scorystal alternatives and similar shards

Based on the "Science and Data analysis" category.
Alternatively, view scorystal alternatives based on common mentions on social networks and blogs.

bottle

9.2 4.2 scorystal VS bottle

Scientific computing in pure Crystal
machine

7.5 0.0 scorystal VS machine

Simple machine learning algorithm for crystal lang

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

crystal-learn

7.4 0.0 scorystal VS crystal-learn

Machine Learning in Crystal
linalg

7.0 7.8 scorystal VS linalg

Linear algebra library based on LAPACK
ishi

7.0 0.0 scorystal VS ishi

Graph plotting package with a small API and sensible defaults powered by gnuplot.
stats

5.7 0.0 scorystal VS stats

An expressive crystal implementation of statistical distributions and usual math functions. :bar_chart:
predict.cr

4.7 0.0 L1 scorystal VS predict.cr

Satellite prediction library for crystal using the sgp4 model
chizge

3.9 0.0 scorystal VS chizge

A Network (Graph) Analysis library for Crystal Language, inspired by NetworkX.
alea

3.2 0.0 scorystal VS alea

Repeatable pseudo-random sampling, CDF over most known probability distributions.

* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.

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README

Scorystal

Crystal scoring API for Predictive Model Markup Language (PMML).

Currently supports random forest and gradient boosted models.

Will be happy to implement new kinds of models by demand, or assist with any other issue.

Contact me here or at [email protected].

Installation

Add this to your application's shard.yml:

dependencies:
  scorystal:
    github: asafschers/scorystal

Usage

require "scorystal"

# Parse PMML file
pmml_text = File.read("spec/pmmls/gbm.pmml")
parsed_pmml = XML.parse(pmml_text, XML::ParserOptions::NOBLANKS)

# Set features hash

json = %({"F1":null,"F2":21371,"F3":"AA"}")
features = Scorystal.features_hash(json)

# Gradient Boosted Model

gbm = Gbm.new(parsed_pmml)
puts gbm.score(features)

# Random Forest

rf = RandomForest.new(parsed_pmml)
puts rf.decisions_count(features)