Data Structure Operations:
| Operation | Math | Python 3 | C++ | Score |
|---|---|---|---|---|
| Column vector instantiation and initialisation | \(\mathbf{c} = \begin{bmatrix} 1 \\ 2 \end{bmatrix}\) |
12 import numpy as np
c = np.array([[1, 2]]).T |
1 |
|
| Row vector instantiation and initialisation | \(\mathbf{r} = \begin{bmatrix} 1 & 2 \end{bmatrix}\) |
12 import numpy as np
r = np.array([[1,2]]) |
1 |
Unordered Collection of Points. Order is not important.
An absolute way of an ecoding a variable.
A set of all possible events.
In a way a basic straight variable is the simplest possible function, where domain equals the codomain, i.e. no transformation is being made.
Mnemonics:
Variable Symbol | Shape | Sound |
M | Massif | Massif |
μ | Mountain | Mountain |
S | Snake | Snake |
σ | Spring | Spring |
x | [1] Fish | NA |
θ | [1] Fishing float. | NA |
A collection of points (real line, plane, solid, ..., etc) in Hilbert Space.
1-D, 2-D, 3-D, ... etc
Any variable can be expressed in simple equation form (polynomial, trigonometric, log or exponential).
Constrained Hilbert Space
Recurion is a way to define a variable, i.e. build-out the solution-space. Because each node only has 1 neighbour people tend to ignore the notation altogether at the detriment of unifying big-picture clarity.
def match(pattern, string):
# Stoppers
if len(pattern)==0: return len(string)==0
# Modifiers
first_match = len(string)!=0 and pattern[0] in {string[0],"."}
return first_match and match(string[1:], pattern[1:])Can be written as:
def match(node):
pattern, string = node
# Stoppers
if len(pattern)==0: return len(string)==0
# Modifiers
first_match = len(string)!=0 and pattern[0] in {string[0],"."}
neighbours = [(string[1:], pattern[1:])]
for neighbour in neighbours:
return first_match and match(neighbour)
A Point that lives in the constrained Hilbert Space.