orcaman

// Predict predicts for a given complex C if it belongs to the mandelbrot set or not
func Predict(svc *machinelearning.MachineLearning, c complex128) bool {
	params := &machinelearning.PredictInput{
		MLModelId:       aws.String(modelID),
		PredictEndpoint: aws.String(endpointURL),
		Record: map[string]*string{
			"real": aws.String(fmt.Sprintf("%.6f", real(c))),
			"imag": aws.String(fmt.Sprintf("%.6f", imag(c))),
		},
	}

orcaman

#include <stdio.h>
#include <stdlib.h>

int *merge(int *left, size_t leftSize, int *right, size_t rightSize) ;
int *mergeSort(int *a, size_t len) ;

const int ARRAY_SIZE = 1000000;

int main() {
    int a[ARRAY_SIZE];

orcaman

// MergeSortMulti sorts the slice s using Merge Sort Algorithm
func MergeSortMulti(s []int) []int {
	if len(s) <= 1 {
		return s
	}

	n := len(s) / 2

	wg := sync.WaitGroup{}
	wg.Add(2)

orcaman

package msort

import "testing"

var a []int

func init() {
    for i := 0; i < 1000000; i++ {
        a = append(a, i)
    }

orcaman

var sem = make(chan struct{}, 100)

// MergeSortMulti sorts the slice s using Merge Sort Algorithm
func MergeSortMulti(s []int) []int {
    if len(s) <= 1 {
        return s
    }

    n := len(s) / 2

orcaman

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

'''
1. first feed forward the data:
input -> weights -> hidden layer 1 -> activation ->
         weights -> hidden layer 2 -> activation ->
         weights -> output layer

2. Compute cost function (cross entropy) to figure out how close we are

orcaman

package fnvbench

import (
	"hash/fnv"
	"math/rand"
	"testing"
)

const (
	maxElems  = 10000000

orcaman

ffmpeg -i trailer.mov -c:v libvpx -crf 10 -b:v 1M -c:a libvorbis trailer.webm

orcaman

// MergeSort sorts the slice s using Merge Sort Algorithm
func MergeSort(s []int) []int {
	if len(s) <= 1 {
		return s
	}

	n := len(s) / 2

	var l []int

orcaman

func merge(l, r []int) []int {
	ret := make([]int, 0, len(l)+len(r))
	for len(l) > 0 || len(r) > 0 {
		if len(l) == 0 {
			return append(ret, r...)
		}
		if len(r) == 0 {
			return append(ret, l...)
		}
		if l[0] <= r[0] {