aussetg

... answer said require line and column. So implement that. Need maybe not touch LSP output normalization in lspPositionToExternal/diagnostic snapshots.

Implementation: modify positions.ts externalPositionToLsp to strict. Maybe rename? The function currently from service. It can return undefined if invalid. But error messages say "requires 1-based line and character". It should not floor floats. It should require Number.isInteger and >=1. For column, if not number returns undefined. Should we default column=1? The old allowed missing char. But "Require column >=1". So no default. This affects codeActions with missing char; okay. We'll update error messages maybe "line and column" not character. Since service methods named character param. But tool uses column alias. In hintForError accepts both. Could leave "character" for legacy? Could make "line and column". But service is generic LSP method; parameter called character. However user-facing contract says column. We could use "line and column" in errors for 

aussetg

This is an OpenPGP proof that connects my OpenPGP key to this Github account. For details check out https://keyoxide.org/guides/openpgp-proofs

[Verifying my OpenPGP key: openpgp4fpr:82E707B0D7283273CB9D53969596671D5E50863D]

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\documentclass{minimal} % Default font size and paper size
\usepackage{fontspec} % For loading fonts

\setmainfont[RawFeature={-calt}, Renderer=Harfbuzz]{PragmataPro Liga}[
  UprightFont={*Regular},
  ItalicFont={*Italic},
  BoldFont={*Bold},
  BoldItalicFont={*Bold Italic},
]

aussetg

using DiffEqFlux
using Zygote

nn = FastChain((x,p) -> p)
p = rand(2, 2)
x = rand(1, 100)

function f(p)
    gz, back = Zygote.pullback(z -> nn(z, p), x)
    back(gz)[1]

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julia> CuArrays.zeros(128, 32) |> nn
┌ Warning: calls to Base intrinsics might be GPU incompatible
│   exception =
│    You called exp(x::T) where T<:Union{Float32, Float64} in Base.Math at special/exp.jl:75, maybe you intended to call exp(x::Float32) in CUDAnative at /home/guillaume/.julia/packages/CUDAnative/hfulr/src/device/cuda/math.jl:101 instead?
│    Stacktrace:
│     [1] exp at special/exp.jl:75
│     [2] mish at /home/guillaume/.julia/packages/NNlib/FAI3o/src/activation.jl:206
│     [3] #25 at /home/guillaume/.julia/packages/GPUArrays/1wgPO/src/broadcast.jl:49
└ @ CUDAnative ~/.julia/packages/CUDAnative/hfulr/src/compiler/irgen.jl:111
┌ Warning: calls to Base intrinsics might be GPU incompatible

aussetg

# -*- coding: utf-8 -*-
import scrapy
import datetime
import json
from TwitterScraper.items import Tweet
from urllib.parse import quote
from bs4 import BeautifulSoup
from scrapy.http import HtmlResponse
from dateutil.parser import parse

aussetg

#!/usr/bin/env python3

import collections
import copy
import datetime
import functools
import itertools
import json
import lzma
import gzip

aussetg

mxnet ) R CMD INSTALL mxnet_0.5.tar.gz
* installing to library ‘/home/guillaume/R/x86_64-pc-linux-gnu-library/3.2’
* installing *source* package ‘mxnet’ ...
** libs
g++-4.9 -I/usr/share/R/include -DNDEBUG -I../inst/include  -I"/home/guillaume/R/x86_64-pc-linux-gnu-library/3.2/Rcpp/include"   -fpic  -g -O2 -fstack-protector-strong -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -g  -c executor.cc -o executor.o
g++-4.9 -I/usr/share/R/include -DNDEBUG -I../inst/include  -I"/home/guillaume/R/x86_64-pc-linux-gnu-library/3.2/Rcpp/include"   -fpic  -g -O2 -fstack-protector-strong -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -g  -c export.cc -o export.o
g++-4.9 -I/usr/share/R/include -DNDEBUG -I../inst/include  -I"/home/guillaume/R/x86_64-pc-linux-gnu-library/3.2/Rcpp/include"   -fpic  -g -O2 -fstack-protector-strong -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -g  -c io.cc -o io.o
g++-4.9 -I/usr/share/R/include -DNDEBUG -I../inst/include  -I"/home/guillaume/R/x86_64-pc-linux-gnu-library/3.2/Rcpp

aussetg

\section{Théorie}

\subsection{L'apprentissage machine}

Initialement une branche des statistiques, l'apprentissage statistique s'est rapidement transformé en une discipline à part entière mêlant plusieurs domaines des mathématiques et de l'informatique: l'apprentissage machine.

Le terme \emph{apprentissage statistique} en lui-même est vague et regroupe plusieurs sous-domaines. De façon générale on dispose d'un échantillon $\mathcal{L}$ d'individus possédant des caractéristiques $X_i \in \mathcal{X}$ propres considérées comme déterministes appelées variables et un attribut aléatoire $Y \in \mathcal{Y}$. Si $\mathcal{Y}$ est un ensemble discret on parle de problème de \emph{classification}, s’il est continu on parle alors de problème de \emph{régression}. Il existe un grand nombre d'autres objectifs comme le \emph{clustering}, la \emph{détection de structures} et autres, mais nous ne nous intéresserons ici qu'à ces deux grandes familles en choisissant à chaque fois la tache qui facilite les explications ou es

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double bsc(double x, double T, double K,double L, double r,double sigma) {
    double lambda = (r+sigma*sigma*0.5)/(sigma*sigma) ;
    double x1 = log(x/L)/(sigma*sqrt(T)) + lambda * sigma * sqrt(T) ;
    double y1 = log(L/x)/(sigma*sqrt(T)) + lambda * sigma * sqrt(T) ;
    double d1 = ( log(x/K) + (r+sigma*sigma*0.5)*T ) / (sigma * sqrt(T)) ;
    double d2 = d1 - sigma * sqrt(T) ;
    double y = log(L*L/(x*K)) / (sigma * sqrt(T)) + lambda * sigma * sqrt(T) ;
    double cui = x * N(x1) - K * exp(-r*T) * N(x1 - sigma * sqrt(T)) - x * pow(L/x,2*lambda) * ( N(-y) - N(-y1) ) + K * exp(-r*T) * pow(L/x,2*lambda-2) * ( N(-y+sigma*sqrt(T)) - N(-y1 + sigma*sqrt(T)) ) ;
    double c = x * N(d1) - K* exp(-r*T) * N(d2) ;
    return(c - cui );