Closed crapthings closed 3 years ago
@crapthings good idea, I can add some distance metrics.
Thanks guys, please wait implementing... ⚙️ I'm also adding dimensionality reductions with PCA and tSNE.
Adding some reference project I'm looking to - thanks @bittlingmayer ;)
Cosine-Similarity - https://github.com/compute-io/cosine-similarity TSNE https://github.com/karpathy/tsnejs TSNE https://github.com/scienceai/tsne-js PCA - https://github.com/mljs/pca, https://github.com/bitanath/pca Adding also (since I was not aware of)
TSNE for TF.js - https://github.com/tensorflow/tfjs-tsne
and also
Annoy - https://github.com/jimkang/annoy-node
About Cosine Distance and Cosine Similarity, keep an eye to this discussion with Gensim author, there are useful ways to normalize and to convert from the distance metrics.
Going to add a minimal implementation with strictly uses external references - if needed (I would prefer NO by the way to keep everything in this package). Stay tuned!
maybe add an api to calculate vector distance
fasttext print-word-vectors trainresult.bin < queries.txt
住宅 -0.3543 -0.36086 -0.1972 -0.48346 -0.4279 0.084653 -0.74038 -0.77876 -0.69068 -0.42149 0.41304 0.9636 -0.11907 -0.081701 0.27681 -0.15278 -0.17322 -0.27368 -0.69611 0.42335 0.11701 -0.43995 0.1868 0.38824 0.42387 0.46397 0.38974 -0.59129 0.69363 0.26292 -0.36955 -0.27438 1.0732 0.0046569 -0.39709 0.44935 0.67039 -0.39564 -0.080179 0.0036072 -0.48187 -0.66577 0.27598 -0.54607 1.0294 -0.29769 0.52144 -0.044384 0.15926 -1.0104 0.80332 -0.60356 0.40641 -0.039965 0.41868 -0.0072699 0.069652 -0.12544 -0.30716 0.21804 -0.36222 -0.51133 -0.24029 -0.7333 0.26404 -0.30949 -0.17224 -0.52331 -1.1139 -0.26803 0.4566 0.28051 -0.50781 0.26043 0.11501 0.17622 -0.1344 -0.46 0.00035005 0.13337 0.50925 -0.82658 0.32135 -0.33323 0.75423 -0.60863 0.42117 0.35665 -0.17826 -0.82987 0.53353 -0.12717 -0.46963 0.15568 0.4642 -0.16868 -0.18377 0.65137 -0.0067536 1.4116
别墅 0.00094935 0.0073073 -0.00094808 -0.0010876 0.0012463 0.0014312 -0.0026107 0.0041731 0.0024454 -0.00093893 0.0045996 0.00050681 -0.00040101 0.0015428 0.0065499 -0.0007207 -0.0022505 -0.0046939 0.0039677 0.0047148 -0.0031379 0.0042863 -0.0056759 -0.0031934 0.0037867 0.006272 0.0050499 -0.0022674 0.0062237 0.00062629 0.0033722 -0.0027245 0.0016423 -0.0037467 -0.00014838 -0.0048198 0.0043823 0.002268 -0.00093589 -0.0034395 -0.0021894 0.0013966 -0.0010953 -0.00073448 0.0012601 0.00037782 -0.0012559 -0.00079777 0.0022461 -0.00085852 -0.001242 0.0039883 0.0017836 -0.00036524 -0.0013768 -0.0036831 0.0023176 0.0027225 0.0010305 0.0020299 0.00057907 -3.4135e-05 0.0029027 -0.00064469 7.3418e-05 -0.0051284 -0.0001829 -0.004983 -0.0024 -0.002313 -2.4026e-05 0.0068082 -0.0062092 0.0045259 -0.0023891 0.0015408 0.00077602 -0.0024638 0.0056508 0.0036942 -0.00089141 -0.0031128 -0.0040772 -0.00063497 -0.006542 -0.0016326 0.002223 -0.0040703 -3.8115e-05 -0.0020506 -0.003437 0.0037226 -0.0062743 0.00098213 0.00030893 0.0013302 -0.002533 0.0038249 -0.0050515 0.0025223