I hereby claim:
- I am mateuszitelli on github.
- I am mzitll (https://keybase.io/mzitll) on keybase.
- I have a public key ASBnWF5dDne7N2ZTLCeXNCR1yZk2P58Q-0o_9woIvMUlnAo
To claim this, I am signing this object:
I hereby claim:
To claim this, I am signing this object:
import face_recognition | |
import csv | |
from PIL import Image, ImageDraw | |
import urllib.request | |
import csv | |
import os.path | |
import pickle | |
import numpy as np | |
# Create arrays of known face encodings and their names |
3 million copies of a photo were printed before asking the right for the photographer, how to negotiate with the photographer a deal?
The BATNA would be Roosevelt capaing administration printing the photos again, lets say, for $500,000
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] |
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
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. |
[(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': |
[(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 |
import autosklearn.classification | |
import sklearn.model_selection | |
import sklearn.datasets | |
import sklearn.metrics | |
import numpy as np | |
classes = { | |
"CYT": 0, | |
"NUC": 1, | |
"MIT": 2, |
#!/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 | |