Remove duplicate numbers Question description Give you N numbers (n≤100), each number is between (0~1000), among which there are many For complex numbers, please keep only one repeated number and arrange the remaining numbers from smallest to largest. sequence and output. Sample Enter copy 10 20 40 32 67 40 20 89 300 400 15 […]
Tag: dimension
One-dimensional array and two-dimensional array
Table of Contents 1. One-dimensional array 1. Creation of one-dimensional array 2. Initialization of one-dimensional array fully initialized Incomplete initialization 3. Types of one-dimensional arrays 4. Subscripts of one-dimensional arrays 5. Input and output of one-dimensional array 6. Storage of one-dimensional array in memory 2. Two-dimensional array 1. Creation of two-dimensional array 2. Initialization of […]
Prefix sum, difference, two-dimensional prefix sum, two-dimensional difference
Prefix sum Function: interval query Definition: prefix and array predix[n], original array a[n], the relationship between them is Interval query: Find the sum of elements between a[left] to a[right]: p[right] – p[left-1] template: #include<iostream> using namespace std; #define int long long const int N = 1e5 + 5; int a[N]; int prefix[N]; signed main() { […]
Array —- one-dimensional array of arrays easier to deduplicate (c++)
Find the number of times each number appears in n numbers Question description Read n integers (n≤100) from the keyboard. These n numbers are all numbers between 1 and 10. Please output each occurring number from small to large, and count each How many times does the number appear? For example: Suppose you read 5 […]
: too many indices for tensor of dimension 3
Table of Contents Solve the problem that tensor with dimension 3 has too many indexes introduction wrong reason Solution 1. Check the number of indexes 2. Make sure the tensor dimensions are correct 3. Check data type 4. Try to reconstruct the tensor 5. Review documentation and reference materials in conclusion scene description Sample code […]
How to convert latitude and longitude coordinates into three-dimensional coordinates in three.js or babylon.js
Requirements There are following requirements. According to the longitude and latitude coordinates returned by the interface, the data must be correctly rendered in the three-dimensional scene in three.js, or the movement trajectory of the object must be drawn based on the longitude and latitude coordinates. Analysis We all know that the rendering of three-dimensional three.js […]
VTK+QT implementation, displaying three two-dimensional three-dimensional views of the three-dimensional model
The renderings are as follows: This code uses QT and VTK libraries to display three three-dimensional views of the three-dimensional model on the interface. First, read the NIFTI image data through vtkNIFTIImageReader, and then create three rendering windows (coronal, sagittal, and axial views) to display images of the coronal, sagittal, and axial views respectively. Next, […]
About pytorch tensor dimension conversion and tensor operations
About pytorch tensor dimension conversion list 1 tensor.view() 2 tensor.reshape() 3 tensor.squeeze() and tensor.unsqueeze() 3.1 tensor.squeeze() dimensionality reduction 3.2 tensor.unsqueeze(idx) dimensionality increase 4 tensor.permute() 5 torch.cat([a,b],dim) 6 torch.stack() 7 torch.chunk() and torch.split() 8 Multiplication operation with tensor 9 Addition operation with tensor 10 tensor.expand() 11 tensor.narrow(dim, start, len) 12 tensor.resize_() 13 tensor.repeat() 14 unbind() refer […]
Solve the problem of converting pytorch model to onnx model, and the grid_sample function input parameter is 5-dimensional and the export fails
When transferring pytorch to onnx, grid_sample does not have a corresponding operator, so the transfer cannot be successful. The solutions on the Internet are all four-dimensional (4D) data input, but my input is five-dimensional (5D). I looked for the code and made modifications based on some codes. . The code does not consider performance, only […]
[Thermodynamics] Simulating two-dimensional transient heat conduction based on MATLAB allows users to analyze the temperature distribution changing with time and space.
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