Linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Treadmil Data - Front thigh filter 1.0 (For result_Image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vel_05 = [90.07698593866574, 94.98510297711027, 95.11833851246287, 80.0415769268055, 23.3276411463577, 226.15559270318536, 86.97159908410208, 100.54546001833134, 91.02151571251906, 118.18574064074153, 87.83712143785579, 93.94913622066804, 92.95005509803593, 90.33217794033175, 91.85408684583126, 91.65513116242937, 78.3481071338127, 89.73877074215774, 91.4328408234176, 93.11317059447856, 104.62805893586378, 90.62351183565475, 86.24122291136061, 84.23397834213719, 85.97676785557428, 87.01678854738064, 85.23272053152448, 88.89245317866715, 65.2350970393752]\n",
    "vel_08=[118.09369259139645, 115.47876192397224, 116.02568469870306, 119.08780828465969, 117.25284457315824, 116.43853749923571, 116.24510839705495, 115.92269314562758, 115.26562536224792, 121.10568642448025, 123.38598812839481, 112.41212111557617]\n",
    "vel_11 = [154.3153683652186, 151.79562138355698, 152.76968031078064, 149.42573042285892, 156.57178737231172, 150.59044437720127, 150.78725993096154, 153.69763600894407, 153.90485035370773, 155.75346736961038, 155.82211770377774, 153.69004560970868, 155.67129261413976, 158.24993478515526, 150.93787904751352, 159.4854401260092, 155.528406634507, 134.69388530753156]\n",
    "vel_14 = [207.14922393437803, 202.37031474301037, 204.34765652793817, 205.42110711709518, 200.38091498116145, 197.47848923770377, 206.25106232860725, 202.544494473707, 200.17770548948323] \n",
    "vel_17 = [231.9893222562082, 234.16174775834153, 237.17136094151195, 232.33745940403338, 234.22533375731132, 232.45613544883398, 229.3764013155987, 228.3968722648312, 235.53622980148978, 231.80839419581199, 231.44061256285033, 226.56621603929554]\n",
    "vel_20 = [258.5071922812823, 255.2489002606823, 252.97395806198577, 261.43352222546037, 262.6071442162892, 261.088992894006, 255.83730213383245, 258.7036105769058, 245.41071872490895, 247.09459251099597, 258.9077221883381, 265.95383963588165, 261.54867518335953, 215.6597169047595]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Treadmil Data - Front thigh filter 3.0 (For result_Image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "vel_05_filter = [72.80928284576643, 74.36227941658433, 73.76497710072707, 75.08354099099722, 74.55180918810197, 73.39927751767694, 74.33232646292804, 74.55317795563153, 73.47156355541499, 76.71510738028243, 74.90387458428235]\n",
    "vel_08_filter = [101.2088878255462, 98.65987925185289, 100.68275566529553, 104.52509248365251, 103.09243180195215, 101.52998774967514, 99.30276637679543, 99.91012409440366, 94.55861333252908, 99.11207352728755, 105.90375818917634, 95.92739017410835]\n",
    "vel_11_filter = [120.6710974074482, 121.27243568448048, 121.61761878695593, 120.75235797351615, 116.96677943961502, 120.39914712945428, 123.35231092790701, 118.17623053063124, 114.5215026787561, 123.86267533042148, 120.42706987474631, 121.42455291642516, 124.30165616238482]\n",
    "vel_14_filter = [146.74213608047523, 149.11770732780994, 144.32631921085087, 144.17114253905538, 149.53044579319038, 148.94763372880558, 149.93495934394497, 148.66619015315953, 147.54656390510695, 147.87279284409442, 151.8903909534615, 134.88276564290464, 51.18709901365427, 30.107788510708133]\n",
    "vel_17_filter = [183.44276119456828, 181.86202629442747, 179.017840363841, 183.47918529627387, 179.7044679047066, 180.5936158849542, 182.5513037969006, 176.14481657459714, 183.0037444338862, 185.5584213391347, 182.56400378283737]\n",
    "vel_20_filter = [217.24369096415398, 218.0330210516482, 211.6235563401096, 217.42546765548775, 223.5074628149544, 219.23048011538157, 221.1261681586638, 221.55431711473062, 214.62095059276564, 220.65804883804975, 216.46485542402826, 193.3318196593738]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Treadmil Data - Side thigh filter 3.0 (Not Image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vel_05_side_filter = [82.84874565808957, 105.67011543387474, 66.25295080733584, 69.54771531863145, 71.32724872742473, 72.58571414339148, 69.38680458435164, 68.09699528339026, 68.21548736506381, 58.267480800230295]\n",
    "vel_08_side_filter = [62.33546104128848, 61.85460724383305, 68.37423615090724, 70.49121473027894, 73.40530599237344, 69.10142659909168, 68.85182681069517, 71.46228825644349, 70.89755495617705, 73.55132933303936, 62.69373017343541, 59.64527542000753, 79.92219710480846, 59.083073160335424]\n",
    "vel_11_side_filter = [78.98722822815687, 75.96648369015763, 76.60582198037386, 79.32985762515736, 78.65326934395695, 79.44379020787572, 78.59268782016625, 78.73283003599747, 78.02069648006614, 78.47989217029834, 74.83145027915968, 75.28911793652854]\n",
    "vel_14_side_filter = [83.7327109438278, 85.68770311178196, 84.63279179795465, 83.0197235536316, 84.89128015816634, 81.19344637123504, 81.77623554932728, 79.68312104341392, 82.08578488975742, 84.55006253937401, 79.74014869275693, 96.7268948730029]\n",
    "vel_17_side_filter = [92.37277693680119, 94.21852232099563, 91.13769720367692, 92.1129562545183, 89.39618364706271, 90.46737519823851, 87.60439796965981, 91.05647126798006, 93.07044852679951, 95.70970773039366, 93.22133856362166, 93.42374595364339, 87.78992668213583, 48.48512996899512]\n",
    "vel_20_side_filter = [101.52986549700341, 96.27187017111089, 91.80986665213382, 95.34993214771855, 99.83197010453131, 99.19905573621314, 99.80570562463892, 104.86986215150041, 104.85630491998542, 98.68307927403, 102.83699969082616, 106.50187210347359, 104.49145419903796]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression ( Front thigh filter-1.0 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### x축-phase portrait 의 radius , y축-treadmil velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vel_05 리스트의 x와 y 값 생성\n",
    "vel_05_x = [(value, 0.5) for value in vel_05]\n",
    "# vel_08 리스트의 x와 y 값 생성\n",
    "vel_08_x = [(value, 0.8) for value in vel_08]\n",
    "vel_11_x = [(value, 1.1) for value in vel_11]\n",
    "vel_14_x = [(value, 1.4) for value in vel_14]\n",
    "vel_17_x = [(value, 1.7) for value in vel_17]\n",
    "vel_20_x = [(value, 2.0) for value in vel_20]\n",
    "\n",
    "vel_list = vel_05_x+vel_08_x+vel_11_x+vel_14_x+vel_17_x+vel_20_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "회귀 계수 (기울기): [0.0081648]\n",
      "절편: -0.1885588449802471\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression 시각화\n",
    "# x축이 phase portrait 의 radius, y축이 treadmil velocity\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 주어진 데이터에서 x와 y 값을 추출\n",
    "X = np.array([pair[0] for pair in vel_list]).reshape(-1, 1)\n",
    "y = np.array([pair[1] for pair in vel_list])\n",
    "\n",
    "# 선형 회귀 모델 생성\n",
    "model = LinearRegression()\n",
    "\n",
    "# 데이터 학습\n",
    "model.fit(X, y)\n",
    "\n",
    "# 회귀 결과 확인\n",
    "print(\"회귀 계수 (기울기):\", model.coef_)\n",
    "print(\"절편:\", model.intercept_)\n",
    "\n",
    "# 회귀선을 그리기 위한 x 값 생성\n",
    "x_values = np.linspace(min(X), max(X), 100)\n",
    "# 회귀선 예측\n",
    "y_values = model.predict(x_values.reshape(-1, 1))\n",
    "\n",
    "# 데이터와 회귀선 시각화\n",
    "plt.scatter(X, y, color='blue', label='Data')  # 데이터 산점도\n",
    "plt.plot(x_values, y_values, color='red', linewidth=2, label='Linear Regression')  # 회귀 선\n",
    "plt.xlabel('X')  # x 축 라벨\n",
    "plt.ylabel('Y')  # y 축 라벨\n",
    "plt.title('Linear Regression')  # 그래프 제목\n",
    "plt.legend()  # 범례 표시\n",
    "plt.grid(True)  # 그리드 표시\n",
    "plt.show()  # 그래프 출력\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 에 들어갈 최종 식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = 0.0081648\n",
    "b1 = -0.1885588449802471\n",
    "def vel(r):\n",
    "  return (0.0081648)*r+b1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### cf ) x축을 velocity 로, y축을 phase portrait 의 radius 로"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Front thigh filter-1.0 (x-vel, y-radius)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [0.5] * len(vel_05)\n",
    "x.extend([0.8] * len(vel_08))\n",
    "x.extend([1.1] * len(vel_11))\n",
    "x.extend([1.4] * len(vel_14))\n",
    "x.extend([1.7] * len(vel_17))\n",
    "x.extend([2.0] * len(vel_20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=[]\n",
    "y.extend(vel_05)\n",
    "y.extend(vel_08)\n",
    "y.extend(vel_11)\n",
    "y.extend(vel_14)\n",
    "y.extend(vel_17)\n",
    "y.extend(vel_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "회귀 계수: 112.66825077036613\n",
      "절편: 34.04031475820561\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "\n",
    "x = np.array(x)\n",
    "y = np.array(y)\n",
    "\n",
    "# 데이터를 적절한 형태로 변환\n",
    "X = x.reshape(-1, 1)  # 2차원 배열로 변환\n",
    "\n",
    "# 선형 회귀 모델 생성 및 학습\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "# 회귀 계수 및 절편 출력\n",
    "print(\"회귀 계수:\", model.coef_[0])\n",
    "print(\"절편:\", model.intercept_)\n",
    "\n",
    "# 그래프 그리기\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x, y, color='blue')\n",
    "plt.plot(x, model.predict(X), color='red')  # 예측 값으로 직선 그리기\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Linear Regression')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Front thigh filter-3.0 (x-vel, y-radius)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_filter = [0.5] * len(vel_05_filter)\n",
    "x_filter.extend([0.8] * len(vel_08_filter))\n",
    "x_filter.extend([1.1] * len(vel_11_filter))\n",
    "x_filter.extend([1.4] * len(vel_14_filter))\n",
    "x_filter.extend([1.7] * len(vel_17_filter))\n",
    "x_filter.extend([2.0] * len(vel_20_filter))\n",
    "y_filter=[]\n",
    "y_filter.extend(vel_05_filter)\n",
    "y_filter.extend(vel_08_filter)\n",
    "y_filter.extend(vel_11_filter)\n",
    "y_filter.extend(vel_14_filter)\n",
    "y_filter.extend(vel_17_filter)\n",
    "y_filter.extend(vel_20_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "회귀 계수: 91.73187527752681\n",
      "절편: 22.13643027406708\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "\n",
    "x_filtered = np.array(x_filter)\n",
    "y_filtered = np.array(y_filter)\n",
    "\n",
    "# 데이터를 적절한 형태로 변환\n",
    "X = x_filtered.reshape(-1, 1)  # 2차원 배열로 변환\n",
    "\n",
    "# 선형 회귀 모델 생성 및 학습\n",
    "model = LinearRegression()\n",
    "model.fit(X, y_filtered)\n",
    "\n",
    "# 회귀 계수 및 절편 출력\n",
    "print(\"회귀 계수:\", model.coef_[0])\n",
    "print(\"절편:\", model.intercept_)\n",
    "\n",
    "# 그래프 그리기\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x_filtered, y_filtered, color='blue')\n",
    "plt.plot(x_filtered, model.predict(X), color='red')  # 예측 값으로 직선 그리기\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Linear Regression')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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