Computational Thinking, Python, Linear Algebra, Linear Regression, Jupyter Notebook.
Best m=100 n=15 t=np.linspace(0, 1, m) # Vandermonde matrix A=np.stack([t**i for i in range(n)], 1) b=np.exp(np.sin(4*t)) # This will turn out to normalize the solution to be 1 b /= 2006.787453080206 from matplotlib import pyplot as plt %matplotlib inline plt.plot(t, b) Check that we get 1: 1 -