thien

def selection(list, i, j, flag):
    size = len(list)
    if (i < size - 1):
        if (flag):
            j = i + 1;
        if (j < size):
            if (list[i] > list[j]):
            	list[i], list[j] = list[j], list[i]
            selection(list, i, j + 1, 0);
        selection(list, i + 1, 0, 1);

thien

def binarySearch(A, i):
    L = 0
    R = len(A)-1
    found = -1
    while L <= R and found == -1:
        M = (L + R)//2
        if A[M] == i:
        	found = M
        else:
            if i < A[M]:

thien

x=[[0,2],[0,1]]
y=[[0,0],[3,4]]
def strassen(a,b):
	S = [b[0][1] - b[1][1],
		 a[0][0] + a[0][1],
		 a[1][0] + a[1][1],
		 b[1][0] - b[0][0],
		 a[0][0] + a[1][1],
		 b[0][0] + b[1][1],
		 a[0][1] - a[1][1],

thien

def fib(n):
	if n is 0:
		return 0
	if n is 1:
		return 1
	else:
		return fib(n-1) + fib(n-2)

for i in range(0,10):
	print(fib(i))

thien

Keybase proof

I hereby claim:

• I am thien on github.
• I am thien (https://keybase.io/thien) on keybase.
• I have a public key ASCA1rbMKEBEIVc2vwp32Wsc9S433KjiE9Q7m6F1nYntqgo

To claim this, I am signing this object:

thien

Multicasting

Before send/receiving IP multicast data, the network needs to support it: - hosts must be configured to send/receive multicast data - routers must support IGMP, multicast forwarding, and routing protocols.

You would send it to a Class D IP address

• to join, you would contact a local router to join the group.
• You'd use the IGMP to join a multicast group.
• routers uses a multicast routing protocol to determine which subnets to forward to.

thien

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

import tensorflow as tf

"""
Input > weight > hidden layer 1 (activation function) > weights > hidden l 2.
repeat until output layer.

compare output to intended output > cost function (cross entropy)

thien

import numpy as np


def simplex(c, A, b, basis):
  z = 0
  tableau = None
  foundOptimal = False
  numRound = 1
  # we'll be using tableau because it's easier and makes more sense.

thien

Artificial Intelligence, Machine Learning, Neural Networks, Neuroevolution, Genetic Algorithms, Draughts, Checkers, Crossover, Monte Carlo Tree Search

Introduction

The intention of this project is to explore the effectiveness of genetic algorithms to improve the neurons of a neural network. Neural networks can be used to evaluate the performance of two players in a zero-sum

thien

from termcolor import *


# simplicity values
def sub(a,b): 
    # substitute
    if a == b:
        return 0
    else:
        return 1