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:
# 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 |
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) |
abstract Abs | |
type Foo <: Abs | |
end | |
type Bar | |
val::Int64 | |
end | |
type Baz |
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 } |
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 |
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) |
I hereby claim:
To claim this, I am signing this object:
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. | |
""" |
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, |