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NicolasHug / Surprise

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measures param for grid search and cross_validate should support callables

NicolasHug
NicolasHug commented Apr 6, 2018

For now only strings are accepted as the measures parameter in GridSearchCV, RandomizedSearchCV, and cross_validate. It's thus impossible to use those with measures that take specific parameters as input (e.g. #156 ), or to use custom measures.

We should then accept callables in addition to strings.

Each callable should only take the predictions parameter. In order to handle measur

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enhancement good first issue
Splitter

benedekrozemberczki / Splitter

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A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).
machine-learning deep-learning clustering word2vec community-detection pytorch deepwalk gensim factorization network-embedding node2vec graph-embedding overlapping-community-detection deep-neural-network graph-representation-learning node-embedding implicit-factorization graph-neural-network ego-splitting word-vector
  • Updated Jun 19, 2021
  • Python

mithi / simple-cryptography

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Scripts that illustrate basic cryptography concepts based on Coursera Standford Cryptography I course and more.
cryptography algorithm crypto coursera ciphertext stanford padding-oracle-attacks factorization public-key-cryptography cryptographic-algorithms meet-in-the-middle symmetric-key-cryptography symmetric-encryption factoring rsa-attacks cryptography-concepts padding-oracle cryptographic-attacks meet-in-the-middle-attacks coursera-cryptography
  • Updated Jul 2, 2020
  • Python
NMFADMM

benedekrozemberczki / NMFADMM

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A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
optimization word2vec deepwalk matrix-factorization feature-extraction pca topic-modeling factorization lda unsupervised-learning admm sparse-matrix principal-component-analysis embedding nmf principal-components node2vec unsupervised-machine-learning word-embedding beta-divergence
  • Updated Jun 19, 2021
  • Python
recursion-and-dynamic-programming

je-suis-tm / recursion-and-dynamic-programming

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Julia and Python recursion algorithm and dynamic programming applications including Edit Distance, Knapsack (Multiple Choice), Stock Trading, Sierpiński Carpet, Pascal Triangle, Prime Factorization, Palindrome, Coin Change, Hanoi Tower, Fibonacci
memoization julia-language recursion edit-distance levenshtein-distance palindrome sierpinski-triangle dynamic-programming recursive-algorithm factorization tabulation fibonacci-numbers knapsack-problem fractals pascal-triangle knapsack hanoi-towers learning-julia julia-training julia-learning
  • Updated May 13, 2021
  • Julia

jannikmi / multivar_horner

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allow multi coefficient polynomials

jannikmi
jannikmi commented Jan 28, 2021

a user might want to represent and evaluate multiple polynomials (different coefficients) with the same properties.
This is useful e.g. for gradients (= partial derivative polynomial for each dimension)!
add support for 2D coefficient arrays and adjust Numba jit compilation.
then the gradient can be returned as a single polynomial with the same exponents but 2D coefficients or multiple distinct

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enhancement good first issue help wanted

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