### On Error Resume Prior   If at first you don't succeed...


# Infix Notation in Scheme

Scheme is well known as a very simple programming language. The general concept behind scheme is that it is composed of S-expressions, or expressions of the form (operator operand1 operand2 ... operandn), which can be nested.

This makes scheme relatively simple to parse and evaluate, however it does lead to some strange compromises on the style of programming. For example, rather than writing 12 * (2 + 3), you have to write (* 12 (+ 2 3)). And in a way, this is elegant in its simplicity.

But simplicity is overrated.

## Homoiconicity, or writing a scheme program to write a scheme program

Scheme has this cool property of being a homoiconic language. This means that you can write scheme code that writes and modifies scheme code.

For example, take this function, which replaces the first element form a scheme expression with a +:

(define (replace-with-plus code)
(cons '+ (cdr code)))

Note that we just treat the code like any other scheme list. To call this function, we can just run

scm> (replace-with-plus '(- 2 3))
(+ 2 3)

Now, if we want to actually run this program, we can wrap it in an eval call as such:

scm> (eval (replace-with-plus '(- 2 3)))
5

## Macros

You’ll note that in the above, we have to quote the arguments and then eval the result. It turns out that there’s a construct in Scheme that will do this for you! Using the same replacing with + function, we can define

(define-macro (replace-with-plus code)
(cons '+ (cdr code)))

Note that this is the same definition, except that we have replaced define with define-macro. Now, Scheme handles the quoting of the arguments and the evaluation of the result for us as such:

(replace-with-plus (- 2 3))
5

Pretty nifty, huh!

## Infix notation

OK, but the post’s title is infix notation. Let’s say we want to be able to write (12 * (2 + 3)) in scheme and have it evaluate the way it would in Python. To accomplish this, let’s define a function (not a macro for now) that takes in (12 * (2 + 3)) and converts it into the scheme form (* 12 (+ 2 3)). First off, we need to identify which things are in fact infix notation:

(define (infix? code)
(and
(list? code)
(= (length code) 3)
(member (cadr code) '(+ * - /, >, <, >=, <=, =))))

The idea here is that we check that the given piece of code is a list with 3 elements, and that its middle element is an operator. Now, let’s write the code to convert infix to prefix notation.

(define (rearrange code)

Where this code just places the elements in second, first, third order.

Now, we can put it together with a recursive function:

(define (to-prefix-func code)
(cond
((infix? code) (to-prefix-func (rearrange code)))
((list? code) (map to-prefix-func code))
(else code)))

The idea behind this code is that if something is infix, we should rearrange it, then call ourselves to finish up the task, if something is a call expression, we should just look at every element, and otherwise, we’re at a base case and can just return.

Let’s try this out on our example:

scm> (to-prefix-func '(12 * (2 + 3)))
(* 12 (+ 2 3))

It works!

Finally, what we really want is a macro. We can just define one directly:

(define-macro (to-prefix code) (to-prefix-func code))

Sadly we can’t just (define-macro to-prefix to-prefix-func) because of a technicality in the scheme language. Anyway, now we can run our scheme code:

scm> (to-prefix (12 * (2 + 3)))
60

And there you have it. Scheme’s power might be in its simplicity but with great power comes the ability to create complexity. Obviously, the usual disclaimer applies: don’t write code like this, but if you do, do it with macros!

## The code

A fully runnable version of this is below: try copy-pasting it into 61A’s scheme interpreter and check it out! (There are a few helpful functions defined at the end that we just kinda assumed the existence of above).

(define (infix? code)
(and
(list? code)
(= (length code) 3)
(member (cadr code) '(+ * - /, >, <, >=, <=, =))))

(define (rearrange code)

(define (to-prefix-func code)
(cond
((infix? code) (to-prefix-func (rearrange code)))
((list? code) (map to-prefix-func code))
(else code)))

(define-macro (to-prefix code)
(to-prefix-func code))

(define (member elem lst)
(cond
((null? lst) #f)
((eq? (car lst) elem) #t)
(else (member elem (cdr lst)))))

(define (cadr lst) (car (cdr lst)))
(define (cddr lst) (cdr (cdr lst)))
(define (caddr lst) (car (cddr lst)))

# (Mini) Scheme in 50 Lines

The scheme language is a programming language that is unique for being easy to parse. Every construct in scheme is of the form

(keyword arg1 ... argn)

## Our Subset

We will start with some standard scheme expressions, which you can think about as being analogous to certain patterns in Python. For example instead of writing a + b, you write (+ a b), where + is just the name of a function. Similarly, instead of writing lambda x: x * 2 for the doubling function, you write (lambda (x) (* 2 x)). Just lambdas and function application are enough to perform any calculation, but we add a few more features for clarity: instead of x = y we write (define x y), instead of x if y else z we write (if y x z) and instead of writing

x = f(y)
return x

we write (begin (define x (f y)) x). The real scheme language has many more constructs, including ones that simulate python’s def statements, and some unique ones that allow you to assign variables in a local frame, simulate elif trees, have short-circuited and/or constructs, or even define your own language constructs. For brevity, we will stick to this subset, which is still very powerful.

## Lexing

The first step in processing a scheme program is to split it up into a list of tokens. What we do is take an expression like '(a b (c))' and convert it into a list of tokens like '(', 'a', 'b', '(', 'c', ')', ')'.

To accomplish this, we first pad all the parentheses with space and replace all space-like characters with a space. We then split on spaces (and filter out empty tokens, which are produced when multiple spaces are consecutive).

def lex(text):
text = re.sub("([()])", r" \1 ", re.sub(r"\s", " ", text))
return [x for x in text.split(" ") if x]

## Parsing

We now need to parse the string. If we have '(a b (c))', we want to parse it to ['a', 'b', ['c']] so we can process it in Python. Parsing scheme is fairly simple using recursion. Whenever we see a symbol or number, we just return and move on. Otherwise, we recursively call parse until we see an end parenthesis. For example, if we have the current state of our input

(+ 2 (4) (* 5 3)) 2 3)

we parse by removing the ( from the front, then parse +, 2, (4), and (* 5 3) before seeing the unmatched ). Then we return, having removed everything but the 2 3). This is implemented as follows:

def parse(text):
text = lex(text)[::-1]
def parse():
start = text.pop()
if start != '(':
return start
vals = []
while text[-1] != ')':
vals += [parse()]
text.pop()
return vals
return parse()

The reason we reverse the text is so that pop() pops from the end rather than the front of the input stream.

## Frames

We need to define an environment diagram so that we can execute code. This is similar to a Python style environment diagram, which is a backwards pointing tree (children point towards the root via the parent annotation rather than the other way around).

To define a frame, we use the defaultdict class from python, which is like a dictionary except that when it can’t find a key, instead of raising a KeyError, it calls a 0 argument function you provide in the constructor and sets that as its value. We want to slightly modify this so that it calls a 1 argument function. Thus we define a frame as such:

class frame(defaultdict):
def __init__(self, f):
super().__init__(lambda: None)
self.__function = f
def __missing__(self, key):
return self.__function(key)

defaultdict calls the __missing__ function when necessary, and in this case, it calls the parent function on a key. We can create a child frame for a given frame simply by saying frame(lambda v: parent[v]). We can now create a global frame as such:

global_frame = frame(int)
global_frame.update({"+" : add, "-" : sub, "*" : mul, "/" : floordiv, "=" : lambda x, y: x == y})

We do something a little hacky here by basically defining integers as just being variables that evaluate to the integer version of themselves via the function int. (this works as int("123") == 123).

## Special Forms

Now we need to somehow handle our special keywords (define, lambda, begin, if).

### Define

We assume that we already have a function seval defined, which is the function that will eval a scheme expression. In scheme, define returns the variable being assigned to, for reasons of tradition.

def define(exp, env):
env[exp[0]] = seval(exp[1])
return exp[0]

### Lambda

A lambda expression has no side effects, but must return a function. First we create a new frame, then we assign the operator to the operands, and then we run the body of the function in that frame and return the last value:

def slambda(exp, env):
def do_lambda(*args):
local_env = frame(lambda x: env[x])
local_env.update(dict(zip(exp[0], args)))
return [seval(u, local_env) for u in exp[1:]][-1]
return do_lambda

### Begin

This is similar to lambda except that we don’t have any arguments or a new frame, and this one is simple enough to just write as a Python lambda function.

lambda exp, env: [seval(u, env) for u in exp][-1]

### If

We can just directly map this to the equivalent Python construct.

lambda exp, env: seval(exp[1], env) if seval(exp[0], env) else seval(exp[2], env)

### Putting it together

We create a dictionary of special forms for easy access:

def define(exp, env):
env[exp[0]] = seval(exp[1])
return exp[0]
def slambda(exp, env):
def do_lambda(*args):
local_env = frame(lambda x: env[x])
local_env.update(dict(zip(exp[0], args)))
return [seval(u, local_env) for u in exp[1:]][-1]
return do_lambda
special_forms = {
"define" : define,
"lambda" : slambda,
"begin" : lambda exp, env: [seval(u, env) for u in exp][-1],
"if" : lambda exp, env: seval(exp[1], env) if seval(exp[0], env) else seval(exp[2], env)
}

## Eval Function

To evaluate a parsed scheme tree, what we need to do is dependent on whether the input is a list or not. If we have a list, then we need to check if its first element is a special form, and if so run its special form function. Otherwise, we evaluate the first item as a function, then evaluate the rest of the items as its arguments, then call the function. If we don’t have a list, we just look up the current element in the current frame.

def seval(tree, env=global_frame):
if isinstance(tree, list):
func, *args = tree
if func in special_forms:
return special_forms[func](args, env)
return seval(func, env)(*(seval(x, env) for x in args))
return env[tree]

We can then run scheme by running seval(parse(text)).

## The Entire Interpreter

The entire interpreter, which is just 50 lines, is as below:

from collections import defaultdict
from operator import *
import re

def lex(text):
text = re.sub("([()])", r" \1 ", re.sub(r"\s", " ", text))
return [x for x in text.split(" ") if x]
def parse(text):
text = lex(text)[::-1]
def parse():
start = text.pop()
if start != '(':
return start
vals = []
while text[-1] != ')':
vals += [parse()]
text.pop()
return vals
return parse()
class frame(defaultdict):
def __init__(self, f):
super().__init__(lambda: None)
self.__function = f
def __missing__(self, key):
return self.__function(key)
global_frame = frame(int)
global_frame.update({"+" : add, "-" : sub, "*" : mul, "/" : floordiv, "=" : lambda x, y: x == y})

def define(exp, env):
env[exp[0]] = seval(exp[1])
return exp[0]
def slambda(exp, env):
def do_lambda(*args):
local_env = frame(lambda x: env[x])
local_env.update(dict(zip(exp[0], args)))
return [seval(u, local_env) for u in exp[1:]][-1]
return do_lambda
special_forms = {
"define" : define,
"lambda" : slambda,
"begin" : lambda exp, env: [seval(u, env) for u in exp][-1],
"if" : lambda exp, env: seval(exp[1], env) if seval(exp[0], env) else seval(exp[2], env)
}
def seval(tree, env=global_frame):
if isinstance(tree, list):
func, *args = tree
if func in special_forms:
return special_forms[func](args, env)
return seval(func, env)(*(seval(x, env) for x in args))
return env[tree]

Of course, a proper scheme interpreter, which has more features (see the section “Subset”), more possible types (like strings, symbols, lists, etc.), and better error handling (we crash on a lot of cases with IndexErrors and ValueErrors). In fact, that’s an entire project in 61a.

But I personally enjoy that you can get quite a few features in such a small amount of space.

Try it out on the following program!

(begin
(define factorial
(lambda (x) (if (= 0 x) 1 (* x (factorial (- x 1))))))
(factorial 40))

# The Efficiency Gap Makes No Sense (but all metrics show Republicans are better at gerrymandering)

## Cracking, Packing, and Wasted Votes

The efficiency gap is a technique for measuring the level of gerrymandering in a state. Gerrymandering generally takes the form of what’s known as “cracking and packing.” Cracking is the process of taking a district with a majority of the opposing party and splitting it into two in which the party has a minority in each, thus denying your opponent that seat. However, this isn’t always possible because your opponent might have too many votes in a given area. To reduce the remaining number of votes your opponent has while minimizing the number of seats they have, you can use a technique called packing, in which you create a district in which your opponent can win by a huge margin.

To measure both cracking and packing, we can use the concept of a wasted vote. A wasted vote is any vote that did not contribute to the victory. There are two kinds of wasted votes, cracked waste and packed waste. A cracked wasted vote for party X is any vote in a district party X has lost. A packed wasted vote is any vote for party X over 50% in a district party X has won.

For example, let’s say that there are just two parties1 and 1000 voters in a district. The number of wasted votes for X in terms of the number of votes for X is as such:

1. The United States has two political parties. A vote for the Green, Libertarian, or American Independent Party is a wasted vote. But if you’re voting for the AIP (the party that nominated Wallace), you should waste your vote, so I guess I’m fine if you vote for the AIP.

# How You Get Homeopathy

Homeopathy is a system that has two primary claims. The first is the law of similars. It says that a dilute solution of a substance cures the ailment caused by a concentrated solution of the same substance. The second is the law of dilution, which says that the more dilute a homeopathic preparation is, the stronger its effect.

The second theory is the one people tend to focus on because it is clearly incorrect. The anti-homeopathy $$10^{23}$$ campaign is named after the order of magnitude of Avogadro’s number, that is the number of molecules of a substance in a 1M solution of a substance (typically a fairly strong solution, 1M saltwater is 5 teaspoons of salt in a liter of water). If you dilute by more than that amount, and many “strong” homeopathic preparations do, then with high probability you literally have 0 molecules of the active ingredient in your preparation. As the $$10^{23}$$ campaign’s slogan makes clear, “There’s nothing in it” and homeopathic medicines are just sugar pills.

# What is NP?

## What is P?

This seems like a silly question. This question seems easily answered by the surprisingly comprehensible explanation on P’s Wikipedia page:

It contains all decision problems that can be solved by a deterministic Turing machine using a polynomial amount of computation time

OK, so it’s the set of all decision problems (yes-or-no questions) that are $$O(n^k)$$ for some integer $$k$$. For example, sorting, which is $$\Theta(n \log n)$$ is P, since it’s also $$O(n^2)$$.

But let’s go for an alternate definition. Let’s take a C-like language and get rid of a bunch of features: subroutine calls, while loops, goto, for loops with anything other than i < n in the second field (where n is the length of the input), and modifying the for loop variable within the loop. Effectively, this language’s only loping construct is for(int varname = 0; varname < n; varname++) {/* Code that never modifies varname */}. Let’s call this language C-FOR.

# Storing Data With the Circle-Tree

## The Pair Abstraction

Computers need some way to store information. More specifically, we want to be able to store multiple pieces of data in one place so that we can retrieve it later. For now, we’ll look at the simplest way of storing data, a pair.

To express the concept of a “pair”, we need three machines.

1. A way to put two things together (construction, or “C” for short)
2. A way to get the first element (first, or “F” for short)
3. A way to get the second element (second, or “S” for short)

# Using Circle-Tree Drawings as a Calculator

So this isn’t just a procrastination game for doodling when you’re bored (and have a lot of colored pencils, for some reason). We can use circle-tree as a way to express math!

## Numbers

For math, the first thing we need are numbers. I’m just going to draw some pictures which are defined to be the numbers. We don’t worry too much about whether they are “really” numbers or not in the way that we don’t worry about whether “102” or “CII” is a better way to explain the concept of “how many cents in a dollar plus how many hands a person has”ness.1

So this is 0:

1. Although, off the record, for doing math CII is a terrible way of representing 102.

# Introducing the Circle-Tree System

If you want to follow along on paper, you’ll want some colored pencils. Like at least 5 colors, not including a pencil to draw links.

# Circtrees

In any case, we’ll call the diagrams we’re interested in “circtrees.”1 Circtrees can be constructed in one of three ways.

## 1. Dots

A dot of any color is a circtree. Here are some examples2:

1. You can probably guess where that name came from

2. I choose to draw pretty big dots, but if you’re doing this by hand, you’ll probably want to use smaller ones for ease of drawing.

# Counting Monoids

## What’s a Monoid?

A monoid is a set $$M$$ with an identity element $$e : M$$ and an operation $$* : M \to M \to M$$, where we have the laws:

• $$x * e = x$$
• $$e * x = x$$
• $$x * (y * z) = (x * y) * z$$

We write a monoid as a tuple $$(M, e, * )$$. Some example monoids (written in pseudo-Haskell) are

(Int, 0, (+))
(Int, 1, (*))
(Bool, True, (||))
((), (), \x y -> ())
([a], [], (++))

# The Porus Mirror Problem, Part 2

## Propagation

From last time, we know the quantity of light that escaped at each location and direction around the unit circle. We want to find how this translates into quantities of light at any point away from the circle. This situation can be modeled as so:

# The Porus Mirror Problem, Part 1

## The porus mirror problem

I’m pretty sure I came up with this problem on my own, but I’m not sure if its an existing problem under a different name. Anyway, the situation is as such:

1. We have a very short cylindrical tube. In fact, we’ll ignore the third dimension and just use a circle. For simplicity, we’re using units such that the radius is 1.
2. The tube is filled with a gas that emits corpuscles in one large burst. We assume a large enough number that we can model them continuously. We additionally assume that the emmissions are uniform in location and direction.
3. The tube itself is semitransparent, with transparency determined by a function $$t: [0, 2\pi] \to [0, 1]$$ of angle around the circle. Any light that does not pass through is reflected across the normal.

We wish to calculate at each point along the surface of the tube what is the total quantity of light emitted in each direction, from which we can easily calculate the light pattern this system will cast on any shape outside.

# Dependent Types and Categories, Part 1

So I’ve been trying to go through Category Theory for Scientists by writing the functions as code. So I obviously decided to use Haskell. Unfortunately, I ran into a wall: Haskell is actually pretty bad at representing categories.

Now, at first this was incredibly surprising. Frankly, I had started learning about category theory because of its association with Haskell. Unfortunately, I didn’t realize that Haskell isn’t category theory, it’s a category.

For instance, look at this definition of a category in math:

# Chaos, Distributions, and a Simple Function

$$\newcommand{\powser}[1]{\left{ #1 \right}}$$

## Iterated functions

The iteration of a function $$f^n$$ is defined as:

$f^0 = id$ $f^n = f \circ f^{n-1}$

For example, $$(x \mapsto 2x)^n = x \mapsto 2^n x$$.

Most functions aren’t all that interesting when iterated. $$x^2$$ goes to $$0$$, $$1$$ or $$\infty$$ depending on starting point, $$\sin$$ goes to 0, $$\cos$$ goes to something close to 0.739. $$\frac{1}{x}$$ has no well defined iteration, as $$(x \to x^{-1})^n = x \to x^{(-1)^n}$$ which does not converge.

OK, so the real reason I covered Products and Coproducts last time last time was to work toward a discussion of limits, which are a concept I have never fully understood in Category Theory.

## Diagram

As before, we have to define a few preliminaries before we can define a limit. The first thing we can define is a diagram, or a functor from another category to the current one. In Haskell, we can’t escape the current category of Hask, so we instead define a diagram as a series of types and associated functions. For example, we can define a diagram from the category $$\mathbf 2$$ containing two elements and no morphisms apart from the identities as a pair of undetermined types.

A diagram from the category $$\mathbf 2*$$, a category containing two elements and an arrow in each direction (along with the identities), (this implies that the two arrows compose in both directions to the identitity):

# Haskell Classes for Products and Coproducts

## Product Data Structures

Product Types are a familiar occurrence for a programmer. For example, in Java, the type int $$\times$$ int can be represented as:

class IntPair {
public final int first;
public final int second;
public IntPair(int first, int second) {
this.first = first;
this.second = second;
}
}

# Monoids, Bioids, and Beyond

## Two View of Monoids.

### Monoids

Monoids are defined in Haskell as follows:

class Monoid a where
m_id :: a
(++) :: a -> a -> a

Monoids define some operation with identity (here called m_id). We can define the required laws, identity, and associativity, as follows:

monoidLaw1, monoidLaw2 :: (Monoid a, Eq a) => a -> Bool
monoidLaw1 x = x ++ m_id == x
monoidLaw2 x = m_id ++ x == x

monoidLaw3 :: (Monoid a, Eq a) => a -> a -> a -> Bool
monoidLaw3 x y z = (x ++ y) ++ z == x ++ (y ++ z)

# Nontrivial Elements of Trivial Types

We know that types can be used to encode how to make useful data structures.

For example, a vehicle can be encoded as:

data Vehicle = Car { vin :: Integer, licensePlate :: String }
| Bike

In this case, valid values include cars with VINs and LPNs, or Bikes, which have neither of these data points.

However, some data types can seem far less useful.

# The Autogeneration of Landscapes

OK, so a break from theory and CS, and to applications. How do we produce reasonably realistic landscapes using differential equations?

## Mountains and Valleys

To simulate mountains and valleys, we can use a fairly simple algorithm that adds a random amount to a region, then recursively calls itself on each quadrant of the region. Afterwards, an image generally looks something like:

We can apply a Gaussian blur to the image to get a reasonably realistic depiction of mountains and valleys.

We can define the elevation as a function $$E(x, y, t)$$. Representing this as a landscape:

# C-Style Pointer Syntax Considered Harmful

## Declaration Follows Use

In C, a pointer to int has type int*, but really, this isn’t the right way of looking at it. For example, the following declaration:

int* x, y;

creates x of type int* and y of type int.

# The Triviality Norm

Feynman famously said that to a mathematician, anything that was proven was trivial. This statement, while clearly a lighthearted dig at mathematics, it does have a striking ring of truth to it.

## Logical Implication

In logic, there is the concept of implication. The idea is that if $$B$$ is true whenever $$A$$ is true we can say that $$A \implies B$$. This makes sense for statements that depend on a variable. For example, we can say that $$x > 2 \implies x > 0$$.

# A Monadic Model for Set Theory, Part 2

$$\newcommand{\fcd}{\leadsto}$$

Last time, we discussed funcads, which are a generalization of functions that can have multiple or no values for each element in their input.

The composition of funcads is defined to be compatible with the composition of functions, and it takes a fairly similar form:

$f :: Y \fcd Z \wedge f :: X \fcd Y \implies \exists (f \odot g :: X \fcd Z), (f \odot g)(x) = \bigcup_{\forall y \in g(x)} f(y)$

In other words, map the second function over the result of the first and take the union. If you’re a Haskell programmer, you probably were expecting this from the title and the name “funcad”: the funcad composition rule is just Haskell’s >=>.

# A Monadic Model for Set Theory, Part 1

$$\newcommand{\fcd}{\leadsto}$$

## Solving an Equation

A lot of elementary algebra involves solving equations. Generally, mathematicians seek solutions to equations of the form

$f(x) = b$

The solution that most mathematicians come up with to this problem is to define left and right inverses: The left inverse of a function $$f$$ is a function $$g$$ such that $$g \circ f = \mathrm {id}$$. This inverse applies to any function that is one-to-one. There is also a right inverse, a function $$g$$ such that $$f \circ g = \mathrm {id}$$ For example, the left inverse of the function

$x \mapsto \sqrt x :: \mathbb R^+ \rightarrow \mathbb R$

is the function

$x \mapsto x^2 :: \mathbb R \rightarrow \mathbb R^+$

# What is Category Theory?

$$\newcommand{\mr}{\mathrm}$$ So, I was reading Category Theory for Scientists recently and kept having people ask me: What is category theory? Well, until I hit chapter 3-4, I didn’t really have an answer. But now I do, and I realize that to just understand what it is doesn’t really require understanding how it works.

## Sets and functions

So, set theory is all about sets and functions. Why are we talking about set theory and not category theory, you may ask? I’ll get to category theory eventually, but for now, let’s discuss something a little more concrete.

Let’s look at subsets of the set $$\{a, b\}$$. We have the possibilities, which we name Empty, A set, B set, Universal set:

$E = \{\}, A = \{a\}, B = \{b\}, U = \{a, b\}$

# Stupid Regex Tricks

## Regexes, the duct tape of CS

Regexes are truly amazing. If you haven’t heard of them, they are a quick way to find (and sometimes replace) some body of text with another body of text. The syntax, while it has its critics, is generally pretty intuitive; for example a zip code might be represented as

[0-9]{5}(\s*-\s*[0-9]{4})?

which is read as “a five digit code optionally followed by a spaced dash and a four digit code”. Some whitespace and named groups might help the reading (use COMMENTS mode when parsing)

(?P<main_code>
[0-9]{5}
)
( # optional extension
(?P<optional_extension>
[0-9]{4}
)
)?

# Dictp Part 2

OK, so to continue the Dictp saga, some syntactic sugar.

## References to local variables.

The parser can be enhanced by rewriting anything that isn’t a built-in command or a string with a lookup to __local__.

E.g.,

• a $$\mapsto$$ __local__['a']
• 0 = 1 $$\mapsto$$ __local__['0'] = __local__['1']
• 0? $$\mapsto$$ __local__['0']?
• '0' $$\mapsto$$ '0'

# Dictp Part 1

So, a friend and I (I’m going to protect his anonymity in case he doesn’t want to be associated with the travesty that is what we ended up thinking up) were discussing ridiculous ideas for programming languages: you know the deal, writing C interpreters in Python, using only integers to represent everything, etc.

One idea, however, seemed a little less ridiculous to us (OK, just to me): a language in which there are two data types: Dictionary and String.

# Infsabot Strategy Part 2

OK, so to continue our Infsaboting.

### Correction

I made a mistake last time. I included SwitchInt, a way to switch on values of ExprDir, which is ridiculous since an ExprDir can only be constructed as a constant or an if branch to begin with.

So just imagine that I never did that.

## Guess and Check

OK, so how should our AI construct these syntax trees? At a very high level, we want to be able to 1) assess trees against collections of trees and 2) modify them randomly. We can represent this as a pair of types:

asses :: [RP] -> RP -> Double
modify :: RP -> StdGen -> (RP, StdGen)

I think the implementation of asses should be pretty clear: simply find the win rate (given that we can simulate an unlimited number of games).

Modify, on the other hand, is a little more complicated. There are a few ways a tree can be modified:

• Modify the value of a constant to be something else.
• Modify the value of a field to point to something else or a field
• Add leaves to the tree
• Remove leaves as a simplification step

# Infsabot Strategy Part 1

OK, so what is this Infsabot? Infsabot is a game I designed for people to write programs to play. Basically, it’s like chess, except where each piece acts independently of each other.

## The Game

There is a square board where each square is either empty or contains material. The game pieces, robots, require material to stay alive and perform tasks. At each turn, every robot reports which of the following it wants to do:

• Noop: The robot does nothing
• Die: The robot dies
• Dig: The robot digs to try to extract material
• Move: The robot can move in one of the four compass directions
• Fire: The robot can fire a quick-moving projectile (e.g., travel time = 0) in a compass direction
• Message: The robot can send a quick-moving message in a compass direction
• Spawn: The robot can create a new robot with its own initial material and appearance

To decide what to do, a robot has access to two things:

• A picture of the positions around it, to a given distance
• Whether or not the spot contains material
• Whether or not the spot contains a robot, and it’s appearance (not it’s team)
• A snapshot of its own hard drive, which is a massive relational database between Strings and Strings
• A list of all messages received

# What's a number?

OK, so a bit of a silly question. Of course, we all know what numbers are. But humor me, and try to define a number without using “number”, “quantity”, or anything that refers back to a number itself.

But, before we learn numbers, we have to first understand something simpler: counting. Couldn’t we define numbers in terms of counting? Well, yes, otherwise I wouldn’t be writing this article.

Now, to not end up with a forty page book, I’m only going to go over how to define the whole numbers (I’m going to ignore negative integers, fractions, $$\pi$$, etc.)

# Recursivized Types

So, a short break from $$\varepsilon—\delta$$.

Anyway, I’m going to look at Algebraic types in this section. Specifically, I am going to use Haskell types.

The provided code is all actual Haskell, in fact, I’m going to have to de-import the standard libraries because some of this is actually defined there. You can find all the code here

# Who Needs Delta? Defining the Hyper-Real Numbers


In high school Calculus, solving differential equations often involves something like the following:

$\frac{\d y}{\d x} = \frac{y}{x}$

$\frac{\d y}{y} = \frac{\d x}{x}$

$\int \frac{\d y}{y} = \int \frac{\d x}{x}$

$\log y = c + \log x$

$y = kx$

Now, I don’t know about anyone else, but this abuse of notation really bothered me at first. Everyone knows that $$\d x$$ isn’t an actual number by itself, but we seem to use it all the time as an independent variable.

# Ancient Greek Abstract Algebra: Introduction

Note: I’ve put the Ancient Greek Abstract Algebra series on hold for now. Please take a look at some of my other post(s).

## Modern constructions of $\mathbb{N}$

The natural numbers are taught to children as a basic facet of mathematics. One of the first things we learn is how to count. In fact, the name “Natural Numbers” itself implies that the natural numbers are basic.

However, modern mathematicians seem to have trouble with accepting the natural numbers as first class features of mathematics. In fact, there are not one but two common definitions of the naturals that can be used to prove the Peano Axioms (the basic axioms from which most of number theory can be proven).