1FfUrl0

Chapter 1
>>> print(res)
[ 3.17389767e+08]
This means that the best straight line fit is the following function:
f(x) = 2.59619213 * x + 989.02487106.
We then use poly1d() to create a model function from the model parameters.
>>> f1 = sp.poly1d(fp1)
>>> print(error(f1, x, y))
317389767.34
We have used full=True to retrieve more details on the fitting process. Normally,
we would not need it, in which case only the model parameters would be returned.
In fact, what we do here is simple curve fitting. You can find out
more about it on Wikipedia by going to http://en.wikipedia.
org/wiki/Curve_fitting.
We can now use f1() to plot our first trained model. In addition to the earlier
plotting instructions, we simply add the following:
fx = sp.linspace(0,x[-1], 1000) # generate X-values for plotting
plt.plot(fx, f1(fx), linewidth=4)
plt.legend(["d=%i" % f1.order], loc="upper left")
The following graph shows our first trained model:
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