vene

""" Fit logistic regression by ADMM. """
# Author: Vlad Niculae <[email protected]>
# License: MIT

import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler

from scipy.linalg import cho_factor, cho_solve

vene

"""Fitting a gaussian to an empirical weighted measure"""

# author: vlad niculae <[email protected]>
# license: bsd

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import logsumexp, softmax

vene

"""wrapped hyperbolic distributions

following https://arxiv.org/abs/1902.02992

"""

# author: vlad niculae <[email protected]>
# license: bsd 3-clause

import torch

vene

""" trying to understand the determinant of sphere-to-cyl """

import numpy as np
import jax.numpy as jnp
from jax import jacfwd


# map from cylinder to sphere
def phi(zr):
    d = zr.shape[0]

vene

"""
Approximating the cross-entropy between two Power Sphericals.

Uses a second-order Taylor expansion to approximate E[log(1+z)].
"""

# author: vlad n <[email protected]>
# license: mit
# documentation: https://hackmd.io/@vladn/SJ93wMevK

vene

"""
Dual p-norms illustrated.

For any norm |.|, the dual norm is defined as |y|_* = max{ <x, y> for |x| <= 1 }.

The figure shows the unit balls of the p-norm, for p = 1.5, 2, and 3.

We compute the dual norm at a dual vector y (short black arrow), rotating
uniformly around the origin over time.

vene

class Base:

    def say(self, val):
        print("base says", val)


class A(Base):

    def say(self, val):
        print("say A")

vene

Example input and output.

$ python conf.py seed=42 lr=.1

project: ???
seed: 42
lr: 0.1
epochs: ???
p_drop: 0.5
baseconf: null

vene

# author: vlad niculae <[email protected]>
# license: mit

import torch
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors

from entmax import sparsemax, entmax15
from entmax.losses import sparsemax_loss, entmax15_loss

vene

# author: vn

import numpy as np
from scipy.optimize import root_scalar
import torch
import matplotlib.pyplot as plt


def entropy(y, a, b):