Welcome to HyperLearn!

HyperLearn aims to make Machine Learning algorithms run in at least 50% of their original time. Algorithms from Linear Regression to Principal Component Analysis are optimized by using LAPACK, BLAS, and parallelized through Numba.

Some key current achievements of HyperLearn:

1. 70% less time to fit Least Squares / Linear Regression than sklearn + 50% less memory usage
2. 50% less time to fit Non Negative Matrix Factorization than sklearn due to new parallelized algo
3. 40% faster full Euclidean / Cosine distance algorithms
4. 50% less time LSMR iterative least squares
5. New Reconstruction SVD - use SVD to impute missing data! Has .fit AND .transform. Approx 30% better than mean imputation
6. 50% faster Sparse Matrix operations - parallelized
7. RandomizedSVD is now 20 - 30% faster

Example code

Singular Value Decomposition

import hyperlearn as hl
U, S, VT = hl.linalg.svd(X)

Pseudoinverse of a matrix using Cholesky Decomposition

from hyperlearn.linalg import pinvc
inv = pinvc(X)

# check if pinv(X) * X = identity
check = inv.dot(X)

QR Decomposition wanting only Q matrix

from hyperlearn import linalg
Q = linalg.qr(X, Q_only = True)

# check if Q == Q from full QR
q, r = linalg.qr(X)

• hyperlearn
  • hyperlearn package
    • Submodules
    • hyperlearn.base module
    • hyperlearn.linalg module
    • hyperlearn.utils module
    • hyperlearn.random module
    • hyperlearn.exceptions module
    • hyperlearn.multiprocessing module
    • hyperlearn.numba module
    • hyperlearn.solvers module
    • hyperlearn.stats module
    • hyperlearn.big_data.base module
    • hyperlearn.big_data.incremental module
    • hyperlearn.big_data.lsmr module
    • hyperlearn.big_data.randomized module
    • hyperlearn.big_data.truncated module
    • hyperlearn.decomposition.base module
    • hyperlearn.decomposition.NMF module
    • hyperlearn.decomposition.PCA module
    • hyperlearn.decomposition.PCA module
    • hyperlearn.discriminant_analysis.base module
    • hyperlearn.discriminant_analysis.QDA module
    • hyperlearn.impute.SVDImpute module
    • hyperlearn.metrics.cosine module
    • hyperlearn.metrics.euclidean module
    • hyperlearn.metrics.pairwise module
    • hyperlearn.sparse.base module
    • hyperlearn.sparse.csr module
    • hyperlearn.sparse.tcsr module
    • Module contents
• hyperlearn.base
  • Example code:
• hyperlearn.linalg
  • Matrix Decompositions
  • Eigenvalue Problems
  • Matrix Inversion
• License
  • Preamble
  • TERMS AND CONDITIONS
• Contact

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