numpy Simple Linear Regression Using np.polyfit
Example
We create a dataset that we then fit with a straight line $f(x) = m x + c$.
npoints = 20
slope = 2
offset = 3
x = np.arange(npoints)
y = slope * x + offset + np.random.normal(size=npoints)
p = np.polyfit(x,y,1) # Last argument is degree of polynomial
To see what we've done:
import matplotlib.pyplot as plt
f = np.poly1d(p) # So we can call f(x)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.plot(x, y, 'bo', label="Data")
plt.plot(x,f(x), 'b-',label="Polyfit")
plt.show()
Note: This example follows the numpy documentation at https://docs.scipy.org/doc/numpy/reference/generated/numpy.polyfit.html quite closely.
