MateusZitelli

backgroud: linear-gradient(90deg, #000, #FFF);background-image

MateusZitelli

### Keybase proof

I hereby claim:

  * I am MateusZitelli on github.
  * I am mzitll (https://keybase.io/mzitll) on keybase.
  * I have a public key whose fingerprint is 5CF5 28A0 B99C 7C91 ED69  0886 51D2 98A3 7832 107E

To claim this, I am signing this object:

MateusZitelli

#!/bin/bash
### BEGIN INIT INFO
# Provides:          YOUR NAME HERE
# Required-Start:    $local_fs $remote_fs $network $syslog $named
# Required-Stop:     $local_fs $remote_fs $network $syslog $named
# Default-Start:     2 3 4 5
# Default-Stop:      0 1 6
# Short-Description: Start/stop the forever nodejs application. Requires sudo
### END INIT INFO
 

MateusZitelli

import autosklearn.classification
import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics
import numpy as np

classes = {
    "CYT": 0,
    "NUC": 1,
    "MIT": 2,

MateusZitelli

[(0.960000, MyDummyClassifier(configuration=1, init_params=None, random_state=None)),
(0.020000, SimpleClassificationPipeline({'one_hot_encoding:minimum_fraction': 0.01, 'imputation:strategy': 'mean', 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:n_estimators': 100, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:criterion': 'gini', 'rescaling:__choice__': 'standardize', 'classifier:random_forest:min_samples_split': 2, 'one_hot_encoding:use_minimum_fraction': 'True', 'balancing:strategy': 'none', 'classifier:random_forest:min_weight_fraction_leaf': 0.0, 'preprocessor:__choice__': 'no_preprocessing', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:max_features': 1.0, 'classifier:__choice__': 'random_forest'},
dataset_properties={
  'multilabel': False,
  'task': 2,
  'signed': False,
  'sparse': False,
  'multiclass': True,
  'target_type': 'classification'})),
(0.020000, SimpleClassif

MateusZitelli

[(0.840000, SimpleClassificationPipeline({'rescaling:__choice__': 'standardize', 'preprocessor:polynomial:degree': 2, 'classifier:adaboost:learning_rate': 0.3740823239105414, 'one_hot_encoding:minimum_fraction': 0.002144117618160979, 'classifier:adaboost:n_estimators': 457, 'preprocessor:__choice__': 'polynomial', 'preprocessor:polynomial:include_bias': 'True', 'one_hot_encoding:use_minimum_fraction': 'True', 'balancing:strategy': 'none', 'preprocessor:polynomial:interaction_only': 'False', 'imputation:strategy': 'most_frequent', 'classifier:__choice__': 'adaboost', 'classifier:adaboost:max_depth': 10, 'classifier:adaboost:algorithm': 'SAMME'},
dataset_properties={
  'target_type': 'classification',
  'multilabel': False,
  'multiclass': True,
  'sparse': False,
  'task': 2,
  'signed': False})),
(0.060000, SimpleClassificationPipeline({'rescaling:__choice__': 'standardize', 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:bootstrap': 'False', 'classifier:random_forest:criterion': 

MateusZitelli

def match_to_sat(xyz, T):
    coverage = [[[] for _ in range(xyz[i])] for i in range(3)]
    for i, t in enumerate(T):
        for j, v in enumerate(t):
            coverage[j][v].append(i)

    instance = set()
    for c in coverage:
        for x in c:
            # if there is points not covered by any set, add a always false statement.

MateusZitelli

OOM error with ExoPlayer and badly interleaved videos.

The problem

Since adopting ExoPlayer in Collect our users started to face crashes in different points of the app. Analysing the crashes we initial and wrongly concluded that was a problem with Samsung phones, however further investigation showed the problem was with Samsung Gear 360 camera videos.

To understand what happened first we need to understand a little about MP4 files. MPEG-4 Part 14 aka MP4 is a multimedia container format that allows the storage of video, audio, subtitles and also images. The different media are stored in different tracks, those tracks are divided in small samples. Those samples are organized in a way where corresponding pieces of different

MateusZitelli

from typing import Tuple, Iterable, Callable, TypeVar
import functools

Solution = TypeVar("Solution")
Population = Iterable[Solution]
EvaluatedPopulation = Iterable[Tuple[float, Solution]]
EvolutionState = Tuple[EvaluatedPopulation, bool]

FitnessFunction = Callable[[Solution], float]
Mutator = Callable[[Solution], Solution]

MateusZitelli

Teddy Roosevelt's presidential campaign negociation:

3 million copies of a photo were printed before asking the right for the photographer, how to negotiate with the photographer a deal?

1. Define your BATNA "Best Alternative to a Negotiated Agreement".

The BATNA would be Roosevelt capaing administration printing the photos again, lets say, for $500,000

2. Define your minimum condition for agreement.