Solved bugs

LinearRegression.r

@@ -233,7 +233,7 @@ for(i in 1:10)
   trSize = 10 * i;
 
   trainingData <- head(dataS , trSize)
-  trmodel <- lm(y ~ x1 + x2 +x3 +x4 +x5 +x6 +x7 , data =dataS , x=T , y=T)
+  trmodel <- lm(y ~ x1 + x2 +x3 +x4 +x5 +x6 +x7 , data =trainingData , x=T , y=T)
   predictedY <- predict ( trmodel , trainingData)
 
   predictedY
@@ -287,4 +287,4 @@ for(i in 1: 10)
 }
 
 plot(MSE)
-lines(x = MSE , y= NULL , type ="l" , col="blue")
+lines(x = MSE , y= NULL , type ="l" , col="blue")

Neural_Network.ipynb

@@ -4,15 +4,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(3, 4)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#Source code for Artificial Neural Network using python\n",
     "import numpy as np\n",
@@ -27,7 +19,7 @@
     "\n",
     "#Derivative of Sigmoid Function\n",
     "def derivatives_sigmoid(x):\n",
-    "    return x * (1 - x)\n",
+    "    return sigmoid(x) * (1 - sigmoid(x))\n",
     "\n",
     "#Variable initialization\n",
     "epoch=5000 #Setting training iterations\n",
@@ -45,79 +37,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
     "for i in range(epoch):\n",
     "#Forward Propogation\n",
     "    hidden_layer_input1=np.dot(X,wh)\n",
     "    hidden_layer_input=hidden_layer_input1 + bh\n",
     "    hiddenlayer_activations = sigmoid(hidden_layer_input)\n",
     "    output_layer_input1=np.dot(hiddenlayer_activations,wout)\n",
     "    output_layer_input= output_layer_input1+ bout\n",
-    "    output = sigmoid(output_layer_input)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
+    "    output = sigmoid(output_layer_input)\n",
+    "    \n",
     "#Backpropagation\n",
-    "E = y-output\n",
-    "slope_output_layer = derivatives_sigmoid(output)\n",
-    "slope_hidden_layer = derivatives_sigmoid(hiddenlayer_activations)\n",
-    "d_output = E * slope_output_layer\n",
-    "Error_at_hidden_layer = d_output.dot(wout.T)\n",
-    "d_hiddenlayer = Error_at_hidden_layer * slope_hidden_layer\n",
-    "wout += hiddenlayer_activations.T.dot(d_output) *lr\n",
-    "bout += np.sum(d_output, axis=0,keepdims=True) *lr\n",
-    "wh += X.T.dot(d_hiddenlayer) *lr\n",
-    "bh += np.sum(d_hiddenlayer, axis=0,keepdims=True) *lr"
+    "    E = y-output\n",
+    "    slope_output_layer = derivatives_sigmoid(output)\n",
+    "    slope_hidden_layer = derivatives_sigmoid(hiddenlayer_activations)\n",
+    "    d_output = E * slope_output_layer\n",
+    "    Error_at_hidden_layer = d_output.dot(wout.T)\n",
+    "    d_hiddenlayer = Error_at_hidden_layer * slope_hidden_layer\n",
+    "    wout += hiddenlayer_activations.T.dot(d_output) *lr\n",
+    "    bout += np.sum(d_output, axis=0,keepdims=True) *lr\n",
+    "    wh += X.T.dot(d_hiddenlayer) *lr\n",
+    "    bh += np.sum(d_hiddenlayer, axis=0,keepdims=True) *lr"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.82938945]\n",
-      " [0.84310929]\n",
-      " [0.83800391]]\n"
+      "[[0.9980349 ]\n",
+      " [0.99553424]\n",
+      " [0.00723341]]\n"
      ]
     }
    ],
    "source": [
+    "\n",
     "print (output)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -136,7 +101,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,

README.md

@@ -1,8 +1,5 @@
 # MLA
-Machine Learning and Algorithms Codes for BE IT.
-
-
-Codes are contributed by Varad and Stony.
+Machine Learning and Applications Codes SPPU BE IT 2015 Course
 
 `my_data.txt` is the dataset for Linear Regression.