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README.md
**Mary Marchini** <******@****.***> (she/her) * [MylesBorins](https://github.com/MylesBorins) - **Myles Borins** <******@****.***> (he/him) * [targos](https://github.com/targos) - **Michaël Zasso** <******@****.***> (he/him) * [tniessen](https://github.com/tniessen) - **Tobias Nießen** <******@****.***> * [Trott](https://github.com/Trott) - **Rich Trott** <******@****.***> (he/him)
Plain Text - Registered: 2021-01-12 21:40 - Last Modified: 2021-01-09 23:35 - 29.2K bytes - Viewed (0) -
AUTHORS
Matthew Chase Whittemore <******@****.***> Matthew King <******@****.***> Matthew Mueller <******@****.***> Mengdi Gao <******@****.***> Michaël Zasso <******@****.***> Michał Gołębiowski-Owczarek <******@****.***> Nathan Rajlich <******@****.***> New Now Nohow <******@****.***> Nick McCurdy <******@****.***> Nicolae Vartolomei <******@****.***>
Plain Text - Registered: 2021-01-13 08:22 - Last Modified: 2018-07-12 06:15 - 6.3K bytes - Viewed (0) -
.mailmap
Maurice Hayward <******@****.***> maurice_hayward <******@****.***> Michael Bernstein <******@****.***> Michael Dawson <******@****.***> <******@****.***> Michaël Zasso <******@****.***> <******@****.***> Michael-Rainabba Richardson <******@****.***> rainabba <******@****.***> Michał Gołębiowski-Owczarek <******@****.***>
Plain Text - Registered: 2021-01-12 21:40 - Last Modified: 2020-10-05 08:24 - 26.6K bytes - Viewed (1) -
benchmarks/bench_lasso.py
clf.fit(X, Y) lars_lasso_results.append(time() - tstart) return lasso_results, lars_lasso_results if __name__ == '__main__': from sklearn.linear_model import Lasso, LassoLars import matplotlib.pyplot as plt alpha = 0.01 # regularization parameter n_features = 10 list_n_samples = np.linspace(100, 1000000, 5).astype(int)
Python - Registered: 2021-01-01 09:24 - Last Modified: 2020-06-24 14:51 - 3.3K bytes - Viewed (0) -
examples/linear_model/plot_lasso_lars.py
#!/usr/bin/env python """ ===================== Lasso path using LARS ===================== Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes dataset. Each color represents a different feature of the coefficient vector, and this is displayed as a function of the regularization parameter. """ print(__doc__) # Author: Fabian Pedregosa <******@****.***>
Python - Registered: 2021-01-01 09:24 - Last Modified: 2019-08-25 03:17 - 1K bytes - Viewed (0) -
sklearn/covariance/_graph_lasso.py
This results from the bound for the all the Lasso that are solved in GraphicalLasso: each time, the row of cov corresponds to Xy. As the bound for alpha is given by `max(abs(Xy))`, the result follows. """ A = np.copy(emp_cov) A.flat[::A.shape[0] + 1] = 0 return np.max(np.abs(A)) # The g-lasso algorithm @_deprecate_positional_args def graphical_lasso(emp_cov, alpha, *, cov_init=None, mode='cd', tol=1e-4,
Python - Registered: 2021-01-01 09:24 - Last Modified: 2020-12-21 13:09 - 32.6K bytes - Viewed (0) -
sklearn/covariance/tests/test_graphical_lasso.py
""" Test the graphical_lasso module. """ import sys import pytest import numpy as np from scipy import linalg from numpy.testing import assert_allclose from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_array_less from sklearn.covariance import (graphical_lasso, GraphicalLasso, GraphicalLassoCV, empirical_covariance)
Python - Registered: 2021-01-01 09:24 - Last Modified: 2020-12-18 16:09 - 8.2K bytes - Viewed (0) -
examples/linear_model/plot_lasso_and_elasticnet.py
# ############################################################################# # Lasso from sklearn.linear_model import Lasso alpha = 0.1 lasso = Lasso(alpha=alpha) y_pred_lasso = lasso.fit(X_train, y_train).predict(X_test) r2_score_lasso = r2_score(y_test, y_pred_lasso) print(lasso) print("r^2 on test data : %f" % r2_score_lasso) # #############################################################################
Python - Registered: 2021-01-01 09:24 - Last Modified: 2019-07-13 16:35 - 2.4K bytes - Viewed (0) -
benchmarks/bench_plot_lasso_path.py
gc.collect() print("benchmarking lasso_path (with Gram):", end='') sys.stdout.flush() tstart = time() lasso_path(X, y, precompute=True) delta = time() - tstart print("%0.3fs" % delta) results['lasso_path (with Gram)'].append(delta) gc.collect() print("benchmarking lasso_path (without Gram):", end='')
Python - Registered: 2021-01-01 09:24 - Last Modified: 2020-06-24 14:51 - 3.9K bytes - Viewed (0) -
examples/linear_model/plot_lasso_coordinate_descent_path.py
# Compute paths eps = 5e-3 # the smaller it is the longer is the path print("Computing regularization path using the lasso...") alphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps, fit_intercept=False) print("Computing regularization path using the positive lasso...") alphas_positive_lasso, coefs_positive_lasso, _ = lasso_path( X, y, eps=eps, positive=True, fit_intercept=False) print("Computing regularization path using the elastic net...")
Python - Registered: 2021-01-01 09:24 - Last Modified: 2020-05-19 07:07 - 2.8K bytes - Viewed (0)