fabianp

fabianp

# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

fabianp

# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

fabianp

fabianp

fabianp

import numpy as np
from scipy import sparse

# .. for plotting ..
import pylab as plt
# .. to generate a synthetic dataset ..
from sklearn import datasets

n_samples, n_features = 1000, 10000
A, b = datasets.make_regression(n_samples, n_features)

fabianp

fabianp

import numpy as np
from scipy import sparse
from datetime import datetime
from numba import njit

@njit
def deriv_logistic(p, b):
    """Derivative of the logistic loss"""
    p *= b
    if p > 0:

fabianp

fabianp

from numba import njit

@njit
def prox_tv1d(w, stepsize):
    """
    
    Parameters
    ----------
    w: array
        vector of coefficieents