Machine learning is used as advance curve fitting tool, i.e We fit a curve amongst the known data points to know hitherto unknown data points ,only now using some sophisticated tools.There is also another characteristic of Machine learning, which in my view is far less utilized than for curve-fit applications and that is the ability to discover the underlying rules of the data which is used to train. This project tries to utilize this aspect of machine learning and deep learning, especially Neural Networks.
In this project, the models used are Long-Short term Memory Neural Networks(LSTM) and the rules are given in the form of differential equations with sufficient boundary conditions and therefore the output will be the solution of the differential equation.
1.Navigate to the folder containing the "setup.py" file
2.Execute the command "pip install ."
1.Navigate to the folder containing the files of test cases(named with suffix tests_)
2.Execute pytest \filename.py (eg. pytest tests_ops.py)
Example usage:
>>> import autodiff as ad
>>> x = ad.Variable(3, name="x")
>>> y = ad.Variable(4, name="y")
>>> z = x * y + ad.Exp(x + 3)
>>> z
< autodiff.core.ops.Add object at 0x7fe91284f080>
>>> z()
array(415.4287934927351)
>>> x_grad = ad.grad(z, [x])[0]
>>> x_grad
< autodiff.core.ops.Add object at 0x7fe9125ecc18>
>>> x_grad()
array(407.4287934927351)This project is inspiried from "https://github.com/bgavran/autodiff" and few function are taken from that repo.