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nullbringernullbringer
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logistic regression
1 parent 4cf90f2 commit 96cad65

2 files changed

Lines changed: 404 additions & 57 deletions

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project_2.ipynb renamed to project_2_linear_regression.ipynb

Lines changed: 38 additions & 57 deletions
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 50,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.cluster import KMeans\n",
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"import math\n",
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"import csv\n",
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"import math\n",
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"import matplotlib.pyplot\n",
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 51,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 54,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -249,6 +248,9 @@
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" L_Erms_Val = []\n",
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" L_Erms_TR = []\n",
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" L_Erms_Test = []\n",
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" L_Acc_Val = []\n",
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" L_Acc_TR = []\n",
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" L_Acc_Test = []\n",
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" W_Mat = []\n",
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"\n",
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" for i in range(0,_loop_range):\n",
@@ -269,22 +271,28 @@
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" #-----------------TrainingData Accuracy---------------------#\n",
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" TR_TEST_OUT = GetValTest( _TRAINING_PHI, W_T_Next) \n",
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" Erms_TR = GetErms(TR_TEST_OUT, _TrainingTarget)\n",
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" L_Acc_TR.append(float(Erms_TR.split(',')[0]))\n",
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" L_Erms_TR.append(float(Erms_TR.split(',')[1]))\n",
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"\n",
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" #-----------------ValidationData Accuracy---------------------#\n",
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" VAL_TEST_OUT = GetValTest( _VAL_PHI, W_T_Next) \n",
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" Erms_Val = GetErms(VAL_TEST_OUT, _ValDataAct)\n",
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" L_Acc_Val.append(float(Erms_Val.split(',')[0]))\n",
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" L_Erms_Val.append(float(Erms_Val.split(',')[1]))\n",
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"\n",
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" #-----------------TestingData Accuracy---------------------#\n",
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" TEST_OUT = GetValTest( _TEST_PHI, W_T_Next) \n",
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" Erms_Test = GetErms(TEST_OUT, _TestDataAct)\n",
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" L_Acc_Test.append(float(Erms_Test.split(',')[0]))\n",
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" L_Erms_Test.append(float(Erms_Test.split(',')[1]))\n",
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" \n",
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" print ('----------Gradient Descent Solution--------------------')\n",
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" print (\"E_rms Training = \" + str(np.around(min(L_Erms_TR),5)))\n",
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" print (\"E_rms Validation = \" + str(np.around(min(L_Erms_Val),5)))\n",
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" print (\"E_rms Testing = \" + str(np.around(min(L_Erms_Test),5)))"
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" print (\"E_rms Testing = \" + str(np.around(min(L_Erms_Test),5)))\n",
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" print (\"Accuracy Training = \" + str(np.around(max(L_Acc_TR),5)))\n",
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" print (\"Accuracy Validation = \" + str(np.around(max(L_Acc_Val),5)))\n",
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" print (\"Accuracy Testing = \" + str(np.around(max(L_Acc_Test),5)))"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 56,
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"metadata": {},
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"outputs": [
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{
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"Testing Phi: (157, 5)\n",
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"Validation Phi: (158, 5)\n",
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"----------Gradient Descent Solution--------------------\n",
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"E_rms Training = 0.49933\n",
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"E_rms Validation = 0.49939\n",
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"E_rms Testing = 0.47696\n"
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"E_rms Training = 0.49923\n",
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"E_rms Validation = 0.49557\n",
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"E_rms Testing = 0.4937\n",
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"Accuracy Training = 54.58136\n",
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"Accuracy Validation = 56.96203\n",
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"Accuracy Testing = 59.87261\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"outputs": [
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{
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"Testing Phi: (157, 5)\n",
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"Validation Phi: (158, 5)\n",
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"----------Gradient Descent Solution--------------------\n",
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"E_rms Training = 0.49949\n",
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"E_rms Validation = 0.49925\n",
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"E_rms Testing = 0.47915\n"
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"E_rms Training = 0.49978\n",
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"E_rms Validation = 0.49704\n",
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"E_rms Testing = 0.49491\n",
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"Accuracy Training = 51.73776\n",
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"Accuracy Validation = 55.6962\n",
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"Accuracy Testing = 57.96178\n"
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]
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}
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],
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},
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"cell_type": "code",
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},
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"cell_type": "code",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"Validation Data: (1017, 14306)\n",
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"Testing Target: (14305,)\n",
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"Testing Data: (1017, 14305)\n",
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"BigSigma: (1017, 1017)\n",
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"Training Phi: (114450, 5)\n",
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"Testing Phi: (14305, 5)\n",
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"Validation Phi: (14306, 5)\n",
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"----------Gradient Descent Solution--------------------\n",
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"E_rms Training = 0.54717\n",
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"E_rms Validation = 0.55033\n",
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"E_rms Testing = 0.55555\n"
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"BigSigma: (1017, 1017)\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"=========================\n",
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"====== GSC Data =========\n",
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"==== Subtraction ======\n",
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"=========================\n",
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"RawData : (509, 143062)\n",
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"Training Target: (114450,)\n",
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"Training Data: (509, 114450)\n",
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"Validation Target: (14306,)\n",
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"Validation Data: (509, 14306)\n",
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"Testing Target: (14305,)\n",
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"Testing Data: (509, 14305)\n",
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"BigSigma: (509, 509)\n",
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"Training Phi: (114450, 5)\n",
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"Testing Phi: (14305, 5)\n",
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"Validation Phi: (14306, 5)\n",
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"----------Gradient Descent Solution--------------------\n",
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"E_rms Training = 0.52324\n",
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"E_rms Validation = 0.5284\n",
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"E_rms Testing = 0.52343\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Now we are merging the image specifications with the dataset, we will use this dataset to train\n",
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"# test or validate data.\n",

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