crawftv

module Main exposing (main)

import Browser
import Html exposing (Html, button, div, text)
import Html.Attributes exposing (style)
import Html.Events exposing (onClick)


type alias Model =
    {

crawftv

view : Model -> Html Msg
view model =
    case model.selectedGender of
        Just x ->
            case x of 
                Masculine -> 
                    div []  [
                    div [] 
                        [ button [onClick (SelectGender Masculine), style "background-color" "blue"] [text "Masculine"]    
                        , button [onClick (SelectGender Feminine)] [text "Feminine"]   

crawftv

view : Model -> Html Msg
view model =
    case model.selectedGender of
    Just Masculine ->
        div []  
           [ button [onClick (SelectGender Masculine), style "background-color" "blue"] [text "Masculine"]    
           , button [onClick (SelectGender Feminine)] [text "Feminine"]   
           , button [onClick (SelectGender Neuter)] [text "Neuter"] 
        ,div [] [text (Debug.toString model)]
          ]

crawftv

module Main exposing (main)

import Browser
import Html exposing (Html, button, div, text)
import Html.Attributes exposing (style)
import Html.Events exposing (onClick)
import Debug


type alias Model =

crawftv

scikit-learn
pandas
numpy
pyod
category-encoders
pytest
pytest-black
coverage
pytest-coverage
feather-format

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#BAD
def f1(input_1,input_2):
  obj(input_1, input_2).save()
  f1(input_2,input1)
f1(a,b)
#NOT Bad
def f(input_1,input_2):
  obj(input_1,input_2).save()

f(input_1,input_2)

crawftv

import pytest
import IPython
from IPython.display import display, HTML
from crawto.charts.charts import Plot, ScatterChart, BarChart, LineChart, Chart_type
from hypothesis import given
from hypothesis.strategies import text, builds, iterables, floats, one_of, integers,composite


class TestChart:
    @composite

crawftv

codecov:
  notify:
    require_ci_to_pass: yes

coverage:
  precision: 2
  round: down
  range: "70...100"

  status:

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# Python CircleCI 2.0 configuration file
version: 2
orbs:
      codecov: codecov/[email protected]
jobs:
  build:
    docker:
      - image: circleci/python:3.6

    working_directory: ~/repo

crawftv

import numpy as np

X = np.array([[0,0,1],[0,1,1],[1,0,1],[0,1,0],[1,0,0],[1,1,1],[0,0,0]])
y = np.array([[0],[1],[1],[1],[1],[0],[0]])

def sigmoid(s):
    return 1/1+np.exp(-s)

def sigmoid_prime(z):
    """Derivative of the sigmoid function."""