kradio3

[alias]
lg1 = log --graph --abbrev-commit --decorate --format=format:'%C(bold blue)%h%C(reset) - %C(bold green)(%ar)%C(reset) %C(white)%s%C(reset) %C(dim white)- %an%C(reset)%C(bold yellow)%d%C(reset)' --all
lg2 = log --graph --abbrev-commit --decorate --format=format:'%C(bold blue)%h%C(reset) - %C(bold cyan)%aD%C(reset) %C(bold green)(%ar)%C(reset)%C(bold yellow)%d%C(reset)%n''          %C(white)%s%C(reset) %C(dim white)- %an%C(reset)' --all
lg = !"git lg1"

kradio3

#!/usr/bin/env python

import os
import csv
import cv2
import numpy as np
import math
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split

kradio3

# ----------
# User Instructions:
# 
# Implement the function optimum_policy2D below.
#
# You are given a car in grid with initial state
# init. Your task is to compute and return the car's 
# optimal path to the position specified in goal; 
# the costs for each motion are as defined in cost.
#

kradio3

/*Goal: sort students by multiple data vaule
**
**student1: Name: Joe Lime, ID#15, Grad. Date: 2019
**student2: Name: Bob Green, ID#3, Grad. Date: 2020
**student3: Name: SallyAnne Green , ID#1, Grad. Date: 2017
**student4: Name: Annie Blue, ID#10, Grad. Date: 2020
**student5: Name: Jose Lemon, ID#25, Grad. Date: 2016
*/

#include "main.hpp"

kradio3

public class ChessMetric {
  private static int[][] moves = {
    { 1, 1, 0,-1,-1,-1, 0, 1, 1, 2, 2, 1,-1,-2,-2,-1}, //x
    { 0, 1, 1, 1, 0,-1,-1,-1,-2,-1, 1, 2, 2, 1,-1,-2} //y
  };


  public long howMany(int size, int[] start, int[] end, int numMoves){

    long dp [][] = new long[size][size];

kradio3

import React from 'react'
import {Navigation, Drawer as MdlDrawer} from 'react-mdl'

export default class Drawer extends React.Component {

  render() {
    return (
        <MdlDrawer title="Title">
          <Navigation>
            <a href="">{"Link"}</a>