# Emel
Turn data into functions! A simple and functional **machine learning** library written in **elixir**.
## Installation
The package can be installed by adding `emel` to your list of dependencies in `mix.exs`:
```elixir
def deps do
[
{:emel, "~> 0.1.0"}
]
end
```
The docs can be found at [https://hexdocs.pm/emel](https://hexdocs.pm/emel).
## Usage
```elixir
# set up the aliases for the module
alias Ml.KNearestNeighbors, as: KNN
dataset = [
%{"x1" => 0.0, "x2" => 0.0, "x3" => 0.0, "y" => 0.0},
%{"x1" => 0.5, "x2" => 0.5, "x3" => 0.5, "y" => 1.5},
%{"x1" => 1.0, "x2" => 1.0, "x3" => 1.0, "y" => 3.0},
%{"x1" => 1.5, "x2" => 1.5, "x3" => 1.5, "y" => 4.5},
%{"x1" => 2.0, "x2" => 2.0, "x3" => 2.0, "y" => 6.0},
%{"x1" => 2.5, "x2" => 2.5, "x3" => 2.5, "y" => 7.5},
%{"x1" => 3.0, "x2" => 3.3, "x3" => 3.0, "y" => 9.0}
]
# turn the dataset into a function
f = KNN.predictor(dataset, ["x1", "x2", "x3"], "y", 2)
# make predictions
f.(%{"x1" => 1.725, "x2" => 1.725, "x3" => 1.725})
# 5.25
```
### Implemented Algorithms
* [Linear Regression](https://hexdocs.pm/emel/Ml.LinearRegression.html)
* [K Nearest Neighbors](https://hexdocs.pm/emel/Ml.KNearestNeighbors.html)
* [Decision Tree](https://hexdocs.pm/emel/Ml.DecisionTree.html)
* [Naive Bayes](https://hexdocs.pm/emel/Ml.NaiveBayes.html)
* [K Means](https://hexdocs.pm/emel/Ml.KMeans.html)
```elixir
alias Ml.DecisionTree, as: DecisionTree
alias Help.Model, as: Mdl
alias Math.Statistics, as: Stat
dataset = [
%{risk: "high", collateral: "none", income: "low", debt: "high", credit_history: "bad"},
%{risk: "high", collateral: "none", income: "moderate", debt: "high", credit_history: "unknown"},
%{risk: "moderate", collateral: "none", income: "moderate", debt: "low", credit_history: "unknown"},
%{risk: "high", collateral: "none", income: "low", debt: "low", credit_history: "unknown"},
%{risk: "low", collateral: "none", income: "high", debt: "low", credit_history: "unknown"},
%{risk: "low", collateral: "adequate", income: "high", debt: "low", credit_history: "unknown"},
%{risk: "high", collateral: "none", income: "low", debt: "low", credit_history: "bad"},
%{risk: "moderate", collateral: "adequate", income: "high", debt: "low", credit_history: "bad"},
%{risk: "low", collateral: "none", income: "high", debt: "low", credit_history: "good"},
%{risk: "low", collateral: "adequate", income: "high", debt: "high", credit_history: "good"},
%{risk: "high", collateral: "none", income: "low", debt: "high", credit_history: "good"},
%{risk: "moderate", collateral: "none", income: "moderate", debt: "high", credit_history: "good"},
%{risk: "low", collateral: "none", income: "high", debt: "high", credit_history: "good"},
%{risk: "high", collateral: "none", income: "moderate", debt: "high", credit_history: "bad"}
]
{training_set, test_set} = Mdl.training_and_test_sets(dataset, 0.75)
f = DecisionTree.classifier(training_set, [:collateral, :income, :debt, :credit_history], :risk)
predictions = Enum.map(test_set, fn row -> f.(row) end)
actual_values = Enum.map(test_set, fn %{risk: v} -> v end)
Stat.similarity(predictions, actual_values)
# 0.75
```
### Mathematics
* [Algebra](https://hexdocs.pm/emel/Math.Algebra.html)
* [Geometry](https://hexdocs.pm/emel/Math.Geometry.html)
* [Statistics](https://hexdocs.pm/emel/Math.Statistics.html)
```elixir
alias Ml.LinearRegression, as: LR
alias Help.Model, as: Mdl
alias Math.Statistics, as: Stat
dataset = [
%{x1: 1, x2: 1, y: -1},
%{x1: 1, x2: 2, y: -1},
%{x1: 1, x2: 3, y: -2},
%{x1: 1, x2: 4, y: -4},
%{x1: 2, x2: 1, y: 1},
%{x1: 2, x2: 2, y: 1},
%{x1: 2, x2: 3, y: 0},
%{x1: 2, x2: 4, y: -1},
%{x1: 3, x2: 1, y: 3},
%{x1: 3, x2: 2, y: 2},
%{x1: 3, x2: 3, y: 1},
%{x1: 3, x2: 4, y: 0},
%{x1: 4, x2: 1, y: 5},
%{x1: 4, x2: 2, y: 4},
%{x1: 4, x2: 3, y: 4},
%{x1: 4, x2: 4, y: 3}
]
{training_set, test_set} = Mdl.training_and_test_sets(dataset, 0.8)
f = LR.predictor(training_set, [:x1, :x2], :y)
predictions = Enum.map(test_set, fn row -> f.(row) end)
actual_values = Enum.map(test_set, fn %{y: v} -> v end)
Stat.mean_absolute_error(predictions, actual_values)
# 0.5889423076923077
```