Popularity
6.2
Stable
Activity
0.0
Declining
94
7
5
Description
Turn data into functions! A simple and functional machine learning library written in elixir.
Monthly Downloads: 24
Programming language: Gleam
License: Apache License 2.0
Emel alternatives and similar packages
Based on the "Statistics" category.
Alternatively, view Emel alternatives based on common mentions on social networks and blogs.
-
Plausible Analytics
Simple, open source, lightweight (< 1 KB) and privacy-friendly web analytics alternative to Google Analytics. -
numerix
A collection of useful mathematical functions in Elixir with a slant towards statistics, linear algebra and machine learning -
-
Statistex
Calculate statistics on data sets, reusing previously calculated values or just all metrics at once. Part of the benchee library family. -
simple_stat_ex
Simple Stat Ex can be used to keep counters around hourly daily or other activity for an elixir project
InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
Promo
www.influxdata.com
Do you think we are missing an alternative of Emel or a related project?
README
# 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:
def deps do
[
{:emel, "~> 0.3.0"}
]
end
The docs can be found at https://hexdocs.pm/emel/0.3.0.
## Usage
# set up the aliases for the module
alias Emel.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
- K Nearest Neighbors
- Decision Tree
- Naive Bayes
- K Means
- Perceptron
- Logistic Regression
- Neural Network
alias Emel.Ml.DecisionTree, as: DecisionTree
alias Emel.Help.Model, as: Mdl
alias Emel.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