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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (0.24.2)\n",
+ "Requirement already satisfied: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\n",
+ "Requirement already satisfied: pytz>=2011k in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2018.9)\n",
+ "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.0)\n",
+ "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)\n",
+ "Requirement already satisfied: xlrd in c:\\programdata\\anaconda3\\lib\\site-packages (1.2.0)\n",
+ "Requirement already satisfied: sklearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
+ "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn) (0.20.3)\n",
+ "Requirement already satisfied: scipy>=0.13.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\n",
+ "Requirement already satisfied: numpy>=1.8.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.16.2)\n",
+ "Collecting imblearn\n",
+ " Using cached https://files.pythonhosted.org/packages/81/a7/4179e6ebfd654bd0eac0b9c06125b8b4c96a9d0a8ff9e9507eb2a26d2d7e/imblearn-0.0-py2.py3-none-any.whl\n",
+ "Collecting imbalanced-learn (from imblearn)\n",
+ " Using cached https://files.pythonhosted.org/packages/e6/62/08c14224a7e242df2cef7b312d2ef821c3931ec9b015ff93bb52ec8a10a3/imbalanced_learn-0.5.0-py3-none-any.whl\n",
+ "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.13.2)\n",
+ "Requirement already satisfied: numpy>=1.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.16.2)\n",
+ "Collecting scikit-learn>=0.21 (from imbalanced-learn->imblearn)\n",
+ " Using cached https://files.pythonhosted.org/packages/75/14/fde90de4fc6722303ddbc183ec253c288dae8c7e4eb5a069ac49f14c9a0f/scikit_learn-0.21.2-cp37-cp37m-win_amd64.whl\n",
+ "Requirement already satisfied: scipy>=0.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.1)\n",
+ "Installing collected packages: scikit-learn, imbalanced-learn, imblearn\n",
+ " Found existing installation: scikit-learn 0.20.3\n",
+ " Uninstalling scikit-learn-0.20.3:\n",
+ " Successfully uninstalled scikit-learn-0.20.3\n",
+ "Successfully installed imbalanced-learn-0.5.0 imblearn-0.0 scikit-learn-0.21.2\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install pandas\n",
+ "!pip install xlrd\n",
+ "!pip install sklearn\n",
+ "!pip install imblearn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xlrd\n",
+ "book = xlrd.open_workbook(\"Datasheets info.xlsx\")\n",
+ "sheetMQ2 = book.sheet_by_name(\"MQ2 - Pololulu\")\n",
+ "sheetMQ3 = book.sheet_by_name(\"MQ3 - Sparkfun\")\n",
+ "sheetMQ4 = book.sheet_by_name(\"MQ4 - Sparkfun\")\n",
+ "sheetMQ5 = book.sheet_by_name(\"MQ5 - Sparkfun\")\n",
+ "sheetMQ6 = book.sheet_by_name(\"MQ6 - Sparkfun\")\n",
+ "sheetMQ7 = book.sheet_by_name(\"MQ7 - Sparkfun\")\n",
+ "sheetMQ8 = book.sheet_by_name(\"MQ8 - Sparkfun\")\n",
+ "sheetMQ9 = book.sheet_by_name(\"MQ9 - Haoyuelectronics\")\n",
+ "sheetMQ131 = book.sheet_by_name(\"MQ131- Sensorsportal\")\n",
+ "sheetMQ135 = book.sheet_by_name(\"MQ135 - HANWEI\")\n",
+ "sheetMQ303A = book.sheet_by_name(\"MQ303A - HANWEI\")\n",
+ "sheetMQ309A = book.sheet_by_name(\"MQ309A - HANWEI\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "RS/R0 \t H2 \t LPG \t CH4 \t CO \t Alcohol \t Propane\n",
+ "0.1 \t \t \t \t \t \t \n",
+ "0.2 \t \t \t \t \t \t \n",
+ "0.3 \t \t \t \t \t \t \n",
+ "0.4 \t \t \t \t \t \t \n",
+ "0.5 \t \t \t \t \t \t \n",
+ "0.6 \t \t 2000.0 \t \t \t \t 2000.0\n",
+ "0.7 \t 2100.0 \t \t \t \t \t \n",
+ "0.8 \t \t 1000.0 \t \t \t \t 1000.0\n",
+ "0.9 \t \t 800.0 \t 5000.0 \t \t 5000.0 \t 800.0\n",
+ "1.0 \t 1000.0 \t \t \t \t \t \n",
+ "2.0 \t 201.0 \t \t 800.0 \t \t 500.0 \t \n",
+ "3.0 \t \t \t 200.0 \t 1000.0 \t 200.0 \t \n",
+ "4.0 \t \t \t \t 500.0 \t \t \n",
+ "5.0 \t \t \t \t 200.0 \t \t \n",
+ "6.0 \t \t \t \t \t \t \n",
+ "7.0 \t \t \t \t \t \t \n",
+ "8.0 \t \t \t \t \t \t \n",
+ "9.0 \t \t \t \t \t \t \n"
+ ]
+ }
+ ],
+ "source": [
+ "for row_index in range(1,20): #reading first columns\n",
+ " RsR0, H2, LPG, CH4, CO, Alcohol, propane = sheetMQ2.row_values(row_index, start_colx=0, end_colx=7)\n",
+ " print(RsR0, \"\t\", H2, \"\t\", LPG, \"\t\", CH4, \"\t\", CO, \"\t\", Alcohol, \"\t\", propane)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_MQ2 = sheetMQ2.col_values(0)[2:]\n",
+ "MQ2_H2 = sheetMQ2.col_values(1)[2:]\n",
+ "MQ2_LPG = sheetMQ2.col_values(2)[2:]\n",
+ "MQ2_CH4 = sheetMQ2.col_values(3)[2:]\n",
+ "MQ2_CO = sheetMQ2.col_values(4)[2:]\n",
+ "MQ2_Alcohol = sheetMQ2.col_values(5)[2:]\n",
+ "MQ2_propane = sheetMQ2.col_values(6)[2:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def zero_to_nan(values):\n",
+ " \"\"\"Replace every 0 with 'nan' and return a copy.\"\"\"\n",
+ " return [float('nan') if x==0 else x for x in values]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MQ2_H2 =zero_to_nan(MQ2_H2)\n",
+ "MQ2_LPG =zero_to_nan(MQ2_LPG)\n",
+ "MQ2_CH4 =zero_to_nan(MQ2_CH4)\n",
+ "MQ2_CO =zero_to_nan(MQ2_CO)\n",
+ "MQ2_Alcohol =zero_to_nan(MQ2_Alcohol)\n",
+ "MQ2_propane =zero_to_nan(MQ2_propane)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sklearn.datasets import load_iris\n",
+ "#from sklearn.cross_validation import train_test_split\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn import datasets\n",
+ "from sklearn import linear_model\n",
+ "\n",
+ "dataH2 = {'RsRo': x_MQ2, 'H2': MQ2_H2}\n",
+ "dataLPG = {'RsRo': x_MQ2, 'LPG': MQ2_LPG}\n",
+ "dataCH4 = {'RsRo': x_MQ2, 'CH4': MQ2_CH4}\n",
+ "dataCO = {'RsRo': x_MQ2, 'CO': MQ2_CO}\n",
+ "dataAlcohol = {'RsRo': x_MQ2, 'Alcohol': MQ2_Alcohol}\n",
+ "dataPropane = {'RsRo': x_MQ2, 'Propane': MQ2_propane}\n",
+ "\n",
+ "dfMQ2_H2 = pd.DataFrame(dataH2)\n",
+ "dfMQ2_LPG = pd.DataFrame(dataLPG)\n",
+ "dfMQ2_CH4 = pd.DataFrame(dataCH4)\n",
+ "dfMQ2_CO = pd.DataFrame(dataCO)\n",
+ "dfMQ2_Alcohol = pd.DataFrame(dataAlcohol)\n",
+ "dfMQ2_Propane = pd.DataFrame(dataPropane)\n",
+ "\n",
+ "dfMQ2_H2['H2'] = pd.to_numeric(dfMQ2_H2['H2'])\n",
+ "dfMQ2_LPG['LPG'] = pd.to_numeric(dfMQ2_LPG['LPG'])\n",
+ "dfMQ2_CH4['CH4'] = pd.to_numeric(dfMQ2_CH4['CH4'])\n",
+ "dfMQ2_CO['CO'] = pd.to_numeric(dfMQ2_CO['CO'])\n",
+ "dfMQ2_Alcohol['Alcohol'] = pd.to_numeric(dfMQ2_Alcohol['Alcohol'])\n",
+ "dfMQ2_Propane['Propane'] = pd.to_numeric(dfMQ2_Propane['Propane'])\n",
+ "\n",
+ "dfMQ2_H2['H2'] = dfMQ2_H2['H2'].replace('',None, regex=True)\n",
+ "dfMQ2_LPG['LPG'] = dfMQ2_LPG['LPG'].replace('',None, regex=True)\n",
+ "dfMQ2_CH4['CH4'] = dfMQ2_CH4['CH4'].replace('',None, regex=True)\n",
+ "dfMQ2_CO['CO'] = dfMQ2_CO['CO'].replace('',None, regex=True)\n",
+ "dfMQ2_Alcohol['Alcohol'] = dfMQ2_Alcohol['Alcohol'].replace('',None, regex=True)\n",
+ "dfMQ2_Propane['Propane'] = dfMQ2_Propane['Propane'].replace('',None, regex=True)\n",
+ "\n",
+ "#Global X_Predict variable\n",
+ "X_Predict = dfMQ2_H2.RsRo.apply(lambda x: [x]).tolist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Model and train\n",
+ "dataset2TrainH2 = dfMQ2_H2.copy()\n",
+ "dataset2TrainH2.dropna(inplace=True)\n",
+ "X_trainH2 = dataset2TrainH2.RsRo.apply(lambda x: [x]).tolist()\n",
+ "y_trainH2 = dataset2TrainH2['H2'].tolist()\n",
+ "model = linear_model.Lasso(alpha=0.1)\n",
+ "model.fit(X_trainH2, y_trainH2)\n",
+ "#Predict\n",
+ "H2_Predicted = model.predict(X_Predict)\n",
+ "#save into MQ2\n",
+ "MQ2_H2 = H2_Predicted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Model and train\n",
+ "dataset2TrainLPG = dfMQ2_LPG.copy()\n",
+ "dataset2TrainLPG.dropna(inplace=True)\n",
+ "X_trainLPG = dataset2TrainLPG.RsRo.apply(lambda x: [x]).tolist()\n",
+ "y_trainLPG = dataset2TrainLPG['LPG'].tolist()\n",
+ "model = linear_model.Lasso(alpha=0.1)\n",
+ "model.fit(X_trainLPG, y_trainLPG)\n",
+ "#Predict\n",
+ "LPG_Predicted = model.predict(X_Predict)\n",
+ "#save into MQ2\n",
+ "MQ2_LPG = LPG_Predicted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Model and train\n",
+ "dataset2TrainCH4 = dfMQ2_CH4.copy()\n",
+ "dataset2TrainCH4.dropna(inplace=True)\n",
+ "X_trainCH4 = dataset2TrainCH4.RsRo.apply(lambda x: [x]).tolist()\n",
+ "y_trainCH4 = dataset2TrainCH4['CH4'].tolist()\n",
+ "model = linear_model.Lasso(alpha=0.1)\n",
+ "model.fit(X_trainCH4, y_trainCH4)\n",
+ "#Predict\n",
+ "CH4_Predicted = model.predict(X_Predict)\n",
+ "#save into MQ2\n",
+ "MQ2_CH4 = CH4_Predicted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Model and train\n",
+ "dataset2TrainCO = dfMQ2_CO.copy()\n",
+ "dataset2TrainCO.dropna(inplace=True)\n",
+ "X_trainCO = dataset2TrainCO.RsRo.apply(lambda x: [x]).tolist()\n",
+ "y_trainCO = dataset2TrainCO['CO'].tolist()\n",
+ "model = linear_model.Lasso(alpha=0.1)\n",
+ "model.fit(X_trainCO, y_trainCO)\n",
+ "#Predict\n",
+ "CO_Predicted = model.predict(X_Predict)\n",
+ "#save into MQ2\n",
+ "MQ2_CO = CO_Predicted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Model and train\n",
+ "dataset2TrainAlcohol = dfMQ2_Alcohol.copy()\n",
+ "dataset2TrainAlcohol.dropna(inplace=True)\n",
+ "X_trainAlcohol = dataset2TrainAlcohol.RsRo.apply(lambda x: [x]).tolist()\n",
+ "y_trainAlcohol = dataset2TrainAlcohol['Alcohol'].tolist()\n",
+ "model = linear_model.Lasso(alpha=0.1)\n",
+ "model.fit(X_trainAlcohol, y_trainAlcohol)\n",
+ "#Predict\n",
+ "Alcohol_Predicted = model.predict(X_Predict)\n",
+ "#save into MQ2\n",
+ "MQ2_Alcohol = Alcohol_Predicted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Model and train\n",
+ "dataset2TrainPropane = dfMQ2_Propane.copy()\n",
+ "dataset2TrainPropane.dropna(inplace=True)\n",
+ "X_trainPropane = dataset2TrainPropane.RsRo.apply(lambda x: [x]).tolist()\n",
+ "y_trainPropane = dataset2TrainPropane['Propane'].tolist()\n",
+ "model = linear_model.Lasso(alpha=0.1)\n",
+ "model.fit(X_trainPropane, y_trainPropane)\n",
+ "#Predict\n",
+ "Propane_Predicted = model.predict(X_Predict)\n",
+ "#save into MQ2\n",
+ "MQ2_propane = Propane_Predicted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Rs/Ro')"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/svg+xml": [
+ "\r\n",
+ "\r\n",
+ "\r\n",
+ "\r\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%config InlineBackend.figure_formats = ['svg']\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.lines as mlines\n",
+ "import matplotlib.transforms as mtransforms\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "\n",
+ "fig.set_size_inches(9, 5.5, forward=True)\n",
+ "fig.set_dpi(200)\n",
+ "\n",
+ "# only these two lines are calibration curves\n",
+ "plt.plot(MQ2_H2, x_MQ2, marker='o', linewidth=1, label='H2')\n",
+ "plt.plot(MQ2_LPG, x_MQ2, marker='o', linewidth=1, label='LPG')\n",
+ "plt.plot(MQ2_CH4, x_MQ2, marker='o', linewidth=1, label='CH4')\n",
+ "plt.plot(MQ2_CO, x_MQ2, marker='o', linewidth=1, label='CO')\n",
+ "plt.plot(MQ2_Alcohol, x_MQ2, marker='o', linewidth=1, label='Alcohol')\n",
+ "plt.plot(MQ2_propane, x_MQ2, marker='o', linewidth=1, label='Propane')\n",
+ "\n",
+ "# reference line, legends, and axis labels\n",
+ "#line = mlines.Line2D([0, 1], [0, 1], color='black')\n",
+ "#transform = ax.transAxes\n",
+ "#line.set_transform(transform)\n",
+ "#ax.add_line(line)\n",
+ "plt.yscale('log')\n",
+ "plt.xscale('log')\n",
+ "\n",
+ "fig.suptitle('Calibration plot for MQ-2 data')\n",
+ "ax.set_xlabel('PPM Concentration')\n",
+ "ax.set_ylabel('Rs/Ro')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ " [-0.05578531]\n",
+ " [ 0.00133873]\n",
+ " [ 0.03043966]\n",
+ " [ 0.00672779]\n",
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+ " [ 0.04552903]\n",
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+ " [ 0.03690653]\n",
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+ " [ 0.03582872]\n",
+ " [ 0.03151747]\n",
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+ " [-0.04177375]\n",
+ " [-0.03961813]\n",
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+ " [-0.02560657]\n",
+ " [-0.02345095]\n",
+ " [-0.06656343]\n",
+ " [ 0.03259528]\n",
+ " [-0.046085 ]\n",
+ " [-0.02991782]\n",
+ " [-0.01267283]\n",
+ " [-0.01590626]\n",
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+ " [ 0.00026092]\n",
+ " [ 0.03690653]\n",
+ " [ 0.03906215]\n",
+ " [-0.01482845]\n",
+ " [ 0.00672779]\n",
+ " [-0.06871905]\n",
+ " [-0.00943939]\n",
+ " [ 0.01966154]\n",
+ " [ 0.07462995]\n",
+ " [-0.00836158]\n",
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+ " [-0.046085 ]\n",
+ " [ 0.05415152]\n",
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+ " [-0.03207344]\n",
+ " [-0.0816528 ]\n",
+ " [ 0.04768465]\n",
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+ " [ 0.05630715]\n",
+ " [ 0.09834182]\n",
+ " [ 0.05954058]\n",
+ " [ 0.03367309]\n",
+ " [ 0.05630715]\n",
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+ " [ 0.16085492]\n",
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+ " [-0.02452876]\n",
+ " [-0.03638469]\n",
+ " [-0.00836158]\n",
+ " [-0.04177375]\n",
+ " [ 0.12744274]\n",
+ " [-0.07734155]\n",
+ " [ 0.02828403]\n",
+ " [-0.02560657]\n",
+ " [-0.06225218]\n",
+ " [-0.00081689]\n",
+ " [ 0.08864151]\n",
+ " [-0.03207344]\n",
+ " [ 0.03043966]\n",
+ " [ 0.00888341]\n",
+ " [ 0.00672779]\n",
+ " [-0.02021751]\n",
+ " [-0.02452876]\n",
+ " [-0.01159501]\n",
+ " [ 0.02612841]\n",
+ " [-0.05901875]\n",
+ " [-0.03638469]\n",
+ " [-0.02452876]\n",
+ " [ 0.01858372]\n",
+ " [-0.0902753 ]\n",
+ " [-0.00512814]\n",
+ " [-0.05255187]\n",
+ " [-0.02237314]\n",
+ " [-0.02021751]\n",
+ " [-0.0547075 ]\n",
+ " [-0.00620595]\n",
+ " [-0.01698407]\n",
+ " [ 0.05522933]\n",
+ " [ 0.07678558]\n",
+ " [ 0.01858372]\n",
+ " [-0.02237314]\n",
+ " [ 0.09295276]\n",
+ " [-0.03099563]\n",
+ " [ 0.03906215]\n",
+ " [-0.06117437]\n",
+ " [-0.00836158]\n",
+ " [-0.0374625 ]\n",
+ " [-0.01375064]\n",
+ " [ 0.07355214]\n",
+ " [-0.02452876]\n",
+ " [ 0.03367309]\n",
+ " [ 0.0347509 ]\n",
+ " [-0.03854032]\n",
+ " [-0.03961813]\n",
+ " [-0.00189471]\n",
+ " [-0.03099563]\n",
+ " [-0.046085 ]\n",
+ " [ 0.00133873]\n",
+ " [ 0.06492964]\n",
+ " [ 0.04013997]\n",
+ " [-0.02345095]\n",
+ " [ 0.05307371]\n",
+ " [ 0.04013997]\n",
+ " [-0.02021751]\n",
+ " [ 0.01427248]\n",
+ " [-0.03422907]\n",
+ " [ 0.00672779]\n",
+ " [ 0.00457217]\n",
+ " [ 0.03043966]\n",
+ " [ 0.0519959 ]\n",
+ " [ 0.06169621]\n",
+ " [-0.00728377]\n",
+ " [ 0.00564998]\n",
+ " [ 0.05415152]\n",
+ " [-0.00836158]\n",
+ " [ 0.114509 ]\n",
+ " [ 0.06708527]\n",
+ " [-0.05578531]\n",
+ " [ 0.03043966]\n",
+ " [-0.02560657]\n",
+ " [ 0.10480869]\n",
+ " [-0.00620595]\n",
+ " [-0.04716281]\n",
+ " [-0.04824063]\n",
+ " [ 0.08540807]\n",
+ " [-0.01267283]\n",
+ " [-0.03315126]\n",
+ " [-0.00728377]\n",
+ " [-0.01375064]\n",
+ " [ 0.05954058]\n",
+ " [ 0.02181716]\n",
+ " [ 0.01858372]\n",
+ " [-0.01159501]\n",
+ " [-0.00297252]\n",
+ " [ 0.01750591]\n",
+ " [-0.02991782]\n",
+ " [-0.02021751]\n",
+ " [-0.05794093]\n",
+ " [ 0.06061839]\n",
+ " [-0.04069594]\n",
+ " [-0.07195249]\n",
+ " [-0.05578531]\n",
+ " [ 0.04552903]\n",
+ " [-0.00943939]\n",
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+ " [ 0.04984027]\n",
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+ " [ 0.00564998]\n",
+ " [ 0.02073935]\n",
+ " [-0.00728377]\n",
+ " [ 0.10480869]\n",
+ " [-0.02452876]\n",
+ " [-0.00620595]\n",
+ " [-0.03854032]\n",
+ " [ 0.13714305]\n",
+ " [ 0.17055523]\n",
+ " [ 0.00241654]\n",
+ " [ 0.03798434]\n",
+ " [-0.05794093]\n",
+ " [-0.00943939]\n",
+ " [-0.02345095]\n",
+ " [-0.0105172 ]\n",
+ " [-0.03422907]\n",
+ " [-0.00297252]\n",
+ " [ 0.06816308]\n",
+ " [ 0.00996123]\n",
+ " [ 0.00241654]\n",
+ " [-0.03854032]\n",
+ " [ 0.02612841]\n",
+ " [-0.08919748]\n",
+ " [ 0.06061839]\n",
+ " [-0.02884001]\n",
+ " [-0.02991782]\n",
+ " [-0.0191397 ]\n",
+ " [-0.04069594]\n",
+ " [ 0.01535029]\n",
+ " [-0.02452876]\n",
+ " [ 0.00133873]\n",
+ " [ 0.06924089]\n",
+ " [-0.06979687]\n",
+ " [-0.02991782]\n",
+ " [-0.046085 ]\n",
+ " [ 0.01858372]\n",
+ " [ 0.00133873]\n",
+ " [-0.03099563]\n",
+ " [-0.00405033]\n",
+ " [ 0.01535029]\n",
+ " [ 0.02289497]\n",
+ " [ 0.04552903]\n",
+ " [-0.04500719]\n",
+ " [-0.03315126]\n",
+ " [ 0.097264 ]\n",
+ " [ 0.05415152]\n",
+ " [ 0.12313149]\n",
+ " [-0.08057499]\n",
+ " [ 0.09295276]\n",
+ " [-0.05039625]\n",
+ " [-0.01159501]\n",
+ " [-0.0277622 ]\n",
+ " [ 0.05846277]\n",
+ " [ 0.08540807]\n",
+ " [-0.00081689]\n",
+ " [ 0.00672779]\n",
+ " [ 0.00888341]\n",
+ " [ 0.08001901]\n",
+ " [ 0.07139652]\n",
+ " [-0.02452876]\n",
+ " [-0.0547075 ]\n",
+ " [-0.03638469]\n",
+ " [ 0.0164281 ]\n",
+ " [ 0.07786339]\n",
+ " [-0.03961813]\n",
+ " [ 0.01103904]\n",
+ " [-0.04069594]\n",
+ " [-0.03422907]\n",
+ " [ 0.00564998]\n",
+ " [ 0.08864151]\n",
+ " [-0.03315126]\n",
+ " [-0.05686312]\n",
+ " [-0.03099563]\n",
+ " [ 0.05522933]\n",
+ " [-0.06009656]\n",
+ " [ 0.00133873]\n",
+ " [-0.02345095]\n",
+ " [-0.07410811]]\n",
+ "[151. 75. 141. 206. 135. 97. 138. 63. 110. 310. 101. 69. 179. 185.\n",
+ " 118. 171. 166. 144. 97. 168. 68. 49. 68. 245. 184. 202. 137. 85.\n",
+ " 131. 283. 129. 59. 341. 87. 65. 102. 265. 276. 252. 90. 100. 55.\n",
+ " 61. 92. 259. 53. 190. 142. 75. 142. 155. 225. 59. 104. 182. 128.\n",
+ " 52. 37. 170. 170. 61. 144. 52. 128. 71. 163. 150. 97. 160. 178.\n",
+ " 48. 270. 202. 111. 85. 42. 170. 200. 252. 113. 143. 51. 52. 210.\n",
+ " 65. 141. 55. 134. 42. 111. 98. 164. 48. 96. 90. 162. 150. 279.\n",
+ " 92. 83. 128. 102. 302. 198. 95. 53. 134. 144. 232. 81. 104. 59.\n",
+ " 246. 297. 258. 229. 275. 281. 179. 200. 200. 173. 180. 84. 121. 161.\n",
+ " 99. 109. 115. 268. 274. 158. 107. 83. 103. 272. 85. 280. 336. 281.\n",
+ " 118. 317. 235. 60. 174. 259. 178. 128. 96. 126. 288. 88. 292. 71.\n",
+ " 197. 186. 25. 84. 96. 195. 53. 217. 172. 131. 214. 59. 70. 220.\n",
+ " 268. 152. 47. 74. 295. 101. 151. 127. 237. 225. 81. 151. 107. 64.\n",
+ " 138. 185. 265. 101. 137. 143. 141. 79. 292. 178. 91. 116. 86. 122.\n",
+ " 72. 129. 142. 90. 158. 39. 196. 222. 277. 99. 196. 202. 155. 77.\n",
+ " 191. 70. 73. 49. 65. 263. 248. 296. 214. 185. 78. 93. 252. 150.\n",
+ " 77. 208. 77. 108. 160. 53. 220. 154. 259. 90. 246. 124. 67. 72.\n",
+ " 257. 262. 275. 177. 71. 47. 187. 125. 78. 51. 258. 215. 303. 243.\n",
+ " 91. 150. 310. 153. 346. 63. 89. 50. 39. 103. 308. 116. 145. 74.\n",
+ " 45. 115. 264. 87. 202. 127. 182. 241. 66. 94. 283. 64. 102. 200.\n",
+ " 265. 94. 230. 181. 156. 233. 60. 219. 80. 68. 332. 248. 84. 200.\n",
+ " 55. 85. 89. 31. 129. 83. 275. 65. 198. 236. 253. 124. 44. 172.\n",
+ " 114. 142. 109. 180. 144. 163. 147. 97. 220. 190. 109. 191. 122. 230.\n",
+ " 242. 248. 249. 192. 131. 237. 78. 135. 244. 199. 270. 164. 72. 96.\n",
+ " 306. 91. 214. 95. 216. 263. 178. 113. 200. 139. 139. 88. 148. 88.\n",
+ " 243. 71. 77. 109. 272. 60. 54. 221. 90. 311. 281. 182. 321. 58.\n",
+ " 262. 206. 233. 242. 123. 167. 63. 197. 71. 168. 140. 217. 121. 235.\n",
+ " 245. 40. 52. 104. 132. 88. 69. 219. 72. 201. 110. 51. 277. 63.\n",
+ " 118. 69. 273. 258. 43. 198. 242. 232. 175. 93. 168. 275. 293. 281.\n",
+ " 72. 140. 189. 181. 209. 136. 261. 113. 131. 174. 257. 55. 84. 42.\n",
+ " 146. 212. 233. 91. 111. 152. 120. 67. 310. 94. 183. 66. 173. 72.\n",
+ " 49. 64. 48.]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Use only one feature\n",
+ "diabetes_X = diabetes.data[:, np.newaxis, 2]\n",
+ "\n",
+ "# Split the data into training/testing sets\n",
+ "diabetes_X_train = diabetes_X[:-5]\n",
+ "diabetes_X_test = diabetes_X[-5:]\n",
+ "\n",
+ "# Split the targets into training/testing sets\n",
+ "diabetes_y_train = diabetes.target[:-5]\n",
+ "diabetes_y_test = diabetes.target[-5:]\n",
+ "\n",
+ "print(diabetes_X_train)\n",
+ "print(diabetes_y_train)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Experiments/.ipynb_checkpoints/.ipynb-checkpoint b/Experiments/.ipynb_checkpoints/.ipynb-checkpoint
new file mode 100644
index 0000000..3c29904
--- /dev/null
+++ b/Experiments/.ipynb_checkpoints/.ipynb-checkpoint
@@ -0,0 +1,795 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (0.24.2)\n",
+ "Requirement already satisfied: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\n",
+ "Requirement already satisfied: pytz>=2011k in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2018.9)\n",
+ "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.0)\n",
+ "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)\n",
+ "Requirement already satisfied: xlrd in c:\\programdata\\anaconda3\\lib\\site-packages (1.2.0)\n",
+ "Requirement already satisfied: sklearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
+ "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn) (0.20.3)\n",
+ "Requirement already satisfied: numpy>=1.8.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.16.2)\n",
+ "Requirement already satisfied: scipy>=0.13.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\n",
+ "Collecting imbalanced-learn\n",
+ " Using cached https://files.pythonhosted.org/packages/e6/62/08c14224a7e242df2cef7b312d2ef821c3931ec9b015ff93bb52ec8a10a3/imbalanced_learn-0.5.0-py3-none-any.whl\n",
+ "Requirement already satisfied: numpy>=1.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.16.2)\n",
+ "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn) (0.13.2)\n",
+ "Requirement already satisfied: scipy>=0.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.2.1)\n",
+ "Collecting scikit-learn>=0.21 (from imbalanced-learn)\n",
+ " Using cached https://files.pythonhosted.org/packages/75/14/fde90de4fc6722303ddbc183ec253c288dae8c7e4eb5a069ac49f14c9a0f/scikit_learn-0.21.2-cp37-cp37m-win_amd64.whl\n",
+ "Installing collected packages: scikit-learn, imbalanced-learn\n",
+ " Found existing installation: scikit-learn 0.20.3\n",
+ " Uninstalling scikit-learn-0.20.3:\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Could not install packages due to an EnvironmentError: [WinError 5] Acceso denegado: 'c:\\\\programdata\\\\anaconda3\\\\lib\\\\site-packages\\\\scikit_learn-0.20.3.dist-info\\\\COPYING'\n",
+ "Consider using the `--user` option or check the permissions.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install pandas\n",
+ "!pip install xlrd\n",
+ "!pip install sklearn\n",
+ "!pip install imbalanced-learn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xlrd\n",
+ "book = xlrd.open_workbook(\"Datasheets info.xlsx\")\n",
+ "sheetMQ2 = book.sheet_by_name(\"MQ2 - Pololulu\")\n",
+ "sheetMQ3 = book.sheet_by_name(\"MQ3 - Sparkfun\")\n",
+ "sheetMQ4 = book.sheet_by_name(\"MQ4 - Sparkfun\")\n",
+ "sheetMQ5 = book.sheet_by_name(\"MQ5 - Sparkfun\")\n",
+ "sheetMQ6 = book.sheet_by_name(\"MQ6 - Sparkfun\")\n",
+ "sheetMQ7 = book.sheet_by_name(\"MQ7 - Sparkfun\")\n",
+ "sheetMQ8 = book.sheet_by_name(\"MQ8 - Sparkfun\")\n",
+ "sheetMQ9 = book.sheet_by_name(\"MQ9 - Haoyuelectronics\")\n",
+ "sheetMQ131 = book.sheet_by_name(\"MQ131- Sensorsportal\")\n",
+ "sheetMQ135 = book.sheet_by_name(\"MQ135 - HANWEI\")\n",
+ "sheetMQ303A = book.sheet_by_name(\"MQ303A - HANWEI\")\n",
+ "sheetMQ309A = book.sheet_by_name(\"MQ309A - HANWEI\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "RS/R0 \t H2 \t LPG \t CH4 \t CO \t Alcohol \t Propane\n",
+ "0.1 \t \t \t \t \t \t \n",
+ "0.2 \t \t \t \t \t \t \n",
+ "0.3 \t \t \t \t \t \t \n",
+ "0.4 \t \t \t \t \t \t \n",
+ "0.5 \t \t \t \t \t \t \n",
+ "0.6 \t \t 2000.0 \t \t \t \t 2000.0\n",
+ "0.7 \t 2100.0 \t \t \t \t \t \n",
+ "0.8 \t \t 1000.0 \t \t \t \t 1000.0\n",
+ "0.9 \t \t 800.0 \t 5000.0 \t \t 5000.0 \t 800.0\n",
+ "1.0 \t 1000.0 \t \t \t \t \t \n",
+ "2.0 \t 201.0 \t \t 800.0 \t \t 500.0 \t \n",
+ "3.0 \t \t \t 200.0 \t 1000.0 \t 200.0 \t \n",
+ "4.0 \t \t \t \t 500.0 \t \t \n",
+ "5.0 \t \t \t \t 200.0 \t \t \n",
+ "6.0 \t \t \t \t \t \t \n",
+ "7.0 \t \t \t \t \t \t \n",
+ "8.0 \t \t \t \t \t \t \n",
+ "9.0 \t \t \t \t \t \t \n"
+ ]
+ }
+ ],
+ "source": [
+ "for row_index in range(1,20): #reading first columns\n",
+ " RsR0, H2, LPG, CH4, CO, Alcohol, propane = sheetMQ2.row_values(row_index, start_colx=0, end_colx=7)\n",
+ " print(RsR0, \"\t\", H2, \"\t\", LPG, \"\t\", CH4, \"\t\", CO, \"\t\", Alcohol, \"\t\", propane)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_MQ2 = sheetMQ2.col_values(0)[2:]\n",
+ "MQ2_H2 = sheetMQ2.col_values(1)[2:]\n",
+ "MQ2_LPG = sheetMQ2.col_values(2)[2:]\n",
+ "MQ2_CH4 = sheetMQ2.col_values(3)[2:]\n",
+ "MQ2_CO = sheetMQ2.col_values(4)[2:]\n",
+ "MQ2_Alcohol = sheetMQ2.col_values(5)[2:]\n",
+ "MQ2_propane = sheetMQ2.col_values(6)[2:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def zero_to_nan(values):\n",
+ " \"\"\"Replace every 0 with 'nan' and return a copy.\"\"\"\n",
+ " return [float('nan') if x==0 else x for x in values]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MQ2_H2 =zero_to_nan(MQ2_H2)\n",
+ "MQ2_LPG =zero_to_nan(MQ2_LPG)\n",
+ "MQ2_CH4 =zero_to_nan(MQ2_CH4)\n",
+ "MQ2_CO =zero_to_nan(MQ2_CO)\n",
+ "MQ2_Alcohol =zero_to_nan(MQ2_Alcohol)\n",
+ "MQ2_propane =zero_to_nan(MQ2_propane)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'imblearn'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mimblearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mover_sampling\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSMOTE\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'imblearn'"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sklearn.datasets import load_iris\n",
+ "#from sklearn.cross_validation import train_test_split\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn import datasets\n",
+ "from imblearn.over_sampling import SMOTE\n",
+ "\n",
+ "\n",
+ "dataH2 = {'RsRo': x_MQ2, 'H2': MQ2_H2}\n",
+ "#, 'LPG': MQ2_LPG, 'CH4': MQ2_CH4, 'CO': MQ2_CO, 'Alcohol': MQ2_Alcohol, 'Propane': MQ2_propane\n",
+ "dfMQ2_H2 = pd.DataFrame(dataH2)\n",
+ "\n",
+ "dfMQ2_H2['H2'] = pd.to_numeric(dfMQ2_H2['H2'])\n",
+ "#dfMQ2_H2['LPG'] = pd.to_numeric(dfMQ2_H2['LPG'])\n",
+ "#dfMQ2_H2['CH4'] = pd.to_numeric(dfMQ2_H2['CH4'])\n",
+ "#dfMQ2_H2['CO'] = pd.to_numeric(dfMQ2_H2['CO'])\n",
+ "#dfMQ2_H2['Alcohol'] = pd.to_numeric(dfMQ2_H2['Alcohol'])\n",
+ "#dfMQ2_H2['Propane'] = pd.to_numeric(dfMQ2_H2['Propane'])\n",
+ "\n",
+ "\n",
+ "dfMQ2_H2['H2'] = dfMQ2_H2['H2'].replace('',None, regex=True)\n",
+ "#dfMQ2_H2['LPG'] = dfMQ2_H2['LPG'].replace('',None, regex=True)\n",
+ "#dfMQ2_H2['CH4'] = dfMQ2_H2['CH4'].replace('',None, regex=True)\n",
+ "#dfMQ2_H2['CO'] = dfMQ2_H2['CO'].replace('',None, regex=True)\n",
+ "#dfMQ2_H2['Alcohol'] = dfMQ2_H2['Alcohol'].replace('',None, regex=True)\n",
+ "#dfMQ2_H2['Propane'] = dfMQ2_H2['Propane'].replace('',None, regex=True)\n",
+ "\n",
+ "dataset2Train = dfMQ2_H2.copy()\n",
+ "dataset2Train.dropna(inplace=True)\n",
+ "\n",
+ "#dfMQ2_H2 = dfMQ2_H2.interpolate(method='polynomial', order=2, axis=0).ffill().bfill()\n",
+ "#print(dfMQ2_H2)\n",
+ "#print(dataset2Train)\n",
+ "\n",
+ "X_train = dataset2Train.RsRo.apply(lambda x: [x]).tolist()\n",
+ "y_train = dataset2Train['H2'].tolist()\n",
+ "\n",
+ "print(X_train)\n",
+ "print(y_train)\n",
+ "\n",
+ "#model = DecisionTreeClassifier()\n",
+ "model = SMOTE(random_state=0)\n",
+ "model.fit(X_train, y_train)\n",
+ "\n",
+ "X_Predict = dfMQ2_H2.RsRo.apply(lambda x: [x]).tolist()\n",
+ "\n",
+ "print(X_Predict)\n",
+ "y_Predicted = model.predict(X_Predict)\n",
+ "print(y_Predicted)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%config InlineBackend.figure_formats = ['svg']\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.lines as mlines\n",
+ "import matplotlib.transforms as mtransforms\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "\n",
+ "fig.set_size_inches(9, 5.5, forward=True)\n",
+ "fig.set_dpi(200)\n",
+ "\n",
+ "# only these two lines are calibration curves\n",
+ "plt.plot(MQ2_H2, x_MQ2, marker='o', linewidth=1, label='H2')\n",
+ "#plt.plot(MQ2_LPG, x_MQ2, marker='o', linewidth=1, label='LPG')\n",
+ "#plt.plot(MQ2_CH4, x_MQ2, marker='o', linewidth=1, label='CH4')\n",
+ "#plt.plot(MQ2_CO, x_MQ2, marker='o', linewidth=1, label='CO')\n",
+ "#plt.plot(MQ2_Alcohol, x_MQ2, marker='o', linewidth=1, label='Alcohol')\n",
+ "#plt.plot(MQ2_propane, x_MQ2, marker='o', linewidth=1, label='Propane')\n",
+ "\n",
+ "# reference line, legends, and axis labels\n",
+ "#line = mlines.Line2D([0, 1], [0, 1], color='black')\n",
+ "#transform = ax.transAxes\n",
+ "#line.set_transform(transform)\n",
+ "#ax.add_line(line)\n",
+ "plt.yscale('log')\n",
+ "plt.xscale('log')\n",
+ "\n",
+ "fig.suptitle('Calibration plot for MQ data')\n",
+ "ax.set_xlabel('PPM Concentration')\n",
+ "ax.set_ylabel('Rs/Ro')\n",
+ "plt.legend()\n",
+ "plt.savefig('demo1.svg')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
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+ " [ 0.06924089]\n",
+ " [-0.06979687]\n",
+ " [-0.02991782]\n",
+ " [-0.046085 ]\n",
+ " [ 0.01858372]\n",
+ " [ 0.00133873]\n",
+ " [-0.03099563]\n",
+ " [-0.00405033]\n",
+ " [ 0.01535029]\n",
+ " [ 0.02289497]\n",
+ " [ 0.04552903]\n",
+ " [-0.04500719]\n",
+ " [-0.03315126]\n",
+ " [ 0.097264 ]\n",
+ " [ 0.05415152]\n",
+ " [ 0.12313149]\n",
+ " [-0.08057499]\n",
+ " [ 0.09295276]\n",
+ " [-0.05039625]\n",
+ " [-0.01159501]\n",
+ " [-0.0277622 ]\n",
+ " [ 0.05846277]\n",
+ " [ 0.08540807]\n",
+ " [-0.00081689]\n",
+ " [ 0.00672779]\n",
+ " [ 0.00888341]\n",
+ " [ 0.08001901]\n",
+ " [ 0.07139652]\n",
+ " [-0.02452876]\n",
+ " [-0.0547075 ]\n",
+ " [-0.03638469]\n",
+ " [ 0.0164281 ]\n",
+ " [ 0.07786339]\n",
+ " [-0.03961813]\n",
+ " [ 0.01103904]\n",
+ " [-0.04069594]\n",
+ " [-0.03422907]\n",
+ " [ 0.00564998]\n",
+ " [ 0.08864151]\n",
+ " [-0.03315126]\n",
+ " [-0.05686312]\n",
+ " [-0.03099563]\n",
+ " [ 0.05522933]\n",
+ " [-0.06009656]\n",
+ " [ 0.00133873]\n",
+ " [-0.02345095]\n",
+ " [-0.07410811]]\n",
+ "[151. 75. 141. 206. 135. 97. 138. 63. 110. 310. 101. 69. 179. 185.\n",
+ " 118. 171. 166. 144. 97. 168. 68. 49. 68. 245. 184. 202. 137. 85.\n",
+ " 131. 283. 129. 59. 341. 87. 65. 102. 265. 276. 252. 90. 100. 55.\n",
+ " 61. 92. 259. 53. 190. 142. 75. 142. 155. 225. 59. 104. 182. 128.\n",
+ " 52. 37. 170. 170. 61. 144. 52. 128. 71. 163. 150. 97. 160. 178.\n",
+ " 48. 270. 202. 111. 85. 42. 170. 200. 252. 113. 143. 51. 52. 210.\n",
+ " 65. 141. 55. 134. 42. 111. 98. 164. 48. 96. 90. 162. 150. 279.\n",
+ " 92. 83. 128. 102. 302. 198. 95. 53. 134. 144. 232. 81. 104. 59.\n",
+ " 246. 297. 258. 229. 275. 281. 179. 200. 200. 173. 180. 84. 121. 161.\n",
+ " 99. 109. 115. 268. 274. 158. 107. 83. 103. 272. 85. 280. 336. 281.\n",
+ " 118. 317. 235. 60. 174. 259. 178. 128. 96. 126. 288. 88. 292. 71.\n",
+ " 197. 186. 25. 84. 96. 195. 53. 217. 172. 131. 214. 59. 70. 220.\n",
+ " 268. 152. 47. 74. 295. 101. 151. 127. 237. 225. 81. 151. 107. 64.\n",
+ " 138. 185. 265. 101. 137. 143. 141. 79. 292. 178. 91. 116. 86. 122.\n",
+ " 72. 129. 142. 90. 158. 39. 196. 222. 277. 99. 196. 202. 155. 77.\n",
+ " 191. 70. 73. 49. 65. 263. 248. 296. 214. 185. 78. 93. 252. 150.\n",
+ " 77. 208. 77. 108. 160. 53. 220. 154. 259. 90. 246. 124. 67. 72.\n",
+ " 257. 262. 275. 177. 71. 47. 187. 125. 78. 51. 258. 215. 303. 243.\n",
+ " 91. 150. 310. 153. 346. 63. 89. 50. 39. 103. 308. 116. 145. 74.\n",
+ " 45. 115. 264. 87. 202. 127. 182. 241. 66. 94. 283. 64. 102. 200.\n",
+ " 265. 94. 230. 181. 156. 233. 60. 219. 80. 68. 332. 248. 84. 200.\n",
+ " 55. 85. 89. 31. 129. 83. 275. 65. 198. 236. 253. 124. 44. 172.\n",
+ " 114. 142. 109. 180. 144. 163. 147. 97. 220. 190. 109. 191. 122. 230.\n",
+ " 242. 248. 249. 192. 131. 237. 78. 135. 244. 199. 270. 164. 72. 96.\n",
+ " 306. 91. 214. 95. 216. 263. 178. 113. 200. 139. 139. 88. 148. 88.\n",
+ " 243. 71. 77. 109. 272. 60. 54. 221. 90. 311. 281. 182. 321. 58.\n",
+ " 262. 206. 233. 242. 123. 167. 63. 197. 71. 168. 140. 217. 121. 235.\n",
+ " 245. 40. 52. 104. 132. 88. 69. 219. 72. 201. 110. 51. 277. 63.\n",
+ " 118. 69. 273. 258. 43. 198. 242. 232. 175. 93. 168. 275. 293. 281.\n",
+ " 72. 140. 189. 181. 209. 136. 261. 113. 131. 174. 257. 55. 84. 42.\n",
+ " 146. 212. 233. 91. 111. 152. 120. 67. 310. 94. 183. 66. 173. 72.\n",
+ " 49. 64. 48.]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Use only one feature\n",
+ "diabetes_X = diabetes.data[:, np.newaxis, 2]\n",
+ "\n",
+ "# Split the data into training/testing sets\n",
+ "diabetes_X_train = diabetes_X[:-5]\n",
+ "diabetes_X_test = diabetes_X[-5:]\n",
+ "\n",
+ "# Split the targets into training/testing sets\n",
+ "diabetes_y_train = diabetes.target[:-5]\n",
+ "diabetes_y_test = diabetes.target[-5:]\n",
+ "\n",
+ "print(diabetes_X_train)\n",
+ "print(diabetes_y_train)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Experiments/Experiment 3.ipynb b/Experiments/Experiment 3.ipynb
index 45dcf5f..d6d7364 100644
--- a/Experiments/Experiment 3.ipynb
+++ b/Experiments/Experiment 3.ipynb
@@ -28,7 +28,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -39,7 +39,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -72,21 +72,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 5,
"metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'sheet' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msheet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mMQ2_R0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msheet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mMQ3_R0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msheet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mMQ4_R0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msheet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mMQ5_R0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msheet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mNameError\u001b[0m: name 'sheet' is not defined"
- ]
- }
- ],
+ "outputs": [],
"source": [
"x = sheet.col_values(0)[5:]\n",
"MQ2_R0 = sheet.col_values(1)[5:]\n",
@@ -101,7 +89,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -141,10 +129,10 @@
" \r\n",
" \r\n",
+ "\" id=\"ma245dfd5f0\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n",
" \r\n",
" \r\n",
- " \r\n",
" \r\n",
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@@ -180,7 +168,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
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@@ -220,7 +208,7 @@
" \r\n",
" \r\n",
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- " \r\n",
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@@ -250,7 +238,7 @@
" \r\n",
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- " \r\n",
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@@ -265,7 +253,7 @@
" \r\n",
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- " \r\n",
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@@ -306,7 +294,7 @@
" \r\n",
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- " \r\n",
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@@ -321,7 +309,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
" \r\n",
" \r\n",
@@ -568,10 +556,10 @@
" \r\n",
" \r\n",
+ "\" id=\"m65264b2057\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n",
" \r\n",
" \r\n",
- " \r\n",
" \r\n",
" \r\n",
@@ -596,7 +584,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
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@@ -613,7 +601,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
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@@ -661,7 +649,7 @@
" \r\n",
" \r\n",
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- " \r\n",
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@@ -718,7 +706,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
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@@ -753,7 +741,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
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@@ -769,7 +757,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
" \r\n",
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@@ -786,7 +774,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
" \r\n",
" \r\n",
@@ -803,7 +791,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
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" \r\n",
@@ -820,7 +808,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
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@@ -837,7 +825,7 @@
" \r\n",
" \r\n",
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- " \r\n",
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@@ -854,7 +842,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
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@@ -882,7 +870,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
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" \r\n",
@@ -931,7 +919,7 @@
" \r\n",
" \r\n",
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- " \r\n",
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@@ -948,7 +936,7 @@
" \r\n",
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- " \r\n",
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@@ -965,7 +953,7 @@
" \r\n",
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- " \r\n",
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@@ -982,7 +970,7 @@
" \r\n",
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- " \r\n",
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@@ -999,7 +987,7 @@
" \r\n",
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- " \r\n",
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@@ -1016,7 +1004,7 @@
" \r\n",
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- " \r\n",
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@@ -1033,7 +1021,7 @@
" \r\n",
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- " \r\n",
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@@ -1050,7 +1038,7 @@
" \r\n",
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- " \r\n",
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@@ -1067,7 +1055,7 @@
" \r\n",
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- " \r\n",
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@@ -1084,7 +1072,7 @@
" \r\n",
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- " \r\n",
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@@ -1101,7 +1089,7 @@
" \r\n",
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- " \r\n",
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@@ -1118,7 +1106,7 @@
" \r\n",
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- " \r\n",
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@@ -1135,7 +1123,7 @@
" \r\n",
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- " \r\n",
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@@ -1152,7 +1140,7 @@
" \r\n",
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- " \r\n",
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@@ -1169,7 +1157,7 @@
" \r\n",
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- " \r\n",
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@@ -1307,7 +1295,7 @@
" \r\n",
" \r\n",
" \r\n",
- " \r\n",
+ "\" id=\"m089f4c0b08\" style=\"stroke:#1f77b4;\"/>\r\n",
" \r\n",
- " \r\n",
- "