interp_vs_linreg.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Example to understand the difference between interpolation and regression

import numpy as np
from scipy import interpolate
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# ===== Regression
x = np.array([5, 15, 25, 35, 45, 55]).reshape((-1, 1))
y = np.array([5, 20, 14, 32, 22, 38])

model = LinearRegression()
model.fit(x, y)

y_pred = model.predict(x)

plt.figure()
plt.plot(x, y, 'o')
plt.plot(x, y_pred)

plt.legend(['Original', 'Regression'])
plt.show()

# ===== Interpolation
x = np.array([5, 15, 25, 35, 45, 55])
y = np.array([5, 20, 14, 32, 22, 38])

f = interpolate.interp1d(x, y)
y_new = f(x)

plt.plot(x, y, 'o', x, y_new, '-')
plt.legend(['Original', 'Interpolation'])
plt.show()