# Integers
1

# Floating point numbers
1.0

# Strings
"a"

# Complex numbers
1 + 2im

# Vectors
[1, 2, 3]

# Matrices
[1.0 2.0; 3.1 4.2]

# Elementary functions
sin(1.0)

subtypes(Any)[1:4]

subtypes(Number)

subtypes(Real)

subtypes(Integer)

subtypes(None)

sqrt(2)

sqrt(2.0)

1 + 1

1.0 + 1.0

"wat" + 1

1 + "wat"

"wat" - 1

1 - "wat"

function foo(a, b)
    return a + b
end

foo(1, 2)

foo(1.0, 2)

bar(a, b) = a * b

bar(2, 3)

bar(1, "wat")

bar("NaN", "NaN")

folly(a::Integer, b::Integer) = 1

folly(a::Float64, b::Float64) = 2

folly(1, 2)

folly(1.0, 2.0)

type NewType
    a::Int
    b::UTF8String
end

x = NewType(1, "this is a string")

x.a, x.b

module NewNamespace
    c = 42
    export c
end

c

using NewNamespace

c

c = 41

NewNamespace.c

foo(a, b) = a + b

code_llvm(foo, (Int, Int))

code_llvm(foo, (Float64, Float64))

code_native(foo, (Int, Int))

code_native(foo, (Float64, Float64))

function foo()
	r = 0
	for i in 1:12
		r += 2
	end
	return r
end

code_llvm(foo, ())

function fib{T <: Integer}(n::T)
    if n == zero(T)
        return zero(T)
    elseif n == one(T)
        return one(T)
    else
        a, b = zero(T), one(T)
        i = 1
        while i < n
            a, b = b, a + b
            i += 1
        end
        return b
    end
end

fib(95)

fib(BigInt(95))

using StatsBase

modes([1, 1, 2, 2, 3])

corspearman(rand(100), rand(100))

using DataArrays

da = @data([1, 2, NA, 4])

using DataFrames

df = DataFrame(
    A = [1, 2, 3],
    B = ["a", "b", "c"],
    C = [1//2, 3//4, 5//6]
)

using Distributions

srand(1)

x = rand(Normal(10, 1), 10)

pdf(Normal(10, 1), 1.0)

loglikelihood(Normal(10, 1), x)

using RDatasets

iris = dataset("datasets", "iris")

using Optim

x = rand(Normal(31, 11), 1_000)

function nll(theta)
    -loglikelihood(Normal(theta[1], exp(theta[2])), x)
end

nll([1.0, 1.0])

fit = optimize(nll, [0.0, 0.0])

theta = fit.minimum

Normal(theta[1], exp(theta[2]))