Final touches 💯

Data_Analysis_Python/Offline Predictions.ipynb → Data_Analysis_Python/.ipynb_checkpoints/Offline Prediction-checkpoint.ipynb

@@ -1,20 +1,41 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook for offline trained ML model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the required libraries"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import csv\n",
+    "import scipy.signal\n",
     "import serial\n",
     "import numpy as np\n",
     "import urllib.request \n",
     "\n",
-    "import scipy.signal\n",
     "from sklearn.externals import joblib"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the serial port"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -28,6 +49,13 @@
     "    print(\"Please check the port.\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Acquisition of the data and parameter extraction"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -65,34 +93,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[490.0, 496.0, 6.0, 496.0, 496.0, 496.0, 495.22]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print('\\nThe parameters of this signal are: ', extracted_parameters)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Viewing the confidence values of the data"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'ceramics': 0.0390936227134829, 'plastic': 0.9253597476988202, 'wood': 0.03554662958769686}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "inputarray = extracted_parameters[:7]\n",
     "clf = joblib.load('model.pkl')\n",

Data_Analysis_Python/.ipynb_checkpoints/Online Prediction-checkpoint.ipynb

@@ -1,6 +1,141 @@
 {
- "cells": [],
- "metadata": {},
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook for Heroku hosted ML API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "import scipy.signal\n",
+    "import serial\n",
+    "import numpy as np\n",
+    "import urllib.request "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the serial port"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    arduino = serial.Serial(\"/COM9\",timeout=1)\n",
+    "    print(\"The port has been successfully loaded.\")\n",
+    "except:\n",
+    "    print(\"Please check the port.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Acquiring the data and extracting the parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = []\n",
+    "data_count = 0\n",
+    "\n",
+    "while data_count<11:\n",
+    "    rawdata = []\n",
+    "    count = 0\n",
+    "    while count<100:\n",
+    "        rawdata.append(int(arduino.readline()))\n",
+    "        count+=1\n",
+    "    dataset.append(rawdata)\n",
+    "    data_count+=1\n",
+    "\n",
+    "# Filtering of the input values\n",
+    "filtered_values = []\n",
+    "\n",
+    "mean_value = np.mean(dataset[10])\n",
+    "value_25 = np.percentile(dataset[10], 25)\n",
+    "value_75 = np.percentile(dataset[10], 75)\n",
+    "\n",
+    "for value in dataset[0]:\n",
+    "    filtered_values.append(value_75 if value > mean_value else value_25)\n",
+    "    \n",
+    "url_parameter = \"\"\n",
+    "for i in range(0,len(filtered_values)):\n",
+    "    if(i<len(filtered_values)-1):\n",
+    "        url_parameter += str(filtered_values[i])\n",
+    "        url_parameter += \",\"\n",
+    "    else:\n",
+    "        url_parameter += str(filtered_values[i])\n",
+    "\n",
+    "# Extraction of the signal parameters\n",
+    "extracted_parameters = [np.amin(filtered_values), np.amax(filtered_values),\n",
+    "                        np.ptp(filtered_values), np.percentile(filtered_values, 75),\n",
+    "                        np.percentile(filtered_values, 25), np.median(filtered_values),\n",
+    "                        np.mean(filtered_values), round(np.std(filtered_values),2),\n",
+    "                        round(np.var(filtered_values),2), round(scipy.stats.kurtosis(filtered_values),2),\n",
+    "                        round(scipy.stats.skew(filtered_values),2)]\n",
+    "\n",
+    "print('\\nThe parameters of this signal are: ', extracted_parameters)\n",
+    "\n",
+    "# Calling the web API\n",
+    "web_api_url = \"https://pacific-harbor-19774.herokuapp.com/predict?myvar=\" + str(url_parameter)\n",
+    "print('\\nThe web API URL is: ', web_api_url)\n",
+    "\n",
+    "urllib.request.urlopen(web_api_url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arduino.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }