Machine Learning in Elixir - Chapter 3

Mix.install([
  {:nx, "~> 0.5"},
  {:exla, "~> 0.5"},
  {:kino, "~> 0.8"},
  {:stb_image, "~> 0.6"},
  {:vega_lite, "~> 0.1"},
  {:kino_vega_lite, "~> 0.1"},
], config: [nx: [default_backend: EXLA.Backend]])

Section

a = Nx.tensor([1, 2, 3])
b = Nx.tensor([4.0, 5.0, 6.0])
c = Nx.tensor([1, 0, 1], type: :u8)

IO.inspect(a, label: "a")
IO.inspect(b, label: "b")
IO.inspect(c, label: "c")

goog_current_price = 2677.32
goog_pe = 23.86
goog_mkt_cap = 1760

meta_current_price = 133.93
meta_pe = 11.10
meta_mkt_cap = 360

stocks_matrix = Nx.tensor([
  [goog_current_price, goog_pe, goog_mkt_cap],
  [meta_current_price, meta_pe, meta_mkt_cap]
])

sales_day_1 = Nx.tensor([32, 10, 14])
sales_day_2 = Nx.tensor([10, 24, 21])
total_sales = Nx.add(sales_day_1, sales_day_2)

keep_rate = 0.9
unreturned_sales = Nx.multiply(keep_rate, total_sales)

price_per_product = Nx.tensor([9.95, 10.95, 5.99])
revenue_per_product = Nx.multiply(unreturned_sales, price_per_product)

sales_matrix = Nx.tensor([
  [32, 10, 14],
  [10, 24, 21]
])
|> Nx.transpose()

invert_color_channels = Nx.tensor([
  [-1, 0, 0],
  [0, -1, 0],
  [0, 0, -1]
])

"/Users/jeanrigotti/dev/machine-learning-in-elixir/capivara.jpg"
|> StbImage.read_file!()
|> StbImage.resize(256, 256)
|> StbImage.to_nx()
|> Nx.dot(invert_color_channels)
|> Nx.as_type({:u, 8})
|> Kino.Image.new()

simulation = fn key ->
  {value, key} = Nx.Random.uniform(key)
  if Nx.to_number(value) < 0.5, do: {0, key}, else: {1, key}
end

key = Nx.Random.key(42)

for n <- [10, 100, 1000, 10_000] do
  Enum.map_reduce(1..n, key, fn _, key -> simulation.(key) end)
  |> elem(0)
  |> Enum.sum()
  |> IO.inspect()
end

defmodule BerryFarm do
  import Nx.Defn

  defn profits(trees) do
    trees
    |> Nx.subtract(1)
    |> Nx.pow(4)
    |> Nx.negate()
    |> Nx.add(Nx.pow(trees, 3))
    |> Nx.add(Nx.pow(trees, 2))
  end

  defn profits_derivative(trees) do
    grad(trees, &profits/1)
  end
end

trees = Nx.linspace(0, 4, n: 100)
profits = BerryFarm.profits(trees)

alias VegaLite, as: Vl

Vl.new(title: "Berry Profits", width: 800, height: 500)
|> Vl.data_from_values(%{
  trees: Nx.to_flat_list(trees),
  profits: Nx.to_flat_list(profits)
})
|> Vl.mark(:line, interpolate: :basis)
|> Vl.encode_field(:x, "trees", type: :quantitative)
|> Vl.encode_field(:y, "profits", type: :quantitative)

trees = Nx.linspace(0, 3, n: 100)
profits = BerryFarm.profits(trees)
profits_derivative = BerryFarm.profits_derivative(trees)

alias VegaLite, as: Vl
title = "Berry Profits and Profits Rate of Change"

Vl.new(title: title, width: 800, height: 500)
|> Vl.data_from_values(%{
  trees: Nx.to_flat_list(trees),
  profits: Nx.to_flat_list(profits),
  profits_derivative: Nx.to_flat_list(profits_derivative)
})
|> Vl.layers([
    Vl.new()
    |> Vl.mark(:line, interpolate: :basis)
    |> Vl.encode_field(:x, "trees", type: :quantitative)
    |> Vl.encode_field(:y, "profits", type: :quantitative),
  
    Vl.new()
    |> Vl.mark(:line, interpolate: :basis)
    |> Vl.encode_field(:x, "trees", type: :quantitative)
    |> Vl.encode_field(:y, "profits_derivative", type: :quantitative)
    |> Vl.encode(:color, value: "#ff0000")
])

defmodule GradFn do
  import Nx.Defn

  defn my_function(x) do
    x
    |> Nx.cos()
    |> Nx.exp()
    |> Nx.sum()
    |> print_expr()
  end

  defn grad_my_function(x) do
    grad(x, &my_function/1) |> print_expr()
  end
end

GradFn.grad_my_function(Nx.tensor([1.0, 2.0, 3.0]))