The resulting segmented image will have the same shape as the original img, but each pixel will be assigned a label (0 to 4) indicating which cluster it belongs to. You can visualize the ...

基于laravel belongsTo使用详解

今天小编就为大家分享一篇基于laravel belongsTo使用详解，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

Transfer the following verbal description into an ER diagram: 1. A group (gourp-ID) has several companies (comp-ID), whereas a company belongs to one group.

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Transfer the following verbal description into an ER diagram: 1. A group (gourp-ID) has several companies (comp-ID), whereas a company belongs to one group.

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Dba.getCluster: This function is not available through a session to a standalone instance (metadata exists, instance belongs to that metadata, but GR is not active) (MYSQLSH 51314)

error while loading shared libraries: /athrun/lib/librockchip_mpp.so.1: file too short

AttributeError: 'numpy.ndarray' object has no attribute 'columns'

以上代码返回错误 Illuminate\Database\Eloquent\RelationNotFoundException: Call to undefined relationship [orderDetail] on model [App\Models\OrderBoxDetail]. in file

and $P_{i,k}$ represents the predicted probability of voxel $i$ belonging to category $k$.这段话润色一下

In your own words, explain, using a most easy example, how the this reference works. What is an advantage of using this reference?

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