There is a data sample. It needs to find the coefficients of the polynomial of the 3rd degree and a piecewise linear function. Polina I found very simple using functions
. It was possible to build a schedule and obtain a specific equation. Now I would like to do the same to obtain the coefficients of several linear functions. I managed to find an example of a code that built a schedule, but to remove the equations for three straight lines. Of course, I can count them manually, but I would like to know if there is a regular method for solving this task?
Import NUMPY AS NP Import Matplotlib.pyPlot AS PLT from scipy import optimize 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 ') 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)) Plt.Show ()
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