Well, version 2.X for the most part was great. Transcoded fast, and more often than not well. I pretty much only go from.TS files to.MP4 and I've done 5-6 with the new version 3 - first 3.0.1 all they way up to 3.0.4 and all of them resulted in nothing but an hour and a half black screen with no sound. Import torch x = torch.randn (2, 3, 5 ) print(x.size ) print (x.permute (2, 0, 1 ).size ) torch.Size ( 2, 3, 5 ) torch.Size ( 5, 2, 3) 2、介绍一下transpose与permute的异同:. 8 permute 2: 90: Find the Number of Possibilities: 7 choose 1: 91: Find the Number of Possibilities: 6 permute 2: 92: Find the Number of Possibilities: 4 permute 2: 93: Find the Number of Possibilities: 4 permute 3: 94: Find the Number of Possibilities: 3 permute 3: 95: Find the Number of Possibilities: 46 choose 6: 96: Find the Number of. Permute (多维数组, 维数的组合) 比如: a=rand (2,3,4);%这是一个三维数组,各维的长度分别为:2,3,4.%现在交换第一维和第二维:. Permute (A, 2,1,3)%变成3.2.4的矩阵. Suppose we have a collection of distinct integers; we have to find all possible permutations. So if the array is like 2,1,3, then the result will be 1,2,3, 1,3,2, 2,1,3, 2,3,1, 3,1,2, 3,2,1 To solve this, we will follow these steps −. We will use the recursive approach, this will make the list, start, curr and res.
- Permute 2 2 3 Pdf
- 1/2 + 2/3 = In Fraction
- Permute 2 2 3 3 Tetramethylbutane
- Permute 2 2 360
- Permute 2 2 300
- 2 Times 2/3
Array order is an important consideration when looping over data in MATLAB/Octave. Choosing which dimension to loop over can actually have a significant impact on computational efficiency. In this tutorial, I introduce and illustrate the concept of array order, create a simple experiment to demonstrate the effects of array order when looping, and explain how the permute() command in MATLAB can be used to write efficient loops every time.
All of the source code for the examples can be found in the GitHub repository.
Array Order
When dealing with multi-dimensional or N-dimensional (ND) arrays it is important to be aware of how the data are stored in memory. This is referred to as array ordering. There are two general conventions: row-major and column-major ordering. These two different methods simply refer to which elements of the ND array area contiguous in memory, as well as, how such arrays should be linearly indexed. In column-major order, the elements of columns are contiguous in memory. and in row-major order, the elements of rows are contiguous in memory. So basically, we count elements down the columns for column-major and along the rows for row-major.
The following code snippet shows an example of a 3 x 3 matrix where each element shows what the linear index is. Similarly, you can think of the numbers in the matrix as the order of the data in memory (e.g. 1 is the first memory address, 2 is the second memory address, etc…).
We can trivially see how MATLAB stores the data by using the ‘:’ operator, which will unwrap the data. Since MATLAB is column-major, you can see that the colMaj matrix returns the index in proper order, but the rowMaj matrix returns the elements out of order.
To make this a little more clear, we can build a simple animation. The animation shows how a matrix is unwrapped using both conventions. The linear index at the bottom shows how the individual elements are actually stored in memory.
Looping Over Data
So why does array order matter? Well, when you are looping over data, array order can actually have a significant impact on the run time. It is much more efficient to access data that is contiguous in memory. So having the knowledge of how our data is organized in memory can also help us write more computationally efficient code.
Let’s demonstrate this with a simple example. Suppose we have a collection of images that we want to operate on. The images are of size (nX, nY) pixels and we have nI images in our data set. There are two likely ways in which these images might be arranged in memory: [nX, nY, nI] or [nI, nX, nY]. In the first orientation, the images will be arranged contiguously in memory, while in the second orientation it will not.
To see what kind of difference array order makes, we can write the following test code. Note, the tic() and toc() functions allow us to time parts of our code execution. If you are not familiar with this functions, tic() essentially starts a timer and toc() records how much time has elapsed since you started the timer. You can find more documentation in the MATLAB help.
To illustrate the effects of array order, the code was run with nX = nY = 128 and nI taking on values of 1,000, 10,000, and 50,000. The brave and curious are encouraged to download the source code and try out more interesting combinations!
Starting with just 1,000 images, we can see that the array order doesn’t seem to have a big impact, however, there is still about a 10% increase in run time.
Increasing the number of images to 10,000, we start to see an actual difference in run times. There is 2.4 factor increase in run time when the images are not stored contiguously in memory.
Further increasing the number of images to 50,000, we continue to see larger increase in the run time. There is now a factor 8 increase in the run time!
It is important to note that this was a simple example with a single loop and only three dimensions. This effect can actually be much worse for arrays with more dimensions or when we are nesting multiple loops. Sometimes it is worthwhile to add some tic() / toc() calls to your code or to run Profiler to where you might want to make some updates to your code.
Permute() in MATLAB
MATLAB includes a function called permute(), which is a generalization of the transpose function but for ND arrays. Permute() takes in an ND-array and the desired array order and then returns the rearranged data. The syntax looks this: newArray = permute( oldArray, [oldDim1, oldDim2, oldIm3, etc…] ). In this syntax the variables oldDim# represent array order index of the old array and the position in the function input represent the new array order position.
For example, suppose we have an ND-array, A, of shape [200, 450, 120, 680], and that we called the permute() function as such: B = permute( A, [3, 1, 4, 2]). The resulting shape of array B would be [120, 200, 680, 450]. Here we have moved dimension 3 to dimension 1, dimension 1 to dimension 2, dimension 4 to dimension 3, and dimension 2 to dimension 4.
Using the permute() function, we can rearrange any ND-array based on what dimension we want to loop over. Returning to our previous image example, we can take our array imageData of shape [nI, nX, nY], and rearrange this to an array of shape [nI, nX, nY] by using the following command.
This would now allow us to loop over the last dimension, which we just showed to be more computational efficient. Adding this simple line to our previous example, we can re-run our test. The updated code will look as follows.
![2-2-3 schedule 2-2-3 schedule](https://cdn.calculatorsoup.com/images/thumbnails/calculators_discretemathematics_permutations.png)
Running this experiment with nI = 50,000, results in the following output.
Using the permute() command has significantly reduced our looping time. There is, of course, a small overhead for using the permute function, but in almost all practical cases it is best to include it. As I mentioned previously, this is especially true with high-dimensional arrays and when we are nesting multiple loops.
Summary
In this tutorial we covered the importance of array order when looping over ND-arrays in MATLAB/Octave. Using some simple MATLAB code, we demonstrated the benefit of looping over data that is contiguous in memory. We further showed that the permute() command allows us to rearrange the array dimensions before looping so we can always ensure that we are looping efficiently.
Here are a few points to remember:
- Data are arranged in column-major order in MATLAB
- We should always loop over the outermost dimensions to make our loops as efficient as possible.
- Permute() can be used before (and after) any loop to ensure that we loop over ND-arrays efficiently.
- For nested loops, the innermost loop should loop over the outermost dimension.
Happy coding!
Endurance 2020 vehicle notification dept. A multidimensional array in MATLAB® is an array with more than two dimensions. In a matrix, the two dimensions are represented by rows and columns.
Each element is defined by two subscripts, the row index and the column index. Multidimensional arrays are an extension of 2-D matrices and use additional subscripts for indexing. A 3-D array, for example, uses three subscripts. The first two are just like a matrix, but the third dimension represents pages or sheets of elements.
Creating Multidimensional Arrays
You can create a multidimensional array by creating a 2-D matrix first, and then extending it. For example, first define a 3-by-3 matrix as the first page in a 3-D array.
Now add a second page. To do this, assign another 3-by-3 matrix to the index value 2 in the third dimension. The syntax
A(:,:,2)
uses a colon in the first and second dimensions to include all rows and all columns from the right-hand side of the assignment. The
cat
function can be a useful tool for building multidimensional arrays. For example, create a new 3-D array B
by concatenating A
with a third page. The first argument indicates which dimension to concatenate along.Another way to quickly expand a multidimensional array is by assigning a single element to an entire page. For example, add a fourth page to
B
that contains all zeros. Accessing Elements
To access elements in a multidimensional array, use integer subscripts just as you would for vectors and matrices. For example, find the 1,2,2 element of
A
, which is in the first row, second column, and second page of A
. Use the index vector
[1 3]
in the second dimension to access only the first and last columns of each page of A
. To find the second and third rows of each page, use the colon operator to create your index vector.
Manipulating Arrays
Permute 2 2 3 Pdf
Elements of multidimensional arrays can be moved around in many ways, similar to vectors and matrices.
reshape
, permute
, and squeeze
are useful functions for rearranging elements. Consider a 3-D array with two pages. Coderunner 2 1 1 download free. Trickster 34 oz spinnerbait. Reshaping a multidimensional array can be useful for performing certain operations or visualizing the data. Use the
reshape
function to rearrange the elements of the 3-D array into a 6-by-5 matrix.1/2 + 2/3 = In Fraction
reshape
operates columnwise, creating the new matrix by taking consecutive elements down each column of A
, starting with the first page then moving to the second page. Permute 2 2 3 3 Tetramethylbutane
Permutations are used to rearrange the order of the dimensions of an array. Consider a 3-D array
M
.Permute 2 2 360
Use the
permute
function to interchange row and column subscripts on each page by specifying the order of dimensions in the second argument. The original rows of M
are now columns, and the columns are now rows.Similarly, interchange row and page subscripts of
M
.When working with multidimensional arrays, you might encounter one that has an unnecessary dimension of length 1. The
squeeze
function performs another type of manipulation that eliminates dimensions of length 1. For example, use the repmat
function to create a 2-by-3-by-1-by-4 array whose elements are each 5, and whose third dimension has length 1. Permute 2 2 300
Use the
squeeze
function to remove the third dimension, resulting in a 3-D array.