maximsch2

function picker{T}(data::AbstractArray{T,1}, selected = T[])
  sinput = Input("")
  inp = Input(nokey)

  getvals = s -> begin

    data = filter(x -> !(x in selected), data)
    ldata = map(lowercase, data)
    ls = lowercase(s)

maximsch2

abstract Abs
type Foo <: Abs
end


type Bar
  val::Int64
end

type Baz

maximsch2

# data is here: http://dl.dropbox.com/u/226605/data.txt.bz2
from pylab import *
from sklearn import linear_model

model = linear_model.Lasso(alpha=0.1)
data = loadtxt("data.txt")
X = np.delete(data, 350, 0).T
y = data[350, :].T
model.fit(X, y)
print model.coef_  # 1

maximsch2

import Html exposing (Html, Attribute, div, input, text, button)
import Html.App as Html
import Html.Attributes exposing (..)
import Html.Events exposing (onInput, onClick)
import String
import Ports exposing (..)

main =
  Html.program { init = init, view = view, update = update, subscriptions = \x -> Sub.none }

maximsch2

module GenericAutocomp exposing (..)

import Autocomplete
import Html exposing (..)
import Html.Attributes exposing (..)
import Html.Events exposing (..)
import Html.App as Html
import String
import Json.Decode as Json
import Dom

maximsch2

using JLD

type VectorOfVectorsSerializer{T}
    data::Vector{T}
    lengths::Vector{Int64}
    VectorOfVectorsSerializer{T}(val::Vector{Vector{T}}) = new(vcat(val...), Int64[length(x) for x in val])
end


JLD.writeas{T}(data::Vector{Vector{T}}) = VectorOfVectorsSerializer{T}(data)

maximsch2

Keybase proof

I hereby claim:

• I am maximsch2 on github.
• I am maximsch2 (https://keybase.io/maximsch2) on keybase.
• I have a public key whose fingerprint is F13E 497C 1056 CEFE 544A CA5F C320 A1D4 ABDC 6271

To claim this, I am signing this object:

maximsch2

from typing import Tuple

import torch
from pytorch_lightning.metrics import Metric


class BinnedRecallAtFixedPrecision(Metric):
    """Returns a tensor of recalls for a fixed precision threshold.
    It is a tensor instead of a single number, because it applies to multi-label inputs.
    """

maximsch2

from typing import Tuple, Union, List

import torch
from pytorch_lightning.metrics import Metric
from pytorch_lightning.metrics.utils import METRIC_EPS, to_onehot


# From Lightning's AveragePrecision metric
def _average_precision_compute(
    precision: torch.Tensor,