{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "44d16018",
   "metadata": {},
   "source": [
    "## Vorbereitungen den Aufgabe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a35b6bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import nnwplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a0dc7253",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ErrorRate(Y,T):\n",
    "    if Y.ndim==1 or Y.shape[0]==1:\n",
    "        errors=Y!=T # gibt boolsche Liste zurück\n",
    "        return errors.sum()/Y.size # summiert True als 1 in errors auf \n",
    "    else: # für mehrere Ausgaben in one-hot Kodierung:\n",
    "        errors=Y.argmax(0)!=T.argmax(0)\n",
    "        return errors.sum()/Y.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b75ac9e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SLN:\n",
    "    def __init__(self, dIn, cOut): # Konstruktor\n",
    "        np.random.seed(42)\n",
    "        self._b=np.zeros((cOut,1))\n",
    "        self._W=np.random.randn(cOut,dIn)/np.sqrt(dIn+1)\n",
    "\n",
    "    def neuron(self, X):\n",
    "        N = X.shape[1]\n",
    "        net = np.zeros(N)\n",
    "        # self._W.shape (1,2) X.shape (2,4)\n",
    "        net = self._W @ X \n",
    "        # b verwenden ...\n",
    "        net = net - self._b\n",
    "        return net > 0 # threshold ....\n",
    "    \n",
    "    def DeltaTrain(self, X, T, eta, maxIter, maxErrorRate):\n",
    "        N=X.shape[1]    # Anzahl Trainingsdaten\n",
    "        plt.ion() # interactive mode on\n",
    "        for it in range(maxIter):\n",
    "            Y = self.neuron(X) # nur ein Aufruf\n",
    "            err = ErrorRate(Y, T)\n",
    "            if (it%20) == 0:\n",
    "                print('#{} err:{}\\n{}\\n{}'.format(it,err,self._W,self._b))\n",
    "                nnwplot.plotTwoFeatures(X, T, self.neuron)\n",
    "                plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)\n",
    "            if err <= maxErrorRate:\n",
    "                break\n",
    "            Y = Y.astype(float)\n",
    "            # update _W\n",
    "            # X.shape = (2,4), T.shape = (1,4), Y.shape = (1,4)\n",
    "            self._W +=  eta * (T - Y) @ X.T  / N\n",
    "            # update _b \n",
    "            self._b -= eta * (T - Y) @ np.ones((1,N)).T / N\n",
    "            \n",
    "        print('#{} err:{}\\n{}\\n{}'.format(it,err,self._W,self._b))\n",
    "        nnwplot.plotTwoFeatures(X, T, self.neuron)\n",
    "        plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)\n",
    "\n",
    "        input(\"Press Enter to continue...\") # wenn gewünscht ...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ea7529b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = np.loadtxt(\"../supplement_files/iris.csv\",delimiter=',')\n",
    "\n",
    "X = iris[:,[0,1]]\n",
    "T = iris[:,-1][iris[:,-1]!=2] # alle Zeilen und nur die letzte Spalte, wo die letzte Spalte ungleich 2 (also 0 oder 1) ist\n",
    "X = X[iris[:,-1]!=2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c3166860",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,)\n",
      "(100, 2)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 2 is out of bounds for axis 1 with size 2",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mIndexError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[37]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# 4d)\u001b[39;00m\n\u001b[32m      2\u001b[39m print(T.shape)\n\u001b[32m      3\u001b[39m print(X.shape)\n\u001b[32m      4\u001b[39m sln = SLN(\u001b[32m2\u001b[39m,\u001b[32m1\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m sln.DeltaTrain(X,T,\u001b[32m0.5\u001b[39m,\u001b[32m1000\u001b[39m,\u001b[32m0.2\u001b[39m)\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[35]\u001b[39m\u001b[32m, line 24\u001b[39m, in \u001b[36mSLN.DeltaTrain\u001b[39m\u001b[34m(self, X, T, eta, maxIter, maxErrorRate)\u001b[39m\n\u001b[32m     20\u001b[39m     \u001b[38;5;28;01mdef\u001b[39;00m DeltaTrain(self, X, T, eta, maxIter, maxErrorRate):\n\u001b[32m     21\u001b[39m         N=X.shape[\u001b[32m1\u001b[39m]    \u001b[38;5;66;03m# Anzahl Trainingsdaten\u001b[39;00m\n\u001b[32m     22\u001b[39m         plt.ion() \u001b[38;5;66;03m# interactive mode on\u001b[39;00m\n\u001b[32m     23\u001b[39m         \u001b[38;5;28;01mfor\u001b[39;00m it \u001b[38;5;28;01min\u001b[39;00m range(maxIter):\n\u001b[32m---> \u001b[39m\u001b[32m24\u001b[39m             Y = self.neuron(X) \u001b[38;5;66;03m# nur ein Aufruf\u001b[39;00m\n\u001b[32m     25\u001b[39m             err = ErrorRate(Y, T)\n\u001b[32m     26\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m (it%\u001b[32m20\u001b[39m) == \u001b[32m0\u001b[39m:\n\u001b[32m     27\u001b[39m                 print(\u001b[33m'#{} err:{}\\n{}\\n{}'\u001b[39m.format(it,err,self._W,self._b))\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[35]\u001b[39m\u001b[32m, line 15\u001b[39m, in \u001b[36mSLN.neuron\u001b[39m\u001b[34m(self, X)\u001b[39m\n\u001b[32m     11\u001b[39m         \u001b[38;5;66;03m# net = self._W @ X\u001b[39;00m\n\u001b[32m     12\u001b[39m         \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;28;01min\u001b[39;00m range(N):\n\u001b[32m     13\u001b[39m             \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;28;01min\u001b[39;00m range(X.shape[\u001b[32m0\u001b[39m]):\n\u001b[32m     14\u001b[39m                 \u001b[38;5;66;03m# print(self._W)\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m15\u001b[39m                 net[n]+= X[j,n] * self._W[\u001b[32m0\u001b[39m, j]\n\u001b[32m     16\u001b[39m         \u001b[38;5;66;03m# b verwenden ...\u001b[39;00m\n\u001b[32m     17\u001b[39m         net = net - self._b\n\u001b[32m     18\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m net > \u001b[32m0\u001b[39m \u001b[38;5;66;03m# threshold ....\u001b[39;00m\n",
      "\u001b[31mIndexError\u001b[39m: index 2 is out of bounds for axis 1 with size 2"
     ]
    }
   ],
   "source": [
    "# 4d)\n",
    "print(T.shape)\n",
    "print(X.shape)\n",
    "sln = SLN(2,1)\n",
    "sln.DeltaTrain(X,T,0.5,1000,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f9a70f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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