apow2

abstract type Person end
abstract type Tourist <:Person end
abstract type Deer end

encounter(a::Deer, b::Tourist) = "bows politely"
encounter(a::Tourist, b::Deer) = "feeds"
encounter(a::Person, b::Deer) = "beckons"
encounter(a::Deer, b::Person) = "ignores"
encounter(a::Tourist, b::Deer, foo::String) = "feeds deer $foo"

apow2

import LinearAlgebra.det

apow2

encounter(a::Person, b::Person) = "hello"
encounter(a::Person, b:: Tourist) = "takes picture"
encounter(a::Person, b::Person, c::Person) = "converses"

dine(a::Tourist) = "picks up fork"

apow2

parfor i = 1:100
  A(i) = eig(rand(500))
end

apow2

x = [1, 1];
for k = 3:1000000
    x(k) = x(k-1) + x(k-2);
end

x = zeros(1000000, 1); x(1:2) = [1, 1]
for k = 3:1000000
    x(k) = x(k-1) + x(k-2);
end

apow2

a = rand(500, 500)
b = rand(500, 500)
c = rand(500, 1)

a*b*c % O(N^3) time complexity

a*(b*c) % O(N^2) time complexity

apow2

ddddd

apow2

t = 0:.05:20;
y = exp(t);

i = 0;
for t = 0:.05:20
    i = i + 1;
    y(i) = exp(t);
end

apow2

x = diag(ones(10000, 1)) % matrix with ones on diagonal and zero elsewhere

z = sparse(x) % compressed adjacency list representation

apow2

using DataFrames
using CSV
using Random
using LinearAlgebra

redwine = DataFrames.DataFrame(CSV.File("winequality-red.csv"))

mat = Matrix(redwine)[:, 1:11]
A = mat[:, 1:end-1] #features
y = mat[:, end]; #labels