diff --git a/pyproject.toml b/pyproject.toml
index 6eb7e1d..771b882 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ dependencies = [
     "jupyterlab>=4.6.4",
     "matplotlib>=3.11.2",
     "numpy>=2.5.4",
-    "pandas>=3.0.6",
+    "pandas==3.1.0",
     "plotly>=7.1.0",
     "scikit-learn>=1.9.1",
 ]
diff --git a/uv.lock b/uv.lock
index bbec391..7352f62 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1211,7 +1211,7 @@ requires-dist = [
     { name = "jupyterlab", specifier = ">=4.6.4" },
     { name = "matplotlib", specifier = ">=3.11.2" },
     { name = "numpy", specifier = ">=2.5.4" },
-    { name = "pandas", specifier = ">=3.0.6" },
+    { name = "pandas", specifier = "==3.1.0" },
     { name = "plotly", specifier = ">=7.1.0" },
     { name = "scikit-learn", specifier = ">=1.9.1" },
 ]
@@ -1293,47 +1293,47 @@ wheels = [
 
 [[package]]
 name = "pandas"
-version = "3.0.6"
+version = "3.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
     { name = "python-dateutil" },
     { name = "tzdata", marker = "sys_platform == 'emscripten' or sys_platform == 'win32'" },
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 ]
 
 [[package]]
diff --git a/Übung 4/SLN.py b/Übung 4/SLN.py
index 6426ebc..beb508d 100644
--- a/Übung 4/SLN.py	
+++ b/Übung 4/SLN.py	
@@ -1,11 +1,12 @@
-import numpy as np
-import nnwplot
 import matplotlib.pyplot as plt
+import numpy as np
+
+import nnwplot
 
 
 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)
@@ -21,15 +22,9 @@ class SLN:
         self._W = np.random.randn(dIn, cOut) / np.sqrt(dIn + 1)
 
     def neuron(self, X):
-        N = X.shape[1]
-        net = np.zeros(N)
+        net = self._W.T.dot(X) + self._b
 
-        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
+        return net >= 0
 
     def delta_train(self, X, T, eta, maxIter, maxErrorRate):
         N = X.shape[1]  # Anzahl Trainingsdaten
@@ -37,22 +32,17 @@ class SLN:
         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 (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
-            ...
+
+            delta = T - Y
+            self._W += X.dot(delta.T) * eta
+            self._b += delta.sum() * eta
+
         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/nnwplot.py b/Übung 4/nnwplot.py
index 8b715cd..44a3155 100644
--- a/Übung 4/nnwplot.py	
+++ b/Übung 4/nnwplot.py	
@@ -16,7 +16,7 @@ def plotTwoFeatures(X,T,pred_func):
         
     # determine canvas borders
     mins = np.amin(X,1); 
-    mins = mins - 0.1*np.abs(mins);
+    mins = mins - np.where(mins == 0, 0.1, 0.1 * np.abs(mins))
     maxs = np.amax(X,1); 
     maxs = maxs + 0.1*maxs;
 
@@ -45,5 +45,5 @@ 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']))
     plt.show()
diff --git a/Übung 4/training.ipynb b/Übung 4/training.ipynb
new file mode 100644
index 0000000..2a1e759
--- /dev/null
+++ b/Übung 4/training.ipynb	
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-27T07:21:20.697208100Z",
+     "start_time": "2026-10-27T07:21:20.549994800Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from importlib import reload\n",
+    "import numpy as np\n",
+    "from nnwplot import plotTwoFeatures\n",
+    "import SLN as import_sln\n",
+    "reload(import_sln)"
+   ],
+   "id": "de83e0a3826971b5",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'SLN' from 'C:\\\\Users\\\\flori\\\\OneDrive - bwstaff\\\\Studium\\\\NNW\\\\NNW\\\\Übung 4\\\\SLN.py'>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-27T07:21:20.748481200Z",
+     "start_time": "2026-10-27T07:21:20.725628100Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "X = np.array([[0., 0., 1., 1.], [0., 1., 0., 1.]])\n",
+    "T = np.array([0., 0., 0., 1.])\n"
+   ],
+   "id": "d04fd0aaafa7b2e3",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-10-27T07:21:20.797664700Z",
+     "start_time": "2026-10-27T07:21:20.755600900Z"
+    }
+   },
+   "source": [
+    "sln = import_sln.SLN(2, 1)\n",
+    "sln._W = np.array([[1], [1]])\n",
+    "sln._b = np.array([[-2]])"
+   ],
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-27T07:21:20.819163400Z",
+     "start_time": "2026-10-27T07:21:20.800115900Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "sln.neuron(X)",
+   "id": "5cb7b328ef2abe20",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-27T07:21:20.868604400Z",
+     "start_time": "2026-10-27T07:21:20.835236400Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plotTwoFeatures(X, T, sln.neuron)",
+   "id": "19d68b97fca5a7e7",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-10-27T07:21:22.319853100Z",
+     "start_time": "2026-10-27T07:21:20.871602500Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "train_sln = import_sln.SLN(2, 1)\n",
+    "train_sln.delta_train(X, T, 0.1, 100, 0.001)"
+   ],
+   "id": "e97b83cdad840616",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#0 err:0.5\n",
+      "[[ 0.28677805]\n",
+      " [-0.07982693]]\n",
+      "[[0.]]\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": [
+      "#1 err:0.25\n",
+      "[[ 0.18677805]\n",
+      " [-0.07982693]]\n",
+      "[[-0.2]]\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": [
+      "#2 err:0.25\n",
+      "[[0.28677805]\n",
+      " [0.02017307]]\n",
+      "[[-0.1]]\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": [
+      "#3 err:0.0\n",
+      "[[0.18677805]\n",
+      " [0.02017307]]\n",
+      "[[-0.2]]\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": [
+      "#3 err:0.0\n",
+      "[[0.18677805]\n",
+      " [0.02017307]]\n",
+      "[[-0.2]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 6
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}