piecewise linear regression python
from scipy import optimize
def piecewise_linear(x, x0, x1, b, k1, k2, k3):
condlist = [x < x0, (x >= x0) & (x < x1), x >= x1]
funclist = [lambda x: k1*x + b, lambda x: k1*x + b + k2*(x-x0), lambda x: k1*x + b + k2*(x-x0) + k3*(x - x1)]
return np.piecewise(x, condlist, funclist)
p , e = optimize.curve_fit(piecewise_linear, x, y)
xd = np.linspace(-30, 30, 1000)
plt.plot(x, y, "o")
plt.plot(xd, piecewise_linear(xd, *p))
4.5
2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf
# plot inline rather than pop out
%matplotlib inline
# change the plot size, default is (6, 4) which is a little small
plt.rcParams['figure.figsize'] = (16, 12)
np.random.seed(9999)
x = np.random.normal(0, 1, 1000) * 10
y = np.where(x < -15, -2 * x + 3 , np.where(x < 10, x + 48, -4 * x + 98)) + np.random.normal(0, 3, 1000)
plt.scatter(x, y, s = 5, color = u'b', marker = '.', label = 'scatter plt')
plt.show()
Thank you!
2
4.5
(2 Votes)
0
Are there any code examples left?
New code examples in category Python
