+def f_from_k(coeffs, support_set, k, x):
+ output = 0
+ for c, s in zip(coeffs, support_set):
+ output += c * s[0] * k(s[1], x)
+ return output
+
+
+def k1(X1, X2):
+ return 1 + X1[0] * X2[0] + X1[1] * X2[1] + X1[0]**2 * X2[0]**2 \
+ + X1[0] * X1[1] * X2[0] * X2[1] + X1[1]**2 * X2[1]**2
+
+
+def kg(x, y, sigma=10):
+ return np.exp(-((x[0] - y[0])**2 + (x[1] - y[1])**2) / sigma**2)
+
+
+def perceptron_k(X, Y, k):
+ coeffs = []
+ support_set = []
+ # Go in the loop at least one time
+ classification_error = 1
+ while not classification_error == 0:
+ classification_error = 0
+ for i in range(X.shape[0]):
+ if Y[i] * f_from_k(coeffs, support_set, k, X[i]) <= 0:
+ classification_error += 1
+ support_set.append([Y[i], X[i]])
+ coeffs.append(1)
+ else:
+ coeffs[len(coeffs) - 1] = coeffs[len(coeffs) - 1] + 1
+ return coeffs, support_set
+
+
+def f(x, y, w):
+ return
+
+
+coeffs, support_set = perceptron_k(X, Y, k1)
+# coeffs, support_set = perceptron_k(X, Y, kg)
+print(coeffs)
+print(support_set)
+
+X = apply_plongement(X, plongement_phi)