feat: polyfeat tutorial

src/klausurvorbereitung/regression_polyfit.py

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+#%%
+# Polyfit tutorial mit Plot
+# Python initialisieren
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Parameter
+x_0 = 1
+x_E = 7.0
+y_a = -2
+y_b = 3
+dg = 1
+pr = 3
+lw = 3
+fig = 1
+tc_x = np.r_[x_0: x_E + 0.5:0.5]
+tc_y = np.r_[y_a:y_b + 0.5:0.5]
+
+# Daten
+x_data = np.r_[x_0:x_E+1]
+y_data = np.array([2.5, 2.2, 1.5, 1.0, 0.6, -0.3, -1.4])
+
+# Berechnungen
+p = np.polyfit(x_data, y_data, dg)
+g_data = np.polyval(p, x_data)
+
+# Ausgabe
+print(60 * "*")
+print("__file__")
+print(60 * "*")
+print(f"Steigung:           m = {p[0]:#.{pr}g}")
+print(f"y-Achsenabschnitt:  q = {p[1]:#.{pr}g}")
+
+# Plot
+fh = plt.figure(fig)
+plt.plot(x_data, g_data, linewidth=lw)
+plt.plot(x_data, y_data, "o", linewidth=lw)
+plt.xlabel(r"$x$")
+plt.ylabel(r"$y$")
+plt.xticks(tc_x)
+plt.yticks(tc_y)
+plt.grid(visible=True)
+plt.axis("image")