jyuan1986/onr2017

Scripts for machine learning at ONR project (2017-)

onr2017

Scripts for machine learning at ONR project (2017-)

Prerequisites: Python 2, Numpy, Sklearn and Matplotlib Packages

Data: unnorma.input.data contains four columns, which are Temperature, Concentration, Dwelling Time and %Mass Change. norm.input.data contains four columns after normalization, consistent with unnorma.input.data.

Machine Learning: Four machine learning models are explored based on 19 bulk sample results. The target property is %Mass Change and they're normalized before the machine learning process.

1. Multivariate Linear Regression

http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html

2. Support Vector Regression

http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html

3. Kernel Ridge Regression

http://scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html

4. Neural Network

http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor