From 74a471726cb5a7dc21caec298f619e8048cc2aae Mon Sep 17 00:00:00 2001
From: Paul <poggel.paul@gmail.com>
Date: Mon, 2 Nov 2020 19:55:06 +0100
Subject: [PATCH] differenzgerade implemented. questionable result

---
 ML_U3_1_Perceptron.ipynb | 199 +++++++++++++++++++++++++++++++++------
 1 file changed, 172 insertions(+), 27 deletions(-)

diff --git a/ML_U3_1_Perceptron.ipynb b/ML_U3_1_Perceptron.ipynb
index 432d6e5..487c60e 100644
--- a/ML_U3_1_Perceptron.ipynb
+++ b/ML_U3_1_Perceptron.ipynb
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,10 @@
     "        >>> Perceptron(2, 100, 0.1)\n",
     "        \"\"\"\n",
     "        ### Dein Code kommt hierhin:\n",
-    "        \n",
+    "        self.weights = np.ones(number_of_inputs+1)\n",
+    "        self.epochs = epochs\n",
+    "        self.eta = eta\n",
+    "        self.bias = 1\n",
     "        ##########################\n",
     "        pass\n",
     "           \n",
@@ -103,11 +106,13 @@
     "        >>> inputs = np.array([0, 1])\n",
     "        >>> h = perceptron.predict(inputs) \n",
     "        \"\"\"\n",
-    "        \n",
-    "        # Dein Code kommt hierhin:        \n",
-    "        \n",
-    "        ##########################\n",
-    "        \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",
+    "        ########################## \n",
     "        pass\n",
     "\n",
     "    def fit(self, training_inputs, labels):\n",
@@ -115,9 +120,13 @@
     "        Beispiel des Funktionsaufrufs:\n",
     "        >>> perceptron.fit(train_input, labels)\n",
     "        \"\"\"\n",
-    "        \n",
     "        # Dein Code kommt hierhin:\n",
-    "\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",
     "        ##########################\n",
     "        pass\n",
     "            \n",
@@ -144,9 +153,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[0.  0.8 0.8]\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f7e777bfa50>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
    "source": [
     "# Beispiel mit AND\n",
     "train_input_AND = np.array([\n",
@@ -160,17 +198,58 @@
     "\n",
     "# Dein Code kommt hier hin:\n",
     "# Perceptron anlegen und trainieren\n",
-    "\n",
+    "perceptron_AND = Perceptron(len(train_input_AND[0]),100,0.1)\n",
+    "perceptron_AND.fit(train_input_AND,labels_AND)\n",
     "\n",
     "# Geradengleichung berechnen und plotten\n",
-    "\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"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[0. 1. 1.]\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f7e76e0b850>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
    "source": [
     "# Beispiel mit OR\n",
     "train_input_OR = np.array([\n",
@@ -184,17 +263,58 @@
     "\n",
     "# Dein Code kommt hier hin:\n",
     "# Perceptron anlegen und trainieren\n",
-    "\n",
+    "perceptron_OR = Perceptron(len(train_input_OR[0]),100,0.1)\n",
+    "perceptron_OR.fit(train_input_OR,labels_OR)\n",
     "\n",
     "# Geradengleichung berechnen und plotten\n",
-    "\n"
+    "weights = perceptron_OR.getWeights()\n",
+    "print(weights)\n",
+    "x = list()\n",
+    "disc = list()\n",
+    "for i in range(len(train_input_OR)):\n",
+    "    x.append(train_input_OR[i].dot(2**np.arange(train_input_OR[i].size)[::-1]))\n",
+    "    sum = weights[0]\n",
+    "    for j in range(len(train_input_OR[i])):\n",
+    "        sum -= 2**j * train_input_OR[i,-j-1] * weights[j+1]\n",
+    "    disc.append(sum)\n",
+    "plt.plot(x,labels_OR,'o')\n",
+    "plt.plot(x,disc)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-0.4  0.8  0.8]\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f7e7678cd90>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
    "source": [
     "# Beispiel mit XOR\n",
     "train_input_XOR = np.array([\n",
@@ -208,9 +328,22 @@
     "\n",
     "# Dein Code kommt hier hin:\n",
     "# Perceptron anlegen und trainieren\n",
+    "perceptron_XOR = Perceptron(len(train_input_XOR[0]),100,0.1)\n",
+    "perceptron_XOR.fit(train_input_XOR,labels_XOR)\n",
     "\n",
-    "\n",
-    "# Geradengleichung berechnen und plotten"
+    "# Geradengleichung berechnen und plotten\n",
+    "weights = perceptron_XOR.getWeights()\n",
+    "print(weights)\n",
+    "x = list()\n",
+    "disc = list()\n",
+    "for i in range(len(train_input_XOR)):\n",
+    "    x.append(train_input_XOR[i].dot(2**np.arange(train_input_XOR[i].size)[::-1]))\n",
+    "    sum = weights[0]\n",
+    "    for j in range(len(train_input_XOR[i])):\n",
+    "        sum -= 2**j * train_input_XOR[i,-j-1] * weights[j+1]\n",
+    "    disc.append(sum)\n",
+    "plt.plot(x,labels_XOR,'o')\n",
+    "plt.plot(x,disc)"
    ]
   },
   {
@@ -236,9 +369,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
    "source": [
     "# Hier wird der Iris-Datensatz geladen und vorbereitet (siehe letzte Übung)\n",
     "\n",
@@ -281,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -320,9 +465,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file