Combinatorics.jl
JLD2.jl
Combinatorics.jl | JLD2.jl | |
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1 | 2 | |
208 | 523 | |
0.0% | 1.3% | |
6.5 | 8.1 | |
24 days ago | 5 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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Combinatorics.jl
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Odds of matching six sided dice
For the exact solution, I split it into a few parts, one of which required iterating over multiset combinations, for which there is already code provided in the Combinatorics.jl package. I copy/pasted the pieces that I needed:
JLD2.jl
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
First, you need to save the model from the notebook to a file. For this you can use JLD2.jl module. This module used to serialize Julia object to HDF5-compatible format (which is well known by Python data scientists) and save it to a file.
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Best format to save matrices to a text file? (R interop)
I didn't realize this was the Julia subreddit. HDF5 or multiple CSV files would be my suggestion. As a side note, check out the JLD2 package. It's a HDF5 compatible format where the package is written in pure Julia.
What are some alternatives?
julia - The Julia Programming Language
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
Plots.jl - Powerful convenience for Julia visualizations and data analysis
Rocket.jl - Functional reactive programming extensions library for Julia
StatsWithJuliaBook
PlotDocs.jl - Documentation for Plots.jl
julia_titanic_model - Titanic machine learning model and web service
seaborn - Statistical data visualization in Python
DataFrames.jl - In-memory tabular data in Julia
NumPy - The fundamental package for scientific computing with Python.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
ScikitLearn.jl - Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/