Mathieu Blondel mblondel
View nmf_cd.py
| """ | |
| NMF by coordinate descent, designed for sparse data (without missing values) | |
| """ | |
| # Author: Mathieu Blondel <mathieu@mblondel.org> | |
| # License: BSD 3 clause | |
| import numpy as np | |
| import scipy.sparse as sp | |
| import numba |
View local_regression.py
| """Local regression""" | |
| # Author: Mathieu Blondel <mathieu@mblondel.org> | |
| # License: BSD 3 clause | |
| import numpy as np | |
| from sklearn.base import BaseEstimator, RegressorMixin | |
| from sklearn.metrics.pairwise import pairwise_kernels | |
| from sklearn.linear_model import Ridge |
View letor_metrics.py
| # (C) Mathieu Blondel, November 2013 | |
| # License: BSD 3 clause | |
| import numpy as np | |
| def ranking_precision_score(y_true, y_score, k=10): | |
| """Precision at rank k | |
| Parameters |
mblondel
/ matrix_sketch.py
Last active Feb 13, 2019
Frequent directions algorithm for matrix sketching.
View matrix_sketch.py
| # (C) Mathieu Blondel, November 2013 | |
| # License: BSD 3 clause | |
| import numpy as np | |
| from scipy.linalg import svd | |
| def frequent_directions(A, ell, verbose=False): | |
| """ | |
| Return the sketch of matrix A. |
View curve_averaging.py
| """Variable-length curve averaging""" | |
| # Author: Mathieu Blondel <mathieu@mblondel.org> | |
| # License: BSD 3 clause | |
| import numpy as np | |
| from scipy.interpolate import interp1d | |
| def curves_mean_std(X, Y, kind="linear"): |
View kernel_kmeans.py
| """Kernel K-means""" | |
| # Author: Mathieu Blondel <mathieu@mblondel.org> | |
| # License: BSD 3 clause | |
| import numpy as np | |
| from sklearn.base import BaseEstimator, ClusterMixin | |
| from sklearn.metrics.pairwise import pairwise_kernels | |
| from sklearn.utils import check_random_state |
View gaussian_process.py
| """Gaussian processes""" | |
| # Author: Mathieu Blondel <mathieu@mblondel.org> | |
| # License: BSD 3 clause | |
| import numpy as np | |
| from scipy.linalg import cholesky, solve_triangular | |
| from sklearn.base import BaseEstimator, RegressorMixin | |
| from sklearn.metrics.pairwise import pairwise_kernels |
mblondel
/ sparse_multiclass_numba.py
Last active Jan 27, 2020
Sparse Multiclass Classification in Numba!
View sparse_multiclass_numba.py
| """ | |
| (C) August 2013, Mathieu Blondel | |
| # License: BSD 3 clause | |
| This is a Numba-based reimplementation of the block coordinate descent solver | |
| (without line search) described in the paper: | |
| Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass | |
| Classification. Mathieu Blondel, Kazuhiro Seki, and Kuniaki Uehara. | |
| Machine Learning, May 2013. |
View imputer.py
| # (C) Mathieu Blondel | |
| # License: BSD 3 clause | |
| import numpy as np | |
| from numpy import ma | |
| import scipy.sparse as sp | |
| def _get_mask(X, missing_values, sparse=False): | |
| if sparse: |
View xref.txt
| paper.tex: main manuscript | |
| supp.tex: supplementary material | |
| Cross-referencing | |
| ----------------- | |
| We want to cross-reference equations in paper.tex from supp.tex. | |