X-Git-Url: https://git.piment-noir.org/?a=blobdiff_plain;f=TP3%2Fexo2%2Ftp3_exo2.py;h=7ceadb1b8185610e7c5d66577261a187324bfe7f;hb=26fd2383c38aa862fd24bdfadf3ba219fa1cd4dd;hp=4ca092ddd0015126efe66d2a3b02fceb8b444433;hpb=c60d868ed5752a8c5adef46881c6a8d792351370;p=TP_AA.git

diff --git a/TP3/exo2/tp3_exo2.py b/TP3/exo2/tp3_exo2.py
index 4ca092d..7ceadb1 100755
--- a/TP3/exo2/tp3_exo2.py
+++ b/TP3/exo2/tp3_exo2.py
@@ -74,10 +74,11 @@ def perceptron_nobias(X, Y):
     classification_error = 1
     while not classification_error == 0:
         classification_error = 0
-        for i in range(X.shape[0]):
-            if Y[i] * np.dot(w, X[i]) <= 0:
+        for x, y in zip(X, Y):
+            if y * np.dot(w, x) <= 0:
                 classification_error += 1
-                w = w + Y[i] * X[i]
+                w = w + y * x
+        print(classification_error)
     return w
 
 
@@ -102,7 +103,7 @@ def apply_plongement(sample, p):
 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)
+        output += c * s[1] * k(s[0], x)
     return output
 
 
@@ -111,25 +112,32 @@ 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)
 
 
 def perceptron_k(X, Y, k):
     coeffs = []
-    support_set = np.array([])
+    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:
+        for x, y in zip(X, Y):
+            if y * f_from_k(coeffs, support_set, k, x) <= 0:
+                if x not in support_set:
+                    support_set.append((x, y))
+                    coeffs.append(1)
+                else:
+                    coeffs[support_set.index((x, y))] += 1
                 classification_error += 1
-                np.append(support_set, X[i])
-                coeffs.append(1)
-            else:
-                coeffs[len(coeffs) - 1] = coeffs[len(coeffs) - 1] + 1
-    return np.array(coeffs), support_set
+        print(classification_error)
+    return np.array(coeffs), np.array(support_set)
 
 
 def f(w, x, y):
@@ -137,19 +145,23 @@ 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 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')
+        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)
+# 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')
 
 pl.show()