+
+# coeffs, support_set = perceptron_k(X, Y, k1)
+# coeffs, support_set = perceptron_k(X, Y, kg)
+res = training_set_size
+# for c, X in zip(coeffs, support_set):
+# pl.plot(X[0], 'xr')
+
+X = apply_plongement(X, plongement_phi)
+w = perceptron_nobias(X, Y)
+for x in range(res):
+ for y in range(res):
+ if abs(f(w, -3 / 2 + 3 * x / res, -3 / 2 + 3 * y / res)) < 0.01:
+ pl.plot(-3 / 2 + 3 * x / res, -3 / 2 + 3 * y / res, 'xb')
+