iui-group-l-name-zensiert/1-first-project/tdt/NeuralNetwork.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f9261918",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a22e78f7",
"metadata": {},
"outputs": [],
"source": [
"delim = ';'\n",
"\n",
"base_path = '/opt/iui-datarelease1-sose2021/'\n",
"\n",
"Xpickle_file = './X.pickle'\n",
"\n",
"ypickle_file = './y.pickle'"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "592e5107",
"metadata": {},
"outputs": [],
"source": [
"THRESH = 70\n",
"LEEWAY = 2\n",
"EPOCH = 30\n",
"\n",
"DENSE_COUNT = 3\n",
"DENSE_NEURONS = 1800\n",
"\n",
"DENSE2_COUNT = 2\n",
"DENSE2_NEURONS = 0\n",
"\n",
"AVG_FROM = 20"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "63671cad",
"metadata": {},
"outputs": [],
"source": [
"def shorten(npList):\n",
" temp = npList['Force']\n",
" thresh = THRESH\n",
" leeway = LEEWAY\n",
" \n",
" temps_over_T = np.where(temp > thresh)[0]\n",
" print(temps_over_T)\n",
" return npList[max(temps_over_T[0]-leeway,0):min(len(npList)-1,temps_over_T[-1]+leeway)]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "166cc6b8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
" 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 196 197 198\n",
" 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214]\n"
]
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f33513f2eb0>]"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Pw8lHRh+TH9zOWBrvpJrUiv4prwf97kpxm3v251duupKJQpYv/OD5IIY1NP1Ev+v0tWRzNJZfsJeu77xfXvlOyJXgobtHH5Mf+jp9Ff20kFrR/+HzixSyGV6+dxPXMwSTxRyve/kCP3g2ng6KrsPmZhO56vQD4tgPbEnjwg2jPc/ELrjudfDcdwMZ1tDUPdHvm+lrvJN0Uiv6Dx69wM/un6aU99FL5RL2zZRj663fdfqbrMidyGv1TiAc+77tlJm9/N94aKb3e/vUxkDX6Wu8k2ZSKfr1VptHTixx6OCIE3MeC1NFao12V4CjZLVPvJPLZijmMro4axTqq3Dqx3Dg1mCeb3KPnSNoxWASupn+Jk4/VwZERT8FpFL0nzi5QqPV4earZwN5viu8bQrjcPur61tX74D9MNA2DCNw8kdg2nDglmCeb8rbpjCO3vVO9DfL9DMZ6/ZV9BNPKkXf7Y27d6YcyPNdMWU3JD8dwy5a3dLTyualpxOFrDr9Uaids5fTVwbzfJN77eVqDNsnunOZ2L357YWKZvopIJWi7zJu15BsVNyG5Gdi2LDkXLVBPitMlzZ3+hOFrDr9UWh6i5Vcb5pRcZuQxyX6hSnIlza/vVDZOF8lsaRU9K3zLQcwiQs98c5yDKK/WmdXpYCIbHr7RCGnTn8UXNzhJjpHZcpz+isxTOZWz0BlC5cPGu+khJSKfrBOf3YiTz4rsWT656sNdvVZa1ApZmPv97+jcSIYlNOvLAASj9OvnoWJ+a1vz2u8kwZSLvoBlOBhe/AsTBY5E4Pon6s22L1Fng/O6avo+8bFHUE5/WzeZuqxxDtn7V64W6FOPxWkUvTXGi1EoJQP7vQXpkucXol+IvfcaoPdk1uLfqWQ1cVZo9Co2lW0fvbG3YqpvbASk9NX0U89qRT9WqPNRD67ZQ7uh/lKgfPV6Dcht/FOH6dfzGkbhlFo1oKLdhyVeeu6o8SY7eOdwqSKfgpIp+g325QDinYcpUKW9Wa04lpvtVmtt/rGO+r0R6RRCy7aceTK0Iz4W2F9GTrNAZy+ZvpJJ52iX28FNonrKOWyrDej3VTafbPoN5FbzmdZa7Zja/2842msBu/086Xo96Ktet8s+k7klm3PfSXRpFP0G+3gRT+fidzpn1u1ot8v0y8VshgD9Va0H0iJoVmDQsCiH4fTd6JfWdj6PvkytNZtFKQkllSK/lqz3d0/NihK+ejjnXOe0+8X77i1CFGPLTE0apu3LRiFfMmKa5S4OYR+dfo5b9FW1GNTIiWVoh+a04/YTZ/1SkTnJ7eOd0pd0Ven74tmNfh4J1eOXlhdr59+ewLkvbYkGvEkmhSLfsATubks7Y6h2Y5OXE8s2v+ce2e2WFbPhtNfU6fvj0YY8U4xemFdOgaS3VgRvBnO6avoJ5ptRV9E7hKR0yLyaM+xj4rICRF52Pt5S89tvyUiR0TkpyLypp7jb/aOHRGRDwV/KoNTa4QwkRtDjPLC4hrzk8W+ewK423RVrk8aVbtSNUjyZdu5s90M9nn7sXTc9vLvt97AfaPReCfRDOL0PwO8eZPj/8MYc5P3cy+AiNwIvAt4hfeYPxWRrIhkgY8DtwE3Au/27hsLYcU7EG2McmJxjf1z/TuFunGp0/dJsxpCyWYMjnrxGMwe6H+fvDr9NLCt6BtjvgMMuhfg7cAXjTF1Y8yzwBHgFu/niDHmGWNMA/iid99YWGu0KecDjndicPonFtfYP7t1tAMb8U5dRd8fYcQ7LjuP0lEvHYeZq/rfJxfDuJTIGSXT/6CIPOLFP3Pesf3AsZ77HPeObXX8MkTkThE5LCKHz5w5M8LwNscYk4h4xxjDC4trXLnNngCuSkmdvg9aDbugKeh4J+oqmXYLlk9sL/pdp6/tlZOMX9H/BPAS4CbgJPDHQQ3IGPMpY8whY8yhhYU+NcU+qbc6dAyhlGxCdPHO+WqD9WZn23hHJ3JHoOnaKofk9KOq1V89ZecQZraLd7zzjHoNgRIpvjIOY0y3W5SI/BnwV96vJ4Dev6yrvGP0OR4pQbdVdnQz/VY04uoqd66c3S7T14lc3zQC3kDFkfNKbKNalbvofcneTvS730A0008yvpy+iOzr+fXtgKvsuQd4l4gUReRa4HrgB8ADwPUicq2IFLCTvff4H7Z/XB+aStAlmxHHOyeXrBvbLt7pjktX5A5Pt61ywIuzchE7/WXPX81smqhuEPU3ECUWtlU+EfkC8DpgXkSOAx8BXiciNwEGeA74NQBjzGMi8iXgcaAFfMAY0/ae54PAN4AscJcx5rGgT2YQnOMNPN7JRRvvrHgbos+U833v585zXZ3+8DTCincidtT1ZXtZmu1/v5xm+mlgW9E3xrx7k8Of7nP/PwD+YJPj9wL3DjW6EAg93onI6a+u2xrvSrH/eZRyWrLpm6D3x3V0q2Qi2nSnvmIvi9t8Y4mjqkiJnNStyHXi129Bkx9KEU+Yut2wJrfYEN2Ry2bIZ0V77/ihK/r9I7Shiboevr4KyPZVSNqGIRWkTvQbXrYd5K5Z9vmirYdfrbfIZ4VibvsPr5LXXlkZkpa3KU6u/1qIoYm6ZLOxauclMtv8zWvDtVSQOtF3LYYL2Z29Ind1vUWlONhkdBwdQBOBE7/c1g3tfBH1itz68vbRDoCIHZs6/USTOtF3Tr8YktOPSlyr9dbAFUjlfFZLNv3Qdk4/YNGPOjuvrw5egaSin3hSJ/p1r46+kA321PPZDNmMRFanv1pvMbVNnu8o56Pf1SsROFHO7nCn31iF4tRg981PaJ1+wkmh6Ifj9MFWykQW79SHiHcKmun7IvRMP8LqnUHiHbCTzFqnn2hSJ/qNbqYfguhHmJ1XhxH9XEZF3w/dTH/rncl8kcnYbw+R1emvQmFApx/HBi9KpKRO9F28Uwy4ZBOirZJZrbeYGlD0ywWdyPVF23PiQTt9iNZRN1aGiHc00086qRP9cJ1+hnpE8U613t52YZajrNU7/mjVAYFMsC07APtBEqXTHzTeyZVV9BNO6kS/3uogAvmsBP7cUcY7Q2X6Wqfvj1bdirME/7diq2Siqt5ZGbx6J1/WidyEkzrRb7Q6FLIZJIT/yKV8NpLqHWMM1cbg8U4pn2WtodU7Q9OqB5/nO6IS11bd7gkwVLyjmX6SSZ3o11sdirlwTruUj6Z6p9ZoYwwDO32Nd3zSroeT54MX70RQvVNftZeDin5OnX7SSaXoFwZoXeCHUi4acV2te+2hBxT9Yj6jou+HVj34Gn1HPqLs3HXYHDje0YncpJNC0W+H5vSL+Ux3HUCYONEfdHFWKZel1TG02hrxDEWrHvxqXEeuFE1pZMOH09d4J9GkTvQbYcY7ETn9an24jWBcX6AoPpASRdiiH4W4duMdnchVLKkTfRvv7HCnvz5cvBN1X6DE0A5R9PMRlWy6XvqDLs7Kl23PoY7+rSSV1Il+mE6/GJHTdxvBDFqnv7F/rzr9oQgz048qRulu7r5NL32HtldOPKkTfZvphzSRm89Gsjir2z9owPMo5tTp+yLMeCcfUabvKoQGPQ/dSCXxpE70G61OKM3WAIq5DI12h07HhPL8jqY3ITvoArNupq+dNoejtR5iyWZEPW5ce+jsgOsNVPQTT+pEv+4tzgqD7u5ZIcco3VYSA8ZUrs9QVG2fE0O7EeLirIhKI4d1+rmIe/0rkZM60Q/b6cNGU7ewqLeHE/2Sxjv+CNXpl8C0od0M5/kdQzv9iHv9K5GTOtGPwumHvSq3OWTTuKLGO/5oNQYXy2GJaiOVYUU/p/FO0kmd6NvqnXAmcqNy+g2fTj/scSWOMJ1+VFsmtobc8tE5fa3VTyypE/16qx1anX5UTn/Y9tBRb9qeGNqNcBdnQfii326AZCEzoNHpTuRqpp9UUij6YdbpR+P0m23bHjqbGbR6RzN9X7TWQyzZjEhc2/XhIqruRK46/aSSOtFvhLgiN0qnP0x7aBV9H3Ta0GmFuDgrohilNWQFUnciV51+UkmV6Lc7hlbHhLg4y8UoIVfvDPnB5b6B6IrcIRi21HFYchGJa7sx3AdXdyK3Fs54lNhJlegPW98+LMVcNHX6zfZwFUjd9QOa6Q9OO2TRj2rCtD1kBVJUE8xKbKRK9Lubooe4iQqE7/SHjaiyGSGfFV2cNQyhO/2IMv1hd//SFbmJJ1WinxSn32gPPy8RVdvnxOBEP7RNVCKs3hnmHLIFQNTpJ5hUif5Go7Kd7fSb7Q75IReYFSPayjExRJXpRyL6+cHvLxLdrl5KLKRS9He80/exqriYy+rirGEIPdOPKEbx0yk0p1smJpmUib7L9ENakTum1Ttgv4XoRO4QdJ1+iL13IAKn3xw+ospPaLyTYFIm+uHGO93FWWM2kQu2gkcz/SFworfje+8MOZELujl6wkmX6HtON6wumyJCMRf+lonDlmyCJ/oa7wyOq6pxMUzQROX0W0OuyIXoev0rsZAq0Xei5+rWwyAKR+2neqeY04ncoXCiF1a8k8nY2CWSeMeP09fFWUllW+UQkbtE5LSIPNpzbJeI3CciT3mXc95xEZGPicgREXlERG7uecwd3v2fEpE7wjmd/rjYpRRSpg9E4vT9TOSW8jqROxStkJ0+eOIaQe+dYSdy8xHt36vEwiDK8RngzZcc+xBwvzHmeuB+73eA24DrvZ87gU+A/ZAAPgLcCtwCfMR9UESJc7qlkOId+9zhO/1m25D3MZGrTn8IXKYdltMHL0aJoPeOr3hHM/2ksq1yGGO+A5y/5PDtwN3e9buBt/Uc/6yxfA+YFZF9wJuA+4wx540xF4D7uPyDJHScGIcZ74yt09fFWcMRhdPPFSPqveMn3lGnn1T8Wt49xpiT3vVTwB7v+n7gWM/9jnvHtjoeKU70wqregWicvp+STV2cNSRdpx9SnT7YD5TQe+/4qdNXp59kRlY/Y4wBTABjAUBE7hSRwyJy+MyZM0E9LbDRZXKnO31bvTNYW2VHKZ8NvZQ0UXQncsN0+hE46taQK3JBV+QmHL+i/6IX2+BdnvaOnwAO9NzvKu/YVscvwxjzKWPMIWPMoYWFBZ/D25x6M3zRLxey1BpjWqevE7mD01yDTA6yufBeo1CJZo/coRdn6URukvEr+vcArgLnDuBrPcff61XxvAZY8mKgbwBvFJE5bwL3jd6xSFlvtclnZeAdp/wwUciyFrbo+yjZLOezNNuGVlsjnoForYfr8sGufG1Ww3v+ThtM22f1jpZsJpVtbYyIfAF4HTAvIsexVTh/CHxJRN4PHAXe6d39XuAtwBGgBrwPwBhzXkR+H3jAu9/vGWMunRwOnfVmO9RyTYBKIUe10Qrt+dsdQ7tjhm64Vna7Z7U6TA752FTSXNvohBkWhQosHg3v+budQn3EO6bt1fgP+Vhl7NlW9I0x797ipjdscl8DfGCL57kLuGuo0QXMerNDMcRoB2CiGG6802z7axrnylTXGm0miyFGFkmhVQ/f6RcmoRGi02837OWw8U7v7lnZmWDHpMROqixfvdkOtUYfPKdfD8/pdzuF+licBbpP7sC0InL6jdXwnt+J/tC9d3QjlSSTKtFfb7VDLdcEmCjkqLc6tDuBFTRdhH+nr6I/FM31cMs1wRP9ELPzbrwzrOhP2EsV/USSLtFvdkKt3AE7kQtQCynXb/h0+i7TX1PRH4zWWgTxTgU6TVtWGQZ+4x33DUdFP5GkSvTrrXb4ol90oh+OuPrd8rFccE5fq3cGorkeQbwzaS/Dinh8xzue09cFWokkVaJvnX74mT4QWq4/8kSuOv3BiMTpe+Ia1mSu331+NdNPNCkT/fBLNjfinXDE1U3kDluy6b7hhL2GIDFE4vQr9jIs0W837aWfhmugop9Q0if6Icc7Fa8cMrR4Z8SJXG2vPCCROH0v3glrgVZ3n1+t3lE2SJnod0LbNcvhsvOwFmi5TL/odyJXnf5gJMHpa7yjbEKqRL/eaoe2KbrDZfq1ejji6jL9Yfvpl7Vkczha9XB76UOE8Y6PFbmgE7kJJVWiH8VE7kRUTt9nvLOm1TuD0VqLQPRd9U7Y8c6Q56FOP9GkSvSjKNnsZvohVe/Uu6I/3Hm4Dwmt3hmATtuWO4a5gQpslEaGVbLp4h0//fRBm64llNSIfrtjaLZNdNU7IYmrm4gd1ulnMmJ7/avob0/Ym6I7uvFOSOLq9zxyRUC0vXJCSY3ob2yVGO4pF3MZMhJepu/2BPAzIV0uZNXpD0Izgq0SIbqJ3GGdvojX9lmdfhJJoeiH6/RFJNT2yn4broGdzNWJ3AFwE5hhO/1s3lbWhBbvOKfvo4dQvrTxeCVRpEf0u1slhn/KE8VseE7fxTs+PrxK+axO5A5CVE4fvKZrYTn9EWKq/IRO5CaU9Ih+d1P0cJ0+hLuRit/qHfBEX+v0t6fr9EPusgnh9tRv1UEydtvHYdHdsxJL6kQ/KqcflrjWWx0yAjkfWz6W8hldkTsIzumHvSIXbP+dsFbkurUG4mN70Cg2bVdiIXWiXy6Ev2tUOZ8NNdMv5rKIj//IZXX6g+EcrmuIFib5iXCdvt9vKzqRm1hSI/quF0455IlcsDFKWC2M682271YS5XyWdXX62+NEOB+R6IflqFvrw7dgcOTLOpGbUFIj+s7hujr6MCmFWCVjnb6/t00z/QHpOv1K+K+VL4XX7mAkp6+ZflJJj+h3452dL/rDdth0hPkNJFFE6fRzpfCqZFrr/stO82Wt3kkoqRH9KOOdcj4Tmrg2vEzfD8V8Ruv0ByFSpx9iaeQoTl8nchNLakQ/8ngnpOy8PsLm7sVcplvyqfQh0kw/xEVQozj9XHGjYZuSKNIj+hHHO2GWbPoV/UIuQ72tor8tzRpINpo6/VyI2fkoTj9bDG/DdiVW0iP6jTYZ8de+YFhK+Sz1VgdjTODPXW+OEO9krdMPY1yJolGz0Y6f+vZhyYcYo7RHiXcK6vQTSmpEv9ZoM1HI+apvHxa3AKweQpRSb/kv2XQTwM22in5fmtVooh2wr9OuQyeEb2CjbASTLdrHq0FIHKkR/bVmK/Rmaw7XvjmMiGeUeMd9Q9BVudvQqEazMAs2RDmMss3W+mhOH7Ox+5aSGNIj+o12JJO4sDFvEMZkbn2E6h3n9HUydxsaNchHULkDPbtUhRDxjOL03eM04kkcqRH9WoSi7+KdMMo2GyPU6XdFXydz+9NUp99dyauTuYkjNaK/1gx/q0SHi3fCqIkfpWTTTWKr09+GRi3aTB/G0OkX7KU6/cSRHtGP0ukX3CbkIYj+KNU7IU4wJ4pmLZqFWWCrd9xrBk0gTl8XaCWN1Ih+pPFOqE6/4796R53+YDSq0Ym+a98ctLh2OnZzd78N15zT13gncaRG9CONd5yjDjjT73QMjfZoi7NAnf62NKOMd9xEbsCZftsT61HaMIDGOwkkPaIf6URuOPGOm4DV6p2QacQR7wQs+qNslQg6kZtgUiP6tUaLiQg2UIGNpm5Bxzv1EbZK7H2c1un3odOJdnFWN94JWvQ9hz5SnT6a6SeQ1Ih+tPGOE/1gHfXGpuijLc5Sp98HJ75RlWx2nX7A4hqU09d4J3GkQvSb7Q7Ntom8Tj/oeMfNEfjtH6R1+gPQ8KpoIluc5Uo2A67eCczpa7yTNEYSfRF5TkR+LCIPi8hh79guEblPRJ7yLue84yIiHxORIyLyiIjcHMQJDIIT36gz/dDiHZ/fWLR6ZwDcJuWRL84Ky+nrRK5yMUE4/dcbY24yxhzyfv8QcL8x5nrgfu93gNuA672fO4FPBPDaA+F64EQV7xRzGUTsfrZB0o13Rl2Rq6K/NV2nv8Ord7pO32+8o04/qYQR79wO3O1dvxt4W8/xzxrL94BZEdkXwutfRpQbqACICMVcJvh4J7CJXBX9LenumjUZzetlCyCZEKt3/Dp9XZyVVEYVfQN8U0QeFJE7vWN7jDEnveungD3e9f3AsZ7HHveOXYSI3Ckih0Xk8JkzZ0YcniXKrRId5RD2o+1m+ur0w6MRcbwjYit4Ao93PKfvd3FWdyJXnX7SGLWG8bXGmBMicgVwn4j8pPdGY4wRkaEachtjPgV8CuDQoUOBNPOuNVoAVIrRlGxCOJuju+fz++GlE7kD0Iw43gFvI5WAnX73PMr+Ht+dyNVMP2mM5PSNMSe8y9PAV4FbgBddbONdnvbufgI40PPwq7xjoVOLON4Bb8vEgEV/4zz8fXi5iVyNd/rQdfoRVe+At2ViSKLv9zx0Ijex+BZ9EamIyJS7DrwReBS4B7jDu9sdwNe86/cA7/WqeF4DLPXEQKHinH5Ui7PAOf1gxXXjPPx9eImI3SdXF2dtTZSbojvy5eAXZ416Hll1+kllFBXcA3zV234wB/xvY8zXReQB4Esi8n7gKPBO7/73Am8BjgA14H0jvPZQVOtW5CrFKDP9TODxThCbu7t9cpUtGNUh+yEfptP3KfoiVvhV9BOHb9E3xjwDvGqT4+eAN2xy3AAf8Pt6o1ALQCyHpVzIdp15UAQRUxVyKvp9icXpT2y8blAEscgsW9SJ3ASSihW5tbo3kRthvFPO57oiHRTu+Uo+G66Biv62NGu2hNJvqaMfChPBr8htVq1Tz47wN59Tp59EUiH61RhKNicKwU/krjValPNZMhnx/Rw201fR35JGzdboi/9/46HJT2w486Boro3+bSVXUtFPIKkQ/SDEclgmCtlQnP6oFUhFdfr9ibLDpqNQ2Wj/EBRBtIfOFrR6J4GkQvSrjXakk7hgM/21gEV/rdEeeV6ikMtonX4/GrXoFmY5QnH6AXx45Yrq9BNIKkS/Vo+ul76jUshRa7Sw89fBEITTL2j1Tn8a1eg6bDoKleAz/SA+vLIFnchNIOkQ/Qh3zXKUC1k6JtiFULVme+R5Ca3T34ZmNXqn70S/E+CHcbM2+odXrqS9dxKIin5IuNcLMuJZDyDeKeay6vT70Yhwf1yHe70gF2g1AvjwyhW1y2YCSYXoVxutSPvuwIbo1wKs4Kk1R4+ptHpnG5oR7o/rcK8XZK4fxObuOpGbSFIh+lFuiu4oe+K8FuACrZpO5IZPI4bqne7uWQFW8ARRvaNOP5GkQvSrEW6K7pjwsvcgyzbXGu3u8/qlmM10WzQrmxCL0/dEP1CnH8CHV7agmX4CSYXox+H0u/FOgKIfSJ1+Xp1+X4JwyMPiJlyDrOAJononV9J4J4GkQvSr9XbkmX45hIlcW6c/2nkUc8H3+U8MnU5Mi7Oc0w8o3ul07KTwqNU7+RI01eknjcSLfqdjWAug1HFYXJwUlNNvtTs02p1AnL5O5G6Bq56JY3EWBOf0gzoPbcOQSBIv+q7/TdQrcjfinWAmcl0V0KiiX/JKNjud4BaNJYYgOlP6oVu9E5DTD2pz91wp+D7/SuwkXvSrMWygAj3xTkBRiouJRq7Tz+uWiVvSjHh/XEfQTr8ZUHvoXMmuyA1y0ZgSO4kX/Vo9+q0Se18vqHgnqM3dXVtmzfU3ISiHPCxB1+m75xn1wyvvbZmoFTyJIvGiv7Junf5UKR/p6zpxDU70R9sq0eGcvub6m1Bftpel6WhfN+g6/WZAMVXO21RdRT9RJF70l9ebAEyXoo13MhmhnM8GtjhrI94Z7TzU6fdhfclelmajfd1c0W7cEpjTDyimchvJqOgnisSL/ooT/XK0Th+C7anvNoKZHHFCWp1+H9ad05+J9nVFrCsPKtPviv7kaM+TV6efRBIv+str1mnHIfpB9tR3Wz6OOiGtTr8P64v2MmrRB+vKA6veCUj0ndPXWv1EkXzRjynegXCc/qj7/KrT74OLd4oRZ/pgc/3AnP6qvRy59446/SSSfNFfayIS7abojkox1y0ZHZXuRO6I8U4pr05/S9aXbJmiq1qJkuJkCE4/gIZroKKfMJIv+ustpoq5SPfHdUwWc6zWgxH9at1l+qO2YfCcvjZdu5z1pXiiHYDCFNRXgnmuoJy+ZvqJJPmiv9aMJc8H++2iGpjot8jIhmj7pev0dfesy1lfiifaAev0gxT9/ARkRlybopl+Ikm+6K83mY64Rt8xWcp1HfqoVBstKoUcIqN9Y1Gn34c4nX5xasOhj0qjGkynUM30E0nyRX+txXQ5+jwfbBTjSkZHpVZvj5zngzr9vtSXY4x3AnT69dWARF8z/SSSfNFfb0a+GtcxWcxRbbQxZvTmZkFt+ahOvw9xO/16kE5/xHJN0Ew/oSRe9FfWW7HFO5VijnbHsB6AwFbrrUAqkNTp9yFu0W9WoRPA+9II2Olrpp8oEi/6diI3rnjHCmwQFTzVgHb/KmTV6W+KMTFX73jOPIhcPyinr5l+Ikm06Lc7hpV6fE5/0lsQFoTo1wKKdzIZoZDLqNO/lNa6bSMcp9OHYHL9oJ2+in6iSLTor67H14IBNhaEBVG2WQtwy8diTjdHv4xus7W4RN9z5kHk+kE5fRFvIxUV/SSRaNE/X2sAMBOT6Dun79o7j8JqvUUloD0BSvksdXX6F1M7Zy/Ls/G8vlsfME5OH6zb10w/USRa9J89a13Twd0Rb4rh4VbPBuL0G+3Adv9Sp78J547Yy13XxfP63Uw/ANGvr258cxiVXFmdfsJItOg/fdr2IHnJQkD/AYbEif6omb4xhmqjNXJbZUcpn9VM/1LOPmkvd18fz+sHlem3GtBpBuv0VfQTRaJF/8jpVXZXCsxVCrG8flCiv97sYAxMaKYfHmeehOmrgnPIwxJUpt/tuxPQeeTV6SeNRIv+02dWeckVMf0nhu7E66jxjuvUGWSmr07/Es4+CfMxuXywDddg9JLNoDpsOjTTTxzJF/2Yoh2w/fRFRnf61YA2UHGo078EY+DsUzD/svjG0HX6y6M9T1AdNh2a6SeOyEVfRN4sIj8VkSMi8qGwXufpM6tcqDV5aYxOX0SYLIzeXtk1bQuqZFOd/iW8+KhdDbsQo+jnipAtBBDvOKc/NfqYQDP9BBKp6ItIFvg4cBtwI/BuEbkx6NcxxvDhrz3KVDHHv3zlvtGerLkOS8ft9U4blk4MNdk2Wcp11wv45euPnQLg4PyQVUhb9PxRp99Dcw3u/c92M/SfuT3esRQD6Kn/xD2AwK5rAxkS+XJwO3qNO42A2mCMOVH3J7gFOGKMeQZARL4I3A48HuSLHD1+nN899n7mJwvMfm7ESdzlF+xX7rlrYe28XcQjWVvaN0C/8i82azQe7fDcE/5bIv+rtuHd0zn2fXmIHZ3Wl6B2HmavvmycH1laZ63Zho8HFAHsZGrnoXoa3vZJmFyIdywTu+Hhz8Nz/2AXRvnh3BF41buDm5+Yuxae/Dp87GbIxrPeJRI6bTj/tP3gnRrRKAbFnlfAO+4K/GmjFv39wLGe348Dt/beQUTuBO4EuPrqq329yMGFadZf9mrbUXLUDbMO3Gpd0wsP29Wa+14FKyc3Svy2Yfd0i+fP11gZYXvCXCbDgT1TkB/ii1l+Asq7YPn4ZTfNzXWYNQz3fEklk4NXvwde8vq4RwLv/Bw8+BlYecH/cxy4BX7pw4ENiV/+KMzsh+MPBPec48qNvwLVs7C+GPdILLPXhPK0EkTb34FfTOQdwJuNMf/W+/09wK3GmA9udv9Dhw6Zw4cPRzY+RVGUJCAiDxpjDm12W9RW7wRwoOf3q7xjiqIoSgRELfoPANeLyLUiUgDeBdwT8RgURVFSS6SZvjGmJSIfBL4BZIG7jDGPRTkGRVGUNBP57iLGmHuBe6N+XUVRFCXhK3IVRVGUi1HRVxRFSREq+oqiKClCRV9RFCVFRLo4a1hE5AxwdISnmAfOBjScuNBziJ+dPn7QcxgXojqHa4wxm/YVGWvRHxURObzVqrSdgp5D/Oz08YOew7gwDueg8Y6iKEqKUNFXFEVJEUkX/U/FPYAA0HOIn50+ftBzGBdiP4dEZ/qKoijKxSTd6SuKoig9qOgriqKkiESKflSbrweNiDwnIj8WkYdF5LB3bJeI3CciT3mXc3GPsxcRuUtETovIoz3HNh2zWD7mvS+PiMjN8Y18gy3O4aMicsJ7Lx4Wkbf03PZb3jn8VETeFM+oL0ZEDojIt0XkcRF5TER+wzu+Y96LPuewY94LESmJyA9E5EfeOfyud/xaEfm+N9Y/91rLIyJF7/cj3u0HQx+kMSZRP9iWzU8D1wEF4EfAjXGPa8CxPwfMX3Lsj4APedc/BPy3uMd5yfh+EbgZeHS7MQNvAf4au4nla4Dvxz3+PufwUeA/bXLfG72/qSJwrfe3lh2Dc9gH3OxdnwKe9Ma6Y96LPuewY94L799z0rueB77v/ft+CXiXd/yTwL/zrv868Env+ruAPw97jEl0+t3N140xDcBtvr5TuR2427t+N/C2+IZyOcaY7wDnLzm81ZhvBz5rLN8DZkUk9l2otziHrbgd+KIxpm6MeRY4gv2bixVjzEljzEPe9RXgCeye1DvmvehzDlsxdu+F9++56v2a934M8EvAX3jHL30f3PvzF8AbRGTUnb37kkTR32zz9X5/OOOEAb4pIg96G8QD7DHGnPSunwL2xDO0odhqzDvtvfmgF33c1ROrjf05eBHBq7Euc0e+F5ecA+yg90JEsiLyMHAauA/7DWTRGNPy7tI7zu45eLcvAbvDHF8SRX8n81pjzM3AbcAHROQXe2809jvgjqqx3Ylj9vgE8BLgJuAk8MexjmZARGQS+DLwm8aY5d7bdsp7sck57Kj3whjTNsbchN0D/BbghnhHdDFJFP0du/m6MeaEd3ka+Cr2D+ZF97Xbuzwd3wgHZqsx75j3xhjzoveftwP8GRuxwdieg4jksWL5eWPMV7zDO+q92OwcduJ7AWCMWQS+Dfw8Nj5zOxX2jrN7Dt7tM8C5MMeVRNHfkZuvi0hFRKbcdeCNwKPYsd/h3e0O4GvxjHAothrzPcB7vcqR1wBLPdHDWHFJvv127HsB9hze5VVdXAtcD/wg6vFdipcDfxp4whjzJz037Zj3Yqtz2EnvhYgsiMisd70M/Avs3MS3gXd4d7v0fXDvzzuAb3nfyMIjzpnusH6wlQlPYrO034l7PAOO+TpsJcKPgMfcuLH53v3AU8DfALviHusl4/4C9it3E5tVvn+rMWMrGz7uvS8/Bg7FPf4+5/A5b4yPYP9j7uu5/+945/BT4La4x++N6bXY6OYR4GHv5y076b3ocw475r0AXgn80Bvro8CHvePXYT+QjgD/Byh6x0ve70e8268Le4zahkFRFCVFJDHeURRFUbZARV9RFCVFqOgriqKkCBV9RVGUFKGiryiKkiJU9BVFUVKEir6iKEqK+P9WHp+7mWvutAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"idata = shorten(X[5])['Force']\n",
"plt.plot(range(len(idata)), idata)\n",
"plt.plot(range(len(X[5]['Force'])),X[5]['Force'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "9d829440",
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"def load_pickles():\n",
" _p = open(Xpickle_file, 'rb')\n",
" X = pickle.load(_p)\n",
" _p.close()\n",
" \n",
" _p = open(ypickle_file, 'rb')\n",
" y = pickle.load(_p)\n",
" _p.close()\n",
" \n",
" return (np.asarray(X, dtype=pd.DataFrame), np.asarray(y, dtype=str))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "0cd6bffc",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"def load_data():\n",
" if os.path.isfile(Xpickle_file) and os.path.isfile(ypickle_file):\n",
" return load_pickles()\n",
" data = []\n",
" label = []\n",
" for user in range(0, user_count):\n",
" user_path = base_path + str(user) + '/split_letters_csv/'\n",
" for file in os.listdir(user_path):\n",
" file_name = user_path + file\n",
" letter = ''.join(filter(lambda x: x.isalpha(), file))[0]\n",
" data.append(pd.read_csv(file_name, delim))\n",
" label.append(letter)\n",
" return (np.asarray(data, dtype=pd.DataFrame), np.asarray(label, dtype=str), np.asarray(file_name))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "0455518d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.58 s, sys: 74.8 ms, total: 3.65 s\n",
"Wall time: 3.65 s\n"
]
},
{
"data": {
"text/plain": [
"(13102,)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"X, y = load_data()\n",
"\n",
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "c96adaf7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f33537e0be0>]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"idata = shorten(X[5])['Force']\n",
"plt.plot(range(len(idata)), idata)\n",
"plt.plot(range(len(X[5]['Force'])),X[5]['Force'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2512addb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.14 s, sys: 9.72 ms, total: 3.15 s\n",
"Wall time: 3.15 s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"<timed exec>:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
]
}
],
"source": [
"%%time\n",
"XX = np.array(list(map(shorten, X)))\n"
]
},
{
"cell_type": "markdown",
"id": "e9c16d84",
"metadata": {},
"source": [
"**How to fix this error**:\n",
"```python\n",
"<timed exec>:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
"```\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "28262137",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 13102.000000\n",
"mean 52.510991\n",
"std 35.307125\n",
"min 4.000000\n",
"50% 48.000000\n",
"95% 95.000000\n",
"96% 101.000000\n",
"97% 108.000000\n",
"98% 124.000000\n",
"99% 157.000000\n",
"max 1512.000000\n",
"dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"\n",
"X_len = np.asarray(list(map(len, XX)))\n",
"l = []\n",
"sq_xlen = pd.Series(X_len)\n",
"ptiles = [x*0.01 for x in range(100)]\n",
"for i in ptiles:\n",
" l.append(sq_xlen.quantile(i))\n",
"plt.plot(l, ptiles)\n",
"sq_xlen.describe(percentiles=[x*0.01 for x in range(95,100)])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "64fce587",
"metadata": {},
"outputs": [],
"source": [
"def plot_data(data):\n",
" fig, axs = plt.subplots(4, 3, figsize=(3*3, 3*4))\n",
" t = data['Millis']\n",
" axs[0][0].plot(t, data['Acc1 X'])\n",
" axs[0][1].plot(t, data['Acc1 Y'])\n",
" axs[0][2].plot(t, data['Acc1 Z'])\n",
" axs[1][0].plot(t, data['Acc2 X'])\n",
" axs[1][1].plot(t, data['Acc2 Y'])\n",
" axs[1][2].plot(t, data['Acc2 Z'])\n",
" axs[2][0].plot(t, data['Gyro X'])\n",
" axs[2][1].plot(t, data['Gyro Y'])\n",
" axs[2][2].plot(t, data['Gyro Z'])\n",
" axs[3][0].plot(t, data['Mag X'])\n",
" axs[3][1].plot(t, data['Mag Y'])\n",
" axs[3][2].plot(t, data['Mag Z'])\n",
"\n",
" for a in axs:\n",
" for b in a:\n",
" b.plot(t, data['Force'])\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "bd86589d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((12973,), (62, 15))"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"threshold_p = 0.99\n",
"threshold = int(sq_xlen.quantile(threshold_p))\n",
"len_mask = np.where(X_len <= threshold)\n",
"\n",
"X_filter = XX[len_mask]\n",
"y_filter = y[len_mask]\n",
"\n",
"X_filter.shape, X_filter[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ce528a76",
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"a = [x.drop(labels='Millis', axis=1) for x in X_filter]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "59bf9140",
"metadata": {},
"outputs": [],
"source": [
"X_filter = pad_sequences(X_filter, dtype=float, padding='post')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "66338e0b",
"metadata": {},
"outputs": [],
"source": [
"def plot_data(data):\n",
" fig, axs = plt.subplots(5, 3, figsize=(3*3, 3*5))\n",
" axs[0][0].plot(data[0])\n",
" axs[0][1].plot(data[1])\n",
" axs[0][2].plot(data[2])\n",
" axs[1][0].plot(data[3])\n",
" axs[1][1].plot(data[4])\n",
" axs[1][2].plot(data[5])\n",
" axs[2][0].plot(data[6])\n",
" axs[2][1].plot(data[7])\n",
" axs[2][2].plot(data[8])\n",
" axs[3][0].plot(data[9])\n",
" axs[3][1].plot(data[10])\n",
" axs[3][2].plot(data[11])\n",
" axs[4][0].plot(data[12])\n",
" axs[4][1].plot(data[13])\n",
"\n",
"# for a in axs:\n",
"# for b in a:\n",
"# b.plot(t, data['Force'])\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8df7f1f4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10378, 157, 15)\n",
"(2595, 157, 15)\n",
"(10378, 26)\n",
"(2595, 26)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x1080 with 15 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder, LabelBinarizer\n",
"import tensorflow as tf\n",
"\n",
"lb = LabelBinarizer()\n",
"\n",
"yt_filter = lb.fit_transform(y_filter)\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X_filter, yt_filter, test_size=0.2, random_state=177013)\n",
"\n",
"print(X_train.shape)\n",
"print(X_test.shape)\n",
"print(y_train.shape)\n",
"print(y_test.shape)\n",
"\n",
"plot_data(X_filter[0].T)"
]
},
{
"cell_type": "markdown",
"id": "289b59cc",
"metadata": {},
"source": [
"fig, axs = plt.subplots(13,2,figsize=(20, 60), sharey=True)\n",
"data_count = int(len(X_train)/10)\n",
"for i,j in zip(X_train[:data_count], lb.inverse_transform(y_train)[:data_count]):\n",
" num = ord(j) - 64\n",
" f = i.T[13]\n",
" r = int((num-1)/2)%13\n",
" c = (num-1)%2\n",
" axs[r][c].title.set_text(f'{j}')\n",
" axs[r][c].plot(f)\n",
"plt.savefig('./all_forces.png')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "13d90f08",
"metadata": {},
"outputs": [],
"source": [
"# FIRST CELL: set these variables to limit GPU usage.\n",
"os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' # this is required\n",
"os.environ['CUDA_VISIBLE_DEVICES'] = '2' # set to '0' for GPU0, '1' for GPU1 or '2' for GPU2. Check \"gpustat\" in a terminal."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "c52f868c",
"metadata": {},
"outputs": [],
"source": [
"accs = []"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "93d225c1",
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout\n",
"from tqdm import tqdm\n",
"\n",
"\n",
"def build_model():\n",
" model = Sequential()\n",
"\n",
" model.add(BatchNormalization(input_shape=X_filter[0].shape))\n",
" \n",
" model.add(Flatten())\n",
"\n",
" for i in range(DENSE_COUNT):\n",
" model.add(Dense(DENSE_NEURONS, activation='relu'))\n",
" \n",
" for i in range(DENSE2_COUNT):\n",
" model.add(Dense(DENSE2_NEURONS, activation='relu'))\n",
" \n",
" model.add(Dense(26, activation='softmax'))\n",
"\n",
" model.compile(\n",
" optimizer=tf.keras.optimizers.Adam(0.001),\n",
" loss=\"categorical_crossentropy\", \n",
" metrics=[\"acc\"],\n",
" )\n",
"\n",
" return model\n",
"# model.summary()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "29d17e4c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/20 [00:00<?, ?it/s]"
]
}
],
"source": [
"for i in tqdm(range(AVG_FROM)):\n",
" model = build_model()\n",
" \n",
" model.fit(X_train, y_train, \n",
" epochs=EPOCH,\n",
" batch_size=128,\n",
" shuffle=True,\n",
" validation_data=(X_test, y_test),\n",
" verbose=0,\n",
" )\n",
" # Evaluate the model on the test data using `evaluate`\n",
"# print(\"Evaluate on test data\")\n",
" results = model.evaluate(X_test, y_test, batch_size=128)\n",
"# print(\"test loss, test acc:\", results)\n",
" accs.append((model,results[1]))\n",
" \n",
"model.save('./model')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5cf54d38",
"metadata": {},
"outputs": [],
"source": [
"np.mean(np.delete(accs,0,1).astype('float64'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc2bcc85",
"metadata": {},
"outputs": [],
"source": [
"exit()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56db820d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}