View k_support_norm.py
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# Author: Mathieu Blondel | |
# License: BSD | |
import numpy as np | |
def find_r(w,k): | |
d = w.shape[0] | |
beta= np.r_[np.Inf,np.sort(np.abs(w))[::-1]] |
View reconstruct_primal_solution.py
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# Author: Mathieu Blondel | |
# License: BSD | |
import numpy as onp | |
import scipy | |
import jaxopt | |
import jax.numpy as jnp | |
View smoothed_conjugate.py
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# Mathieu Blondel, 2022 | |
# BSD license | |
import numpy as np | |
from scipy.ndimage import convolve1d | |
from sklearn.metrics.pairwise import euclidean_distances | |
def smoothed_conjugate_conv(f, x, eps=1.0): | |
""" |
View check_convex.py
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# Authors: Mathieu Blondel, Vlad Niculae | |
# License: BSD 3 clause | |
import numpy as np | |
def _gen_pairs(gen, max_iter, max_inner, random_state, verbose): | |
rng = np.random.RandomState(random_state) | |
# if tuple, interpret as randn |
View projection_simplex_isotonic.py
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# Author: Mathieu Blondel | |
# License: BSD 3 clause | |
import numpy as np | |
from sklearn.isotonic import isotonic_regression | |
def projection_simplex(x, z=1): | |
""" | |
Compute argmin_{p : p >= 0 and \sum_i p_i = z} ||p - x|| |
View projection_simplex_vectorized.py
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# Author: Mathieu Blondel | |
# License: BSD 3 clause | |
import numpy as np | |
def projection_simplex(V, z=1, axis=None): | |
""" | |
Projection of x onto the simplex, scaled by z: | |
P(x; z) = argmin_{y >= 0, sum(y) = z} ||y - x||^2 |
View ot_dual_lp.py
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# Author: Mathieu Blondel | |
# License: BSD 3 clause | |
import numpy as np | |
from scipy.optimize import linprog | |
def dual_lp(a, b, C, verbose=0): | |
"""Solves the dual optimal transport problem: |
View fista.py
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""" | |
Efficient implementation of FISTA. | |
""" | |
# Author: Mathieu Blondel | |
# License: BSD 3 clause | |
import numpy as np | |
View seminb.py
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# -*- coding: utf-8 -*- | |
# Copyright (C) 2010 Mathieu Blondel | |
# | |
# This program is free software; you can redistribute it and/or modify | |
# it under the terms of the GNU General Public License as published by | |
# the Free Software Foundation; either version 2 of the License, or | |
# (at your option) any later version. | |
# | |
# This program is distributed in the hope that it will be useful, |
View einsum.py
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import numpy as np | |
rng = np.random.RandomState(0) | |
print "Trace" | |
A = rng.rand(3, 3) | |
print np.trace(A) | |
print np.einsum("ii", A) | |
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