Implementing LSH and Annoy methods as a classifierss
Budget: $20 – $50 USD
Implement LSH and annoy algorithm for data classification so that they become fully fledged classifiers following scikit learn estimator rules. So that can be able to train and predict values for given datasets.
Implement LSH and annoy algorithm for data classification so that they become fully fledged classifiers following scikit learn estimator rules. So that can be able to train and predict values for given datasets. .... I'm writing a scientific paper where I am evaluating time complexity for different KNN speedup methods using datasets with different number of dimensions. So now I have already evaluated KNN EXACT methods which are : brute force, kd tree and ball tree. But Now I have to also evaluate for Approximate Nearest Neighbour methods which are; Annoy, HNSW and LSH. The problem is now I have to implement LSH CLASS so that I can use it as a classifier as my datasets are meant for classification
Implement LSH and annoy algorithm for data classification so that they become fully fledged classifiers following scikit learn estimator rules. So that can be able to train and predict values for given datasets. .... I'm writing a scientific paper where I am evaluating time complexity for different KNN speedup methods using datasets with different number of dimensions. So now I have already evaluated KNN EXACT methods which are : brute force, kd tree and ball tree. But Now I have to also evaluate for Approximate Nearest Neighbour methods which are; Annoy, HNSW and LSH. The problem is now I have to implement LSH CLASS so that I can use it as a classifier as my datasets are meant for classification