Mixed degree

examples/mixed_degree.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import pwlf
+
+x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+y = [1, 2, 3, 4, 4.25, 3.75, 4, 5, 6, 7]
+
+degree_list = [1, 0, 1]
+
+my_pwlf = pwlf.PiecewiseLinFit(x, y, degree=degree_list)
+
+breaks = my_pwlf.fit(3)
+
+# generate predictions
+x_hat = np.linspace(min(x), max(x), 1000)
+y_hat = my_pwlf.predict(x_hat)
+
+plt.figure()
+plt.plot(x, y, 'o')
+plt.plot(x_hat, y_hat)
+plt.show()

examples/mixed_degree_forcing_slope.py

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+import numpy as np
+from scipy.optimize import differential_evolution
+import matplotlib.pyplot as plt
+import pwlf
+
+x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+y = [1, 2, 3, 4, 4.25, 3.75, 4, 5, 6, 7]
+
+my_pwlf = pwlf.PiecewiseLinFit(x, y, degree=1)
+
+# perform initial fit
+breaks = my_pwlf.fit(2)
+
+
+def my_fun(beta):
+    # assing variables to the pwlf object
+    my_pwlf.beta[0] = beta[0]  # first line offset
+    my_pwlf.beta[1] = beta[1]  # first line slope
+    my_pwlf.beta[2] = -1*beta[1]
+    my_pwlf.fit_breaks[1] = beta[2]  # breakpoint
+    # generate predictions
+    y_temp = my_pwlf.predict(my_pwlf.x_data)
+    # compute ssr
+    e = y_temp - my_pwlf.y_data
+    return np.dot(e, e)
+
+
+bounds = np.zeros((3, 2))
+# first line offset
+bounds[0, 0] = -100.0  # lower bound
+bounds[0, 1] = 100.0  # upper bound
+# first line slope
+bounds[1, 0] = -100.0  # lower bound
+bounds[1, 1] = 100.0  # upper bound
+# breakpont
+bounds[2, 0] = 2.  # lower bound
+bounds[2, 1] = 6.  # upper bound
+
+res = differential_evolution(my_fun, bounds, maxiter=1000, popsize=30,
+                             disp=True)
+
+# assign optimum to my_pwlf object
+my_fun(res.x)
+
+# generate predictions
+x_hat = np.linspace(min(x), max(x), 1000)
+y_hat = my_pwlf.predict(x_hat)
+
+plt.figure()
+plt.plot(x, y, 'o')
+plt.plot(x_hat, y_hat)
+plt.show()