Vlad Niculae vene

## nls_solvers.ipynb

      
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                Non-negative least squares in Python
              
          
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## lbfgs_l1logistic.py
"""l-bfgs-b L1-Logistic Regression solver"""

# Author: Vlad Niculae <vlad@vene.ro>
# Suggested by Mathieu Blondel

from __future__ import division, print_function

import numpy as np
from scipy.optimize import fmin_l_bfgs_b

## weighted_empirical_cov.py
"""Fitting a gaussian to an empirical weighted measure"""

# author: vlad niculae <v.niculae@uva.nl>
# license: bsd

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

## omp.py
# -*- coding: utf-8 -*-
"""
http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf

parametrization by error is still in progress
"""
from time import time
import numpy as np
from scipy import linalg
import matplotlib.pyplot as pl

## hyperbolic.py
"""wrapped hyperbolic distributions

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

"""

# author: vlad niculae <v.niculae@uva.nl>
# license: bsd 3-clause

import torch

## check_det_without_rotat.py
""" 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]

## README.txt
Example input and output.

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

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

## kl.py
"""
Approximating the cross-entropy between two Power Sphericals.

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

# author: vlad n <vlad@vene.ro>
# license: mit
# documentation: https://hackmd.io/@vladn/SJ93wMevK

## plot_dual_norms.py
"""
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.

## multi_mixins.py
class Base:

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


class A(Base):

    def say(self, val):
        print("say A")
	"""l-bfgs-b L1-Logistic Regression solver"""

	# Author: Vlad Niculae <vlad@vene.ro>
	# Suggested by Mathieu Blondel

	from __future__ import division, print_function

	import numpy as np
	from scipy.optimize import fmin_l_bfgs_b
	"""Fitting a gaussian to an empirical weighted measure"""

	# author: vlad niculae <v.niculae@uva.nl>
	# license: bsd

	import numpy as np
	import matplotlib.pyplot as plt
	from scipy.special import logsumexp, softmax
	# -- coding: utf-8 --
	"""
	http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf

	parametrization by error is still in progress
	"""
	from time import time
	import numpy as np
	from scipy import linalg
	import matplotlib.pyplot as pl
	"""wrapped hyperbolic distributions

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

	"""

	# author: vlad niculae <v.niculae@uva.nl>
	# license: bsd 3-clause

	import torch
	""" 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]
	Example input and output.

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

	project: ???
	seed: 42
	lr: 0.1
	epochs: ???
	p_drop: 0.5
	baseconf: null
	"""
	Approximating the cross-entropy between two Power Sphericals.

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

	# author: vlad n <vlad@vene.ro>
	# license: mit
	# documentation: https://hackmd.io/@vladn/SJ93wMevK
	"""
	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.
	class Base:

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


	class A(Base):

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