egonSchiele

require 'rubygems'
require 'celluloid'

class Waiter
  include Celluloid
  FORK_FREE = 0
  FORK_USED = 1
  attr_reader :philosophers
  attr_reader :forks
  attr_reader :eating

egonSchiele

If you have already taken a course in algorithms, why read Grokking Algorithms (manning.com/bhargava)?

If you were learning graph algorithms, which approach would you prefer:

1. Imagine you have to take public transit from your home to your office. How do you figure out the fastest route? Use graph algorithms! OR

2. We can choose between two standard ways to represent a graph G = (V, E): as a collection of adjacency lists or as an adjacency matrix. Either way applies to both directed and undirected graphs.

I prefer the first way: lead with lots of examples, and clear writing. The second way is an excerpt from "Introduction to Algorithms"...that's how they start their section on graph algorithms.

egonSchiele

{-# LANGUAGE TemplateHaskell #-}

import Control.Lens

data Point = Point {
           _x :: Double,
           _y :: Double
           } deriving (Show)

data Mario = Mario { _location :: Point } deriving (Show)

egonSchiele

require 'benchmark'
require 'ostruct'
require 'rubygems'
require 'deep_struct'
require 'classy_struct'
require 'structure'
require 'deepopenstruct'
require 'recursive-open-struct'
require './fast_struct/lib/fast_struct'

egonSchiele

% my training data.
% so if x > 3 || x < 7, y = 1, otherwise y = 0.
x = 1:100;
y = [0, 0, 0, 1, 1, 1, 1, zeros(1, 93)];

% instead of theta' * x, I'm trying to create
% a non-linear decision boundary.
% So instead of y = theta_0 + theta_1 * x, I use:
function result = h(x, theta)
  result = sigmoid(theta(1) + theta(2) * x + theta(3) * ((x - theta(4))^2));

egonSchiele

#!/usr/sbin/dtrace -s

#pragma D option quiet
#pragma D option defaultargs
#pragma D option switchrate=10hz

/* from http://dtrace.org/blogs/brendan/2011/10/10/top-10-dtrace-scripts-for-mac-os-x/ */

dtrace:::BEGIN
{

egonSchiele

diff --git a/src/Phan/Analysis/PreOrderAnalysisVisitor.php b/src/Phan/Analysis/PreOrderAnalysisVisitor.php
index 8650cd3..feaaee4 100644
--- a/src/Phan/Analysis/PreOrderAnalysisVisitor.php
+++ b/src/Phan/Analysis/PreOrderAnalysisVisitor.php
@@ -508,11 +508,16 @@ class PreOrderAnalysisVisitor extends ScopeVisitor
      */
     public function visitIfElem(Node $node) : Context
     {
+        $closure_fqsen =
+            FullyQualifiedFunctionName::fromClosureInContext(

egonSchiele

x = [1000, 2000, 4000];
y = [200000, 250000, 300000];

% given a theta_0 and theta_1, this function calculates
% their cost. We don't need this function, strictly speaking...
% but it is nice to print out the costs as gradient descent iterates.
% We should see the cost go down every time the values of theta get updated.
function distance = cost(theta)
  theta_0 = theta(1);
  theta_1 = theta(2);

egonSchiele

% my training data.
% so if x > 3 || x < 7, y = 1, otherwise y = 0.
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
y = [0, 0, 0, 1, 1, 1, 0, 0, 0, 0];

% instead of theta' * x, I'm trying to create
% a non-linear decision boundary.
function result = h(x, theta)
  result = sigmoid(theta(1) + theta(2) * x + theta(3) * ((x - theta(4))^2));
end

egonSchiele

#!/usr/bin/env ruby
require 'tempfile'

def make_row cols, style=""
  "<tr style='#{style}'><td>" + cols.join("</td><td>") + "</td></tr>"
end

if ARGV.empty?
  puts %{
Usage: #{$0} [file] where file has col list for a hive table, then a blank line, then some sample rows