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How To Install Different JDK Versions on MacOS with Homebrew

Keywords: Java, JDK (Java Development Kit), MacOS, Homebrew, Specific Version

This how-to guide covers how to install different versions of the JDK on MacOS with Homebrew.

Table of Contents

• Preface
• This Guide Favors OpenJDK

Gerst20051

function mapValues(obj, fn) {
  return Object.keys(obj).reduce((result, key) => {
    result[key] = fn(obj[key], key);
    return result;
  }, {});
}

function pick(obj, fn) {
  return Object.keys(obj).reduce((result, key) => {
    if (fn(obj[key])) {

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# My take on Mike's source_for method.
# (see http://pragmaticstudio.com/blog/2013/2/13/view-source-ruby-methods)
#
# (1) I named it 'src' rather than source_for (ok, I'm a lazy typer).
# (2) The edit function was broken out as a separate function.
# (3) The edit function is for emacs
# (4) If the method is not defined on the object, and the object 
#     is a class, then see if it is an instance method on the class.
#
# The fourth point allows my to say:

Gerst20051

import hashlib, struct, codecs
 
ver = 2
prev_block = "000000000000000117c80378b8da0e33559b5997f2ad55e2f7d18ec1975b9717"
mrkl_root = "871714dcbae6c8193a2bb9b2a69fe1c0440399f38d94b3a0f1b447275a29978a"
time_ = 0x53058b35 # 2014-02-20 04:57:25
bits = 0x19015f53
 
# https://en.bitcoin.it/wiki/Difficulty
exp = bits >> 24

Gerst20051

import hashlib, struct
 
ver = 2
prev_block = "000000000000000117c80378b8da0e33559b5997f2ad55e2f7d18ec1975b9717"
mrkl_root = "871714dcbae6c8193a2bb9b2a69fe1c0440399f38d94b3a0f1b447275a29978a"
time_ = 0x53058b35 # 2014-02-20 04:57:25
bits = 0x19015f53
 
# https://en.bitcoin.it/wiki/Difficulty
exp = bits >> 24

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System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

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import math
import random
import pickle
import os

class NeuralNetwork():
	def __init__(self):

		# Seed the random number generator, so we get the same random numbers each time
		random.seed(1)

Gerst20051

from numpy import exp, array, random, dot


class NeuralNetwork():
    def __init__(self):
        # Seed the random number generator, so it generates the same numbers
        # every time the program runs.
        random.seed(1)

        # We model a single neuron, with 3 input connections and 1 output connection.

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from numpy import exp, array, random, dot
training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
training_set_outputs = array([[0, 1, 1, 0]]).T
random.seed(1)
synaptic_weights = 2 * random.random((3, 1)) - 1
for iteration in xrange(10000):
    output = 1 / (1 + exp(-(dot(training_set_inputs, synaptic_weights))))
    synaptic_weights += dot(training_set_inputs.T, (training_set_outputs - output) * output * (1 - output))
print 1 / (1 + exp(-(dot(array([1, 0, 0]), synaptic_weights))))

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class NeuralNetwork:
    def __init__(self, x, y):
        self.input      = x
        self.weights1   = np.random.rand(self.input.shape[1],4) 
        self.weights2   = np.random.rand(4,1)                 
        self.y          = y
        self.output     = np.zeros(self.y.shape)

    def feedforward(self):
        self.layer1 = sigmoid(np.dot(self.input, self.weights1))