From 2ac48b6a210cab1304898258df01ba86398f6568 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=95=D0=BB=D0=B5=D0=BD=D0=B0=20=D0=91=D0=B0=D0=BA=D0=B0?= =?UTF-8?q?=D0=BB=D1=8C=D1=81=D0=BA=D0=B0=D1=8F?= Date: Sat, 1 Mar 2025 01:32:13 +0400 Subject: [PATCH] =?UTF-8?q?=D1=83=D1=84=20=D0=BB=D0=B0=D0=B1=D0=B0=201=20?= =?UTF-8?q?=D1=81=D0=B4=D0=B5=D0=BB=D0=B0=D0=BD=D0=B0=20=D1=87=D0=B5=D1=80?= =?UTF-8?q?=D0=B5=D0=B7=20=D0=BC=D0=B5=D1=81=D1=8F=D1=86=20=D0=BF=D0=BE?= =?UTF-8?q?=D1=81=D0=BB=D0=B5=20=D1=82=D0=BE=D0=B3=D0=BE,=20=D0=BA=D0=B0?= =?UTF-8?q?=D0=BA=20=D0=B5=D0=B5=20=D0=B4=D0=B0=D0=BB=D0=B8...?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab_7/lab_7.ipynb | 2456 +++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 2384 insertions(+), 72 deletions(-) diff --git a/lab_7/lab_7.ipynb b/lab_7/lab_7.ipynb index 52c218a..f67a866 100644 --- a/lab_7/lab_7.ipynb +++ b/lab_7/lab_7.ipynb @@ -19,9 +19,48 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 140, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 5110 entries, 0 to 5109\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 5110 non-null int64 \n", + " 1 gender 5110 non-null object \n", + " 2 age 5110 non-null float64\n", + " 3 hypertension 5110 non-null int64 \n", + " 4 heart_disease 5110 non-null int64 \n", + " 5 ever_married 5110 non-null object \n", + " 6 work_type 5110 non-null object \n", + " 7 Residence_type 5110 non-null object \n", + " 8 avg_glucose_level 5110 non-null float64\n", + " 9 bmi 4909 non-null float64\n", + " 10 smoking_status 5110 non-null object \n", + " 11 stroke 5110 non-null int64 \n", + "dtypes: float64(3), int64(4), object(5)\n", + "memory usage: 479.2+ KB\n", + "id 0\n", + "gender 0\n", + "age 0\n", + "hypertension 0\n", + "heart_disease 0\n", + "ever_married 0\n", + "work_type 0\n", + "Residence_type 0\n", + "avg_glucose_level 0\n", + "bmi 0\n", + "smoking_status 0\n", + "stroke 0\n", + "dtype: int64\n" + ] + } + ], "source": [ "import pandas as pd\n", "import skfuzzy.control as control\n", @@ -30,7 +69,11 @@ "\n", "\n", "# считаем данные и поределим входные и выходные переменные\n", - "data = pd.read_csv(\"./csv/option4.csv\")" + "data = pd.read_csv(\"./csv/option4.csv\")\n", + "data.info()\n", + "\n", + "data['bmi'] = data['bmi'].fillna(data['bmi'].mean())\n", + "print(data.isnull().sum())\n" ] }, { @@ -44,15 +87,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 141, "metadata": {}, "outputs": [], "source": [ "age = control.Antecedent(np.arange(0, 101, 1), 'age') # возраст от 0 до 100 с шагом 1 год и т.д.\n", "glucose = control.Antecedent(np.arange(50, 301, 1), 'glucose')\n", "bmi = control.Antecedent(np.arange(10, 50, 0.1), 'bmi')\n", - "hypertension = control.Antecedent(np.arange(0, 2, 1), 'hypertension')\n", - "heart_disease = control.Antecedent(np.arange(0, 2, 1), 'heart_disease')\n", + "hypertension = control.Antecedent(np.arange(0, 2, 0.1), 'hypertension')\n", + "heart_disease = control.Antecedent(np.arange(0, 2, 0.1), 'heart_disease')\n", "\n", "# а теперь выходная переменная (Consequent)\n", "stroke_risk = control.Consequent(np.arange(0, 1.1, 0.1), 'stroke_risk')" @@ -68,12 +111,12 @@ "\n", "

\n", "теперь самое время определить нечеткие переменные, которые сложатся... в лингвистические\n", - "

\n" + "

" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 142, "metadata": {}, "outputs": [ { @@ -96,7 +139,7 @@ }, { "data": { - "image/png": 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", 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", 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tWrSw+POJfh9zU/KhQ4dkA3JxVpAsVpa628qNf33gxgUgLdFaURERFUokNMP/HI5rKdcwr+s8VClXRXVImsXkxgBE2Wj16tXw8fFBhw4dZLJTs2ZNLFu2rEyeb/r06fK5xBZxsUtKNB43a9YMVmXeLXUn5jEMseFWCYmIqDBZpiyM+WsMjl87Lqd71/SuqTokTbPLFvt4DUQ0vXp7eyM+Pl42v+aVkZGBmJgY2VSbv3+FrKvU78X7VYBOo4F2wwu/T3oS8H4loPdsoOkzpYqXiKikxK/hD/d8iCUnlmBGpxm4r+p9qkPSPK7ckP5kZQDpN+9elnL2AHyqs6mYiJT69ti3WHR8Eca2HsvExkKY3JD+pMbnfLxbWcrcd8PkhogU+e3cb/hk7ycY2mgonqyXc54YlR6TG9Lx0MwibJ8UfTcxJ8o8JCKi/PZf3Y+x28aiZ82eGN70DiV0KjYmN6Q/uUMzyxctuUm49P8JERGRFVxKvISRm0YiLCAME9tNzD0vjCyDyQ3pj9gGXtSylHnHVAx3TBGRdSRlJOHVja/Cw8kD0ztOh5MDN7BYGpMb0vHKTRHKUhXq5EwPjz5W5mEREYkt3+9sfQdRSVFyy3f5ovwRRsXGE4pJf0SJycEZcHK7+32dXAHfWuy7ISKr+PzA59h6aStm3j8TtcrXUh2ObnHlhvTHPDSzqDVsOWOKKzdEVLZ+PvMzFh5ZiFHNR6FDlQ6qw9E1JjekP0UZmnnbdnCu3BBR2TkccxgTtk/AI7UfwcDQgarD0T0mNzrRqVMnjBw5stCvi078VatWFfnxNm/eLL9HDNfUZFmqKDul8jYVJ0UDSdfKMioiMqi41DiM2jIK9X3rY1ybcdwZZQVMbgwiKioKDz30kEUf87///S/CwmxwWnpRhmYWuGOKh/kRkeUbiN/e+jbSs9IxrdM0OIt+QCpzTG4MIigoCC4uLjBMWao4OxBEQ7G9E08qJiKLm3VwFvZc2YOPOnyEII8g1eEYBpMbHTGZTHj77bfh6+srkxmxslJYWWr79u1y1cXV1RUtWrSQXxP3OXjw4C2PuW/fPvl1d3d3OeU7PDznPJivv/4aEydOxKFDh+T3iYu4TZNlKUdnoEJtJjdEZFF/RvyJ+YfnY0SzEWhdsbXqcAyFyY2OfPPNN/Dw8MCuXbvw0UcfYdKkSdiwYUOBk9F79uyJRo0aYf/+/Zg8eTJGjx5d4GOOHTsW06ZNw969e+Ho6IjnnntO3t63b1+88cYbaNCggSx5iYu4TZMNxebSFJMbIrKQCwkX5GiFzlU7Y3CDwarDMRyec1MEKZkpOBd/zurPW8O7Btwci3BWy78aN26MCRMmyM/r1KmDWbNmYePGjXjggQduud/ixYvlSsu8efPkyk1oaCguXbqEoUOH3vaY7733Hjp27Cg/f+edd9CjRw+kpqbCzc0Nnp6eMuERq0Q2uRW8uMnN2U1AdnbRt5ATERUgOSNZjlbwc/PDlPZT2ECsAJObIhCJTd9frL8qsezhZQitEFqs5CavihUrIjo6+rb7idKSuK9IbMxatWp118cUjyeIx6xatSpsUkYKkJlaspUbseKTeBUoZ2PJGhFpyge7P8DFmxexuMdieDp7qg7HkJjcFHEFRSQaKp63OJycbp1PIv5aEH04pZH3Mc1/fZT2Ma0zEbyYKzfirBtBlKaY3BBRCa09uxYrT6/EpHaTUMenjupwDIvJTRGI0lBxVlBsXb169fD9998jLS0tdwfVnj17iv04zs7OyMrKgmaHZublWwNwcMlJbmrdVyahEZH++2wm7ZiE7jW6y8P6SB02FBvQ008/LVdfhg0bhuPHj2P9+vX45JNP5NeKUxuuXr06zp07J3dYxcbGymRJU0Mz87J3APzr8qwbIioRcY7NW1vekn0249uOZ5+NYkxuDMjLyws///yzTErEdnCxI2r8+PHya3n7cO7m8ccfx4MPPoj77rsP/v7+WLJkCTRblhICQrljiohK5NN9n+L0jdP4uOPH8HDyUB2O4bEspRNiXEJ+ec+1yRa7gPIQZ9aIM2rMFi1aJPtrzI3CYpxD/u8RiVDe20RJ64cffoAuylKCfwhw4lfumCKiYtkUsQnfH/8e77R6R1ctDFrG5Magvv32W9SsWROVK1eWSY445+bJJ5+UW7w1TZSlxF9N4mC+kqzcpN8E4i8C5YPLIjoi0pkrSVcwbvs4dAruhKdDnlYdDv2LyY1BXblyRZaixEexxfuJJ56QZ9poXnFPJ84rICTnY8wJJjdEdFembBPe/ftduNi7YHK7yeyzsSFMbgxKjGkQF90pyenEZt5Vc1Z9RN9NnVsPPiQiym/x8cXYFbULcx+Yi/IlKYVTmWFDMelLSU4nNrO3B/zrsamYiO7q9PXTsom4f/3+aFepnepwKB8mNwXI30hLGnoPSlOWMvfdcDs4Ed1BRlYGxmwbg+BywRjZbKTqcKgATG7ycHBwkB/T09NVh2J45vfA/J4UryxVmuQmBIgJF8cwl/wxiEjXZh+cLbd9T713Klwdi358Bhmo52b27Nn4+OOPZWNrkyZNMHPmzELnHAkzZszAF198gYiICPj5+aFPnz6YOnVqsc5nKYy9vT3c3d3l1GzzCbxsELP+io1IbMR7IN4L8Z5YrSxlnjGVkQzcuJBzajERUR77ru7DwiML8Vqz11C/wr9jW8jmKE1uli1bhlGjRmHOnDlo3bq1TFy6desmBzsGBATcdn8xzVpMpl64cKE8p+XkyZN49tlnZQIyffp0i8Tk7e0tP5oTHFJDJDbm98JqDcX5Z0wxuSGiPJIykjB221g0DWiKwQ0Gqw6HbDW5EQnJ0KFDMXhwzj8SkeSsXbtWJi8iiclv+/btaN++vRwfYD7+/6mnnsKuXbssFpNIlMqXLy9P8bW5uUkGIUpRxV6xEUSfTml7brwqAS7eOX03Id1L/jhEpDvT905HXGoc5nWdBwcxsoVslrLkRpQe9u3bhzFjxuTeJn6hdenSBTt27Cjwe8RqjRj4uHv3blm6Onv2LH799VcMGDCg0OcR847yzjwq6oqMiKVEv2BJnfREIDurdCs3ogwp+m64Y4qI8tgZtRPLTy7H2NZjZSMx2TZlyY0YtChWRgIDA2+5XVw/ceJEgd8jVmzE991zzz2yNyMzMxMvvvgi/vOf/xT6PKIfZ+LEiRaPn2yQeWhmac+bEH03F/dZJCQi0kc5asLfE9AyqCWerPek6nCoCOy1Nj/p/fffx//+9z/s378fP/30kyxjTZ48udDvEStD8fHxuZfIyEirxkwaGZqZv+8mNhzIyrRIWESkbeI8m+tp1zGx3UTY22nq16ZhKVu5ETudRG/F1atXb7ldXA8KCirwe8aNGydLUEOGDJHXGzVqhKSkJAwbNkxOti6ojCSGO4oLGYB5aGZpylLmlZusdOD6OcCvjkVCIyJt2nNlD5aFL8OYVjnn2pA2KEtBxTbr5s2bY+PGjbm3mUwmeb1t27YFfk9ycvJtCYz5HBQevEcWLUsJ0cdKHxMRaVZyRjLG/T0OzQObo19IP9XhkFZ2S4lt4IMGDUKLFi1kg7DYCi5WYsy7pwYOHCinVou+GaFnz55yh1XTpk3l1vHTp0/L1Rxxe7EPeyP9lqVcS7CFPC8Pf8C9AhB9AgjtbZHQiEh7Ptv/Ga6lXMO8B+axHKUxSpObvn37IiYmJnc6dVhYGNatW5fbZCwO6su7UvPuu+/Krdri46VLl+Dv7y8TG11MsybLrNyIxKa0WzTFjinRd8OVGyLDOhB9AItPLMbolqMR7MVylNbYZRusniO2govD4URzsTjLhnTkj/8CR34CRv5T+sda+wZwfhvwiuXOUCIibUjPSscTPz8BT2dPfPvgtzzTRoO4zkb6IQ/wK2Uzcd6+m2ungUzOGSMyGjFeISIhAhPaTmBio1FMbkg/Sjs0My9RljJl5iQ4RGQY5+LP4ct/vsSzDZ9FXZ+6qsOhEmJyQ/pR2qGZeXHHFJHhiC6NSTsmIcgjCC80fkF1OKTlqeBEFl258bHQsEt3X8AzEIgp+LRsItKfVadXYe/VvXJ2lKujq+pwqBS4ckP6UdqhmQWt3nDGFJEhxKbE4pO9n6BXrV5oU7GN6nColJjckL7KUpZqKBbkdnAmN0RG8NGej+Bg54A3W7ypOhSyACY3pA+mLCA13nI9N+aVm7izQEaK5R6TiGzO9svb8du53/BWy7fg42rBP5BIGSY3pA8isREsXZZCNhB70nKPSUQ2d6bN1F1T0SKwBR6u+bDqcMhCmNyQvuZKWbQsFZLzkaUpIt369ti3iLwZibGtx8oT8EkfmNyQviaCW7Is5eoFeFVhckOkU1GJUZh7aC6eqf8MavvUVh0OWRCTG9LX0ExLrtwI3DFFpFsf7vkQXs5eeCnsJdWhkIUxuSGdlaUsuHIjBIQAMUxuiPTmr4t/YWPERtlE7OHkoTocsjAmN6SfspSdA+DsadnHDQgFbkQAaYmWfVwiUiYtKw1Td09F64qt0a16N9XhUBlgckM6mivlA1i6IdDcVBwTbtnHJSJlvjryFaKSovCf1v9hE7FOMbkhfbD06cRm/vUA2LE0RaQTlxIvYf7h+RgYOhA1vWuqDofKCJMb0gdLn05s5uwB+FRjUzGRTkzfO102EXMwpr4xuSH9rNxYcht4/r4bJjdEmrf3yl78fuF3vN78dbg7uasOh8oQkxvSh7IqS5n7bpjcEGlalilLbv1u7NcYPWr2UB0OlTEmN6QPZVWWMq/c3Lz8/2fpEJHmrDy9EifiTmB0q9Gwt+OvPr3jO0z62S1VZmUp846pE2Xz+ERUpm6m38TMAzPRs2ZPNPZvrDocsgImN6QPZVmWqlAn5wwdlqaINEmMWEjJTMGIZiNUh0JWwuSGtC8zHchIKruylJMr4FuTyQ2RBp2LP4dFxxdhSKMhCPQIVB0OWQmTG9K+shiaWdCMKZ51Q6Q5n+z9RCY14lwbMg4mN6R9ZTU0My8O0CTSnB2Xd2Drxa1y67ero6vqcMiKmNyQ9pXV0Mz8yU1SDJAUW3bPQUQWY8o2Yfq+6QjzD0PXal1Vh0NWxuSGtM8aZSn/+jkfuXpDpAm/nP1Fbv1+o8UbnB9lQExuSPussXJToRZg78Tt4EQakJqZKrd+d6naBWEBYarDIQWY3JA+em5EPd3Jreyew8EJ8KsDRB8ru+cgIosQu6Nik2MxsvlI1aGQIkxuSPvK8nTi28YwcOWGyJZdT70up34/Ue8JVPOqpjocUoTJDWlfWZ5OfNsAzWNAdnbZPxcRlcjcf+bKjy82eVF1KKQQkxvSvrI8nTj/GAaxSpR4teyfi4iKLSIhAstOLMPzjZ6Hr6uv6nBIISY3pI+VG2uUpcTKjcC+GyKbNGP/DPi6+aJ//f6qQyHFmNyQ9onVFGuUpXyq5zQus++GyOYcijmEDRc2YHjT4XBzLMPNBaQJTG5IJ2UpK6zc2DsAfnW5ckNkY7KzszFt7zTU9akrJ38TOaoOgMgyZSkrrNzkzpjiyg2RLdkUuQkHog9gTpc5cBB/hJDhceWGtE3sXLJWWSp3xtQJ7pgishFZpix5YF/roNZoX7m96nDIRjC5IW3LSAay0q1TljKPYUi/CcRftM7zEdEdrTu/DqdvnMbwZsNVh0I2hMkN6WQiuBVXbgTOmCJSLsOUgdkHZ6NTlU5o4t9EdThkQ5jckD6GZlpr5cY7GHDyAGKY3BCptur0KkTejMSrTV9VHQrZGCY3pI+hmdbqubG3zznMjys3RMqHY845NAcP1XgI9XzrqQ6HbAyTG9I2a5elzH03TG6IlFoWvgzXUq7hlbBXVIdCNojJDemjLGWtlZvc7eDhgMlkveckolxJGUlYcHgBHqn9CIdjUoGY3JD2y1LO5QAHKx7ZJMpSmSnAjfPWe04iyvXdse9kgsPhmFQYJjekbdY6nbjAGVM8zI/I2m6k3sA3R79B35C+CPIIUh0O2SgmN6SD04m9rfuc5SoCLt4cw0CkwMKjC2HKNmFIoyGqQyEbxuSGtM2apxOb2dlxDAORAjHJMVhyfAkGhA6Ar6uv6nDIhjG5IW1TUZYSuB2cyOrmH54PZwdnDGowSHUoZOOY3JC2WXNoZv6+m9iTQFam9Z+byICik6Pxw8kfMDB0IMqJTQREd8DkhrRNRVlK8A/JmWkVd9b6z01kQAuPLISroyuerv+06lBIA5jckLbJlRsVZal/d0xxDAORVVZtVoSv4KoNFRmTG9IucYhearyaspSnP+BegX03RFbw1ZGv4OLowlUbKjImN6Rd6TeBbJOalRvz6g2TG6Iy3yG14iRXbah4mNyQdll7aGZBfTdMbojKvNdG7JDqX7+/6lBIQ5jckHapGJqZlzjrJu4MkJmm5vmJDLJqI8614aoNFQeTG9L+yo2yslR9wJQJXDut5vmJdI6rNlRSTG5Iu1RMBM9flhJYmiKyuNiU2JxVm/oD4OXspToc0hgmN6TtspSdPeCi6D987r6AZxCTG6KyWrWxd0b/UK7aUPExuSFtl6VcvQF7hf+MxRgGzpgisviqzfLw5Xgm9Bmu2lCJMLkh7VJ1OvFt28E5HZzI0ufaiFUbkdwQlQSTG9IuVacT5++7iTsHZKSojYNIJ66nXpe9Nk/Vf4qrNlRiTG5I4xPBbWDlBtlATLjaOIh0YtHxRfLjM/W5akMlx+SGtF2WUr5yUy/nI/tuiEotKSMJi08sRp+6feDjqvhnmzRNeXIze/ZsVK9eHa6urmjdujV27959x/vfuHEDr7zyCipWrAgXFxfUrVsXv/76q9XiJVtrKFa8cuPqBXgHs++GyAJEE3FKZgoGhQ5SHQoZNbnZuHEjHn74YdSqVUtexOd//PFHsR5j2bJlGDVqFCZMmID9+/ejSZMm6NatG6Kjowu8f3p6Oh544AGcP38eP/zwA8LDwzFv3jxUrly5pC+DtCxF0dDMAscwcOWGqDTSstLwzdFv0LtWbwR6BKoOh4yY3Pzvf//Dgw8+iHLlymHEiBHy4uXlhe7du8uVmKKaPn06hg4disGDByM0NBRz5syBu7s7Fi5cWOD9xe1xcXFYtWoV2rdvL1d8OnbsKJMiMiBbKEuZTyrmWTdEpbLq1CpcT7uOwQ0Hqw6FjJrcvP/++/j000+xZMkSvPbaa/KyePFieZv4WlGIVZh9+/ahS5cu/x+Mvb28vmPHjgK/Z82aNWjbtq0sSwUGBqJhw4by+bKysgp9nrS0NCQkJNxyIR3IygTSEtSXpczJTXwEkHZTdSREmpRhysBXR79Ct2rdUM2rmupwyKjJjeh7ESs3+XXt2hXx8fFFeozY2FiZlIgkJS9x/cqVKwV+z9mzZ2U5Snyf6LMZN24cpk2bhilTphT6PFOnToW3t3fuJTg4uEjxkY1L/fffma2s3AjcMUVUIuvOrcOlxEt4vtHzqkMhIyc3vXr1wsqVK2+7ffXq1bL3pqyYTCYEBATgyy+/RPPmzdG3b1+MHTtWlrMKM2bMGJlwmS+RkZFlFh+pGJppAys3fmLHlB1LU0QlYMo2Yf7h+ehYpSPq+f67+5ColBxL8k2iP+a9997D5s2bZZlI2LlzJ/7++2+88cYb+Pzzz3PvK0pWBfHz84ODgwOuXr16y+3ielBQUIHfI3ZIOTk5ye8zq1+/vlzpEWUuZ2fn275H7KgSF9IZ1UMz83J2B3yqM7khKoFNEZtwNv4sJrabqDoUMnpys2DBAvj4+ODYsWPyYla+fHn5NTM7O7tCkxuRiIjVF7Hr6pFHHsldmRHXX3311QK/RzQRi94ecT/RnyOcPHlSJj0FJTak8wP8bKUsZS5NxTC5ISqO7OxszDs8Dy0CWyAsIEx1OGT05ObcuXMWeXKxDXzQoEFo0aIFWrVqhRkzZiApKUnunhIGDhwot3mLvhnhpZdewqxZs+TurOHDh+PUqVOyobiwBIp0zJbKUubk5uBi1VEQacqOqB04eu0o5naZqzoU0pkSJTeWInpmYmJiMH78eFlaCgsLw7p163KbjCMiInJXaATRDLx+/Xq8/vrraNy4sUx8RKIzevRoha+ClJWlHJwBJ3fYBP/6wM0o2xgJQaQRotcmtEIo2lbKaW8gsnpyI1ZZJk+eDA8PD/n53c6vKSpRgiqsDCV6evITPT6iv4cMznw6sZ0dbELujqkTQNU2qqMhsnlHYo9gz5U9mNZxmmxhIFKS3Bw4cAAZGRm5nxeG/0jJKmxthcSvDmDnkDOGgckN0V19ffRrBJcLRueqnVWHQkZObjZt2lTg50SGPp3YzNEFqFCLYxiIiuDizYvYcGEDxrQaAwf7/9/9SqSbwZlEmh2aWeCMKQ7QJLqb7459By9nL/Su3Vt1KKRTJWooFjuaPvjgA7ltWwy5FFuz858kTFTmZanyNnbadEAosPf/j0IgotvdSL2BladXYlCDQXBzdFMdDulUiZKbIUOGYMuWLRgwYIA8Y4Z9NqRk5aZiY9iUgBAgKQZIigU8/FRHQ2STlp9cLk8l7levn+pQSMdKlNz89ttvWLt2rTxUj0hZz42tlaXEyo0gTiquca/qaIhsTlpWGhYfX4xetXqhglsF1eGQjpWo50acTuzr62v5aIiKtVvKhhqKBd+agL0TxzAQFeKXM78gLjUOA0MHqg6FdK5EyY0470YcvJecnGz5iIjuJiMVyEyxra3ggoMT4FeXYxiICiBKUWL7933B96G6d3XV4ZDOFbks1bRp01t6a06fPi1PEq5evbocZpnX/v37LRslka0OzSyo74YrN0S32RK5BecTzmNy+8mqQyEDKHJyYx5uSWQ7c6VsrCxlPqn49EYxEdB2Tk8msgFi1SbMP4wDMsm2kpsJEyaUbSRExZ4IboMrN2LGlFhZunkF8KqoOhoim/BPzD/YH70fMzrNUB0KGUSJem4iIyNx8eLF3Ou7d+/GyJEj8eWXX1oyNqI7l6VsdeVGYN8N0S2rNtW8qqFTcCfVoZBBlCi5efrpp3NHMIhp3l26dJEJztixYzFp0iRLx0hUcFnKFntufKoDjq7suyH6V+TNSGyM2Ch3SHHUAtl0cnPkyBG0atVKfr58+XI0atQI27dvx6JFi/D1119bOkai28tSTu6AozNsjviPt9gxxeSGSFpyYgnKOZdDz1o9VYdCBlKi5EZMB3dxcZGf//HHH+jVq5f8PCQkBFFRUZaNkKiglRtbLEnlPcyPyQ0RkjKSsPLUSvSp04ejFsj2k5sGDRpgzpw5+Ouvv7BhwwY8+OCD8vbLly+jQgWeOkkGPJ04/3bwmBM5O6aIDGz16dVIyUxBvxCOWiANJDcffvgh5s6di06dOuGpp55CkyZN5O1r1qzJLVcRGep04vwrN+mJQHyk6kiIlB7at/jEYnSu2hlBHkGqwyGDKfZsqezsbNSsWRMRERHIzMyUoxjMhg0bBnd3d0vHSFRAWcqGV278Q3I+Rp8AyldVHQ2REtsubcOFhAs8tI+0sXIjkpvatWvLXVJ5ExtBnFYcEBBgyfiItFeW8g4GnD2B6GOqIyFSZtHxRQitECoP7iOy+eTG3t4ederUwbVr18omIqIilaVsOLmxtwf86+X03RAZ0NkbZ7H98nY8U/+ZW8b2ENl0z80HH3yAt956S24JJ7I6Wy9LmQ/z48oNGZTotangWgHdqndTHQoZVLF7boSBAwfKieCikdjZ2Rlubrdu8YuLi7NUfES3EjuQUm28odg8huHwj4ApK+fsGyKDiE+Lx5ozazC4wWA4O9jgWVRkCCVKbmbM4HwQUkTsQjJl2nbPjXnlJjMFuH4eqFBLdTREViPOtck0ZeKJek+oDoUMrETJzaBBgywfCZHWh2YWOGPqBJMbMgyR1IgTiR+q8RD83PxUh0MGVqKeG+HMmTN499135Tk30dHR8rbffvsNR48etWR8RNoZmplXuYqAqzf7bshQNkduxuWky+hfv7/qUMjgSpTcbNmyRc6T2rVrF3766SckJibK2w8dOoQJEyZYOkYibQzNzEvsEBF9N+KsGyKD+P7492gW0ExuASfSXHLzzjvvYMqUKXL0gmgoNrv//vuxc+dOS8ZHVEhZysZXbnJ3THHGFBnDibgT2Hd1H1dtSLvJzeHDh/Hoo4/edrs4wC82NtYScRHdZeXGG5pIbq6dArIyVUdCVOa+P/Y9KnpUxP1V71cdClHJkpvy5csXOP37wIEDqFy5siXiIiq858bFWxvbq0Vyk5UOxJ1VHQlRmbqWcg2/nvtVDsh0tC/RPhUi9clNv379MHr0aDmCQZw+aTKZ8Pfff+PNN9+UZ+AQGfZ04rxEz43ApmLSuRUnV8DBzgGP13lcdShEJU9u3n//fYSEhCA4OFg2E4eGhqJDhw5o166d3EFFZOjTic08/QF3P45hIF3LyMrAsvBl6FmrJ7zFqiqRDSjR+qFoIp43bx7GjRsnRzCIBKdp06Zy5hSRoYdm5scxDKRz6y+sR2xKLBuJyaaUqjhatWpVuXojcDgaWW/lRgM7pfImN2e3qI6CqMwsPr4YbSu2Ra3yPKySdHCI34IFC9CwYUO4urrKi/h8/vz5lo2OSMs9N4J/CHDtNJCZpjoSIos7FHMIh2MPc9WG9LFyM378eEyfPh3Dhw9H27Zt5W07duzA66+/joiICEyaNMnScRLl0MLQzLwCQoHsrJwEJ7CB6miILGrRsUWoWq4q7q1yr+pQiEqf3HzxxRey50aMXjDr1asXGjduLBMeJjdUpmUpTfXchOR8FIf5MbkhHbmSdAUbLmzAmy3fhL1diYsARGWiRP8iMzIy0KJFi9tub968OTIzeWAZlRFTFpCaoK2ylFhlEnOmeFIx6czSE0vh6uiKR2o/ojoUIsskNwMGDJCrN/l9+eWX6N+ftVcqI6nxALK1VZYy990wuSEdSc5IlmfbPFbnMXg4eagOh6jkZalRo0blfi52Ronm4d9//x1t2rSRt4khmqLfhof4UZlPBNdSWcrcd3PyN9VREFnMz2d+RmJGIp6u/7TqUIhKl9yI0Qr5S1DCmTNn5Ec/Pz95OXr0aFEfkki/QzPz993s/B+Qngw4u6uOhqhUTNkmOf27c9XOqOzJcTuk8eRm06ZNZRsJUVGHZmqp5yZ3DEM2EHsSqBSmOhqiUtl2aRvOJ5zH5PaTVYdCVCi2uJN2aLUs5V8v5yP7bkgHvjv2HRpWaIgm/k1Uh0Jk2a3gqampmDlzplzNiY6OloMz89q/f39JHpbo7mUpOwfApRw0xdUL8A4GYpjckLadvH4SO6N24sN7P+Sp9KS/5Ob555+XzcR9+vRBq1at+I+crDs0U4v/3uSMKSY3pG3fH/sege6BeKD6A6pDIbJ8cvPLL7/g119/Rfv27Uvy7UTGOJ04/3bwo6tUR0FUYtdSrmHt2bV4OexlONk7qQ6HyPI9N5UrV0a5chorDZD2ae104vzbweMjgLSbqiMhKpHl4cvhYO+APnX7qA6FqGySm2nTpmH06NG4cOFCSb6dyBhDMwsawxATrjoSomJLz0rH0vCl6FWrF7xdvFWHQ1Q2ZSkxekE0FdesWRPu7u5wcrp1iTIuLq4kD0t09xOKywVBk/zEjik7IPoYUOX20SVEtuzXc78iLjWO079J38mNGJh56dIlvP/++wgMDNRkQ3FiWia8VAdBxS9Lid4VLRKH9/lUB6JPqI6EqFiys7Pl9u8OVTqghncN1eEQlV1ys337duzYsQNNmmj3nINNJ66iv7+v6jCo2GUpjTYUm/tuxMoNkYbsurJLbgF/s8WbqkMhKtuem5CQEKSkpEDL1h6OUh0ClXQruFaJvpsYrtyQtnx95GuE+IagTcWcOYJEuk1uPvjgA7zxxhvYvHkzrl27hoSEhFsuWrDzTBziktJVh0FFlZUBZCRpd7eUeeXmZtT/j5EgsnHhceH4+/LfeLbBs5psPyDjKlFZ6sEHH5QfO3fufFttVvwAZGVlwdZliya5w1F4pk011aGQnodm5mXuFxJ9N9Xaqo6G6K6+Pvo1KnpURNfqXVWHQlT2yY0ehmi2quGLNYcuM7nRCq0OzczLr07O+AjRd8PkhmxcVGIUfjv3m+y14aF9ZIjkpmPHjtC6Ho0qYsK6s7h8IwWVyrupDof0OjQzL0cXoEIt9t2QJnx3/Dt4OHngsTqPqQ6FyHpTwf/66y8888wzaNeundwWLnz33XfYtm0btKBz/QA4Odjjl38uqw6FirVyo+GylMAZU6QB8Wnx+OHkD+hbry/cndxVh0NkneTmxx9/RLdu3eDm5iYngKelpcnb4+Pj5dk3WlDO1Qn31wuQpSnSUs+NhlduBH8mN2T7VpxcgSxTFp6u/7TqUIisl9xMmTIFc+bMwbx58245nVgM0hTJjlb0CquEI5cScDYmUXUoVJSylKMr4OSm/ZWb5FggMUZ1JEQFSstKk9O/e9fuDT83P9XhEFkvuQkPD0eHDh1uu93b2xs3bvz7F7YG3B8SAE8XR67eaIGWh2bmT26EGK7ekG36+czPctTCoAaDVIdCZN3kJigoCKdPn77tdtFvI+ZNaYWrkwO6NgiUyY3Yxk42TMtDM/PyrQk4OHMMA9kkU7YJ3xz9Bp2rdkY1L+4kJYMlN0OHDsWIESOwa9cuea7N5cuXsWjRIrz55pt46aWXoCW9mlTC2ZgkHL2sjcMHjX06scabiQUHJ6BCHY5hIJu0KXITziecx7MNn1UdCpH1t4K/8847MJlM8hC/5ORkWaJycXGRyc3w4cOhJe1r+8HXw1mu3jSs7K06HLpTz40eylLm0hS3g5MN+urIV2gW0AxN/LU7N5CoxCs3YrVm7NixiIuLw5EjR7Bz507ExMRg8uTJmvt/VWwH794oCD8fugyTiaUpm6X1oZn5Z0yJlRuWQsmGHIg+gEMxh/Bcw+dUh0Jk3ZWb554r2j/6hQsXFiuI2bNn4+OPP8aVK1fkpPGZM2eiVatWd/2+pUuX4qmnnkLv3r2xatUqlNSjTSvj+50R2HnuGtrV4u4Am6T1oZn5Z0ylxgM3rwBeFVVHQyTN+2ceannXwr1V7lUdCpF1V26+/vprOXpB7Ii6fv16oZfiWLZsGUaNGoUJEybIbeQiuRFn6ERHR9/x+86fPy/LYPfeW/ofxGZVfVCtgjt+2p9zGCHZID2VpXJnTLHvhmzD8WvH8delv/B8o+dhb1fis12JtLlyI5qFlyxZgnPnzmHw4MHyhGJfX99SBTB9+nTZoCweTxDn56xdu1au/ojenoKIwZz9+/fHxIkT5UnJd9p+Lg4YNB8yKBQ0tVyU2cTqzbytZzG5d0O4OTuU6jWRhYnyjZ7KUj7VAUe3nL6b2rcOnyVSYd7heajiWQUP1XhIdShEFmFf3PJRVFQU3n77bfz8888IDg7Gk08+ifXr15doK3V6ejr27duHLl26/H9A9vby+o4dOwr9vkmTJiEgIADPP//8XZ9j6tSp8vwd80XEXBCR3CSlZ+H3Y1eK/TqojGWkAFlp+ilL2TsA/nW5ckM24eyNs/jjwh94rtFzcLQv0R4TIptT7PVHsStK9Lls2LABx44dQ4MGDfDyyy+jevXqSEws3km/sbGxchUmMDDwltvFddF/UxBxls6CBQvk6chFMWbMGDkWwnyJjIws8H7VKnigeTUflqZseWimXlZuzH03POuGbMCCIwvg7+6P3rV6qw6FyGJKVVwVqyyipCNWbUSSUtZu3ryJAQMGyMTGz8+vyMmYl5fXLZfCPNasMv46FYPom6kWjJosNjRTLz035r4bUZbijilS6OLNi1h7di2ebfAsnMXhkkRGTW5E/4rou3nggQdQt25dHD58GLNmzUJERAQ8PT2L9VgiQXFwcMDVq1dvuV1cF6cg53fmzBnZSNyzZ084OjrKy7fffos1a9bIz8XXS+PhRpXgaG+PNQc5jsGm6GVoZv6zbtITgfiCVxKJrHWujZezFx6v87jqUIjUJTei/FSxYkV88MEHePjhh2WJZ8WKFejevbtcxSkuZ2dnNG/eHBs3bsy9TRwOKK63bdv2tvuHhITIZOrgwYO5l169euG+++6TnxfWT1NU3u5Oct4US1M2RpdlqX9nTHFCOCkSnRyNladXYkDoALg7uasOh8iiitU9JnYyVa1aVc6P2rJli7wU5KeffiryY4pt4IMGDUKLFi3k2TYzZsxAUlJS7u6pgQMHonLlyrIx2NXVFQ0bNrzl+8uXz/lrPv/tJSVKU8O+24cTVxIQElR4CYtUlKV0dIK0dzDg7JmT3NTtpjoaMiAxQ8rVwRX9QvqpDoVIbXIjEg3RY2NJffv2lacbjx8/XjYRh4WFYd26dblNxqLcVZJVoZLqVC8APu5OWLn/EsZ0Z3JjM2Up53I5c5n0Qvwcib4brtyQAtdTr2PFyRV4pv4zKCd+toh0xi7bYOOwxTk3Yku42DlVWHPx+NVHsP7oFWx/pzMc7C2bzFEJbJwM/LMMeP0IdGX1K8CVw8ALW1VHQgYz88BMfHfsO6x/fD18XHVU7iX6F4+iLOTMm6sJadh+JlZ1KKS304nzbwePCQdMZb/TkMjsZvpNLDm+BH3q9mFiQ7rF5KYAYcHlUcPPQ5amyAbI04l1mNyIslRmKnD9vOpIyECWhS9Dalaq3P5NpFdMbgog+ooea1oZvx25gsS0TNXhkJ6GZuZfuRHYd0NWkpyRLBuJH639KALcA1SHQ1RmmNwU4rHmVZCamYW1//DMG5soS+lpG7hZuaCcHWAxTG7IOhafWIzEjEQMaTREdShEZYrJTSEql3fDPbX9sHzvRdWhkFi50WPPjdgxJccwMLmhspeUkYSvj34tD+yr6FlRdThEZYrJzR30bRmMfReu43R08WZmkYXptedGkNvBOWOKyt7i44tlWYqrNmQETG7u4IHQQJR3d8KKfTwiXxmTSb9lKUGs3MSeBLIyVEdCOpaYnohvjn0jV22CPG4fbUOkN0xu7sDF0QGPhFXGj/suISPLpDocY0q/CWSb9FmWEgJCAFMGEHdWdSSk814bsWrzfKPnVYdCZBVMbu7iiRZVEJuYhi3hMapDMSY9Ds0scMfUMdWRkI7PtRE7pMS5Nly1IaNgcnMXDSp5o0ElLyzfy9KU0rlSei1LefgB7n7su6Ey7bVJzUxlrw0ZCpObIniyRTD+PBGNmJtpqkMx7kRwvZalzBPCuXJDZbVqc+wbPFHvCZ5rQ4bC5KYIeodVgr2dHVYd4InF6spSOl25MSc3MVy5Icv7/vj3SM9Kx3MNn1MdCpFVMbkpgvLuzujaIFCWpgw2Z9RGylJ2gIuXvpOba2eATK4MkuUkpCfgu6Pf4Ym6XLUh42FyU4zS1KnoRByI/Hclgaw4NNMbsNfxP1X/+kB2FhB7SnUkpCOLji1CuomrNmRMOv6NYVnta/uhkrcrVrCxWMEBfjouSZm3gwssTZGFxKfF47tjOas2/u7+qsMhsjomN0XkYG+HPs2rYM3By0jiME3r0evQzLxE8lauIpuKyWIWHFmAzOxM7pAiw2JyUwx9W1VFSkYW1hziME2r0fPpxLftmOLKDZXe1aSrcvv3wNCBqOBWQXU4REowuSnmMM1O9QKwaNcF1aEYh16HZhbUd8OVG7KAuf/MhaujKwY1GKQ6FCJlmNwUU//WVXHkUgL+ucjGYqvQ89DM/Cs3188D6cmqIyENi0iIwMpTKzGk4RCUcy6nOhwiZZjcFJNYuRGNxYt3RagOxRiMVJZCNhAbrjoS0rBZB2fB180X/UL6qQ6FSCkmNyVoLO7bsqrsu0lI5SRnq6zcGKIsVS/nI/tuqIROxJ3Ab+d+w0tNXpJlKSIjY3JTAn1bBiMt04TVPLG4bGVlAmkJxli5cSkHeFdl3w2V2Of7P0c1r2p4pPYjqkMhUo7JTQkEebuic4hoLI7gicVlKTU+56MRem7M593wrBsqgf1X9+OvS3/h1bBX4WjvqDocIuWY3JTQ062r4sSVmzyxuCwZYWjmbdvBj6uOgjRG/IE1Y/8MhPiGoGv1rqrDIbIJTG5KqEMdf1TxcWNjcVkywtDM/NvB4yOB1ATVkZCGiBWbA9EHMKLZCNjb8T/pRAJ/EkrI3t4OT7Wqip8PXUZ8MhuLy25oppHKUvX/HcPAHVNUNKZsk+y1aR7YHO0rtVcdDpHNYHJTCk+0qIIsUzZ+OnBRdSj6LksZZeXGr27OBPQYlqaoaNaeXYvw6+EY2Wwk7OzsVIdDZDOY3JRCQDlXdGsQxMbisly5sXcCnNxhCM7ugG8N9t1QkaRmpuLzA5+jS9UuCAsIUx0OkU1hclNKz7SphtPRidh+5prqUPR7OrGR/iKVYxiY3NDdfX/8e8Qmx2Jk85GqQyGyOUxuSqlNTV/UCyyHr7efVx2K/hjldOK8uGOKiiAuNQ4LDi/Ak/WelGfbENGtmNyUkqhzD2pXHRuPX0VkHOcCWZRRhmbmT24SrwDJcaojIRs259Ac+fHFJi+qDoXIJjG5sYBHmlaCp4sjvt/JaeGWL0sZcOVG4GF+VIjz8eexInwFhjYeCh9Xg/18EBURkxsLcHd2lCMZlu6JREp6lupw9EOs3BhlG7hZhdqAnQNLU1QocWCfv7s/+tfvrzoUIpvF5MZCBrSpLgdprj7IeVMW7bkxWlnK0SUnwWFyQ4WMWdgYsRGvNXsNLg4uqsMhsllMbiykagV3OW9KNBZzW7iFGLEsJXDGFBVA/Hdl2t5pqO9bH91rdFcdDpFNY3JjQaKxWMyb2n2OzaAWYcSylBAQyungdJtfz/2Kf2L/wVst3+KYBaK74E+IBd1T2w+1/D3wzQ5uCy+1zDQgM8V4ZSnBPwRIvgYkxqiOhGxEckYyPt33qTywr2VQS9XhENk8JjdlsC18/dGruHwjRXU42ma0oZn5V24Ert7Qv746+hWup17HqBajVIdCpAlMbizssWZV4ObkgEW7uC28VIw2NDMv35qAgzP7bki6nHgZXx35CgMbDERwuWDV4RBpApMbCxPn3fRpXgWLd0VwW3hpGG1oZl4OjjlDNLlyQwCm75sOL2cvDGk0RHUoRJrB5KYMPNe+BuJTMvDjfk4LL/XKjRF7bsx9N9FcuTG6fVf3Yf359XJ+lIeTh+pwiDSDyU0ZbQt/sGEQFmw7B5OJ28JL13Nj0ORGnFQcc1zs/1UdCSmSZcrCh7s/RCO/Rni45sOqwyHSFCY3ZWTIvTVxLjYJfxy/qjoU7ZalnNxzDrUzanKTGg/cjFIdCSmy6vQqHI87jrdbvs2t30TFxJ+YMtKsqg9aVPPB/L/OqQ5Fm4w4NLOgGVM8qdiQEtIT8PmBz+WKTVhAmOpwiDSHyU0Zr97sPh+Hg5H/llio6Ix6OrFZ+eqAoxuTG4OafWA2UjNTMbLZSNWhEGkSk5sy9EBoIKpVcMe8v86qDkV7jHo6sZm9PeBfL6fvhgzlRNwJLA1fipfDXkagR6DqcIg0iclNGXKwt8OQe2rgt8NRiIxLVh2OthhxaGZBpSmu3BiKKduEKTunoIZXDTxd/2nV4RBpFpObMtaneTC83Jyw8G/23hSL0ctSuTumwgGTSXUkZCWrT6/GoZhDGNtmLJzsnVSHQ6RZTG7KmJuzAwa0qYZleyIRn5yhOhztMHpZSvCvD6QnAvGRqiMhK4hPi5fzo3rU7MH5UUSlxOTGCga0rYbMrGws3h2hOhRtlaWMntyYd0xxDIMhzDwwExmmDLzR/A3VoRBpHpMbKwgo54pHm1bGV3+fQ2oGRzLclTi4zuhbwQXvKoBzOY5hMICjsUexPHw5Xgl7Bf7u/qrDIdI8JjdW8kLHmohJTONIhqJITwJMmey5sbPL2THFMQy6P4n4vV3vobZPbfQL6ac6HCJdYHJjJTX9PdG9UUXM2XIGmVlsEC3a0EyDr9zk7pjiyo2eLT+5HIdjD+Pd1u/C0d5RdThEusDkxope6VQbkXEpWHPosupQNDI00+ArN+bkJvYkYGI5U4+uJl3FZ/s/Q5+6fdAssJnqcIh0g8mNFYVW8kLnkAD8b/MZDtS8E6MPzcyf3GSmAtfPq46EysDU3VPh5uiG15u/rjoUIl1hcmNlL99XG6ejE/H7sSuqQ7H9lRuj99yYt4MLPMxPdzZe2IiNERvxTqt34OXspTocIl1hcmNlzav5oG3NCpi16TSyxa4gKrznxtVbdSTqlQvK2TXG5EZXEtMT8f7u99GxSkd0rdZVdThEusPkRoFX7quNI5cSsPVUrOpQbLcs5eIN2DuojsQ2dkzJk4qZ3OiJmPh9M/0mxrYeCzvxHhORRTG5UaB97QpoElwes/88rToUGz6dmKs2uThjSlfEeIWlJ5bitaavoaJnRdXhEOkSkxsFxF9qr3Sqhd3n47D7XJzqcGz0dGL229zSdxN7Csji+A6ty8jKwMQdExFaIRRPhTylOhwi3WJyo0iX+oGoF1gOM/88pToU2yxLGf104vwrN6YM4NoZ1ZFQKc07PA/nbpzDf9v9Fw4suxKVGSY3itjb22F459r461Qs9l3g6s0tODSzkBlTLE1pWXhcOOb9Mw9DGg9BiG+I6nCIdM0mkpvZs2ejevXqcHV1RevWrbF79+5C7ztv3jzce++98PHxkZcuXbrc8f62rHvDiggJKodpv59UHYptYVnqVh5+gIc/+240TAzEfPfvd1GjfA0MazRMdThEuqc8uVm2bBlGjRqFCRMmYP/+/WjSpAm6deuG6OjoAu+/efNmPPXUU9i0aRN27NiB4OBgdO3aFZcuXYIWV29GdqmL7WeuYceZa6rDsR0cmnk7/xAmNxq24PACnLp+ClPaT4GTg5PqcIh0T3lyM336dAwdOhSDBw9GaGgo5syZA3d3dyxcuLDA+y9atAgvv/wywsLCEBISgvnz58NkMmHjxo3Qom4NAtGwshembwjnuTd5e25YlrpVQCiTGw2Xo+b+MxfPN3peNhITkc6Tm/T0dOzbt0+WlnIDsreX18WqTFEkJycjIyMDvr6+BX49LS0NCQkJt1xsbefUqAfqYs/569h2mufewGQCUuNZlsovIASIOwtkpKqOhIpZjhr39zhU96qOFxq/oDocIsNQmtzExsYiKysLgYGBt9wurl+5UrTxBKNHj0alSpVuSZDymjp1Kry9vXMvooxla+6rF4Cw4PKy98bwqzdp8QCyWZYqaOUmOwu4xt11WvLVka9w8vpJWY5ydnBWHQ6RYSgvS5XGBx98gKVLl2LlypWyGbkgY8aMQXx8fO4lMjIStkas3rzRtS4ORt7ApvCCe42MNzSTKze39dwI0SdUR0JFdCLuBL449AWebfAsGvg1UB0OkaE4qnxyPz8/ODg44OrVq7fcLq4HBQXd8Xs/+eQTmdz88ccfaNy4caH3c3FxkRdbd09tP7Ss7oPpG07KlRzDHsmeOzSTKze3EP9/lKsERB9THQkVQVpWGsb8NQa1vGvh5bCXVYdDZDhKV26cnZ3RvHnzW5qBzc3Bbdu2LfT7PvroI0yePBnr1q1DixYtoAc5vTf15Myp34/dmuwZc2gmk5sC+25iuHKjBTP2zUBEQgSm3juV5SgiI5alxDZwcXbNN998g+PHj+Oll15CUlKS3D0lDBw4UJaWzD788EOMGzdO7qYSZ+OI3hxxSUxMhNa1rVUB7WpVwLTfw5FlMmjvDctSheOOKU3YcXkHvj/+PUY0G4E6PnVUh0NkSMqTm759+8oS0/jx4+X27oMHD8oVGXOTcUREBKKionLv/8UXX8hdVn369EHFihVzL+Ix9ODtB0Nw8moiftx/EYYtS9k5AC7lVEdim303188D6cmqI6FCxKfFy8P6Wge1xjOhz6gOh8iwlPbcmL366qvyUtihfXmdP38eeiZ2TfVoVBGfbjiJXk0qwdXJwYCnE5cXdTrVkdjmyo3YSRYbDlRqqjoaKsB7O99DSmYKptwzBfZ2yv92JDIs/vTZoLe61UPMzTR89be+E7kC8XTiwvnXy/nI0pRNWnt2LX47/xvebf0ugjzuvCGCiMoWkxsbVN3PA0+3ror/bT6N60npMBSeTlw4F0+gfFUmNzbo4s2LctXmoRoPoXvN7qrDITI8Jjc26rXOdWAyZWP2ptMwFA7NvDP/+kxubPAU4tFbR8PLxQvvtnlXdThExOTGdvl5umBYh1r4dscFRMYlG2vlhmWpwgXU53ZwGzPzwEwcu3YMH3X4CF7OXqrDISImN7ZtyL014OXmhI/Wh8NYZSmu3NwxuYmPBFJta0aaUW27tE2OWHit2Wto7F/4YaJEZF1MbmyYh4sj3upWFz8fuox9F+JgmIZi9tzcObkRYgyU8Nqo6ORojN02Fu0rt8egBoNUh0NEeTC5sXF9mgejQSUvTPr5mOzBMUTPDctShfOrC4gtxhzDoFSWKUuOV3Cwc8B77d/jtm8iG8OfSBvnYG+HCT0b4NDFeKw8cAm6lpUBpCeyLHUnTm6AT3X23Sg2//B87LmyR45XqOBWQXU4RJQPkxsNaFXDVx7s99H6E0hKy4T+Ry9w5ebuYxi4cqPKzqid+N+h/2FY42FoXbG16nCIqABMbjTinYdCcD05A3O2nIHuh2Zy5ebuYxiiuXKjwpWkK3h7y9toFdQKLzV5SXU4RFQIJjcaEezrjqH31sCXW8/qd2u4eeWGPTd3bypOvAIkG6TJ3EakZ6Vj1OZRcHV0ldu+HewNNhqFSEOY3GjIy51qw8fdGZN+OabfnVICy1JF3DHF1Rtr+nD3hzgRdwLTO02HjytXF4lsGZMbjW0NH98zFBuOXcXG41ehOyxLFU2FOoC9I/turGj16dVYfnI5xrQeg4Z+DVWHQ0R3weRGYx5qGIR76/hhwpqjSEnPgu5WbhxccnYEUeEcnQHfWuy7sRKxWjN552Q8UvsR9KnTR3U4RFQETG40xs7ODpN6N0R0QpocrKkrHJpZvNIUZ0yVueup1zFy00jU9K6Jsa3Hyp8/IrJ9TG40qIafB17sWBNzt5zFudgk6AaHZhYzuTkGZBvgYEdFMrIyZANxckYyPr3vU9lITETawORGo16+rzYCvV0wfvURZOvlF5woS3GnVNGTm5Q4IClGdSS6JH6m3tv1Hg7GHMSM+2agsmdl1SERUTEwudEoVycHTOrVEH+ditXPycUcmll0/v/umGJpqkwsPrEYP576EePbjEezwGaqwyGiYmJyo2H3hQSgd1gluTU8NjENmsehmUXnWxNwcGZyUwa2X9qOj/Z8hIGhA/FonUdVh0NEJcDkRuPGPxwK0eL43zVHoXkcmll0Do45QzRjmNxY0rn4c3hzy5toV6kdRjUfpTocIiohJjcaV8HTBf/t1QC//BOFP45p/OwblqWKhzumLL4zavifw+Hv7s8TiIk0jsmNDvRqUgn31fPHu6uOICE1A5rFslTJZkzppaFcoZTMFLz656u4mX4TszrPQjnncqpDIqJSYHKjA+LsjSmPNsLN1AxM/VWjB7tlpABZaVy5Ke508LR4IOGy6kg0LcuUhdFbR+PU9VP4X+f/IbhcsOqQiKiUmNzoROXybvhPj/pYsjsCm8Kjodm5Uuy5KbqAkJyP7Lsp1ZbvqbunYuvFrfik4ydo4NdAdUhEZAFMbnTk6VZV0bGuP97+4R9cT0qHJieCsyxVdOWrA45u7LsphQVHFmBZ+DKMazMOHap0UB0OEVkIkxudlac+6tMY6Zkm2X+jqcP9ODSz+OztAf96nDFVQj+f+Rmf7f8MLzZ5EY/XfVx1OERkQUxudCbQyxVTHmmItYejsOaQhnoxWJYqed8Ny1LFtjlyM8b/PV4Ow3y5ycuqwyEiC2Nyo0M9m1SSO6jGrTqCqPgUaALLUiXvuxErNyaT6kg0Y2fUTryx+Q10Cu6ECW0ncBgmkQ4xudGpSb0bwN3ZESOXHkSWKVsbKzfOnoCDk+pItLdyk5EExEeqjkQTDkYfxGt/voaWFVviww4fwtHeUXVIRFQGmNzoVHl3Z3zWLwx7zsfh842nYPN4OnHJz7oR2FR8VyfiTuDlP15Gfd/6+LTTp3AW4yuISJeY3OhY65oVMKJzXcz88xR2nLkGm8bTiUvGuwogDpxj380dnY0/ixc2vICqXlUxu/NsuIldZkSkW0xudO7V+2ujdY0KGLH0AK7Z8nBNnk5cMqJfRPbdMLm507yooeuHwtfVF3O6zIGnKH8Ska4xudE5B3s7zOgXJvtu3lhxCCZb7b8RZSkmNyXDGVOFOnPjDAavGyzHKczrOg/lWfokMgQmNwbZHj69bxi2nIzBrE2nYbNlKf7iKRn/+kDsScCUpToSmxIeF47n1j+HCm4VsPDBhfBz81MdEhFZCZMbgxAnF7/epS4+/eMkNh63wenhLEuVbuUmMxW4fl51JDbVPDzk9yEIdA/Egq4LZEmKiIyDyY2BvHpfbXSpHyi3h5+NSYTtlaXYUFzi5EaIPqY6EptwNPYonl//PCp7VmYpisigmNwYiL29HaY/2QT+Xi544bt9SEzLhE0QYyJYlio5z8Cc/+84hgG7onbh+d+fR3Wv6viy65fwdvFWHRIRKcDkxmDKuTrhywEtEBWfijeWH7SNBuO0m0B2FlduSrVjKtTwKzfrzq/DS3+8hCb+TeSKjZezl+qQiEgRJjcGVDvAEzP6huH3Y1fx4boTNjQ0kys3JSa2g8fYwHupyOLji/H2lrfRtXpXzLp/Ftyd3FWHREQKMbkxqC6hgRjXIxRzt57Fol0X1AbDoZmlJ1ZuYk8BWRkwEjH5/vP9n2Pq7qkYEDoA79/zPpw4woPI8DhYxcCeu6cGIuKSMX71UVQu74ZO9QIUD81kWapUYxhMGcC1MzmrOAaQkZWBSTsnYdXpVXij+Rt4tuGzqkMiIhvBlRuDG/dwKO6r549XFu3HscsJalduWJYqOYPtmIpLjZNbvdeeXStXa5jYEFFeTG4MTpxg/PlTTVHT3xMDF+7GudgkRT03dgB3tpSchx/g4W+IvptT10/h6bVP43zCeSzsthA9a/VUHRIR2RgmNwR3Z0d8PbglvN0c8cz8Xbh8I8W6Acht4N5ir7p1n1eXYxj0vXKzOXIznvn1GXg6eWJJjyUICwhTHRIR2SD+NiGpgqcLFg1pI3cViwQn5qYVh2zydGLLjWHQ6Vk3pmwT5h+ej9f+fA1tK7XFtw99i0qelVSHRUQ2iskN5QrydsWiIa3l4X6iRHUjOd06T8zTiS23chN3BshIhZ7Ep8XLpOaz/Z9haOOhmN5pOrd6E9EdMbmhW1Sr4IHvh7TG1YRUPDVvF64lplln5YbbwC2T3GSbgGunoKdRCn1/6YsD0Qcwu/NsDG86HPZ2/M8WEd0Z/ytBt6kbWA5Lh7WRpam+X+6UiU6Z99ywLGWZ7eBC9HHooQz1/bHvMeC3AfBx8cGKnivQoUoH1WERkUYwuaFCE5zlL7RBUlomnpy7AxevJ5fdk7EsZRkiQSxXSfPJTUxyDF7+42V8uOdDPFnvSXzz0DfsryGiYmFyQ4US28OXv9AWpuxs9J27Eyev3iybJ2JZysI7prSb3PwZ8SceW/MYwq+HY06XOXin1TtwdnBWHRYRaQyTG7qjYF93meCUc3XE419sx/bTsZZ/kpR4rtxYMrmJ0V5ycz31Osb8NQYjNo1A04Cm+LHXj2hfub3qsIhIo5jc0F1V9HbDihfbIiy4vNxF9cO+i5Z7cFMWkCaSG67cWCy5uX4eSFdwGGMJZ0OtO7cOj6x+BFsvbsWU9lPw2X2fwdfVV3VoRKRhTG6oSMq5OmHhsy3Rp3kVvLniED5efwJZpuzSP3BqfM5HlqUsd9aNEBMOW3fx5kW5xfutrW+heWBzrH5kNXrX7g07cdgSEVEpcHAmFZmTgz2mPtZIbhcXyc2hyHh81i9MHgBY+rlSLEtZhH+9nI+i76ZyM9ii1MxUfHXkKyw4sgDeLt74tNOn6FKti+qwiEhHmNxQsYi/ql/qVAuNq3jjtSUH0OPzbZjdvymaV/Mt5URwrtxYhIsnUL6qTfbdiBLUHxF/YNreabiafBXPNngWQxsN5YF8RGRxLEtRibSv7Ye1r92LKj5ucifV/zafRmaWqfgPlMqVG4sLCLW5MQx7ruyRM6FGbR6F6t7VsbLXSoxoNoKJDRGVCa7cUKnGNSwZ1gbTfj+JT9aHY/3Rq5j2RGPUDihX/JUb9txY9jC/wz/AVk4YnnVwFrZd2oYGFRpgXtd5aFOxjeqwiEjnuHJDpe7DeeehEPzwUjvcTM1A98+3Yc6WM0VfxRE9N/aOgLNHWYdqrJWbhItAaoKyEPZe2YsXNryAfmv7ycbhTzp+Iqd4M7EhImvgyg1ZRLOqPvj1tXsx7fdwfLjuBH7cdxHvPhyKjnX9i3Y6MXfIWE7Av2MYYk4Awa2s9rSZpkxsitwkxybsj96Puj518XGHj/FAtQfgYO9gtTiIiJjckMW4OjlgbI9Q9A6rjEm/HMOghbtxXz1/jO1Rv/BSlShLsSRlWX51ATFcUuyYskJycy3lGn469ROWhS+TjcLNApph5v0z0bFKR27rJiIlmNyQxTWs7I1lw9pg3ZEreP+34+j66VZ0b1RR7rJqUMm7gKGZbCa2KCc3wKdGmY5hSM9Kx5aLW7DmzBpsu7hNrsz0qNkD/er1Q/0K/561Q0SkCJMbKhPiL/aHGlXEfSEBWLHvIuZuOSO3jXeq54+h99ZE25oVYG9v929Ziis3WhjDIM6n2Rm1U5ae/rjwBxLSE2ST8Jst30SPGj1QnitwRGQjbKKhePbs2ahevTpcXV3RunVr7N69+473X7FiBUJCQuT9GzVqhF9//dVqsVLxS1UD2lTD5jc7YUbfMETdSEX/+btw37TNmL3pNFITYlmWstEBmuJcmnPx57A8fDlGbhqJDss6YPifw7H/6n48UfcJrOq9CksfXor+9fszsSEim6J85WbZsmUYNWoU5syZIxObGTNmoFu3bggPD0dAQMBt99++fTueeuopTJ06FQ8//DAWL16MRx55BPv370fDhg2VvAa6O0cHezzStDJ6h1XC3gvXsXR3JGb+eQr3213CqWv+OL3hJNrXqoCmVX3g7GgTObf2t4MnXgWS4wD3oh2wmJieiONxx3Hs2jEcvXYU+67sQ3RKNBzsHNDQryGGNR6G+6vej5reNcs8fCKi0rDLFn+eKSQSmpYtW2LWrFnyuslkQnBwMIYPH4533nnntvv37dsXSUlJ+OWXX3Jva9OmDcLCwmSCdDcJCQnw9vZGfHw8vLy8oFV3etuykW3577vD1+78pcK/mJSeAbvPm2KLWxeMu9EDCSkZcHWyR70gLzSo6IXaAR6o7OuOyt5uKO/uBC9XJ5n4lPT13Y21/38raaxFikXMlvrqIeCppblNxVnZWXL6trjEpcbJ5t/Im5GISIhAxM0IXEq8JO/n6uCKur510TygOVoGtUSzwGbwcOJWfSLSDqUrN+np6di3bx/GjBmTe5u9vT26dOmCHTt2FPg94nax0pOXWOlZtWpVgfdPS0uTFzOR1Aht57eAg1vxtqdml8XXjL6bxFv8E9wMBGyG+azaU+JyFYC4UMkFVAQ2vl7ol11NJlTOyELlzCzck5mJ6umZqJeWgeDMLDjiDIDf5P1S/r0QEanm/J/zKFeu3F13YipNbmJjY5GVlYXAwMBbbhfXT5wo+Pj4K1euFHh/cXtBRPlq4sSJt91+7A3xK5TI2A6oDoCIqDg+LFrlRXnPTVkTq0J5V3pu3LiBatWqISIiQpanjEKU40S5LzIyUtPluJIw6mvn6+brNgK+bmO9bkGs3NyN0uTGz88PDg4OuHr11vqDuB4UFFTg94jbi3N/FxcXeclPJDZG+wchiNdsxNdt5NfO120sfN3GYtTXfTdKt6U4OzujefPm2LhxY+5toqFYXG/btm2B3yNuz3t/YcOGDYXen4iIiIxFeVlKlIwGDRqEFi1aoFWrVnIruNgNNXjwYPn1gQMHonLlyrJ3RhgxYgQ6duyIadOmoUePHli6dCn27t2LL7/8UvErISIiIlugPLkRW7tjYmIwfvx42RQstnSvW7cut2lY9MaIHVRm7dq1k2fbvPvuu/jPf/6DOnXqyJ1SRT3jRpSoJkyYUGCpSs+M+rqN/Nr5uvm6jYCv21ivWzPn3BARERFZEo+CJSIiIl1hckNERES6wuSGiIiIdIXJDREREemKbpObrVu3omfPnqhUqZKcQZF/9pTooxY7tCpWrAg3Nzc5z+rUqVO6f93PPvusvD3v5cEHH4TWiaMCxABWcXKlmCYvJsWLyfJ5paam4pVXXkGFChXg6emJxx9//LYDIfX4ujt16nTbe/7iiy9Cy7744gs0btw49wAzcc7Vb7/lzMLS63tdlNetx/e6IB988IF8bSNHjtT9e363122U97y4dJvciLNymjRpgtmzZxf49Y8++giff/65nCS+a9cueHh4yAGc4gdEz69bEMlMVFRU7mXJkiXQui1btsj/sO3cuVMe6piRkYGuXbvK/z/MXn/9dfz8889YsWKFvP/ly5fx2GOPQe+vWxg6dOgt77n4969lVapUkf+hF4N3xTlX999/P3r37o2jR4/q9r0uyuvW43ud3549ezB37lyZ5OWl1/f8bq/bCO95iWQbgHiZK1euzL1uMpmyg4KCsj/++OPc227cuJHt4uKSvWTJkmy9vm5h0KBB2b17987Wu+joaPn6t2zZkvv+Ojk5Za9YsSL3PsePH5f32bFjR7ZeX7fQsWPH7BEjRmTrnY+PT/b8+fMN817nf91GeK9v3ryZXadOnewNGzbc8lr1/p4X9rqN8J6XlG5Xbu7k3Llz8sBAUYrKO2uqdevW2LFjB/Ru8+bNsoRRr149vPTSS7h27Rr0RkyNFXx9feVH8ZeuWNXI+56HhISgatWqunrP879us0WLFslZbuKwSzFMNjk5GXqRlZUlTyoXq1WiTGOU9zr/6zbCey1WKcXJ9HnfW0Hv73lhr9sI77lmTyhWQSQ2gvkUZDNx3fw1vRIlKbFUW6NGDZw5c0ae8vzQQw/J/wCIIaZ6IOaTiZp0+/btc0+uFu+rmGVWvnx53b7nBb1u4emnn0a1atVkH9Y///yD0aNHy76cn376CVp2+PBh+UtdlJJFj8XKlSsRGhqKgwcP6vq9Lux16/m9FkQit3//flmeyU/PP993et16f89Lw5DJjZH169cv9/NGjRrJ+m2tWrXkak7nzp2hB+KvnCNHjmDbtm0wksJe97Bhw255z0UTvXivRXIr3nutEiuPIpERq1U//PCDnFEnei30rrDXLRIcvb7XkZGRcq6g6CtzdXWFURTldev1PS8tQ5algoKC5Mf8nfTiuvlrRlGzZk25nHn69GnowauvvopffvkFmzZtks2XZuJ9TU9Px40bN3T5nhf2ugsiyq+C1t9z8Zd67dq10bx5c7lrTDTSf/bZZ7p/rwt73Xp+r0XZKTo6Gs2aNYOjo6O8iIRObAoRn4sVGj2+53d73aI0qdf3vLQMmdyIkoz4B79x48bc2xISEuSuqby1ayO4ePGi7LkR2b6Wif5p8QteLNH/+eef8j3OS/wicHJyuuU9F0u3YjCrlt/zu73ugoi/+gWtv+cFleXS0tJ0+17f7XXr+b0WKxGiHCdej/nSokUL9O/fP/dzPb7nd3vdBbUS6OU9Ly3dlqUSExNvyVxFE7F400WjpWgyE70JU6ZMkVPFxS+EcePGyZqlOCdEr69bXCZOnCjPfxDJnVi2fPvtt+VfgWIbvNZLMmJa/OrVq+WZL+Y6u2gUF+cYiY/PP/88Ro0aJf9/EGeEDB8+XP6Hr02bNtDr6xbvsfh69+7d5fkfoiYvtsx26NChwC2lWiGaJkWvmPhZvnnzpnyNorS6fv163b7Xd3vden2vBfFvO28fmSCO7xCv03y7Ht/zu71uPb/npZatU5s2bZLbAPNfxFZo83bwcePGZQcGBsot4J07d84ODw/P1vPrTk5Ozu7atWu2v7+/3DZZrVq17KFDh2ZfuXIlW+sKes3i8tVXX+XeJyUlJfvll1+WW2fd3d2zH3300eyoqKhsPb/uiIiI7A4dOmT7+vrKf+e1a9fOfuutt7Lj4+Oztey5556T/36dnZ3lv2fx8/v777/r+r2+2+vW63tdmPxboPX6nt/pdRvtPS8OO/E/pU+RiIiIiGyDIXtuiIiISL+Y3BAREZGuMLkhIiIiXWFyQ0RERLrC5IaIiIh0hckNERER6QqTGyIiItIVJjdERESkK0xuiMimderUSY5LsaSvv/4a5cuXt+hjEpHtYHJDRIbTt29fnDx5UnUYRFRGdDs4k4ioMGKoqLgQkT5x5YaIbF5mZiZeffVVOfHbz88P48aNE0N/5deqV6+OKVOmYODAgfD09ES1atWwZs0axMTEoHfv3vI2MSF57969uY/HshSRvjG5ISKb980338DR0RG7d+/GZ599hunTp2P+/Pm5X//000/Rvn17HDhwAD169MCAAQNksvPMM89g//79qFWrlrzOOcFExsDkhohsXnBwsExg6tWrh/79+2P48OHyuln37t3xwgsvoE6dOhg/fjwSEhLQsmVLPPHEE6hbty5Gjx6N48eP4+rVq0pfBxFZB5MbIrJ5bdq0gZ2dXe71tm3b4tSpU8jKypLXRdnJLDAwUH5s1KjRbbdFR0dbMWoiUoXJDRFpnpOTU+7n5iSooNtMJpOC6IjI2pjcEJHN27Vr1y3Xd+7cKUtQDg4OymIiItvF5IaIbF5ERARGjRqF8PBwLFmyBDNnzsSIESNUh0VENorn3BCRzRM7nVJSUtCqVSu5WiMSm2HDhqkOi4hslF0290YSERGRjrAsRURERLrC5IaIiIh0hckNERER6QqTGyIiItIVJjdERESkK0xuiIiISFeY3BAREZGuMLkhIiIiXWFyQ0RERLrC5IaIiIh0hckNERERQU/+D22m+M/LqjwlAAAAAElFTkSuQmCC", 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", 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", + "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -157,12 +200,12 @@ "# сахар\n", "glucose['low'] = fuzzy.zmf(glucose.universe, 50, 80)\n", "glucose['normal'] = fuzzy.trapmf(glucose.universe, [70, 80, 90, 100])\n", - "glucose['hight'] = fuzzy.smf(glucose.universe, 100, 300)\n", + "glucose['high'] = fuzzy.smf(glucose.universe, 100, 300)\n", "\n", "# ИМТ\n", "bmi['low'] = fuzzy.zmf(bmi.universe, 0, 19)\n", "bmi['normal'] = fuzzy.trimf(bmi.universe, [18, 20, 25])\n", - "bmi['hight'] = fuzzy.smf(bmi.universe, 25, 50)\n", + "bmi['high'] = fuzzy.smf(bmi.universe, 25, 50)\n", "\n", "# гипертония\n", "hypertension['low'] = fuzzy.zmf(hypertension.universe, 0, 0.6)\n", @@ -209,41 +252,54 @@ "\n", "\n", "

\n", - " ну.... с написанием правил мне чат гпт помог, я не медик, поэтому... спасибо чату\n", + " ну.... с написанием правил мне чат гпт помог, ну а что, у меня 5 входных переменных... я не знала, на что наткнусь, когда дойду до этого момента, поэтому... спасибо чату\n", "

" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 143, "metadata": {}, "outputs": [], "source": [ - "rule1 = control.Rule(age['aged'] & hypertension['high'], stroke_risk['high'])\n", - "rule2 = control.Rule(age['old'] & hypertension['high'], stroke_risk['high'])\n", - "rule3 = control.Rule(age['middle'] & hypertension['high'] & glucose['hight'], stroke_risk['high'])\n", - "rule4 = control.Rule(age['middle'] & hypertension['high'] & glucose['normal'], stroke_risk['medium'])\n", - "rule5 = control.Rule(age['young'] & hypertension['high'] & glucose['low'], stroke_risk['medium'])\n", - "rule6 = control.Rule(age['young'] & hypertension['low'] & glucose['normal'], stroke_risk['low'])\n", - "rule7 = control.Rule(age['aged'] & glucose['hight'] & bmi['hight'], stroke_risk['high'])\n", - "rule8 = control.Rule(age['old'] & glucose['normal'] & bmi['hight'], stroke_risk['medium'])\n", - "rule9 = control.Rule(age['old'] & glucose['low'] & bmi['low'], stroke_risk['low'])\n", - "rule10 = control.Rule(age['middle'] & bmi['hight'] & glucose['hight'], stroke_risk['high'])\n", - "rule11 = control.Rule(age['young'] & bmi['hight'] & glucose['hight'], stroke_risk['medium'])\n", - "rule12 = control.Rule(age['young'] & bmi['normal'] & glucose['normal'], stroke_risk['low'])\n", - "rule13 = control.Rule(age['aged'] & heart_disease['high'], stroke_risk['high'])\n", - "rule14 = control.Rule(age['old'] & heart_disease['high'], stroke_risk['high'])\n", - "rule15 = control.Rule(age['middle'] & heart_disease['high'], stroke_risk['medium'])\n", - "rule16 = control.Rule(age['young'] & heart_disease['high'], stroke_risk['medium'])\n", - "rule17 = control.Rule(age['aged'] & hypertension['high'] & glucose['hight'] & bmi['hight'], stroke_risk['high'])\n", - "rule18 = control.Rule(age['middle'] & hypertension['low'] & glucose['low'] & bmi['low'], stroke_risk['low'])\n", - "rule19 = control.Rule(heart_disease['high'] & glucose['hight'] & bmi['hight'], stroke_risk['high'])\n", - "rule20 = control.Rule(heart_disease['low'] & glucose['normal'] & bmi['normal'], stroke_risk['low'])\n" + "rules = []\n", + "\n", + "ages = ['young', 'middle', 'old', 'aged']\n", + "hypertension_levels = ['low', 'high']\n", + "glucose_levels = ['low', 'normal', 'high']\n", + "bmi_levels = ['low', 'normal', 'high']\n", + "heart_disease_levels = ['low', 'high']\n", + "\n", + "for ag in ages:\n", + " for hl in hypertension_levels:\n", + " for gl in glucose_levels:\n", + " for bm in bmi_levels:\n", + " for hd in heart_disease_levels:\n", + " # Определяем уровень риска\n", + " if ag in ['aged', 'old'] and hl == 'high' and gl == 'high' and bm == 'high' and hd == 'high':\n", + " risk = 'high'\n", + " elif ag in ['middle', 'old'] and hl == 'high' and gl == 'high' and bm in ['normal', 'high']:\n", + " risk = 'high'\n", + " elif ag == 'young' and hl == 'low' and gl == 'low' and bm == 'low' and hd == 'low':\n", + " risk = 'low'\n", + " elif gl == 'normal' and bm == 'normal' and hd == 'low':\n", + " risk = 'low'\n", + " elif ag == 'middle' and hl == 'low' and gl == 'low' and bm == 'low':\n", + " risk = 'low'\n", + " else:\n", + " risk = 'medium'\n", + " \n", + " # Создаем правило\n", + " rule = control.Rule(\n", + " age[ag] & hypertension[hl] & glucose[gl] & bmi[bm] & heart_disease[hd],\n", + " stroke_risk[risk]\n", + " )\n", + " rules.append(rule)\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -256,7 +312,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -266,51 +322,2307 @@ } ], "source": [ - "stroke_ctrl = control.ControlSystem([\n", - " rule1, \n", - " rule2, \n", - " rule3,\n", - " rule4,\n", - " rule5,\n", - " rule6,\n", - " rule7,\n", - " rule8,\n", - " rule9,\n", - " rule10,\n", - " rule11,\n", - " rule12,\n", - " rule13,\n", - " rule14,\n", - " rule15,\n", - " rule16,\n", - " rule17,\n", - " rule18,\n", - " rule19,\n", - " rule20,\n", - "])\n", + "stroke_ctrl = control.ControlSystem(\n", + " rules\n", + ")\n", "\n", - "stroke = control.ControlSystemSimulation(stroke_ctrl)\n", + "stroke_simulation = control.ControlSystemSimulation(stroke_ctrl)\n", "stroke_ctrl.view()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 145, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 72\n", + " - young : 0.0\n", + " - middle : 0.0\n", + " - old : 0.0\n", + " - aged : 0.18\n", + "Antecedent: hypertension = 1\n", + " - low : 0.0\n", + " - high : 1.0\n", + "Antecedent: glucose = 220\n", + " - low : 0.0\n", + " - normal : 0.0\n", + " - high : 0.6799999999999999\n", + "Antecedent: bmi = 30\n", + " - low : 0.0\n", + " - normal : 0.0\n", + " - high : 0.08000000000000002\n", + "Antecedent: heart_disease = 0\n", + " - low : 1.0\n", + " - high : 0.0\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #1:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #2:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #3:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #4:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #5:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #6:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #7:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #8:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #9:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #10:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #11:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #12:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #13:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #14:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #15:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #16:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #17:\n", + " IF (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #18:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #19:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #20:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #21:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #22:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #23:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #24:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #25:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #26:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #27:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #28:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #29:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #30:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #31:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #32:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #33:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #34:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #35:\n", + " IF (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[young] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #36:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #37:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #38:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #39:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #40:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #41:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #42:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #43:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #44:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #45:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #46:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #47:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #48:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #49:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #50:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #51:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #52:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #53:\n", + " IF (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #54:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #55:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #56:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #57:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #58:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #59:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #60:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #61:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #62:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #63:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #64:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #65:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #66:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #67:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #68:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[high] : 0.0\n", + "\n", + "RULE #69:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[high] : 0.0\n", + "\n", + "RULE #70:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[high] : 0.0\n", + "\n", + "RULE #71:\n", + " IF (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[middle] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[high] : 0.0\n", + "\n", + "RULE #72:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #73:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #74:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #75:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #76:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #77:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #78:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #79:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #80:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #81:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #82:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #83:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #84:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #85:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #86:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #87:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #88:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #89:\n", + " IF (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #90:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #91:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #92:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #93:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #94:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #95:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #96:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #97:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #98:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #99:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #100:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #101:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #102:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #103:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #104:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[high] : 0.0\n", + "\n", + "RULE #105:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[high] : 0.0\n", + "\n", + "RULE #106:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[high] : 0.0\n", + "\n", + "RULE #107:\n", + " IF (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[old] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[high] : 0.0\n", + "\n", + "RULE #108:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #109:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #110:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #111:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #112:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #113:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #114:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #115:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #116:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #117:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #118:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #119:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #120:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #121:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #122:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #123:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #124:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #125:\n", + " IF (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[low] : 0.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[low]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #126:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #127:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #128:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #129:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #130:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #131:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[low] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[low]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #132:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #133:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #134:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[low]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[low] : 0.0\n", + "\n", + "RULE #135:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #136:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #137:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[normal] : 0.0\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[normal]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #138:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #139:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[low] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[low]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #140:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[low] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #141:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[normal] : 0.0\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[normal]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.0\n", + "\n", + "RULE #142:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] THEN stroke_risk[medium]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[low] : 1.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[low] = 0.08000000000000002\n", + " Activation (THEN-clause):\n", + " stroke_risk[medium] : 0.08000000000000002\n", + "\n", + "RULE #143:\n", + " IF (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] THEN stroke_risk[high]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[aged] : 0.18\n", + " - hypertension[high] : 1.0\n", + " - glucose[high] : 0.6799999999999999\n", + " - bmi[high] : 0.08000000000000002\n", + " - heart_disease[high] : 0.0\n", + " (((age[aged] AND hypertension[high]) AND glucose[high]) AND bmi[high]) AND heart_disease[high] = 0.0\n", + " Activation (THEN-clause):\n", + " stroke_risk[high] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: stroke_risk = 0.49999999999999994\n", + " low:\n", + " Accumulate using accumulation_max : 0.0\n", + " medium:\n", + " Accumulate using accumulation_max : 0.08000000000000002\n", + " high:\n", + " Accumulate using accumulation_max : 0.0\n", + "\n", + "0.49999999999999994\n" + ] + } + ], + "source": [ + "\n", + "from pprint import pprint\n", + "\n", + "def fuzzy_pred(row):\n", + " stroke_simulation.input[\"age\"] = row[\"age\"]\n", + " stroke_simulation.input[\"glucose\"] = row[\"avg_glucose_level\"]\n", + " stroke_simulation.input[\"bmi\"] = row[\"bmi\"]\n", + " stroke_simulation.input[\"hypertension\"] = row[\"hypertension\"]\n", + " stroke_simulation.input[\"heart_disease\"] = row[\"heart_disease\"]\n", + "\n", + "\n", + " stroke_simulation.compute()\n", + " return stroke_simulation.output[\"stroke_risk\"]\n", + "\n", + "stroke_simulation.input[\"age\"] = 72\n", + "stroke_simulation.input[\"glucose\"] = 220\n", + "stroke_simulation.input[\"bmi\"] = 30\n", + "stroke_simulation.input[\"hypertension\"] = 1\n", + "stroke_simulation.input[\"heart_disease\"] = 0\n", + "\n", + "stroke_simulation.compute()\n", + "stroke_simulation.print_state()\n", + "stroke_predict = stroke_simulation.output[\"stroke_risk\"]\n", + "print(stroke_predict)\n", + "\n", + "# result = data.copy()\n", + "# result = result.sample(frac=0.01)\n", + "# result[\"stroke_predicted\"] = result.apply(fuzzy_pred, axis=1)\n", + "# # result[\"stroke_predicted\"] = result['stroke'].apply(lambda x: 1 if x> 0.5 else 0, axis=1)\n", + "\n", + "# print(result.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE: 0.4596\n", + "MSE: 0.2265\n", + "RMSE: 0.4760\n", + "R² Score: -3.0918\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "import numpy as np\n", + "\n", + "# Создаем копию данных и прогнозируем значения\n", + "result = data.copy()\n", + "result = result.sample(frac=0.01) # Берем 1% данных для теста\n", + "result[\"stroke_predicted\"] = result.apply(fuzzy_pred, axis=1)\n", + "\n", + "# Истинные и предсказанные значения\n", + "y_true = result[\"stroke\"]\n", + "y_pred = result[\"stroke_predicted\"]\n", + "\n", + "# Вычисляем метрики\n", + "mae = mean_absolute_error(y_true, y_pred)\n", + "mse = mean_squared_error(y_true, y_pred)\n", + "rmse = np.sqrt(mse)\n", + "r2 = r2_score(y_true, y_pred)\n", + "\n", + "# Выводим результаты\n", + "print(f\"MAE: {mae:.4f}\") # ближе к 0 - лучше\n", + "print(f\"MSE: {mse:.4f}\") # ближе к 0 - лучше\n", + "print(f\"RMSE: {rmse:.4f}\") # тоже\n", + "print(f\"R² Score: {r2:.4f}\") # а тут ближе к 1 - лучше" + ] }, { "cell_type": "markdown", "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "#### ну короче... противоречивые результаты, но в основном они говорят о том, что модель данная - такое себе. в чем я, собственно, не знаю, согласна или нет... по показателям людей здесь и правда мог бы быть инсульт, ведь риск его развития есть, а если вероятность его развития больше 0,2 вроде, то это высокая вероятность уже... короче сложно такие задачи решать с помощью нечетких переменных" + ] } ], "metadata": {