ahorn42

function Share-PartialScreen {
    [CmdletBinding(DefaultParameterSetName='Preset')]
    Param(
        [Parameter(Mandatory = $false)]
        [string] $preset,
        [Parameter(Mandatory = $false)]
        [int] $Width = 1920,
        [Parameter(Mandatory = $false)]
        [int] $Height = 1080
    )

veetaw

import 'package:flutter/material.dart';

void main() {
 runApp(Application());
}

class Application extends StatelessWidget {
 @override
 Widget build(BuildContext context) {
   return MaterialApp(

MattHealy

Description: API Gateway to JSON payloads and send to SQS
Outputs:
  ApiEndpoint:
    Description: Endpoint for this stage of the api
    Value: !Join
      - ''
      - - https://
        - !Ref 'SQSProxy'
        - .execute-api.ap-southeast-2.amazonaws.com/
        - prod

miguelmota

package crypto

import (
	"crypto/sha256"
)

// NewSHA256 ...
func NewSHA256(data []byte) []byte {
	hash := sha256.Sum256(data)
	return hash[:]

yossan

// Method 1
// String → UnsafePointer<Int8> → UnsafeMutablePointer<Int8>
// Note: func withCString<Result>(_ body: (UnsafePointer<Int8>) throws -> Result) rethrows -> Result
// Note: String.UTF8View doesn't include null character.

func makeCString(from str: String) -> UnsafeMutablePointer<Int8> {
    let count = str.utf8.count + 1
    let result = UnsafeMutablePointer<Int8>.allocate(capacity: count)
    str.withCString { (baseAddress) in
        // func initialize(from: UnsafePointer<Pointee>, count: Int) 

bgadrian

type Set struct {
	list map[int]struct{} //empty structs occupy 0 memory
}

func (s *Set) Has(v int) bool {
	_, ok := s.list[v]
	return ok
}

hamdifourati

#!/bin/bash
set -ex

yum update -y

# install python3.6
yum -y install yum-utils
yum -y groupinstall development
yum -y install https://centos7.iuscommunity.org/ius-release.rpm
yum -y install python36u

trymbill

data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/wAALCAABAAEBAREA/8QAFAABAAAAAAAAAAAAAAAAAAAACf/EABQQAQAAAAAAAAAAAAAAAAAAAAD/2gAIAQEAAD8AKp//2Q==

matti

apk add --no-cache -X http://dl-cdn.alpinelinux.org/alpine/edge/testing \
  x11vnc

dyerrington

Overfitting in decision trees

Overfitting can be one problem that describes if your model no longer generalizes well.

Overfitting happens when any learning processing overly optimizes training set error at the cost test error. While it’s possible for training and testing to perform equality well in cross validation, it could be as the result of the data being very close in characteristics, which may not be a huge problem. In the case of decision tree’s they can learn a training set to a point of high granularity that makes them easily overfit. Allowing a decision tree to split to a granular degree, is the behavior of this model that makes it prone to learning every point extremely well — to the point of perfect classification — ie: overfitting.

I recommend the following steps to avoid overfitting:

• Use a test set that is not exactly like the training set, or different enough that error rates are going to be easy to see.