diff --git a/Experiments/.ipynb_checkpoints/MQ131_Regression-checkpoint.ipynb b/Experiments/.ipynb_checkpoints/MQ131_Regression-checkpoint.ipynb
new file mode 100644
index 0000000..2e5152f
--- /dev/null
+++ b/Experiments/.ipynb_checkpoints/MQ131_Regression-checkpoint.ipynb
@@ -0,0 +1,1705 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "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: 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: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\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) (0.21.2)\n",
+ "Requirement already satisfied: scipy>=0.17.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\n",
+ "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.13.2)\n",
+ "Requirement already satisfied: numpy>=1.11.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.16.2)\n",
+ "Requirement already satisfied: imblearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
+ "Requirement already satisfied: imbalanced-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from imblearn) (0.5.0)\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: scipy>=0.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.1)\n",
+ "Requirement already satisfied: numpy>=1.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.16.2)\n",
+ "Requirement already satisfied: scikit-learn>=0.21 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (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": 1,
+ "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": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.1 \t \t \t \n",
+ "0.2 \t \t \t \n",
+ "0.3 \t \t \t \n",
+ "0.4 \t \t \t \n",
+ "0.5 \t \t \t 100.0\n",
+ "0.6 \t \t \t \n",
+ "0.7 \t \t \t \n",
+ "0.8 \t \t \t \n",
+ "0.9 \t \t \t \n",
+ "1.0 \t \t 50.0 \t \n",
+ "2.0 \t \t \t 20.0\n",
+ "3.0 \t 100.0 \t \t \n",
+ "4.0 \t \t \t \n",
+ "5.0 \t \t \t \n",
+ "6.0 \t \t 10.0 \t 5.0\n",
+ "7.0 \t 10.0 \t \t \n",
+ "8.0 \t \t 5.0 \t \n",
+ "9.0 \t 5.0 \t \t \n",
+ "10.0 \t \t \t \n"
+ ]
+ }
+ ],
+ "source": [
+ "for row_index in range(1,20): #reading first columns\n",
+ " RsR0, Nox, CL2, O3 = sheetMQ131.row_values(row_index, start_colx=0, end_colx=4)\n",
+ " print(RsR0, \"\t\", Nox, \"\t\", CL2, \"\t\", O3)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_MQ131 = sheetMQ131.col_values(0)[2:]\n",
+ "MQ131_Nox = sheetMQ131.col_values(1)[2:]\n",
+ "MQ131_CL2 = sheetMQ131.col_values(2)[2:]\n",
+ "MQ131_O3 = sheetMQ131.col_values(3)[2:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "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": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MQ131_Nox =zero_to_nan(MQ131_Nox)\n",
+ "MQ131_CL2 =zero_to_nan(MQ131_CL2)\n",
+ "MQ131_O3 =zero_to_nan(MQ131_O3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "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",
+ "dataNox = {'RsRo': x_MQ131, 'Nox': MQ131_Nox}\n",
+ "dataCL2 = {'RsRo': x_MQ131, 'CL2': MQ131_CL2}\n",
+ "dataO3 = {'RsRo': x_MQ131, 'O3': MQ131_O3}\n",
+ "\n",
+ "dfMQ131_Nox = pd.DataFrame(dataNox)\n",
+ "dfMQ131_CL2 = pd.DataFrame(dataCL2)\n",
+ "dfMQ131_O3 = pd.DataFrame(dataO3)\n",
+ "\n",
+ "dfMQ131_Nox['Nox'] = pd.to_numeric(dfMQ131_Nox['Nox'])\n",
+ "dfMQ131_CL2['CL2'] = pd.to_numeric(dfMQ131_CL2['CL2'])\n",
+ "dfMQ131_O3['O3'] = pd.to_numeric(dfMQ131_O3['O3'])\n",
+ "\n",
+ "dfMQ131_Nox['Nox'] = dfMQ131_Nox['Nox'].replace('',None, regex=True)\n",
+ "dfMQ131_CL2['CL2'] = dfMQ131_CL2['CL2'].replace('',None, regex=True)\n",
+ "dfMQ131_O3['O3'] = dfMQ131_O3['O3'].replace('',None, regex=True)\n",
+ "\n",
+ "#Global X_Predict variable\n",
+ "X_Predict = dfMQ131_Nox.RsRo.apply(lambda x: [x]).tolist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Model and train Nox\n",
+ "dataset2TrainNox = dfMQ131_Nox.copy()\n",
+ "dataset2TrainNox.dropna(inplace=True)\n",
+ "X_trainNox = dataset2TrainNox.RsRo.apply(lambda x: [x]).tolist()\n",
+ "y_trainNox = dataset2TrainNox['Nox'].tolist()\n",
+ "model = linear_model.Lasso(alpha=0.1)\n",
+ "model.fit(X_trainNox, y_trainNox)\n",
+ "#Predict\n",
+ "Nox_Predicted = model.predict(X_Predict)\n",
+ "#save into MQ2\n",
+ "MQ131_Nox = Nox_Predicted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Model and train CL2\n",
+ "dataset2TrainCL2 = dfMQ131_CL2.copy()\n",
+ "dataset2TrainCL2.dropna(inplace=True)\n",
+ "X_trainCL2 = dataset2TrainCL2.RsRo.apply(lambda x: [x]).tolist()\n",
+ "y_trainCL2 = dataset2TrainCL2['CL2'].tolist()\n",
+ "model = linear_model.Lasso(alpha=0.1)\n",
+ "model.fit(X_trainCL2, y_trainCL2)\n",
+ "#Predict\n",
+ "CL2_Predicted = model.predict(X_Predict)\n",
+ "#save into MQ2\n",
+ "MQ131_CL2 = CL2_Predicted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Model and train O3\n",
+ "dataset2TrainO3 = dfMQ131_O3.copy()\n",
+ "dataset2TrainO3.dropna(inplace=True)\n",
+ "X_trainO3 = dataset2TrainO3.RsRo.apply(lambda x: [x]).tolist()\n",
+ "y_trainO3 = dataset2TrainO3['O3'].tolist()\n",
+ "model = linear_model.Lasso(alpha=0.1)\n",
+ "model.fit(X_trainO3, y_trainO3)\n",
+ "#Predict\n",
+ "O3_Predicted = model.predict(X_Predict)\n",
+ "#save into MQ2\n",
+ "MQ131_O3 = O3_Predicted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "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(MQ131_Nox, x_MQ131, marker='o', linewidth=1, label='Nox')\n",
+ "plt.plot(MQ131_CL2, x_MQ131, marker='o', linewidth=1, label='CL2')\n",
+ "plt.plot(MQ131_O3, x_MQ131, marker='o', linewidth=1, label='O3')\n",
+ "\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",
+ "plt.legend()\n",
+ "\n",
+ "plt.grid(b=True, which='minor', color='lightgrey', linestyle='--')\n",
+ "\n",
+ "fig.suptitle('Calibration plot for MQ-131 data')\n",
+ "ax.set_xlabel('PPM Concentration')\n",
+ "ax.set_ylabel('Rs/Ro')\n",
+ "\n",
+ "\n",
+ "#Save image\n",
+ "plt.savefig('MQ131.svg', format = 'svg', dpi = 1200)\n",
+ "plt.savefig('MQ131.png')\n",
+ "plt.savefig('MQ131.eps', format = 'eps', dpi = 1200)"
+ ]
+ },
+ {
+ "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/MQ9_Regression -checkpoint.ipynb b/Experiments/.ipynb_checkpoints/MQ9_Regression -checkpoint.ipynb
index 67cbacd..0eeca8b 100644
--- a/Experiments/.ipynb_checkpoints/MQ9_Regression -checkpoint.ipynb
+++ b/Experiments/.ipynb_checkpoints/MQ9_Regression -checkpoint.ipynb
@@ -38,7 +38,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -60,7 +60,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -98,7 +98,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -110,7 +110,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -121,32 +121,18 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 13,
"metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'MQ9_H2' 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[0mMQ9_H2\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mzero_to_nan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMQ9_H2\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[0mMQ9_LPG\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mzero_to_nan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMQ9_LPG\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[0mMQ9_CH4\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mzero_to_nan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMQ9_CH4\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[0mMQ9_CO\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mzero_to_nan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMQ9_CO\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[0mMQ9_Alcohol\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mzero_to_nan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMQ9_Alcohol\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 'MQ9_H2' is not defined"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "MQ9_H2 =zero_to_nan(MQ9_H2)\n",
"MQ9_LPG =zero_to_nan(MQ9_LPG)\n",
"MQ9_CH4 =zero_to_nan(MQ9_CH4)\n",
- "MQ9_CO =zero_to_nan(MQ9_CO)\n",
- "MQ9_Alcohol =zero_to_nan(MQ9_Alcohol)"
+ "MQ9_CO =zero_to_nan(MQ9_CO)"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 14,
"metadata": {
"scrolled": false
},
@@ -160,61 +146,34 @@
"from sklearn import datasets\n",
"from sklearn import linear_model\n",
"\n",
- "dataH2 = {'RsRo': x_MQ9, 'H2': MQ8_H2}\n",
- "dataLPG = {'RsRo': x_MQ9, 'LPG': MQ8_LPG}\n",
- "dataCH4 = {'RsRo': x_MQ9, 'CH4': MQ8_CH4}\n",
- "dataCO = {'RsRo': x_MQ8, 'CO': MQ8_CO}\n",
- "dataALcohol = {'RsRo': x_MQ8, 'Alcohol': MQ8_Alcohol}\n",
+ "dataLPG = {'RsRo': x_MQ9, 'LPG': MQ9_LPG}\n",
+ "dataCH4 = {'RsRo': x_MQ9, 'CH4': MQ9_CH4}\n",
+ "dataCO = {'RsRo': x_MQ9, 'CO': MQ9_CO}\n",
"\n",
- "dfMQ8_H2 = pd.DataFrame(dataH2)\n",
- "dfMQ8_LPG = pd.DataFrame(dataLPG)\n",
- "dfMQ8_CH4 = pd.DataFrame(dataCH4)\n",
- "dfMQ8_CO = pd.DataFrame(dataCO)\n",
- "dfMQ8_Alcohol = pd.DataFrame(dataALcohol)\n",
+ "dfMQ9_LPG = pd.DataFrame(dataLPG)\n",
+ "dfMQ9_CH4 = pd.DataFrame(dataCH4)\n",
+ "dfMQ9_CO = pd.DataFrame(dataCO)\n",
"\n",
- "dfMQ8_H2['H2'] = pd.to_numeric(dfMQ8_H2['H2'])\n",
- "dfMQ8_LPG['LPG'] = pd.to_numeric(dfMQ8_LPG['LPG'])\n",
- "dfMQ8_CH4['CH4'] = pd.to_numeric(dfMQ8_CH4['CH4'])\n",
- "dfMQ8_CO['CO'] = pd.to_numeric(dfMQ8_CO['CO'])\n",
- "dfMQ8_Alcohol['Alcohol'] = pd.to_numeric(dfMQ8_Alcohol['Alcohol'])\n",
+ "dfMQ9_LPG['LPG'] = pd.to_numeric(dfMQ9_LPG['LPG'])\n",
+ "dfMQ9_CH4['CH4'] = pd.to_numeric(dfMQ9_CH4['CH4'])\n",
+ "dfMQ9_CO['CO'] = pd.to_numeric(dfMQ9_CO['CO'])\n",
"\n",
- "dfMQ8_H2['H2'] = dfMQ8_H2['H2'].replace('',None, regex=True)\n",
- "dfMQ8_LPG['LPG'] = dfMQ8_LPG['LPG'].replace('',None, regex=True)\n",
- "dfMQ8_CH4['CH4'] = dfMQ8_CH4['CH4'].replace('',None, regex=True)\n",
- "dfMQ8_CO['CO'] = dfMQ8_CO['CO'].replace('',None, regex=True)\n",
- "dfMQ8_Alcohol['Alcohol'] = dfMQ8_Alcohol['Alcohol'].replace('',None, regex=True)\n",
+ "dfMQ9_LPG['LPG'] = dfMQ9_LPG['LPG'].replace('',None, regex=True)\n",
+ "dfMQ9_CH4['CH4'] = dfMQ9_CH4['CH4'].replace('',None, regex=True)\n",
+ "dfMQ9_CO['CO'] = dfMQ9_CO['CO'].replace('',None, regex=True)\n",
"\n",
"#Global X_Predict variable\n",
- "X_Predict = dfMQ8_LPG.RsRo.apply(lambda x: [x]).tolist()"
+ "X_Predict = dfMQ9_LPG.RsRo.apply(lambda x: [x]).tolist()"
]
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Model and train H2\n",
- "dataset2TrainH2 = dfMQ8_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",
- "MQ8_H2 = H2_Predicted\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#Model and train LPG\n",
- "dataset2TrainLPG = dfMQ8_LPG.copy()\n",
+ "dataset2TrainLPG = dfMQ9_LPG.copy()\n",
"dataset2TrainLPG.dropna(inplace=True)\n",
"X_trainLPG = dataset2TrainLPG.RsRo.apply(lambda x: [x]).tolist()\n",
"y_trainLPG = dataset2TrainLPG['LPG'].tolist()\n",
@@ -223,17 +182,17 @@
"#Predict\n",
"LPG_Predicted = model.predict(X_Predict)\n",
"#save into MQ2\n",
- "MQ8_LPG = LPG_Predicted"
+ "MQ9_LPG = LPG_Predicted"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"#Model and train CH4\n",
- "dataset2TrainCH4 = dfMQ8_CH4.copy()\n",
+ "dataset2TrainCH4 = dfMQ9_CH4.copy()\n",
"dataset2TrainCH4.dropna(inplace=True)\n",
"X_trainCH4 = dataset2TrainCH4.RsRo.apply(lambda x: [x]).tolist()\n",
"y_trainCH4 = dataset2TrainCH4['CH4'].tolist()\n",
@@ -242,17 +201,17 @@
"#Predict\n",
"CH4_Predicted = model.predict(X_Predict)\n",
"#save into MQ2\n",
- "MQ8_CH4 = CH4_Predicted"
+ "MQ9_CH4 = CH4_Predicted"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"#Model and train CO\n",
- "dataset2TrainCO = dfMQ8_CO.copy()\n",
+ "dataset2TrainCO = dfMQ9_CO.copy()\n",
"dataset2TrainCO.dropna(inplace=True)\n",
"X_trainCO = dataset2TrainCO.RsRo.apply(lambda x: [x]).tolist()\n",
"y_trainCO = dataset2TrainCO['CO'].tolist()\n",
@@ -261,31 +220,12 @@
"#Predict\n",
"CO_Predicted = model.predict(X_Predict)\n",
"#save into MQ2\n",
- "MQ8_CO = CO_Predicted"
+ "MQ9_CO = CO_Predicted"
]
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Model and train Alcohol\n",
- "dataset2TrainAlcohol = dfMQ8_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",
- "MQ8_Alcohol = Alcohol_Predicted"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -295,7 +235,7 @@
"\r\n",
"\r\n",
- "