GitHub - bakineggs/mrscake: data prediction library

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 195 Commits
data		data
lib		lib
.gitignore		.gitignore
Makefile		Makefile
README		README
ast.c		ast.c
ast.h		ast.h
ast_transforms.c		ast_transforms.c
ast_transforms.h		ast_transforms.h
autoupdate.py		autoupdate.py
build_model.py		build_model.py
codegen.c		codegen.c
codegen.h		codegen.h
codegen_c.c		codegen_c.c
codegen_js.c		codegen_js.c
codegen_python.c		codegen_python.c
codegen_ruby.c		codegen_ruby.c
config.h		config.h
constant.c		constant.c
constant.h		constant.h
cvtools.cpp		cvtools.cpp
cvtools.h		cvtools.h
dataset.c		dataset.c
dataset.h		dataset.h
dict.c		dict.c
dict.h		dict.h
easy_ast.h		easy_ast.h
environment.c		environment.c
environment.h		environment.h
io.c		io.c
io.h		io.h
job.c		job.c
job.h		job.h
list.c		list.c
list.h		list.h
model.c		model.c
model_cv_ann.cpp		model_cv_ann.cpp
model_cv_dtree.cpp		model_cv_dtree.cpp
model_cv_linear.cpp		model_cv_linear.cpp
model_cv_svm.cpp		model_cv_svm.cpp
model_perceptron.c		model_perceptron.c
model_select.c		model_select.c
model_select.h		model_select.h
mrscake.h		mrscake.h
mrscake.py.c		mrscake.py.c
mrscake.rb.c		mrscake.rb.c
multimodel.cpp		multimodel.cpp
net.c		net.c
net.h		net.h
python_interpreter.c		python_interpreter.c
serialize.c		serialize.c
serialize.h		serialize.h
server.c		server.c
settings.c		settings.c
settings.h		settings.h
stringpool.c		stringpool.c
stringpool.h		stringpool.h
test_ast.c		test_ast.c
test_model.c		test_model.c
test_python_module.py		test_python_module.py
test_ruby_module.rb		test_ruby_module.rb
test_server.c		test_server.c
test_subset.c		test_subset.c
var_selection.c		var_selection.c
var_selection.h		var_selection.h
varselect_cv_dtree.cpp		varselect_cv_dtree.cpp

Repository files navigation

mrscake (read: "Mrs. Cake") is a model selection algorithm built 
on top of machine learning algorithms from OpenCV.

Compile it using
    make
    make install
.
(Potentially tweak the Makefile first)

It has a Ruby and a Python interface.

Python:
-------

import mrscake
data = mrscake.DataSet()
data.add(["a", 1.0, "blue"], output="yes")
data.add(["a", 3.0, "red"], output="yes")
data.add(["b", 2.0, "red"], output="no")
data.add(["b", 3.0, "blue"], output="no")
data.add(["a", 5.0, "blue"], output="yes")
data.add(["b", 4.0, "blue"], output="yes")
model = data.train()

result = model.predict(["a", 2.0, "red"])

code = model.generate_code("python") # or: ruby, javascript, c

Ruby:
-----

require 'mrscake'
data = MrsCake::DataSet.new
data.add([:a, 1.0, :blue], :yes)
data.add([:a, 3.0, :red], :yes)
data.add([:b, 2.0, :red], :no)
data.add([:b, 3.0, :blue], :no)
data.add([:a, 5.0, :blue], :yes)
data.add([:b, 4.0, :blue], :yes)
model = data.train()
model.print

result = model.predict([:a, 2.0, :red])

code = model.generate_code("ruby") # or: ruby, javascript, c