Testing-CWRU-SNR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy.stats import kurtosis\n",
    "from scipy.stats import skew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the data\n",
    "data = np.load('CWRU_dataset.npy')\n",
    "label = np.load('CWRU_lables.npy').reshape(2800,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-4.53178939269873 8.912316771674277 -17.347619303686315\n",
      "-2.8765261920516005 10.614111083274357 -15.606513853349128\n",
      "-0.6501830075600307 12.780734773037395 -13.472719710796694\n",
      "2.6121302086704725 16.05304041915961 -10.249454358025131\n",
      "4.983190366141585 18.452000618669377 -7.825380660794393\n",
      "8.380983795925037 21.814525899787782 -4.45959680283449\n",
      "10.820503024412867 24.263957885115612 -2.022623637711112\n"
     ]
    }
   ],
   "source": [
    "# Generating white noise\n",
    "# Calculating the SNR\n",
    "white_noise_1 = np.clip(np.random.normal(0, 1, data.shape), -0.5, 0.5) # -4.5\n",
    "signal_energy = np.mean(data**2, axis = 1)\n",
    "noise_energy = np.mean(white_noise_1**2, axis = 1)\n",
    "SNR =  10*np.log10(signal_energy/noise_energy)\n",
    "print(np.mean(SNR), np.max(SNR), np.min(SNR))\n",
    "###\n",
    "white_noise_2 = np.clip(np.random.normal(0, 1, data.shape), -0.4, 0.4) # -2.9\n",
    "signal_energy = np.mean(data**2, axis = 1)\n",
    "noise_energy = np.mean(white_noise_2**2, axis = 1)\n",
    "SNR =  10*np.log10(signal_energy/noise_energy)\n",
    "print(np.mean(SNR), np.max(SNR), np.min(SNR))\n",
    "###\n",
    "white_noise_3 = np.clip(np.random.normal(0, 1, data.shape), -0.3, 0.3) # -0.6\n",
    "signal_energy = np.mean(data**2, axis = 1)\n",
    "noise_energy = np.mean(white_noise_3**2, axis = 1)\n",
    "SNR =  10*np.log10(signal_energy/noise_energy)\n",
    "print(np.mean(SNR), np.max(SNR), np.min(SNR))\n",
    "###\n",
    "white_noise_4 = np.clip(np.random.normal(0, 1, data.shape), -0.2, 0.2) # 2.6\n",
    "signal_energy = np.mean(data**2, axis = 1)\n",
    "noise_energy = np.mean(white_noise_4**2, axis = 1)\n",
    "SNR =  10*np.log10(signal_energy/noise_energy)\n",
    "print(np.mean(SNR), np.max(SNR), np.min(SNR))\n",
    "###\n",
    "white_noise_5 = np.clip(np.random.normal(0, 1, data.shape), -0.15, 0.15) # 5\n",
    "signal_energy = np.mean(data**2, axis = 1)\n",
    "noise_energy = np.mean(white_noise_5**2, axis = 1)\n",
    "SNR =  10*np.log10(signal_energy/noise_energy)\n",
    "print(np.mean(SNR), np.max(SNR), np.min(SNR))\n",
    "###\n",
    "white_noise_6 = np.clip(np.random.normal(0, 1, data.shape), -0.1, 0.1) # 8.4\n",
    "signal_energy = np.mean(data**2, axis = 1)\n",
    "noise_energy = np.mean(white_noise_6**2, axis = 1)\n",
    "SNR =  10*np.log10(signal_energy/noise_energy)\n",
    "print(np.mean(SNR), np.max(SNR), np.min(SNR))\n",
    "###\n",
    "white_noise_7 = np.clip(np.random.normal(0, 1, data.shape), -0.075, 0.075) # 10.8\n",
    "signal_energy = np.mean(data**2, axis = 1)\n",
    "noise_energy = np.mean(white_noise_7**2, axis = 1)\n",
    "SNR =  10*np.log10(signal_energy/noise_energy)\n",
    "print(np.mean(SNR), np.max(SNR), np.min(SNR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Noisy data\n",
    "data1 = data + white_noise_1\n",
    "data2 = data + white_noise_2\n",
    "data3 = data + white_noise_3\n",
    "data4 = data + white_noise_4\n",
    "data5 = data + white_noise_5\n",
    "data6 = data + white_noise_6\n",
    "data7 = data + white_noise_7\n",
    "del data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving noisy data\n",
    "np.save('noisy_data_1.npy',data1)\n",
    "np.save('noisy_data_2.npy',data2)\n",
    "np.save('noisy_data_3.npy',data3)\n",
    "np.save('noisy_data_4.npy',data4)\n",
    "np.save('noisy_data_5.npy',data5)\n",
    "np.save('noisy_data_6.npy',data6)\n",
    "np.save('noisy_data_7.npy',data7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    M=data.mean()\n",
    "    return M\n",
    "def Max(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    Max1=data.max()\n",
    "    return Max1\n",
    "def Min(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    Min1=data.min()\n",
    "    return Min1\n",
    "def pp(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    PP=data.max()-data.min()\n",
    "    return PP\n",
    "def Variance(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    Var=data.var()\n",
    "    return Var\n",
    "def rms(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    Rms=np.sqrt(np.mean(data**2))\n",
    "    return Rms\n",
    "def Ab_mean(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    Abm=np.mean(np.absolute(data))\n",
    "    return Abm\n",
    "def Shapef(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    shapef=rms(data)/Ab_mean(data)\n",
    "    return shapef\n",
    "def Impulsef(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    impulse=Max(data)/Ab_mean(data)\n",
    "    return impulse\n",
    "def crestf(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    crest=Max(data)/rms(data)\n",
    "    return crest\n",
    "def SQRT_AMPL(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    SQRTA=(np.mean(np.sqrt(np.absolute(data))))**2\n",
    "    return SQRTA\n",
    "def clearancef(data):\n",
    "    data = np.asarray(data)\n",
    "    data.flatten()\n",
    "    clrf=Max(data)/SQRT_AMPL(data)\n",
    "    return clrf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noisy Data 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "[0.74642857 0.75714286 0.73928571 0.76428571 0.77142857]\n",
      "0.7557142857142857\n",
      "**********\n",
      "Support Vector Classifier\n",
      "[0.62678571 0.60892857 0.60357143 0.61071429 0.62857143]\n",
      "0.6157142857142858\n",
      "**********\n",
      "Logistic Regression\n",
      "[0.65714286 0.65178571 0.66785714 0.68928571 0.69821429]\n",
      "0.6728571428571428\n",
      "**********\n",
      "K Nearest Neighbors\n",
      "[0.67857143 0.66964286 0.65892857 0.6875     0.68928571]\n",
      "0.6767857142857142\n",
      "**********\n",
      "MLP\n",
      "[0.72857143 0.7125     0.71964286 0.72678571 0.7375    ]\n",
      "0.725\n",
      "**********\n"
     ]
    }
   ],
   "source": [
    "data = data1\n",
    "feature = []\n",
    "for i in range(2800):\n",
    "    feature.append(mean(data[i]))\n",
    "    feature.append(Max(data[i]))\n",
    "    feature.append(Min(data[i]))\n",
    "    feature.append(pp(data[i]))\n",
    "    feature.append(Variance(data[i]))\n",
    "    feature.append(rms(data[i]))\n",
    "    feature.append(Ab_mean(data[i]))\n",
    "    feature.append(Shapef(data[i]))\n",
    "    feature.append(Impulsef(data[i]))\n",
    "    feature.append(crestf(data[i]))\n",
    "    feature.append(max(abs(data[i])))\n",
    "    feature.append(clearancef(data[i]))\n",
    "    K=kurtosis(data[i])\n",
    "    feature.append(K)\n",
    "    S=skew(data[i], axis=0, bias=False)\n",
    "    feature.append(S)\n",
    "data_f = np.array(feature).reshape(2800,14)\n",
    "x = data_f\n",
    "y = label.ravel()\n",
    "print(\"Random Forest\")\n",
    "RF=RandomForestClassifier(n_estimators=15,random_state=42)\n",
    "RF.fit(x,y)\n",
    "score = cross_val_score(RF, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Support Vector Classifier\")\n",
    "svc=SVC(random_state=100, tol=1e-1)\n",
    "score = cross_val_score(svc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Logistic Regression\")\n",
    "LR=LogisticRegression(random_state=6, solver='lbfgs',multi_class='multinomial')\n",
    "score = cross_val_score(LR, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print('K Nearest Neighbors')\n",
    "knn=KNeighborsClassifier(n_neighbors=75)\n",
    "score = cross_val_score(knn, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"MLP\")\n",
    "mlpc=MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(512), random_state=10)\n",
    "score = cross_val_score(mlpc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noisy Data 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "[0.78928571 0.75892857 0.77321429 0.78214286 0.7625    ]\n",
      "0.7732142857142857\n",
      "**********\n",
      "Support Vector Classifier\n",
      "[0.64107143 0.63035714 0.60892857 0.625      0.62857143]\n",
      "0.6267857142857143\n",
      "**********\n",
      "Logistic Regression\n",
      "[0.68571429 0.70535714 0.71071429 0.71428571 0.72142857]\n",
      "0.7075000000000001\n",
      "**********\n",
      "K Nearest Neighbors\n",
      "[0.7        0.71428571 0.675      0.68392857 0.66964286]\n",
      "0.6885714285714286\n",
      "**********\n",
      "MLP\n",
      "[0.75535714 0.76607143 0.75       0.74464286 0.75357143]\n",
      "0.7539285714285714\n",
      "**********\n"
     ]
    }
   ],
   "source": [
    "data = data2\n",
    "feature = []\n",
    "for i in range(2800):\n",
    "    feature.append(mean(data[i]))\n",
    "    feature.append(Max(data[i]))\n",
    "    feature.append(Min(data[i]))\n",
    "    feature.append(pp(data[i]))\n",
    "    feature.append(Variance(data[i]))\n",
    "    feature.append(rms(data[i]))\n",
    "    feature.append(Ab_mean(data[i]))\n",
    "    feature.append(Shapef(data[i]))\n",
    "    feature.append(Impulsef(data[i]))\n",
    "    feature.append(crestf(data[i]))\n",
    "    feature.append(max(abs(data[i])))\n",
    "    feature.append(clearancef(data[i]))\n",
    "    K=kurtosis(data[i])\n",
    "    feature.append(K)\n",
    "    S=skew(data[i], axis=0, bias=False)\n",
    "    feature.append(S)\n",
    "data_f = np.array(feature).reshape(2800,14)\n",
    "x = data_f\n",
    "y = label.ravel()\n",
    "print(\"Random Forest\")\n",
    "RF=RandomForestClassifier(n_estimators=15,random_state=42)\n",
    "RF.fit(x,y)\n",
    "score = cross_val_score(RF, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Support Vector Classifier\")\n",
    "svc=SVC(random_state=100, tol=1e-1)\n",
    "score = cross_val_score(svc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Logistic Regression\")\n",
    "LR=LogisticRegression(random_state=6, solver='lbfgs',multi_class='multinomial')\n",
    "score = cross_val_score(LR, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print('K Nearest Neighbors')\n",
    "knn=KNeighborsClassifier(n_neighbors=75)\n",
    "score = cross_val_score(knn, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"MLP\")\n",
    "mlpc=MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(512), random_state=10)\n",
    "score = cross_val_score(mlpc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noisy Data 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "[0.79464286 0.79821429 0.78928571 0.77857143 0.80714286]\n",
      "0.7935714285714285\n",
      "**********\n",
      "Support Vector Classifier\n",
      "[0.64642857 0.63928571 0.61607143 0.64821429 0.63571429]\n",
      "0.6371428571428571\n",
      "**********\n",
      "Logistic Regression\n",
      "[0.70178571 0.71607143 0.72142857 0.71607143 0.73214286]\n",
      "0.7175\n",
      "**********\n",
      "K Nearest Neighbors\n",
      "[0.68214286 0.69107143 0.675      0.69285714 0.68392857]\n",
      "0.685\n",
      "**********\n",
      "MLP\n",
      "[0.76607143 0.74642857 0.74642857 0.75178571 0.78035714]\n",
      "0.7582142857142858\n",
      "**********\n"
     ]
    }
   ],
   "source": [
    "data = data3\n",
    "feature = []\n",
    "for i in range(2800):\n",
    "    feature.append(mean(data[i]))\n",
    "    feature.append(Max(data[i]))\n",
    "    feature.append(Min(data[i]))\n",
    "    feature.append(pp(data[i]))\n",
    "    feature.append(Variance(data[i]))\n",
    "    feature.append(rms(data[i]))\n",
    "    feature.append(Ab_mean(data[i]))\n",
    "    feature.append(Shapef(data[i]))\n",
    "    feature.append(Impulsef(data[i]))\n",
    "    feature.append(crestf(data[i]))\n",
    "    feature.append(max(abs(data[i])))\n",
    "    feature.append(clearancef(data[i]))\n",
    "    K=kurtosis(data[i])\n",
    "    feature.append(K)\n",
    "    S=skew(data[i], axis=0, bias=False)\n",
    "    feature.append(S)\n",
    "data_f = np.array(feature).reshape(2800,14)\n",
    "x = data_f\n",
    "y = label.ravel()\n",
    "print(\"Random Forest\")\n",
    "RF=RandomForestClassifier(n_estimators=15,random_state=42)\n",
    "RF.fit(x,y)\n",
    "score = cross_val_score(RF, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Support Vector Classifier\")\n",
    "svc=SVC(random_state=100, tol=1e-1)\n",
    "score = cross_val_score(svc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Logistic Regression\")\n",
    "LR=LogisticRegression(random_state=6, solver='lbfgs',multi_class='multinomial')\n",
    "score = cross_val_score(LR, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print('K Nearest Neighbors')\n",
    "knn=KNeighborsClassifier(n_neighbors=75)\n",
    "score = cross_val_score(knn, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"MLP\")\n",
    "mlpc=MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(512), random_state=10)\n",
    "score = cross_val_score(mlpc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noisy Data 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "[0.81071429 0.80357143 0.81785714 0.78928571 0.83035714]\n",
      "0.8103571428571428\n",
      "**********\n",
      "Support Vector Classifier\n",
      "[0.675      0.66785714 0.64107143 0.65714286 0.65535714]\n",
      "0.6592857142857144\n",
      "**********\n",
      "Logistic Regression\n",
      "[0.725      0.71785714 0.72142857 0.73928571 0.73214286]\n",
      "0.7271428571428571\n",
      "**********\n",
      "K Nearest Neighbors\n",
      "[0.71071429 0.69464286 0.675      0.70178571 0.69821429]\n",
      "0.6960714285714287\n",
      "**********\n",
      "MLP\n",
      "[0.76964286 0.75714286 0.75892857 0.75357143 0.77678571]\n",
      "0.7632142857142857\n",
      "**********\n"
     ]
    }
   ],
   "source": [
    "data = data4\n",
    "feature = []\n",
    "for i in range(2800):\n",
    "    feature.append(mean(data[i]))\n",
    "    feature.append(Max(data[i]))\n",
    "    feature.append(Min(data[i]))\n",
    "    feature.append(pp(data[i]))\n",
    "    feature.append(Variance(data[i]))\n",
    "    feature.append(rms(data[i]))\n",
    "    feature.append(Ab_mean(data[i]))\n",
    "    feature.append(Shapef(data[i]))\n",
    "    feature.append(Impulsef(data[i]))\n",
    "    feature.append(crestf(data[i]))\n",
    "    feature.append(max(abs(data[i])))\n",
    "    feature.append(clearancef(data[i]))\n",
    "    K=kurtosis(data[i])\n",
    "    feature.append(K)\n",
    "    S=skew(data[i], axis=0, bias=False)\n",
    "    feature.append(S)\n",
    "data_f = np.array(feature).reshape(2800,14)\n",
    "x = data_f\n",
    "y = label.ravel()\n",
    "print(\"Random Forest\")\n",
    "RF=RandomForestClassifier(n_estimators=15,random_state=42)\n",
    "RF.fit(x,y)\n",
    "score = cross_val_score(RF, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Support Vector Classifier\")\n",
    "svc=SVC(random_state=100, tol=1e-1)\n",
    "score = cross_val_score(svc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Logistic Regression\")\n",
    "LR=LogisticRegression(random_state=6, solver='lbfgs',multi_class='multinomial')\n",
    "score = cross_val_score(LR, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print('K Nearest Neighbors')\n",
    "knn=KNeighborsClassifier(n_neighbors=75)\n",
    "score = cross_val_score(knn, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"MLP\")\n",
    "mlpc=MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(512), random_state=10)\n",
    "score = cross_val_score(mlpc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noisy Data 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "[0.82321429 0.82321429 0.82321429 0.81071429 0.84464286]\n",
      "0.825\n",
      "**********\n",
      "Support Vector Classifier\n",
      "[0.68392857 0.675      0.67678571 0.66964286 0.675     ]\n",
      "0.6760714285714287\n",
      "**********\n",
      "Logistic Regression\n",
      "[0.71607143 0.74821429 0.71607143 0.73392857 0.73035714]\n",
      "0.7289285714285714\n",
      "**********\n",
      "K Nearest Neighbors\n",
      "[0.69821429 0.69285714 0.70178571 0.71071429 0.69821429]\n",
      "0.7003571428571428\n",
      "**********\n",
      "MLP\n",
      "[0.76785714 0.77321429 0.76428571 0.76607143 0.78928571]\n",
      "0.7721428571428571\n",
      "**********\n"
     ]
    }
   ],
   "source": [
    "data = data5\n",
    "feature = []\n",
    "for i in range(2800):\n",
    "    feature.append(mean(data[i]))\n",
    "    feature.append(Max(data[i]))\n",
    "    feature.append(Min(data[i]))\n",
    "    feature.append(pp(data[i]))\n",
    "    feature.append(Variance(data[i]))\n",
    "    feature.append(rms(data[i]))\n",
    "    feature.append(Ab_mean(data[i]))\n",
    "    feature.append(Shapef(data[i]))\n",
    "    feature.append(Impulsef(data[i]))\n",
    "    feature.append(crestf(data[i]))\n",
    "    feature.append(max(abs(data[i])))\n",
    "    feature.append(clearancef(data[i]))\n",
    "    K=kurtosis(data[i])\n",
    "    feature.append(K)\n",
    "    S=skew(data[i], axis=0, bias=False)\n",
    "    feature.append(S)\n",
    "data_f = np.array(feature).reshape(2800,14)\n",
    "x = data_f\n",
    "y = label.ravel()\n",
    "print(\"Random Forest\")\n",
    "RF=RandomForestClassifier(n_estimators=15,random_state=42)\n",
    "RF.fit(x,y)\n",
    "score = cross_val_score(RF, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Support Vector Classifier\")\n",
    "svc=SVC(random_state=100, tol=1e-1)\n",
    "score = cross_val_score(svc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Logistic Regression\")\n",
    "LR=LogisticRegression(random_state=6, solver='lbfgs',multi_class='multinomial')\n",
    "score = cross_val_score(LR, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print('K Nearest Neighbors')\n",
    "knn=KNeighborsClassifier(n_neighbors=75)\n",
    "score = cross_val_score(knn, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"MLP\")\n",
    "mlpc=MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(512), random_state=10)\n",
    "score = cross_val_score(mlpc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noisy Data 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "[0.85178571 0.85714286 0.82678571 0.81071429 0.86785714]\n",
      "0.8428571428571429\n",
      "**********\n",
      "Support Vector Classifier\n",
      "[0.69642857 0.69821429 0.66607143 0.69107143 0.7       ]\n",
      "0.6903571428571429\n",
      "**********\n",
      "Logistic Regression\n",
      "[0.7375     0.74464286 0.73035714 0.72857143 0.74642857]\n",
      "0.7375\n",
      "**********\n",
      "K Nearest Neighbors\n",
      "[0.70357143 0.7125     0.7        0.71071429 0.69464286]\n",
      "0.7042857142857144\n",
      "**********\n",
      "MLP\n",
      "[0.78928571 0.78928571 0.75178571 0.74285714 0.79642857]\n",
      "0.7739285714285714\n",
      "**********\n"
     ]
    }
   ],
   "source": [
    "data = data6\n",
    "feature = []\n",
    "for i in range(2800):\n",
    "    feature.append(mean(data[i]))\n",
    "    feature.append(Max(data[i]))\n",
    "    feature.append(Min(data[i]))\n",
    "    feature.append(pp(data[i]))\n",
    "    feature.append(Variance(data[i]))\n",
    "    feature.append(rms(data[i]))\n",
    "    feature.append(Ab_mean(data[i]))\n",
    "    feature.append(Shapef(data[i]))\n",
    "    feature.append(Impulsef(data[i]))\n",
    "    feature.append(crestf(data[i]))\n",
    "    feature.append(max(abs(data[i])))\n",
    "    feature.append(clearancef(data[i]))\n",
    "    K=kurtosis(data[i])\n",
    "    feature.append(K)\n",
    "    S=skew(data[i], axis=0, bias=False)\n",
    "    feature.append(S)\n",
    "data_f = np.array(feature).reshape(2800,14)\n",
    "x = data_f\n",
    "y = label.ravel()\n",
    "print(\"Random Forest\")\n",
    "RF=RandomForestClassifier(n_estimators=15,random_state=42)\n",
    "RF.fit(x,y)\n",
    "score = cross_val_score(RF, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Support Vector Classifier\")\n",
    "svc=SVC(random_state=100, tol=1e-1)\n",
    "score = cross_val_score(svc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Logistic Regression\")\n",
    "LR=LogisticRegression(random_state=6, solver='lbfgs',multi_class='multinomial')\n",
    "score = cross_val_score(LR, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print('K Nearest Neighbors')\n",
    "knn=KNeighborsClassifier(n_neighbors=75)\n",
    "score = cross_val_score(knn, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"MLP\")\n",
    "mlpc=MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(512), random_state=10)\n",
    "score = cross_val_score(mlpc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noisy Data 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "[0.86964286 0.87321429 0.83035714 0.86785714 0.86607143]\n",
      "0.8614285714285714\n",
      "**********\n",
      "Support Vector Classifier\n",
      "[0.69285714 0.69285714 0.67321429 0.70892857 0.68928571]\n",
      "0.6914285714285715\n",
      "**********\n",
      "Logistic Regression\n",
      "[0.73928571 0.75892857 0.71607143 0.74285714 0.75178571]\n",
      "0.7417857142857143\n",
      "**********\n",
      "K Nearest Neighbors\n",
      "[0.7        0.70178571 0.67142857 0.68928571 0.71071429]\n",
      "0.6946428571428572\n",
      "**********\n",
      "MLP\n",
      "[0.77321429 0.7875     0.75178571 0.75892857 0.78571429]\n",
      "0.7714285714285714\n",
      "**********\n"
     ]
    }
   ],
   "source": [
    "data = data7\n",
    "feature = []\n",
    "for i in range(2800):\n",
    "    feature.append(mean(data[i]))\n",
    "    feature.append(Max(data[i]))\n",
    "    feature.append(Min(data[i]))\n",
    "    feature.append(pp(data[i]))\n",
    "    feature.append(Variance(data[i]))\n",
    "    feature.append(rms(data[i]))\n",
    "    feature.append(Ab_mean(data[i]))\n",
    "    feature.append(Shapef(data[i]))\n",
    "    feature.append(Impulsef(data[i]))\n",
    "    feature.append(crestf(data[i]))\n",
    "    feature.append(max(abs(data[i])))\n",
    "    feature.append(clearancef(data[i]))\n",
    "    K=kurtosis(data[i])\n",
    "    feature.append(K)\n",
    "    S=skew(data[i], axis=0, bias=False)\n",
    "    feature.append(S)\n",
    "data_f = np.array(feature).reshape(2800,14)\n",
    "x = data_f\n",
    "y = label.ravel()\n",
    "print(\"Random Forest\")\n",
    "RF=RandomForestClassifier(n_estimators=15,random_state=42)\n",
    "RF.fit(x,y)\n",
    "score = cross_val_score(RF, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Support Vector Classifier\")\n",
    "svc=SVC(random_state=100, tol=1e-1)\n",
    "score = cross_val_score(svc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"Logistic Regression\")\n",
    "LR=LogisticRegression(random_state=6, solver='lbfgs',multi_class='multinomial')\n",
    "score = cross_val_score(LR, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print('K Nearest Neighbors')\n",
    "knn=KNeighborsClassifier(n_neighbors=75)\n",
    "score = cross_val_score(knn, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)\n",
    "print(\"MLP\")\n",
    "mlpc=MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(512), random_state=10)\n",
    "score = cross_val_score(mlpc, x, y, cv=5)\n",
    "print(score)\n",
    "print(np.mean(np.array(score)))\n",
    "print('*'*10)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}