cosine-distance | Computes the cosine distance between two arrays | Topic Modeling library
kandi X-RAY | cosine-distance Summary
kandi X-RAY | cosine-distance Summary
Cosine Distance === [NPM version][npm-image]][npm-url] [Build Status][travis-image]][travis-url] [Coverage Status][coveralls-image]][coveralls-url] [Dependencies][dependencies-image]][dependencies-url]. [Cosine similarity] defines vector similarity in terms of the angle separating two vectors. . The computed similarity resides on the interval [-1,1], where vectors with the same orientation have a similarity equal to 1, orthogonal orientation a similarity equal to 0, and opposite orientation a similarity equal to -1. The [cosine distance] seeks to express vector dissimilarity in positive space and does so by subtracting the similarity from 1. .
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Lines of Code : 71License : Non-SPDX (Apache License 2.0)
Copydef mean_cosine_distance(labels,
predictions,
dim,
weights=None,
metrics_collections=None,
updates_collections=None,
def cosine_distance(
labels, predictions, axis=None, weights=1.0, scope=None,
loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS,
dim=None):
"""Adds a cosine-distance loss to the training procedure.
def _compute_cosine_distance(cls, inputs, clusters, inputs_normalized=True):
"""Computes cosine distance between each input and each cluster center.
Args:
inputs: list of input Tensor.
clusters: cluster Tensor
inputs_normal Community Discussions
Trending Discussions on cosine-distance
QUESTION
I am trying to understand the performance of neo4j in real-time recommendation systems.
The following is a cypher query (taken from their sandbox) which computes top 100 most similar users (in cosine-distance) to the query user "Cynthia Freeman":
...ANSWER
Answered 2020-Oct-20 at 21:38If you want to do real-time recommendations using some sort of cosine distance metric on tens of thousands of nodes or more, it is probably best to store the precomputed values as relationships.
As for making the graph dense, you can limit the SIMILAR relationship to top K similar nodes and also define the similarity cutoff threshold, which can make your graph as sparse as you would like to. You can only store relevant results. So, for example, in a graph of 10 thousand nodes, if every item has a connection to the top 10 other nodes, this is not a really dense graph. If you also remove duplicate relationships that point from one node to another and back, you could remove them even more. So if there are 10k*10k (divided by two if you are treating the relationships as undirected) relationships possible, you won't have a billion possible relationships, but only 100k at most.
The Graph Data Science library supports two algorithms for calculating cosine distance:
The first naive version calculates the distance between all pairs and can be tuned with topK and similarityCutoff parameters.
Just recently, the optimized implementation of the kNN algorithm was added in the GDS 1.4 pre-release. It uses the implementation described in this article: https://dl.acm.org/doi/abs/10.1145/1963405.1963487
However, for real-time calculation of similarity between 10k+ nodes, it might still take more than 100ms you would max the real-time response, so going with the pre-computed similarity relationships makes sense.
QUESTION
My task is to compare documents in a corpus by the cosine similarity. I use tm package and obtain the TermDocumentMatrix (in td-idf form) tdm. The following task should as simple as stated in here
...ANSWER
Answered 2017-May-07 at 03:39120,000 x 120,000 matrix * 8 bytes (dbl float) = 115.2 gigabytes. This isn't necessarily beyond the capability of R, but you do need at least that much memory, regardless of what language you use. Realistically, you'll probably want to write to the disk, either using some database such as Sql (e.g. RSQLite package) or if you plan to only use R in your analysis, it might be better to use the "ff" package for storing/accessing large matrices on disk.
You could do this iteratively and multithread it to improve the speed of calculation.
To find the distance between two docs, you can do something like this:
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