übung 3

Übung 3/SLN.py

@@ -37,23 +37,22 @@ class SLN:
         for it in range(maxIter):
             Y = self.neuron(X)  # nur ein Aufruf
             err = error_rate(Y, T)
-            if (it % 10) == 0:
+            # if (it % 10) == 0:
             print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b))
             nnwplot.plotTwoFeatures(X, T, self.neuron)
             plt.pause(0.05)  # warte auf GUI event loop (je nach python Version notwendig oder nicht)
             if err <= maxErrorRate:
                 break
-            # update _W
             for n in range(N):
-                for j in range(self.dIn):
-                    weighted_error = err * X[j, n]
-                    to_learn = weighted_error * eta
-                    #print("{:.2f} = {} * {} * {}".format(to_learn, err, X[j, n], eta))
-                    self._W[j, 0] += to_learn
-            # update _b
-            self._b[0][0] += err * eta
+                delta = T[n] - Y[n]  # Fehlersignal pro Beispiel (mit Vorzeichen!)
+
+                # update _W
+                for j in range(self.dIn):
+                    self._W[j, 0] += eta * delta * X[j, n]
+
+                # update _b
+                self._b[0][0] += eta * delta
 
-            ...
         print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b))
         nnwplot.plotTwoFeatures(X, T, self.neuron)
         plt.pause(0.05)  # warte auf GUI event loop (je nach python Version notwendig oder nicht)

Übung 3/nnwplot.py

@@ -15,15 +15,14 @@ def plotTwoFeatures(X,T,pred_func):
         raise ValueError('X must contain exactly 2 features.')
 
     # determine canvas borders
-    mins = np.amin(X,1); 
-    mins = mins - 0.1*np.abs(mins);
-    maxs = np.amax(X,1); 
-    maxs = maxs + 0.1*maxs;
+    mins = np.amin(X, 1)
+    mins = mins - np.where(mins == 0, 0.1, 0.1 * np.abs(mins))
+    maxs = np.amax(X, 1)
+    maxs = maxs + 0.1 * maxs
 
     ## generate dense grid
     xs, ys = np.meshgrid(np.linspace(mins[0], maxs[0], 300),
-            np.linspace(mins[1], maxs[1], 300));
-
+                         np.linspace(mins[1], maxs[1], 300))
 
     # evaluate model on the dense grid
     try:
@@ -45,5 +44,5 @@ def plotTwoFeatures(X,T,pred_func):
     # Plot the contour and training examples
     plt.contourf(xs, ys, Z, cmap=plt.cm.Spectral)
     plt.scatter(X[0, :], X[1, :], c=T, s=50,
-            cmap=colors.ListedColormap(['orange', 'blue', 'green']))
+                cmap=colors.ListedColormap(['blue', 'green', 'red']))
     plt.show()