Finish TP3 exo2.

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/TP3/exo2/tp3_exo2.py b/TP3/exo2/tp3_exo2.py
index b97c6ea4df6424608316272322dc3840da29428f..7ceadb1b8185610e7c5d66577261a187324bfe7f 100755 (executable)
--- a/TP3/exo2/tp3_exo2.py
+++ b/TP3/exo2/tp3_exo2.py
@@ -112,7 +112,12 @@ def k1(X1, X2):
              + X1[0] * X1[1] * X2[0] * X2[1] + X1[1]**2 * X2[1]**2
 
 
-def kg(x, y, sigma=10):
+def kg(x, y):
+    # sigma = 20  # do not converge
+    # sigma = 10  # do not converge
+    sigma = 1
+    # sigma = 0.5  # overfitting
+    # sigma = 0.2  # overfitting
     return np.exp(-((x[0] - y[0])**2 + (x[1] - y[1])**2) / sigma**2)
 
 
@@ -140,14 +145,16 @@ def f(w, x, y):
 
 
 pl.scatter(X[:, 0], X[:, 1], c=Y, s=training_set_size)
-pl.title(u"Perceptron - hyperplan")
+pl.title(u"Perceptron - prolontaged hyperplan")
 
-coeffs, support_set = perceptron_k(X, Y, k1)
-# coeffs, support_set = perceptron_k(X, Y, kg)
+# k = k1
+# coeffs, support_set = perceptron_k(X, Y, k)
+k = kg
+coeffs, support_set = perceptron_k(X, Y, k)
 res = training_set_size
 for x in range(res):
     for y in range(res):
-        if abs(f_from_k(coeffs, support_set, k1, [-3 / 2 + 3 * x / res, -3 / 2 + 3 * y / res])) < 0.01:
+        if abs(f_from_k(coeffs, support_set, k, [-3 / 2 + 3 * x / res, -3 / 2 + 3 * y / res])) < 0.01:
             pl.plot(-3 / 2 + 3 * x / res, -3 / 2 + 3 * y / res, 'xr')
 
 # X = apply_plongement(X, plongement_phi)