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HcmV?d00001 From da2196e1860ed8a050cd2a8899b9b5286f2e47ad Mon Sep 17 00:00:00 2001 From: dinesh9-ai <63300423+dinesh9-ai@users.noreply.github.com> Date: Fri, 12 Feb 2021 15:08:10 +0530 Subject: [PATCH 2/2] Add files via upload --- attempt_1.ipynb | 649 ++++++++++++++++++++++++++++++++++++++++++++++++ attempt_2.ipynb | 218 ++++++++++++++++ 2 files changed, 867 insertions(+) create mode 100644 attempt_1.ipynb create mode 100644 attempt_2.ipynb diff --git a/attempt_1.ipynb b/attempt_1.ipynb new file mode 100644 index 0000000..0284b05 --- /dev/null +++ b/attempt_1.ipynb @@ -0,0 +1,649 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SCIKIT TUTORIALS" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "from sklearn.linear_model import *" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'C:\\\\Users\\\\Anish'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "data=pd.read_csv(os.getcwd()+'\\\\Downloads\\\\2019\\\\final.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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trainx.append([data['tmin'][i],data['tmax'][i],data['hmin'][i],data['hmax'][i],data['wmin'][i],data['wmax'][i],\n", + " data['tmin'][i-1],data['tmax'][i-1],data['hmin'][i-1],data['hmax'][i-1],data['wmin'][i-1],data['wmax'][i-1],data['r'][i-1]])\n", + " trainy.append(data['r'][i])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "xt=[]\n", + "for i in range(0,70):\n", + " xt.append(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "3/3 [==============================] - 0s 39ms/step - loss: 72.9765\n", + "Epoch 2/100\n", + "3/3 [==============================] - 0s 25ms/step - loss: 6.8027\n", + "Epoch 3/100\n", + "3/3 [==============================] - 0s 29ms/step - loss: 6.8027\n", + "Epoch 4/100\n", + "3/3 [==============================] - 0s 20ms/step - loss: 6.8027\n", + "Epoch 5/100\n", 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[==============================] - 0s 16ms/step - loss: 6.8027\n", + "Epoch 90/100\n", + "3/3 [==============================] - 0s 16ms/step - loss: 6.8027\n", + "Epoch 91/100\n", + "3/3 [==============================] - 0s 16ms/step - loss: 6.8027\n", + "Epoch 92/100\n", + "3/3 [==============================] - 0s 16ms/step - loss: 6.8027\n", + "Epoch 93/100\n", + "3/3 [==============================] - 0s 17ms/step - loss: 6.8027\n", + "Epoch 94/100\n", + "3/3 [==============================] - 0s 19ms/step - loss: 6.8027\n", + "Epoch 95/100\n", + "3/3 [==============================] - 0s 16ms/step - loss: 6.8027\n", + "Epoch 96/100\n", + "3/3 [==============================] - 0s 18ms/step - loss: 6.8027\n", + "Epoch 97/100\n", + "3/3 [==============================] - 0s 22ms/step - loss: 6.8027\n", + "Epoch 98/100\n", + "3/3 [==============================] - 0s 26ms/step - loss: 6.8027\n", + "Epoch 99/100\n", + "3/3 [==============================] - 0s 14ms/step - loss: 6.8027\n", + "Epoch 100/100\n", + "3/3 [==============================] - 0s 22ms/step - loss: 6.8027\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m=tf.keras.models.Sequential([tf.keras.layers.Dense(1,activation='relu'),\n", + " tf.keras.layers.Dense(1024,activation='relu'),\n", + " tf.keras.layers.Dense(512,activation='relu'),\n", + " tf.keras.layers.Dense(1024,activation='relu'),\n", + " tf.keras.layers.Dense(1024,activation='relu'),\n", + " tf.keras.layers.Dense(1,activation='relu')])\n", + "m.compile(optimizer='adam',loss='mean_squared_error')\n", + "m.fit(trainx,trainy,epochs=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "t11=[]\n", + "h11=[]\n", + "ti=[]\n", + "t12=[]\n", + "for i in range(70):\n", + " t11.append([data['temp_max '][i]-data['temp_min '][i]])\n", + " q=-data['humidity_min (%)'][i]+data['Humidity_max (%)'][i]\n", + " h11.append([q])\n", + " ti.append(i)\n", + " t12.append(data['temp_max '][i]-data['temp_min '][i])" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [], + "source": [ + "reg=LinearRegression().fit(h11,t11)" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "metadata": {}, + "outputs": [], + "source": [ + "yp=[]\n", + "for i in range(70):\n", + " yp.append(reg.predict([h11[i]]).item())" + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 311, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(ti,t12)\n", + "plt.plot(ti,yp,color='red')" + ] + }, + { + "cell_type": "code", + "execution_count": 308, + "metadata": {}, + "outputs": [], + "source": [ + "reg.coef_=reg.coef_+0.035" + ] + }, + { + "cell_type": "code", + "execution_count": 309, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.2518092]])" + ] + }, + "execution_count": 309, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg.coef_" + ] + }, + { + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/attempt_2.ipynb b/attempt_2.ipynb new file mode 100644 index 0000000..361db73 --- /dev/null +++ b/attempt_2.ipynb @@ -0,0 +1,218 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import *\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "d=pd.read_csv(os.getcwd()+'\\\\Downloads\\\\2019\\\\final.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " r tmin tmax hmin hmax wmin wmax date month\n", + "0 0.0 9.8 28.6 18.60 73.2 0.0 8.1 1 1\n", + "1 0.0 14.5 29.7 18.50 63.4 0.0 5.2 2 1\n", + "2 0.0 12.3 30.1 25.10 83.2 0.0 9.1 3 1\n", + "3 0.0 13.9 31.8 21.60 83.1 0.0 7.1 4 1\n", + "4 0.0 15.1 30.7 32.30 89.0 0.0 0.1 5 1\n", + ".. ... ... ... ... ... ... ... ... ...\n", + "339 0.0 19.6 27.2 74.20 100.0 0.0 11.0 27 12\n", + "340 0.0 18.9 28.3 74.60 95.4 0.0 10.5 28 12\n", + "341 0.0 16.9 27.1 72.40 88.0 0.0 10.5 29 12\n", + "342 0.0 16.0 27.0 69.75 98.6 0.0 8.6 30 12\n", + "343 0.0 19.9 26.5 61.80 94.4 0.0 10.9 31 12\n", + "\n", + "[344 rows x 9 columns]\n" + ] + } + ], + "source": [ + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.0\n", + "1 0.0\n", + "2 0.0\n", + "3 0.0\n", + "4 0.0\n", + " ... \n", + "339 0.0\n", + "340 0.0\n", + "341 0.0\n", + "342 0.0\n", + "343 0.0\n", + "Name: r, Length: 344, dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d['r']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TRAINING" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "x1=[]\n", + "y1=[]\n", + "t1=[]\n", + "for i in range(2,344):\n", + " x1.append([d['r'][i-2], d['tmin'][i-2], d['tmax'][i-2], d['hmin'][i-2], d['hmax'][i-2], d['wmin'][i-2], d['wmax'][i-2],\n", + " d['r'][i-1], d['tmin'][i-1], d['tmax'][i-1], d['hmin'][i-1], d['hmax'][i-1], d['wmin'][i-1], d['wmax'][i-1],\n", + " d['hmax'][i-2]-d['hmax'][i-1],\n", + " d['hmin'][i-2]-d['hmin'][i-1],\n", + " d['tmax'][i-2]-d['tmax'][i-1],\n", + " d['tmin'][i-2]-d['tmin'][i-1],\n", + " \n", + " \n", + " d['date'][i],d['month'][i],2019])\n", + " y1.append(d['r'][i])\n", + " t1.append(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "reg=LinearRegression().fit(x1,y1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "yp=[]\n", + "s=0\n", + "q=0\n", + "for i in x1:\n", + " yp.append(reg.predict([i]))\n", + " s=abs(y1[q])-abs(reg.predict([i]))\n", + " q=q+1" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(t1,y1)\n", + "plt.plot(t1,yp)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "error is [-0.01089493]\n" + ] + } + ], + "source": [ + "print('error is '+str(s/len(yp)))" + ] + }, + { + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}

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