JVero

import numpy as np
from math import sqrt, log10
from scipy.io import loadmat
from sklearn.linear_model import LinearRegression
from matplotlib import pyplot as plt
import seaborn as sns

"""
This code can be found on GitHub using the link below
https://git.io/vH2um

JVero

natures = {
    "Adamant":
        {"increased": "Attack",
         "decreased": "Sp. Atk"},
    "Bashful":
        {"increased": None, 
         "decreased": None},
    "Bold":
        {"increased": "Defense",
         "decreased": "Attack"},

JVero

package main

import (
	"bytes"
	"fmt"
	"strconv"
	"sync"
)

type Matrix struct {

JVero

package matrix

import "math"

func Pad(m Matrix) Matrix {
	println("old width", m.width)
	newDim := int(math.Pow(2, float64(int(math.Log2(float64(m.width-1))+1))))
	println(newDim, "dab")
	retMat := Empty(newDim, newDim)
	for i := range retMat.values {

JVero

package main

import "fmt"

func main() {
  fmt.Println("This is some sample code")
}

JVero

package main

import(
        "fmt"
        "log"
        "golang.org/x/crypto/ssh/terminal"
        "bufio"
        "os"
        "bytes"
)

JVero

#include<iostream>
struct GameOfLife {
	static const int xdim = 10, ydim = 10;
	bool grid[xdim][ydim];
	int turns;
	int getNeighbors(int x, int y) {
		if (x == 0) { // Left edge
			if (y == 0) // Top Left Corner
				return grid[0][1] + grid[1][1] + grid[1][0];
			if (y > 0 && y < ydim-1) // Left Edge

JVero

package client

import (
	"fmt"
	"testing"

	a "../go_server"
)

func TestPort(t *testing.T) {

JVero

func TestPort(t *testing.T) {
	s := a.CreateServer("9000")
	defer s.Close()
	go func() {
		for {
			var buf [512]byte
			_, addr, _ := s.ReadFromUDP(buf[0:])
			s.WriteToUDP([]byte{1, 3, 3, 4, 5, 6, 4, 65, 4, 6, 4, 6, 4, 6, 3, 5, 3, 5, 5}, addr)
		}
	}()

JVero

import numpy as np
from matplotlib import pyplot as plt

def generate_generic_kernel(bandwidth, kernel_type):
    def u(x): return x/bandwidth - 1 # u normalizes the range of the kernel supports between [-1, 1]
    kernels = {
        'epanechnikov': lambda x: 3/4 * (1 - u(x)**2),
        'triangular':   lambda x: 1 - np.abs(u(x)),
        'knn': lambda x: 1/2
        }