diff --git a/Übung 3/SLN.py b/Übung 3/SLN.py
index 6426ebc..a7f4f87 100644
--- a/Übung 3/SLN.py	
+++ b/Übung 3/SLN.py	
@@ -5,7 +5,7 @@ import matplotlib.pyplot as plt
 
 def error_rate(Y, T):
     if Y.ndim == 1 or Y.shape[0] == 1:
-        errors = Y - T
+        errors = Y != T
         return errors.sum() / Y.size
     else:  # für mehrere Ausgaben in one-hot Kodierung:
         errors = Y.argmax(0) != T.argmax(0)
@@ -29,7 +29,7 @@ class SLN:
             for i in range(self.dIn):
                 value += X[i, n] * self._W[i][0]
             net[n] += value + self._b[0][0]
-        return net
+        return net > 0
 
     def delta_train(self, X, T, eta, maxIter, maxErrorRate):
         N = X.shape[1]  # Anzahl Trainingsdaten
@@ -50,8 +50,9 @@ class SLN:
                     to_learn = weighted_error * eta
                     #print("{:.2f} = {} * {} * {}".format(to_learn, err, X[j, n], eta))
                     self._W[j, 0] += to_learn
-                    self._b[0][0] -= to_learn
             # update _b
+            self._b[0][0] += err * eta
+
             ...
         print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b))
         nnwplot.plotTwoFeatures(X, T, self.neuron)
diff --git a/Übung 3/training.ipynb b/Übung 3/training.ipynb
index 9d8b454..df8258e 100644
--- a/Übung 3/training.ipynb	
+++ b/Übung 3/training.ipynb	
@@ -3,8 +3,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-10-20T07:08:24.185618200Z",
-     "start_time": "2026-10-20T07:08:24.097258900Z"
+     "end_time": "2026-10-20T07:38:30.524944100Z",
+     "start_time": "2026-10-20T07:38:30.469285800Z"
     }
    },
    "cell_type": "code",
@@ -20,14 +20,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-10-20T07:08:24.212521900Z",
-     "start_time": "2026-10-20T07:08:24.190597900Z"
+     "end_time": "2026-10-20T07:38:30.573897500Z",
+     "start_time": "2026-10-20T07:38:30.548936700Z"
     }
    },
    "cell_type": "code",
    "source": [
     "X = np.array([[0., 0., 1., 1.], [0., 1., 0., 1.]])\n",
-    "T = np.array([-2., -1., -1., 0.])\n"
+    "T = np.array([0., 0., 0., 1.])\n"
    ],
    "id": "d04fd0aaafa7b2e3",
    "outputs": [],
@@ -39,8 +39,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2026-10-20T07:08:24.255643Z",
-     "start_time": "2026-10-20T07:08:24.218508400Z"
+     "end_time": "2026-10-20T07:38:30.627791200Z",
+     "start_time": "2026-10-20T07:38:30.576891300Z"
     }
    },
    "source": [
@@ -54,8 +54,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-10-20T07:08:24.333864700Z",
-     "start_time": "2026-10-20T07:08:24.266697800Z"
+     "end_time": "2026-10-20T07:38:30.698915700Z",
+     "start_time": "2026-10-20T07:38:30.630785600Z"
     }
    },
    "cell_type": "code",
@@ -65,7 +65,7 @@
     {
      "data": {
       "text/plain": [
-       "array([-2., -1., -1.,  0.])"
+       "array([False, False, False, False])"
       ]
      },
      "execution_count": 4,
@@ -78,8 +78,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-10-20T07:08:24.845491900Z",
-     "start_time": "2026-10-20T07:08:24.336878Z"
+     "end_time": "2026-10-20T07:38:31.198791Z",
+     "start_time": "2026-10-20T07:38:30.723236300Z"
     }
    },
    "cell_type": "code",
@@ -91,7 +91,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -102,8 +102,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-10-20T07:09:21.417290200Z",
-     "start_time": "2026-10-20T07:09:18.825672100Z"
+     "end_time": "2026-10-20T07:38:36.186121200Z",
+     "start_time": "2026-10-20T07:38:31.344738100Z"
     }
    },
    "cell_type": "code",
@@ -117,7 +117,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#0 err:1.1034755592254166\n",
+      "#0 err:0.25\n",
       "[[ 0.28677805]\n",
       " [-0.07982693]]\n",
       "[[0.]]\n"
@@ -128,7 +128,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -137,10 +137,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#10 err:0.11848478597021123\n",
-      "[[1.27176882]\n",
-      " [0.90516384]]\n",
-      "[[-1.96998155]]\n"
+      "#10 err:0.75\n",
+      "[[1.24927805]\n",
+      " [0.88267307]]\n",
+      "[[0.675]]\n"
      ]
     },
     {
@@ -148,7 +148,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -157,10 +157,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#20 err:0.012722207020390064\n",
-      "[[1.3775314 ]\n",
-      " [1.01092642]]\n",
-      "[[-2.1815067]]\n"
+      "#20 err:0.75\n",
+      "[[2.37427805]\n",
+      " [2.00767307]]\n",
+      "[[1.425]]\n"
      ]
     },
     {
@@ -168,7 +168,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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QECMORpBAESRQBAmiMUiIEYcjSKAIEiiCBNEWJMSICxAkUAQJFEGCaAoSYsQlCBIoggSKIEG0BAkx4iIECRRBAkWQIBqChBhxGYIEiiCBIkgQ6SAhRlyIIIEiSKAIEkQySIgRlyJIoAgSKIIEkQoSYsTFCBIoggSKIEEkgoQYcTmCBIoggSJIEO4gIUZAkMCDIIEiSBDOICFGYGGEBIoggSJIEK4gIUbgQZBAESRQBAnCESTECLwQJFAECRRBglAHCTGCMxAkUAQJFEGCUAYJMYIiESRQBAkUQYJQBQkxAp8IEiiCBIogQSgQIygWQQJFkEARJAj26AgxghIRJFAECRRBgmAGCTECvxAkUAQJFEGCYAUJMQK/ESRQBAmAYAYJMYKAECRQBAkMRkcQjCAhRhAwggSKIIFBkKCsQUKMoFQIEiiCBAZBgrIECTGCUiNIoAgSGAQJShskxAjKhCABUBhBgsJB0qveCfEHMYIyI0hgMDoCRZAgUMQIgoIggUGQQBEkCAQxgqAhSGAQJFAECfxFjAAIOoIEiiCBP4gRBBWjI1AECRRBgpIQIwg6ggSKIIEiSFAcYgQhQZBAESRQBAl8IUYQMgQJFEECRZCgKMQIQooggSJIoAgSnI4YQcgRJFAECRRBgsKIEYQFQQJFkEARJFDECMKGIIEiSKAIEhAjCDuCBIoggSJIwMgIwo4ggSJIoAgSdws4Rq7p3FnmzpktX637Uv793jvSrl3bEtdJTa0sj40YLp8vXSJLly6Wh4Y9KPHx8aXdZjgAQQJFkEARJO4VUIy0bdtWxo9/Wd59913p3qOXfP75F/LG61OlYcOGPtdJTk6Wf7/3rjRo2EDuvOsuuf76G+R4To507twpGNsPGyNIoAgSKILEnWLS0jMK/H3w1CmvSX5BvgwceKtn2byPP5Rvv/1OHhz2UJHrPPzwQzLg5pukbbvL5OjRo6XayAoVKsimjeslf95QkZPHS/V3IHrFtCl5dA3ukJ2SGOlNQJRYvCsv0puAIIiLiZeu5/WVBg0by5EjR4IzMnLJJRfL0qWfey1bvHiJXHzJxT7X6da1i3z44UelDhE4HyMkUIyQQDFC4i5nBTI6kZKSIvv37fdavn//z3LuOef4XK927dqyZ88eGTN6lKz9crXMn/+xDB16vyQkJPhcx3zOfD29mUM9cDaCBIoggSJI3CPgCaz5+fle90/mnZSYmBifj4+NjZUhQwbLF8uWSZeu3eWxEY/LwAE3y9/++n8+1xkyeJB1WEZvJmLgfAQJFEECRZC4g98xYo71HDt2TKpUSfVaXrVqVdm/33u0pDDzOXNo54033rRGSBYvWSITJkyU3n16+1xn1Ogx1vElvbVs1drfzYTNESRQBAkUQeJ8AY2MrFv3lbRp08ZrWbu2bWTt2nU+11mz5kvJzc31WpaTmytxsbE+1zGPN/GjN+abuAtBAkWQQBEkzhZQjEycNEmuuaazdO7UyTr80veGG6Rly5YyefIrnscMHnSvrFyxzHN//ISJcuWVV8oVHTpY9+vUqS233nqLfPDBh8H8PuAwBAkUQQJFkDhXXCAPNmfFPPbY4/LMM09LpUqV5D/79sn9Qx+QVat/n9ORmJhoTXRVK1eulAcfHCZPP/2UVKtWzTrU8+/3Z8njj48M7ncCRwYJp/1Cg4TTfqFBwmm/Lr/OSGHlypWT48fPvOaHiRFzNszhw4eL/FxOTk7AX4vrjLgbQQJFkEARJC6+zkhhRYWIYWKjqBDRzwGB4pANFIdsoDhk4yy8UR5sgSCBIkigCBLnIEZgGwQJFEECRZA4AzECWyFIoAgSKILE/ogR2A5BAkWQQBEk9marGDn8wdZIbwKiBEECRZBAEST2ZasYMQ7P+iHSm4AoQZBAESRQBIk92S5GDIIEiiCBIkigCBL7sWWMGAQJFEECRZBAEST2YtsYMQgSKIIEiiCBIkjsw9YxYhAkUAQJFEECRZDYg+1jxCBIoAgSKIIEiiCJfo6IEYMggSJIoAgSKIIkujkmRgyCBIoggSJIoAiS6OWoGDEIEiiCBIoggSJIopPjYsQgSKAIEiiCBIogiT6OjBGDIIEiSKAIEiiCJLo4NkYMggSKIIEiSKAIkujh6BgxCBIoggSKIIEiSKKD42PEIEigCBIoggSKIIk8V8SIQZBAESRQBAkUQRJZrokRgyCBIkigCBIogiRyXBUjBkECRZBAESRQBElkuC5GDIIEiiCBIkigCJLwc2WMGAQJFEECRZBAESTh5doYMQgSKIIEiiCBIkjCx9UxYhAkUAQJFEECRZCEh+tjxCBIoAgSKIIEiiAJPWLkNwQJFEECRZBAESShRYwUQpBAESRQBAkUQRI6xMhpCBIoggSKIIEiSEKDGCkCQQJFkEARJFAESfARIz4QJFAECRRBAkWQBBcxUgyCBIoggSJIoAiS4CFGSkCQQBEkUAQJFEESHMSIHwgSKIIEiiABgocY8RNBAkWQQBEkMBgdKTtiJAAECRRBAkWQwCBIyoYYCRBBAkWQQBEkMAiS0iNGSoEggSJIABRGkJQOMVJKBAkUQQKD0REogiRwxEgZECRQBAkMggSKIAkMMVJGBAkUQQKDIIEiSPxHjAQBQQJFkMAgSKAIEv8QI0FCkAAojCCBIkhKRowEEUECg9ERKIIEiiApHjESZAQJDIIEiiCBIkh8I0ZCgCCBQZBAESRQBEnRiBEghAgSKIIEiiA5EzESIoyOQBEkUAQJFEHijRgJIYIEiiCBIkigCJLfESMhRpBAESRQBAkUQXIKMRIGBAkUQQJFkEB1SI91/c4gRsKEIAFBgtMRJFAdXB4kxEgYESRQjJBAESRQbg4SYiTMCBIoggSKIIHbgyTgGKlb93x54omRMmXKqzJ8+COSnp7m97pt2rSR6dPelAeG3i9uRpBAESRQBAncHCQBxcj559eRuXNmS/nySTJjxkypVbOWdb9atWolrpuaWlleeP5ZqVWrljRu0ljcjiCBIkigCBK4NUgCipEHhg6VLVt+kKFDH5Q5c+bKn+68S3Jzc+Xuu+4scd3nnn1W3njzLdm0eXNZttdRCBIoggSKIIEbgySgGLniig7y8bx5nvsnT56UBQs+kQ5XdCh2vdtvv00qVqwoo0ePKf2WOhRBAkWQQBEkcFuQ+B0jSeXKSdWqVWX37t1ey839mhkZPtdresEFct+QwTLkvvuloKDAr6+VkJAgFSpU8NySk5PFyQgSKIIEiiCBm4LE7xiJT0iw/jx+/LjX8mPHjkl8/KnPna58+fIy9uWX5NFHR8jOnTv93qghgwfJpo3rPbe1X64WpyNIoAgSKIIEbgkSv2Pk6NGjcuLECalcubLX8tTUVMnOzi5ynfaXXy4ZNWpIv343WGfRmFurli2lzSWXWB+bkZaijBo9Rho0bOy5tWzVWtyAIIEiSKAIErghSOL8fWBeXp5s3LhRmjVr6rXc3P/uu++KXGfV6tUyYOAtXsv+/OeH5UTuCRk95iU5fPhwkeuZSbHm5kYmSFJ61o30ZiBKgiSmTdtIbwaiJEiyUxIjvRmIkiBZvCtPXD2BdfqMmdK9W3epXbu2db958+bSoUMHmTFzpucx11+fKZMnTbQ+PnDggCxZstTrZpbt/3m/9XFOTk6wvx9HYIQEihESKEZI4OQRkoBi5JVXXpX5C+bLgvkfy0cfzpX33n1HXn1tisyePcfzmPNq1ZJ27fhtrqwIEiiCBIoggVODJCYtPcO/U1wKqVGjhnXLysqSvXv3en3OXNQsPT1dli9fXuS6TZo0lvz8AtmwYYPfX8+cUWMmsmYP7SVy/FdxEw7ZQHHIBopDNlDRfsgmLiZeup7X15r/eeTIkeDGSLi5OUYMggSKIIEiSGCHIPE3RnijPBvgkA0Uh2ygOGQDJx2yIUZsgiCBIkigCBI4JUiIERshSKAIEiiCBE4IEmLEZggSKIIEiiCB3YOEGLEhggSKIIEiSGDnICFGbIoggSJIoAgS2DVIiBEbI0igCBIoggR2DBJixOYIEiiCBIoggd2ChBhxAIIEiiCBIkhgpyAhRhyCIIEiSKAIEtglSIgRByFIoAgSKIIEdggSYsRhCBIoggSKIEG0Bwkx4kAECRRBAkWQIJqDhBhxKIIEiiCBIkgQrUFCjDgYQQJFkEARJIjGICFGHI4ggSJIoAgSRFuQECMuQJBAESRQBAmiKUiIEZcgSKAIEiiCBNESJMSIixAkUAQJFEGCaAgSYsRlCBIoggSKIEGkg4QYcSGCBIoggSJIEMkgIUZciiCBIkigCBJEKkiIERcjSKAIEiiCBJEIEmLE5QgSKIIEiiBBuIOEGAFBAg+CBIogQTiDhBiBhRESKIIEiiBBuIKEGIEHQQJFkEARJAhHkBAj8EKQQBEkUAQJQh0kxAjOQJBAESRQBAlCGSTECIpEkEARJFAECUIVJMQIfCJIoAgSKIIEoQgSYgTFIkigCBIoggTBDhJiBCUiSKAIEiiCBMEMEmIEfiFIoAgSKIIEwUKMwG8ECRRBAkWQIBijI8QIAkKQQBEkUAQJyhokxAgCRpBAESRQBAnKEiTECEqFIIEiSACUNUiIEZQaQQJFkMBgdASlDRJiBGVCkEARJDAIEhQOkkvT/MsMYgRlRpBAESQwCBIEihhBUBAkUAQJDIIEgSBGEDQECYDCCBL4ixhBUBEkMBgdgSJI4A9iBEFHkMAgSKAIEpSEGEFIECQwCBIoggTFIUYAhBRBAkWQwBdiBCHD6AgUQQJFkKAoxAhCiiCBIkigCBKcjhhByBEkUAQJFEGCwogRhAVBAkWQQBEkUMQIwoYggSJIoAgSECMIO4IEiiCBIkjAyAjCjiCBIkigCBJ3I0YQEQQJFEECRZC4V1ygK5QvX1569ughGRk15MetW2X27Dly4sSJYtdpesEF0rZdW4mPi5N1X30ly5YtL8s2w0FBktKzbqQ3A1ESJDFt2kZ6MxAlQZKdkhjpzUA0j4ykplaWjz6cKwMHDpDExES5b8hg+fd770pSuXI+15k4Ybw8/fQ/pWbNDKlWvbpMGD9Oxr40JhjbDgdghASKERIoRkjcJ6CRkfvuu0/Oio2VPn0y5djx4/LS2LGyZMliueWWgTL25XFFrjP25ZdlzZovPfdnzpwpnyyYL9Omz5DPPvus7N8BbI8REihGSKAYIXGXgEZGuna5VubMmWuFiHHw4C8yf/4C6dK1i891CoeIsWHDRsnJyZEa6Wml3WY4ECMkUIyQQDFC4h5+x0hCQoLUrFlTtm3d5rV827Ztcv755/v9Bc18k/j4eFm1ek2xX6tChQqeW3Jyst9/P+yLIIEiSKAIEnfwO0aSkpKsPw8fOey1/PChQ1L+t8+VpF69evLkkyNl/ISJsnnzZp+PGzJ4kGzauN5zW/vlan83EzZHkEARJFAEifP5HSO//vqr5OfnS8WUFK/lFStVsj5Xktq1a8v0aW/JJ58slMcee7zYx44aPUYaNGzsubVs1drfzYQDECRQBAkUQeJsfseIOX03K2u71K3rfSpm3fPPl82btxS77nnnnSdvz5wuy5cvl/uHPiAFBQXFPj43N1eOHDniuR09etTfzYRDECRQBAkUQeJcAU1gnTNnjvTo0d2ax2FUr15dOna82lqu2re/XB58YKjnfq1ateSdt2fI8uUrZMh991ujK4A/CBIoggSKIHGmmLT0jOKHKQpJSUmxwsJcY2T5ihVy5RVXSNb27XLTTQOs0Qxj2IMPyB133C6NmzS17n++dIlUq1ZVpk59XfLy8jx/15KlS2XJkqV+fV0TP2buSPbQXiLHSz4kBGfhwmhQXBgNiguj2UWcVE74L2vKhTnS4ftRATh8+LB0695TOnXqKDVq1LBO6124cJHXaIcJjOxDhzz3TYTExcWe8XflHM8J5EvDxbgOCRTXIYHiOiQuHhmJFEZGYDBCAsUICRQjJM4YGeGN8mAbzCGBYg4JFHNInIEYga0QJFAECRRBYn/ECGyHIIEiSKAIEnuzVYx8uyQ+0puAKEGQQBEkUASJfdkqRoyvPk2I9CYgShAkUAQJFEFiT7aLEYMggSJIoAgSKILEfmwZIwZBAkWQQBEkUASJvdg2RgyCBIoggSJIoAgS+7B1jBgECRRBAkWQQBEk9mD7GDEIEiiCBIoggSJIop8jYsQgSKAIEiiCBIogiW6OiRGDIIEiSKAIEiiCJHo5KkYMggSKIIEiSKAIkujkuBgxCBIoggSKIIEiSKKPI2PEIEigCBIoggSKIIkujo0RgyCBIkigCBIogiR6ODpGDIIEiiCBIkigCJLo4PgYMQgSKIIEiiCBIkgizxUxYhAkUAQJFEECRZBElmtixCBIoAgSKIIEiiCJHFfFiEGQQBEkUAQJFEESGa6LEYMggSJIoAgSKIIk/FwZIwZBAkWQQBEkUARJeLk2RgyCBIoggSJIoAiS8HF1jBgECRRBAkWQQBEk4eH6GDEIEiiCBIoggSJIQo8Y+Q1BAkWQQBEkUARJaBEjhRAkUAQJFEECRZCEDjFyGoIEiiCBIkigCJLQIEaKQJBAESRQBAkUQRJ8xIgPBAkUQQJFkEARJMFFjBSDIIEiSKAIEiiCJHiIkRIQJFAECRRBAkWQBAcx4geCBIoggSJIoAiSsiNG/ESQQBEkUAQJFEFSNsRIAAgSKIIEiiCBIkhKjxgJEEECRZBAESRA2RAjpUCQQBEkUAQJDEZHSocYKSWCBIoggSJIYBAkgSNGyoAggSJIoAgSGARJYIiRMiJIoAgSKIIEBkHiP2IkCAgSKIIEQGEEiX+IkSAhSKAIEhiMjkARJCUjRoKIIIEiSGAQJFAESfGIkSAjSKAIEhgECRRB4hsxEgIECYDCCBIogqRoxEiIECQwGB2BIkigCJIzESMhRJDAIEigCBIogsQbMRJiBAkMggSKIIEiSH5HjIQBQQKDIIEiSKAIklOIESCMCBIoggSq0uEc1+8MYiRMGB0BQYLTESRQlVweJMRIGBEkUIyQQBEkUG4OEmIkzAgSKIIEiiCB24PEXjESUyBOQJBAESRQBAmKC5Ldu5Jk7ZoqsmVTihQ440dh2WOkceNG0qlTR6lXt25I1zndRXcvkYx2P0rMWflidwQJFEECRZDg9CD5bOE50vOaq6RJnevkD5deIxc36y6tm3aTyePrSb79fxSWLkbi4+Nl8qSJMnPmDLnjjttl7tzZ8sy/ng76Or4kphyXOh03yAX9VhMkcBSCBIoggXp7bC3p3fUq+WJJdSls6w8pMmxIa7nntraOCZKAYuRPd9wuF1/cWjp16ix9+94oPXpeJ9ddd51kZvYJ6jq+xMScuqWev19qddgiTsAICRRBAkWQ4LsNleXOBy61Dsnk5Xn/qC4oiDE/EWXGW7Vl0rj67ouRzMxMmTVrtuzevce6v2nTJlm0aJFcn5kZ1HVKEnOWSPrFWRITmydOQJBAESRQBIm7jZnUSGLPMpNDTHgUzfxyPvbFho6YQxLn7wNjY2Olfv16MvmVV7yWr1+/Xm6++aagrWMkJCRYN5WcnPzb1qZ4lsWniKQ2zJXsrCriBN+uSpKm7U9EejMQBQ7P2y0pXetEejMQBQrWrJOY1hdHejMQAR8trC1J5UsaL4iR/ftSZPPGKtKg0SGxc2b4HSMmCOLi4iQ7O9tr+cGDB6VixYpBW8cYMniQDBv24Jmf6LPT627TG/zdegAA7GPrtkAefY1EO9MDR44cKXuM5ObmWn8mJSV5LS+fnCw5OTlBW8cYNXqMjBs/weubWPvlamnZqrUcPXrU301GgNjP4cF+Zj87Cc9n9rU/z5G9e/cW+xi/Y+T48ePWX1ajRg2v5eb+9u3bg7aORoyGTGEmRIorKwQH+zk82M/sZyfh+cy+9sWfn9sBTWBdtOhT6drlWokxs2bMqbaJida1QxYu+tTzmPr160vnTp0CWgcAALhXQDHy7HPPS1pamkycMF76979Rpkx5VfJOnpTx48d7HtOzR3d54YXnAloHAAC4V0AxsmPHDrnm2i6yZcsWubRdO1m5YqV06dpdDh78xfOYTZs3y8fz5gW0TknMIZtnnnm2yEM3CB72c3iwn9nPTsLzmX0dDDFp6RkOOEMZAADYlb3eKA8AADgOMQIAACKKGAEAABHl93VGQslc+t28mV5y+WRZu26d7Nu3LyTruJ05vbplywulerXqsnHTRtnqxyX+KlWqJM2bN5P8/Hz57rvv5Zdf/J947GaNGjWS2rVry46ffpJvv/vO7/XKlSsnnTt3kp9//lk+//yLkG6jE2RkZEjTCy6QAwcOyOo1a6znqT//Dy68sIWcffbZsnbtOmtfo3ipqZXloosuktycXFm1apUcO368xF1Ws2ZNadCgvpw8cdL6P8B+LtlZZ50ll192mVSqXElmz57j19MyqVw5ueSSSyQ+IV5Wr15j29foiE9gNS/Y0956w5qRvW/ffmnRorn89a9/l2nTpwd1HberUKGCvPH6FMnIqGm9WWHr1hfJ1Kmvy4jHHve5zhNPjJRrOneWzZs3SWJiObnggiby6PAR8uabb4V12+32YvLC88/J1Vf/Qdat+8oKuVWrVsudd90tJ06U/N5DT//zKbn++kxZs+ZLuf6/eL+D4gwZMljuv2+IrFmzRurUqSPZhw5Jv379i/2hZ66DNHHCOImLi5cNGzZY7531zDPPyfuzZpXq39sNunS51npOf//995KSUlFSU1Pl5gEDrF9OfPnnU/+QPn16y7Jly60rcLds2VL+8dRTMmHCxLBuu53cfvtt1rvc5+cXSEZGDal1XsnvT9WsWTOZOuVV2bd/v/x69Kj1S9B99w31OqPVLiIeI2/PnGFFxU03D7B+qzFvoDdi+KNy+eUdZOeuXUFbx+2GD39EOl7dUbp07SaHDh2Siy5qJe//+z35400D5LPPPitynQEDbpZp06Z7Tqk214n5x5NPyOXtryj2Crpu1v/GfvLoo49Yp7Obkaca6eny8byPZNSo0TJuXPHX1unRo7sMHnSvbNnyg5xzzjnESDFatGghH34wR/r/8Wb59NNPrd8OZ81+33oTTvNiXJTy5cvLp4s+keUrVsjQoQ9arx3mIoxt27SRzxYvLts/vENVrlxZli/7XEaPHiOjx7xkLRs3bqzUq1tPru74+8UtCzO/pf/7vXekR89eVlQbt94yUEaMGC5NLmgmhw8fDuv3YBcDBtwsn3yyUC677FL519P/9CtGPvt0oXz99Tcy5L77rfvDHnzAipo2bS+13X6O6JyRtLRz5dJL28nESZM8w6tvvTVNjh07Jt17dA/aOhDp07u3NXJkQsQwLxLmN8rMzN4+d8+UKVO9ru3ywQcfWm982KhhQ3apD3369JH58xd4DoGZOJ47d65k9vG9n3VI2wT1oMH3Sa4fIyhu16fPddYInwkRwxw2MCN93bp2swKjKJmZfaR69eryyCOPel47zHtkESK+mUOGZn++8uprnmXjx0+Qxo0bSZMmjYtcJzHx1Duu//TTDs+yrN9+eSn8buw48/V2507vN4MtTvPmza2RPvPvoSZNfsV6H5iOHa+23e6NaIyYISVj44aNnmUnT56UH3780fO5YKzjduYF2BwfL7zPjPUbNkjjAPZZ+/aXW39u3LQp6NvoFI0aN5ING73384b1G6RBgwY+1zGB99JLo+X5F160Lg4IP/Zzo6L3c1JSOeswblHMCMjX33wjx48dlyuvvFIuv/wyay4Eit/PO3bu9HqD0vXrN3g+V5QlS5bK9BkzZcL4l63R1Ntvu1Ue+fvfZOTIJ5k3EkSNf9v/hf8fmPkie/bsCeh1PVpEdAJrxZSK1p8HT5twc/DgQalUsWLQ1nG7iikp1p+nT2wyV8Gt6Oc+M4cbHn9shEx9/XXJysoKyXY6ZV8XtZ/Nb4TmMMGvv/56xjr//eeHrQmYr702JYxbam/mdWDr1q1eyw4cPGj96et1oGrVsyUuNk4++ugD2bFjp1SokCyNGzeW//nfv8i7774Xlu12wvPZPIfNiFJxr7dr166VS9u1lW5du1qTsgsKCs6IR5RNSsUUKxJPn4tmfhZWrGS/n4URjZGc3w4BmGGlwi/S5gyZ//znP0Fbx+10n5VPTvZanly+vPWi4s/IyrRpb1rHJs1EYfhmDmuZ52Jh5ZPLn/p3KGJfm2HWu+66U4YPHyG9eva0ltWqWVPOPruKdf/Tzz6T7OxsdvkZ+znnjP1sXhOM4z6e02Z5+/YtpHef62XFihXWsnvvuVue/uc/ZdGiRQG9RYWbn89mJM8cuvH12mHeKPWxEcOlW7cenjPJzCGyVyZPlA5XXCU//fRTWLbdDf825cqVs84OM7FX+P+BP6/r0Saih2mysk4dV69Ro4bX8ho10j3HGIOxjtvt3r3benKafXT6aZFZWcXvs2rVqsnMGdNk+/af5I4/3enXGSFutm1bVpH72RwLzsvLO+Px+fl5MnfuB9K6dWu59tprrJtZv0qVKtbH5tRqFLGfs7LOeA0wZyAYviZXZ23Lsg4TaIgYcz/40Dq006AB86CKfD5nZcm5555jnSVW+Pls7U8f+7ltu7aybds2r1Pa58yZawWMuRwDgiNrW5bExsZKelqaZ5m5bya/by/hdT0aRTRGzLFH8yLdo3s3zzJzHYxatWrJJws+8ZqrYM7+CGQdiNecmsVLlnjtMzNL3uxXM3u78ISoP/zhKs/9qlWrWiFihrRvu/0OW9Z2uC1cuNCa9KcT9cyLQ9cu18qCQvu5Tp3anlGQH374Ue65d5DXbdnyFbJp02brY85a8rGfP1lkvSace+65nmU9e/awrjWihxWqpKZa+1mDbv6CBdbH5nmtzKm9xi7OwvPxfF5kHcpt37691342+9hc00Kf42Y/m9dg3ZfmB6K5nIBnP9er5/nFCKXXqVNH67o6xsqVK60zZgqfuNHx6qutw8ELFy2y3W6O+EXPHnl0uIx9aYz18e49e+SuO/8k778/S1atXu15zJ///LDs+GmH5zQxf9aBtyeffEpmvf+ejB71orWf+vXta/328ta0aZ7H9O/fz3pnZfMCFB8fLzOmT7Mmvr409mW59pprPI9b99VXzBvx4eWXx0mvXj1l6tTXrBGPTh07WuH34osveh5z5RVXyMiRj8ucuXOLHC1ByWbPmWOdCvnmG6/L1KlTpUmTJtbhgRtu6Od5TL169WTs2DFybZdu8vXXX1sTKz/86CN56603rDMXKiQnW4fIzDwoDh0UzUyoNvtq1IvPy0svvWzNUxh07z3yl//7q+eXEzPiYfbzQw89LG9u3y4zZ75tXS9j+rS3rDP4zKGEO26/TT7/4gtZvvz3USl4a9WqpdTMqGldk8UcetFfWJYsWeKZD2Xm7X308cfy7SPfWWeQjXziSXn0kb9LSoUK1vyRQYPulUmTJ/t1QctoE/HrjOg/gjn11BzrWrFipcyYOdPrSooPP/yQHPj5Z+u0JX/XwZnMWQZ/7H+jVKtezTqz5rUpU73m3dzYr6/Ub9BARox4zLpuw7PPPlPkbnzzrbesF3YUzfxGPnDgADmv9nmyc8dOaz8Xns90RYcO0q9fX7l30GCvY73K/JA9u0oVee75F9jFxTA/BG/6Y39p1ryZNd/DXBNnY6FJknXrni8PDRsmT/7jKc8IkzncYC4qZ86sOXLkiHzxxTLrxR3Fu65XL2sk1cw/mzNnjrXflBkFNBdFM1Gny82oiHmON6hf3zq0+9XXX8s777xLfBfjloEDpU2bS85Y/q9nnrFGUDVGzC+Db7/9jufz5t+la9eukhAfb52mPmvWbFs+naMiRgAAgHvxRnkAACCiiBEAABBRxAgAAIgoYgQAAEQUMQIAACKKGAEAABFFjAAAgIgiRgAAQEQRIwAAIKKIEQAAEFHECAAAiChiBAAASCT9P6khsaeNshoBAAAAAElFTkSuQmCC"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -177,10 +177,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#30 err:0.0013660365771364091\n",
-      "[[1.38888757]\n",
-      " [1.02228259]]\n",
-      "[[-2.20421905]]\n"
+      "#30 err:0.75\n",
+      "[[3.49927805]\n",
+      " [3.13267307]]\n",
+      "[[2.175]]\n"
      ]
     },
     {
@@ -188,7 +188,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -197,10 +197,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#32 err:0.0008742634093676926\n",
-      "[[1.38937935]\n",
-      " [1.02277436]]\n",
-      "[[-2.20520259]]\n"
+      "#40 err:0.75\n",
+      "[[4.62427805]\n",
+      " [4.25767307]]\n",
+      "[[2.925]]\n"
      ]
     },
     {
@@ -208,19 +208,139 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#50 err:0.75\n",
+      "[[5.74927805]\n",
+      " [5.38267307]]\n",
+      "[[3.675]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#60 err:0.75\n",
+      "[[6.87427805]\n",
+      " [6.50767307]]\n",
+      "[[4.425]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#70 err:0.75\n",
+      "[[7.99927805]\n",
+      " [7.63267307]]\n",
+      "[[5.175]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#80 err:0.75\n",
+      "[[9.12427805]\n",
+      " [8.75767307]]\n",
+      "[[5.925]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#90 err:0.75\n",
+      "[[10.24927805]\n",
+      " [ 9.88267307]]\n",
+      "[[6.675]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#99 err:0.75\n",
+      "[[11.37427805]\n",
+      " [11.00767307]]\n",
+      "[[7.425]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 7
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-10-20T07:08:27.930652900Z",
-     "start_time": "2026-10-20T07:08:27.849345600Z"
+     "end_time": "2026-10-20T07:38:36.409641400Z",
+     "start_time": "2026-10-20T07:38:36.245355400Z"
     }
    },
    "cell_type": "code",
diff --git a/Übung 4/SLN.py b/Übung 4/SLN.py
new file mode 100644
index 0000000..6426ebc
--- /dev/null
+++ b/Übung 4/SLN.py	
@@ -0,0 +1,58 @@
+import numpy as np
+import nnwplot
+import matplotlib.pyplot as plt
+
+
+def error_rate(Y, T):
+    if Y.ndim == 1 or Y.shape[0] == 1:
+        errors = Y - T
+        return errors.sum() / Y.size
+    else:  # für mehrere Ausgaben in one-hot Kodierung:
+        errors = Y.argmax(0) != T.argmax(0)
+        return errors.sum() / Y.shape[1]
+
+
+class SLN:
+    def __init__(self, dIn, cOut):
+        self.dIn = dIn
+        self.cOut = cOut
+        np.random.seed(42)
+        self._b = np.zeros((cOut, 1))
+        self._W = np.random.randn(dIn, cOut) / np.sqrt(dIn + 1)
+
+    def neuron(self, X):
+        N = X.shape[1]
+        net = np.zeros(N)
+
+        for n in range(0, N):
+            value = 0
+            for i in range(self.dIn):
+                value += X[i, n] * self._W[i][0]
+            net[n] += value + self._b[0][0]
+        return net
+
+    def delta_train(self, X, T, eta, maxIter, maxErrorRate):
+        N = X.shape[1]  # Anzahl Trainingsdaten
+        plt.ion()  # interactive mode on
+        for it in range(maxIter):
+            Y = self.neuron(X)  # nur ein Aufruf
+            err = error_rate(Y, T)
+            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
+                    self._b[0][0] -= to_learn
+            # update _b
+            ...
+        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)
diff --git a/Übung 4/data/iris.csv b/Übung 4/data/iris.csv
new file mode 100644
index 0000000..c620a3d
--- /dev/null
+++ b/Übung 4/data/iris.csv	
@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,0
+4.9,3.0,1.4,0.2,0
+4.7,3.2,1.3,0.2,0
+4.6,3.1,1.5,0.2,0
+5.0,3.6,1.4,0.2,0
+5.4,3.9,1.7,0.4,0
+4.6,3.4,1.4,0.3,0
+5.0,3.4,1.5,0.2,0
+4.4,2.9,1.4,0.2,0
+4.9,3.1,1.5,0.1,0
+5.4,3.7,1.5,0.2,0
+4.8,3.4,1.6,0.2,0
+4.8,3.0,1.4,0.1,0
+4.3,3.0,1.1,0.1,0
+5.8,4.0,1.2,0.2,0
+5.7,4.4,1.5,0.4,0
+5.4,3.9,1.3,0.4,0
+5.1,3.5,1.4,0.3,0
+5.7,3.8,1.7,0.3,0
+5.1,3.8,1.5,0.3,0
+5.4,3.4,1.7,0.2,0
+5.1,3.7,1.5,0.4,0
+4.6,3.6,1.0,0.2,0
+5.1,3.3,1.7,0.5,0
+4.8,3.4,1.9,0.2,0
+5.0,3.0,1.6,0.2,0
+5.0,3.4,1.6,0.4,0
+5.2,3.5,1.5,0.2,0
+5.2,3.4,1.4,0.2,0
+4.7,3.2,1.6,0.2,0
+4.8,3.1,1.6,0.2,0
+5.4,3.4,1.5,0.4,0
+5.2,4.1,1.5,0.1,0
+5.5,4.2,1.4,0.2,0
+4.9,3.1,1.5,0.1,0
+5.0,3.2,1.2,0.2,0
+5.5,3.5,1.3,0.2,0
+4.9,3.1,1.5,0.1,0
+4.4,3.0,1.3,0.2,0
+5.1,3.4,1.5,0.2,0
+5.0,3.5,1.3,0.3,0
+4.5,2.3,1.3,0.3,0
+4.4,3.2,1.3,0.2,0
+5.0,3.5,1.6,0.6,0
+5.1,3.8,1.9,0.4,0
+4.8,3.0,1.4,0.3,0
+5.1,3.8,1.6,0.2,0
+4.6,3.2,1.4,0.2,0
+5.3,3.7,1.5,0.2,0
+5.0,3.3,1.4,0.2,0
+7.0,3.2,4.7,1.4,1
+6.4,3.2,4.5,1.5,1
+6.9,3.1,4.9,1.5,1
+5.5,2.3,4.0,1.3,1
+6.5,2.8,4.6,1.5,1
+5.7,2.8,4.5,1.3,1
+6.3,3.3,4.7,1.6,1
+4.9,2.4,3.3,1.0,1
+6.6,2.9,4.6,1.3,1
+5.2,2.7,3.9,1.4,1
+5.0,2.0,3.5,1.0,1
+5.9,3.0,4.2,1.5,1
+6.0,2.2,4.0,1.0,1
+6.1,2.9,4.7,1.4,1
+5.6,2.9,3.6,1.3,1
+6.7,3.1,4.4,1.4,1
+5.6,3.0,4.5,1.5,1
+5.8,2.7,4.1,1.0,1
+6.2,2.2,4.5,1.5,1
+5.6,2.5,3.9,1.1,1
+5.9,3.2,4.8,1.8,1
+6.1,2.8,4.0,1.3,1
+6.3,2.5,4.9,1.5,1
+6.1,2.8,4.7,1.2,1
+6.4,2.9,4.3,1.3,1
+6.6,3.0,4.4,1.4,1
+6.8,2.8,4.8,1.4,1
+6.7,3.0,5.0,1.7,1
+6.0,2.9,4.5,1.5,1
+5.7,2.6,3.5,1.0,1
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+5.8,2.7,3.9,1.2,1
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+6.7,3.1,4.7,1.5,1
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+6.2,3.4,5.4,2.3,2
+5.9,3.0,5.1,1.8,2
+
diff --git a/Übung 4/nnwplot.py b/Übung 4/nnwplot.py
new file mode 100644
index 0000000..8b715cd
--- /dev/null
+++ b/Übung 4/nnwplot.py	
@@ -0,0 +1,49 @@
+# based on: https://github.com/eakbas/tf-svm/blob/master/plot_boundary_on_data.py
+import numpy as np
+from matplotlib import pyplot as plt
+from matplotlib import colors
+
+
+def plotTwoFeatures(X,T,pred_func):
+    if X.ndim!=2:
+        raise ValueError('X be a matrix (2 dimensional array).')
+    tp=False
+    if X.shape[0]!=2 and X.shape[1]==2: 
+        X=X.T
+        tp=True
+    if X.shape[0]!=2:
+        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;
+
+    ## generate dense grid
+    xs,ys = np.meshgrid(np.linspace(mins[0],maxs[0],300), 
+            np.linspace(mins[1], maxs[1], 300));
+
+
+    # evaluate model on the dense grid
+    try:
+        Z = pred_func(np.c_[xs.flatten(), ys.flatten()].T);
+    except:
+        Z = pred_func(np.c_[xs.flatten(), ys.flatten()]);
+
+    if tp:
+        Z=Z.T
+        T=T.T
+    
+    if Z.ndim>1 and Z.shape[0]>1: # onehot? -> convert
+        Z=Z.argmax(0)
+    Z = Z.reshape(xs.shape)
+    
+    if T.ndim>1 and T.shape[0]>1: # onehot? -> convert
+        T=T.argmax(0)
+        
+    # 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']))
+    plt.show()