vene

"""
Generate data triples (x, y, z) for deterministic classification p(y | x; z)

Generative story:

    Given: n_clusters; for each cluster:
      - a cluster center (mean) center[z]
      - a linear model y=sign(w[z] * x + b[z])

    pick z from uniform Categorical(n_clusters)

vene

# compare FY entmax losses with (log)-likelihood objectives
# author: vlad niculae

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

from entmax import entmax_bisect, entmax_bisect_loss

def main(alpha=1.5):

vene

"""
Check sampling from a sequential CRF model.
 - Code is for n_states=2 but the strategy is general.
 - TODO; cythonize or numbaize
 - TODO; write general impl for clarity
"""

# author: vlad niculae <vlad@vene.ro>
# license: MIT

vene

# author: vlad niculae <vlad@vene.ro>
# license: mit

import numpy as np
from scipy.special import softmax
from mayavi import mlab


def barycenter(X, w):
    # riemannian opt

vene

"""Sum-and-sample estimator for learning a stochastic Bernoulli.

Reproduces Experiment 1 from
    Liu et al, Rao-Blackwellized Stochastic Gradients for Discrete Distributions
    https://arxiv.org/abs/1810.04777
"""

# author: vlad niculae <vlad@vene.ro>
# license: mit

vene

// k-best assignments for independent binary variables
// (optimized version of zeroth order viterbi)
// author: vlad niculae <vlad@vene.ro>
// license: mit

#pragma once
#include <vector>
#include <algorithm>
#include <cassert>
#include <bitset>

vene

ffmpeg \
    -i slideslive-recorder_[...].364Z_user.webm \
    -i slideslive-recorder_[...].364Z_display.webm \
    -filter_complex \
'[0:v]scale=640:360,pad=640:720[left];[1:v]crop=1680:945:0:0,scale=1280:720[right];[left][right]hstack[v]' \
    -map [v] \
    -map 0:a \
    -c:v libx264 \
    -preset veryslow \
    -crf 18 \

vene

# https://arxiv.org/abs/2002.08676
# code by vlad niculae
# license: mit

import numpy as np
import matplotlib.pyplot as plt


def main():

vene

# linearity of expectation under mixture model
# license: mit
# author: vlad niculae

from scipy.stats import norm

import numpy as np

def main():
    rng = np.random.RandomState(42)

vene

# Geometric intepretation of the gradient of the mapping:
# f : (0, inf) x Sphere(k-1) -> R^k
# f(r, u) -> r*u
# The *catch*: R can vary on (0, inf) but u may only vary on the
# k-1--dimensional tangent plane!

import numpy as np

def main():