diff --git a/ML_U3_1_Perceptron.ipynb b/ML_U3_1_Perceptron.ipynb
index 347708c..08d7f80 100644
--- a/ML_U3_1_Perceptron.ipynb
+++ b/ML_U3_1_Perceptron.ipynb
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,6 +99,10 @@
     "        self.bias = 1\n",
     "        ##########################\n",
     "        pass\n",
+    "\n",
+    "    def net_input(self, X):\n",
+    "        \"\"\"Calculate net input\"\"\"\n",
+    "        return np.dot(X, self.weights[1:]) + self.weights[0]\n",
     "           \n",
     "    def predict(self, inputs):\n",
     "        \"\"\"\n",
@@ -108,10 +112,7 @@
     "        \"\"\"\n",
     "        # Dein Code kommt hierhin:\n",
     "        #bias\n",
-    "        sum = self.bias * self.weights[0]\n",
-    "        for i in range(len(inputs)):\n",
-    "            sum += self.weights[i+1]*inputs[i]\n",
-    "        return np.sign(sum)\n",
+    "        return np.where(self.net_input(inputs) >= 0.0, 1, -1)\n",
     "        ########################## \n",
     "        pass\n",
     "\n",
@@ -121,12 +122,11 @@
     "        >>> perceptron.fit(train_input, labels)\n",
     "        \"\"\"\n",
     "        # Dein Code kommt hierhin:\n",
-    "        for i in range(len(training_inputs)):\n",
-    "            for j in range(len(training_inputs[i])+1):\n",
-    "                #bias\n",
-    "                if j == 0:\n",
-    "                    self.weights[j] = self.eta * (labels[i] - self.predict(training_inputs[i])) * self.bias\n",
-    "                self.weights[j] += self.eta * (labels[i] - self.predict(training_inputs[i])) * training_inputs[i,j-1]\n",
+    "        for e in range(self.epochs):\n",
+    "            for xi, target in zip(training_inputs, labels):\n",
+    "                delta_w = self.eta * (target - self.predict(xi))\n",
+    "                self.weights[1:] += delta_w * xi\n",
+    "                self.weights[0] += delta_w\n",
     "        ##########################\n",
     "        pass\n",
     "            \n",
@@ -153,32 +153,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[0.  0.8 0.8]\n"
+      "[-0.6  0.4  0.4]\n"
      ]
     },
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa6ce749090>]"
+       "[<matplotlib.lines.Line2D at 0x7fa8c9cba190>]"
       ]
      },
      "metadata": {},
-     "execution_count": 86
+     "execution_count": 22
     },
     {
      "output_type": "display_data",
      "data": {
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      },
      "metadata": {
       "needs_background": "light"
@@ -198,25 +198,17 @@
     "\n",
     "# Dein Code kommt hier hin:\n",
     "# Perceptron anlegen und trainieren\n",
-    "perceptron_AND = Perceptron(len(train_input_AND[0]),100,0.1)\n",
+    "perceptron_AND = Perceptron(2,100,0.1)\n",
     "perceptron_AND.fit(train_input_AND,labels_AND)\n",
     "\n",
     "# Geradengleichung berechnen und plotten\n",
     "weights = perceptron_AND.getWeights()\n",
     "print(weights)\n",
-    "#x = list()\n",
-    "#disc = list()\n",
-    "#for i in range(len(train_input_AND)):\n",
-    "#    x.append(train_input_AND[i].dot(2**np.arange(train_input_AND[i].size)[::-1]))\n",
-    "#    sum = weights[0]\n",
-    "#    for j in range(len(train_input_AND[i])):\n",
-    "#        sum -= 2**j * train_input_AND[i,-j-1] * weights[j+1]\n",
-    "#    disc.append(sum)\n",
-    "#plt.plot(x,labels_AND,'o')\n",
-    "#plt.plot(x,disc)\n",
     "\n",
-    "x_AND = [1-weights[1],1]\n",
-    "y_AND = [1+weights[0],1-weights[2]+weights[0]]\n",
+    "slope = -weights[1]/weights[2]\n",
+    "offset = -weights[0]/weights[2]\n",
+    "x_AND = np.linspace(0,1)\n",
+    "y_AND = slope * x_AND + offset\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.scatter(train_input_AND[(labels_AND==-1),0] , train_input_AND[(labels_AND==-1),1])\n",
@@ -226,32 +218,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[0. 1. 1.]\n"
+      "[-0.2  1.   1. ]\n"
      ]
     },
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa6ce45ab90>]"
+       "[<matplotlib.lines.Line2D at 0x7fa8c9946fd0>]"
       ]
      },
      "metadata": {},
-     "execution_count": 87
+     "execution_count": 25
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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      },
      "metadata": {
       "needs_background": "light"
@@ -278,8 +270,10 @@
     "weights = perceptron_OR.getWeights()\n",
     "print(weights)\n",
     "\n",
-    "x_OR = [1-weights[1],1]\n",
-    "y_OR = [1+weights[0], 1-weights[2]+weights[0]]\n",
+    "slope = -weights[1]/weights[2]\n",
+    "offset = -weights[0]/weights[2]\n",
+    "x_OR = np.linspace(0,1)\n",
+    "y_OR = slope * x_OR + offset\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.scatter(train_input_OR[(labels_OR==-1),0] , train_input_OR[(labels_OR==-1),1])\n",
@@ -289,32 +283,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[-0.4  0.8  0.8]\n"
+      "[ 5.55111512e-17 -2.00000000e-01  5.55111512e-17]\n"
      ]
     },
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa6ce533ad0>]"
+       "[<matplotlib.lines.Line2D at 0x7fa8c90b0ad0>]"
       ]
      },
      "metadata": {},
-     "execution_count": 88
+     "execution_count": 32
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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      },
      "metadata": {
       "needs_background": "light"
@@ -341,8 +335,10 @@
     "weights = perceptron_XOR.getWeights()\n",
     "print(weights)\n",
     "\n",
-    "x_XOR = [1-weights[1],1]\n",
-    "y_XOR = [1+weights[0], 1-weights[2]+weights[0]]\n",
+    "slope = -weights[1]/weights[2]\n",
+    "offset = -weights[0]/weights[2]\n",
+    "x_XOR = np.linspace(0,1)\n",
+    "y_XOR = slope * x_XOR + offset\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.scatter(train_input_XOR[(labels_XOR==-1),0] , train_input_XOR[(labels_XOR==-1),1])\n",
@@ -376,14 +372,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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      },
      "metadata": {
@@ -433,15 +429,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[ 0.38 -0.12  0.14]\n"
+      "[-22.2   1.9   8. ]\n"
      ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
     }
    ],
    "source": [
@@ -451,7 +458,19 @@
     "percepton_iris.fit(X_train,y_train)\n",
     "\n",
     "weights = percepton_iris.getWeights()\n",
-    "print(weights)"
+    "print(weights)\n",
+    "\n",
+    "slope = -weights[1]/weights[2]\n",
+    "offset = -weights[0]/weights[2]\n",
+    "x_iris = np.linspace(min(X_train[0]),max(X_train[0]))\n",
+    "y_iris = slope * x_iris + offset\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(X[(y==-1),0] , X[(y==-1),1])\n",
+    "ax.scatter(X[(y==1),0] , X[(y==1),1])\n",
+    "ax.set(xlabel = iris_features[0], ylabel = iris_features[1])\n",
+    "plt.plot(x_iris, y_iris)\n",
+    "plt.show()"
    ]
   },
   {