# Category Pytorch all pairwise distances

## Pytorch all pairwise distances

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Learn more. How can I determine neighborhood from pairwise distance matrix efficiently? Ask Question. Asked 2 months ago. Active 2 months ago. Viewed 20 times.

I want to get the list of neighbor points from group B for each points from group A. Is there any efficient code for this problem using pytorch? Newbie Newbie 33 4 4 bronze badges. Please include a minimal reproducible example to clarify your question, ideally with a small example. Also, please share any implementation be it pseudocode or actual code that you would use to brute-force your solution as described above.

This method takes either a vector array or a distance matrix, and returns a distance matrix. If the input is a vector array, the distances are computed. If the input is a distances matrix, it is returned instead. This method provides a safe way to take a distance matrix as input, while preserving compatibility with many other algorithms that take a vector array.

If Y is given default is Nonethen the returned matrix is the pairwise distance between the arrays from both X and Y. These metrics support sparse matrix inputs. From scipy. These metrics do not support sparse matrix inputs.

Read more in the User Guide. The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by scipy. Alternatively, if metric is a callable function, it is called on each pair of instances rows and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them.

The number of jobs to use for the computation. None means 1 unless in a joblib. See Glossary for more details. Any further parameters are passed directly to the distance function. If using a scipy. See the scipy docs for usage examples. Toggle Menu. Prev Up Next. Only allowed if metric! The possibilities are: True: Force all values of array to be finite. False: accept both np. Values cannot be infinite. New in version 0. Examples using sklearn.See Conv1d for details and output shape.

If this is undesirable, you can try to make the operation deterministic potentially at a performance cost by setting torch.

## Advances in few-shot learning: reproducing results in PyTorch

Please see the notes on Reproducibility for background. Default: None. Can be a single number or a one-element tuple sW.

Cosine Similarity and Cosine Distance

Default: 1. Can be a single number or a one-element tuple padW. Default: 0. Can be a single number or a one-element tuple dW. See Conv2d for details and output shape. Can be a single number or a tuple sH, sW. Can be a single number or a tuple padH, padW. Can be a single number or a tuple dH, dW. See Conv3d for details and output shape. Can be a single number or a tuple sT, sH, sW. Can be a single number or a tuple padT, padH, padW. Can be a single number or a tuple dT, dH, dW. See ConvTranspose1d for details and output shape. Can be a single number or a tuple sW. Can be a single number or a tuple padW. Can be a single number or a tuple dW. See ConvTranspose2d for details and output shape. See ConvTranspose3d for details and output shape. More than one element of the unfolded tensor may refer to a single memory location.

As a result, in-place operations especially ones that are vectorized may result in incorrect behavior. If you need to write to the tensor, please clone it first. See torch. Unfold for details. Fold for details. See AvgPool1d for details and output shape. Can be a single number or a tuple kW. Default: False. Default: True. The number of output features is equal to the number of input planes. See AvgPool2d for details and output shape.

Can be a single number or a tuple kH, kW. See AvgPool3d for details and output shape. Can be a single number or a tuple kT, kH, kW.The torch package contains data structures for multi-dimensional tensors and mathematical operations over these are defined. Additionally, it provides many utilities for efficient serializing of Tensors and arbitrary types, and other useful utilities. Returns True if the data type of input is a floating point data type i.

Sets the default floating point dtype to d. This type will be used as default floating point type for type inference in torch. The default floating point dtype is initially torch. Get the current default floating point torch. Sets the default torch. Tensor type to floating point tensor type t.

This type will also be used as default floating point type for type inference in torch. The default floating point tensor type is initially torch. Returns the total number of elements in the input tensor. Thresholded matrices will ignore this parameter. Can override with any of the above options. Returns True if your system supports flushing denormal numbers and it successfully configures flush denormal mode. Random sampling creation ops are listed under Random sampling and include: torch.

Tensor s with values sampled from a broader range of distributions. Constructs a tensor with data. If you have a Tensor data and want to avoid a copy, use torch. If you have a NumPy ndarray and want to avoid a copy, use torch. When data is a tensor xtorch. Therefore torch. The equivalents using clone and detach are recommended.

Can be a list, tuple, NumPy ndarrayscalar, and other types. Default: if Noneinfers data type from data. Default: if Noneuses the current device for the default tensor type see torch. Default: False. Works only for CPU tensors. Constructs a sparse tensors in COO rdinate format with non-zero elements at the given indices with the given values. A sparse tensor can be uncoalescedin that case, there are duplicate coordinates in the indices, and the value at that index is the sum of all duplicate value entries: torch.

Will be cast to a torch. LongTensor internally. The indices are the coordinates of the non-zero values in the matrix, and thus should be two-dimensional where the first dimension is the number of tensor dimensions and the second dimension is the number of non-zero values. Sizeoptional — Size of the sparse tensor.

If not provided the size will be inferred as the minimum size big enough to hold all non-zero elements. Default: if None, infers data type from values.

Default: if None, uses the current device for the default tensor type see torch. Convert the data into a torch. Similarly, if the data is an ndarray of the corresponding dtype and the device is the cpu, no copy will be performed.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Looking at the documentation of nn. PairWiseDistancepytorch expects two 2D tensors of N vectors in D dimensions, and computes the distances between the N pairs.

Learn more. How does pytorch calculate matrix pairwise distance? Why isn't 'self' distance not zero? Ask Question. Asked 1 year, 4 months ago. Active 1 year, 4 months ago. Viewed 4k times. If this is a naive question, please forgive me, my test code like this: import torch from torch. Is it to calculate row vectors distance? Why isn't 'self' distance 0? Shikkediel 4, 12 12 gold badges 38 38 silver badges 68 68 bronze badges. Alex Luya Alex Luya 6, 9 9 gold badges 40 40 silver badges 71 71 bronze badges.

Active Oldest Votes. Shai Shai Shai,sorry,I didn't get it clear enough,when I say "calculation",I want to know what kinds of calculation on what kind of elments,and "Computes the p-norm distance between every pair of row vectors in the input" told me that the p-norm distance will be calculated on corresponded row vectors.

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Stack Overflow works best with JavaScript enabled.The distance metric to use. Extra arguments to metric : refer to each metric documentation for a list of all possible arguments. The output array If not None, condensed distance matrix Y is stored in this array. Note: metric independent, it will become a regular keyword arg in a future scipy version. Returns a condensed distance matrix Y. See squareform for information on how to calculate the index of this entry or to convert the condensed distance matrix to a redundant square matrix.

Computes the distance between m points using Euclidean distance 2-norm as the distance metric between the points. The points are arranged as m n-dimensional row vectors in the matrix X.

Computes the standardized Euclidean distance. The standardized Euclidean distance between two n-vectors u and v is. If not passed, it is automatically computed. Computes the normalized Hamming distance, or the proportion of those vector elements between two n-vectors u and v which disagree.

To save memory, the matrix X can be of type boolean. Computes the Jaccard distance between the points. Given two vectors, u and vthe Jaccard distance is the proportion of those elements u[i] and v[i] that disagree. Computes the Chebyshev distance between the points.

The Chebyshev distance between two n-vectors u and v is the maximum norm-1 distance between their respective elements. More precisely, the distance is given by. Computes the Canberra distance between the points. The Canberra distance between two points u and v is.

Computes the Bray-Curtis distance between the points. The Bray-Curtis distance between two points u and v is. Computes the Mahalanobis distance between the points.

### Source code for torch.nn.modules.loss

Computes the Yule distance between each pair of boolean vectors. Computes the Dice distance between each pair of boolean vectors. Computes the Kulsinski distance between each pair of boolean vectors.

Computes the Rogers-Tanimoto distance between each pair of boolean vectors. Computes the Russell-Rao distance between each pair of boolean vectors. Computes the Sokal-Michener distance between each pair of boolean vectors. Computes the Sokal-Sneath distance between each pair of boolean vectors. Computes the weighted Minkowski distance between each pair of vectors. Computes the distance between all pairs of vectors in X using the user supplied 2-arity function f. For example, Euclidean distance between the vectors could be computed as follows:.

Note that you should avoid passing a reference to one of the distance functions defined in this library. For example,:. Instead, the optimized C version is more efficient, and we call it using the following syntax. Distance computations scipy. See Notes for common calling conventions. Parameters X ndarray An m by n array of m original observations in an n-dimensional space.

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