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Created September 2, 2021 05:43
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Excercise code: Machine Learning in Python
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Machine Learning in Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Input of dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_selection import SelectKBest\n",
"from sklearn import svm\n",
"from sklearn.neighbors import NearestCentroid\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn import tree\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.neural_network import MLPClassifier\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset input"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(\"train.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>tBodyAcc-mean()-X</th>\n",
" <th>tBodyAcc-mean()-Y</th>\n",
" <th>tBodyAcc-mean()-Z</th>\n",
" <th>tBodyAcc-std()-X</th>\n",
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" <th>tBodyAcc-std()-Z</th>\n",
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" <th>tBodyAcc-mad()-Y</th>\n",
" <th>tBodyAcc-mad()-Z</th>\n",
" <th>tBodyAcc-max()-X</th>\n",
" <th>...</th>\n",
" <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
" <th>angle(tBodyAccMean,gravity)</th>\n",
" <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
" <th>angle(tBodyGyroMean,gravityMean)</th>\n",
" <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
" <th>angle(X,gravityMean)</th>\n",
" <th>angle(Y,gravityMean)</th>\n",
" <th>angle(Z,gravityMean)</th>\n",
" <th>subject</th>\n",
" <th>Activity</th>\n",
" </tr>\n",
" </thead>\n",
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" <td>STANDING</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>0.279653</td>\n",
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" <td>-0.963668</td>\n",
" <td>-0.977469</td>\n",
" <td>-0.938692</td>\n",
" <td>...</td>\n",
" <td>-0.760104</td>\n",
" <td>-0.118559</td>\n",
" <td>0.177899</td>\n",
" <td>0.100699</td>\n",
" <td>0.808529</td>\n",
" <td>-0.848933</td>\n",
" <td>0.180637</td>\n",
" <td>-0.049118</td>\n",
" <td>1</td>\n",
" <td>STANDING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.279174</td>\n",
" <td>-0.026201</td>\n",
" <td>-0.123283</td>\n",
" <td>-0.996091</td>\n",
" <td>-0.983403</td>\n",
" <td>-0.990675</td>\n",
" <td>-0.997099</td>\n",
" <td>-0.982750</td>\n",
" <td>-0.989302</td>\n",
" <td>-0.938692</td>\n",
" <td>...</td>\n",
" <td>-0.482845</td>\n",
" <td>-0.036788</td>\n",
" <td>-0.012892</td>\n",
" <td>0.640011</td>\n",
" <td>-0.485366</td>\n",
" <td>-0.848649</td>\n",
" <td>0.181935</td>\n",
" <td>-0.047663</td>\n",
" <td>1</td>\n",
" <td>STANDING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.276629</td>\n",
" <td>-0.016570</td>\n",
" <td>-0.115362</td>\n",
" <td>-0.998139</td>\n",
" <td>-0.980817</td>\n",
" <td>-0.990482</td>\n",
" <td>-0.998321</td>\n",
" <td>-0.979672</td>\n",
" <td>-0.990441</td>\n",
" <td>-0.942469</td>\n",
" <td>...</td>\n",
" <td>-0.699205</td>\n",
" <td>0.123320</td>\n",
" <td>0.122542</td>\n",
" <td>0.693578</td>\n",
" <td>-0.615971</td>\n",
" <td>-0.847865</td>\n",
" <td>0.185151</td>\n",
" <td>-0.043892</td>\n",
" <td>1</td>\n",
" <td>STANDING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>0.039905</td>\n",
" <td>0.077078</td>\n",
" <td>-0.282301</td>\n",
" <td>0.043616</td>\n",
" <td>0.060410</td>\n",
" <td>0.210795</td>\n",
" <td>...</td>\n",
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" <td>0.206972</td>\n",
" <td>-0.425619</td>\n",
" <td>-0.791883</td>\n",
" <td>0.238604</td>\n",
" <td>0.049819</td>\n",
" <td>30</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7348</th>\n",
" <td>0.273853</td>\n",
" <td>-0.007749</td>\n",
" <td>-0.147468</td>\n",
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" <td>0.004816</td>\n",
" <td>0.059280</td>\n",
" <td>-0.322552</td>\n",
" <td>-0.029456</td>\n",
" <td>0.080585</td>\n",
" <td>0.117440</td>\n",
" <td>...</td>\n",
" <td>-0.680744</td>\n",
" <td>0.064907</td>\n",
" <td>0.875679</td>\n",
" <td>-0.879033</td>\n",
" <td>0.400219</td>\n",
" <td>-0.771840</td>\n",
" <td>0.252676</td>\n",
" <td>0.050053</td>\n",
" <td>30</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7349</th>\n",
" <td>0.273387</td>\n",
" <td>-0.017011</td>\n",
" <td>-0.045022</td>\n",
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" <td>0.274533</td>\n",
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" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>0.101702</td>\n",
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" <td>0.700740</td>\n",
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" <td>-0.785181</td>\n",
" <td>0.246432</td>\n",
" <td>0.025339</td>\n",
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" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>-0.280088</td>\n",
" <td>-0.007739</td>\n",
" <td>-0.056088</td>\n",
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" <td>-0.783267</td>\n",
" <td>0.246809</td>\n",
" <td>0.036695</td>\n",
" <td>30</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7352 rows × 563 columns</p>\n",
"</div>"
],
"text/plain": [
" tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z \\\n",
"0 0.288585 -0.020294 -0.132905 \n",
"1 0.278419 -0.016411 -0.123520 \n",
"2 0.279653 -0.019467 -0.113462 \n",
"3 0.279174 -0.026201 -0.123283 \n",
"4 0.276629 -0.016570 -0.115362 \n",
"... ... ... ... \n",
"7347 0.299665 -0.057193 -0.181233 \n",
"7348 0.273853 -0.007749 -0.147468 \n",
"7349 0.273387 -0.017011 -0.045022 \n",
"7350 0.289654 -0.018843 -0.158281 \n",
"7351 0.351503 -0.012423 -0.203867 \n",
"\n",
" tBodyAcc-std()-X tBodyAcc-std()-Y tBodyAcc-std()-Z tBodyAcc-mad()-X \\\n",
"0 -0.995279 -0.983111 -0.913526 -0.995112 \n",
"1 -0.998245 -0.975300 -0.960322 -0.998807 \n",
"2 -0.995380 -0.967187 -0.978944 -0.996520 \n",
"3 -0.996091 -0.983403 -0.990675 -0.997099 \n",
"4 -0.998139 -0.980817 -0.990482 -0.998321 \n",
"... ... ... ... ... \n",
"7347 -0.195387 0.039905 0.077078 -0.282301 \n",
"7348 -0.235309 0.004816 0.059280 -0.322552 \n",
"7349 -0.218218 -0.103822 0.274533 -0.304515 \n",
"7350 -0.219139 -0.111412 0.268893 -0.310487 \n",
"7351 -0.269270 -0.087212 0.177404 -0.377404 \n",
"\n",
" tBodyAcc-mad()-Y tBodyAcc-mad()-Z tBodyAcc-max()-X ... \\\n",
"0 -0.983185 -0.923527 -0.934724 ... \n",
"1 -0.974914 -0.957686 -0.943068 ... \n",
"2 -0.963668 -0.977469 -0.938692 ... \n",
"3 -0.982750 -0.989302 -0.938692 ... \n",
"4 -0.979672 -0.990441 -0.942469 ... \n",
"... ... ... ... ... \n",
"7347 0.043616 0.060410 0.210795 ... \n",
"7348 -0.029456 0.080585 0.117440 ... \n",
"7349 -0.098913 0.332584 0.043999 ... \n",
"7350 -0.068200 0.319473 0.101702 ... \n",
"7351 -0.038678 0.229430 0.269013 ... \n",
"\n",
" fBodyBodyGyroJerkMag-kurtosis() angle(tBodyAccMean,gravity) \\\n",
"0 -0.710304 -0.112754 \n",
"1 -0.861499 0.053477 \n",
"2 -0.760104 -0.118559 \n",
"3 -0.482845 -0.036788 \n",
"4 -0.699205 0.123320 \n",
"... ... ... \n",
"7347 -0.880324 -0.190437 \n",
"7348 -0.680744 0.064907 \n",
"7349 -0.304029 0.052806 \n",
"7350 -0.344314 -0.101360 \n",
"7351 -0.740738 -0.280088 \n",
"\n",
" angle(tBodyAccJerkMean),gravityMean) angle(tBodyGyroMean,gravityMean) \\\n",
"0 0.030400 -0.464761 \n",
"1 -0.007435 -0.732626 \n",
"2 0.177899 0.100699 \n",
"3 -0.012892 0.640011 \n",
"4 0.122542 0.693578 \n",
"... ... ... \n",
"7347 0.829718 0.206972 \n",
"7348 0.875679 -0.879033 \n",
"7349 -0.266724 0.864404 \n",
"7350 0.700740 0.936674 \n",
"7351 -0.007739 -0.056088 \n",
"\n",
" angle(tBodyGyroJerkMean,gravityMean) angle(X,gravityMean) \\\n",
"0 -0.018446 -0.841247 \n",
"1 0.703511 -0.844788 \n",
"2 0.808529 -0.848933 \n",
"3 -0.485366 -0.848649 \n",
"4 -0.615971 -0.847865 \n",
"... ... ... \n",
"7347 -0.425619 -0.791883 \n",
"7348 0.400219 -0.771840 \n",
"7349 0.701169 -0.779133 \n",
"7350 -0.589479 -0.785181 \n",
"7351 -0.616956 -0.783267 \n",
"\n",
" angle(Y,gravityMean) angle(Z,gravityMean) subject Activity \n",
"0 0.179941 -0.058627 1 STANDING \n",
"1 0.180289 -0.054317 1 STANDING \n",
"2 0.180637 -0.049118 1 STANDING \n",
"3 0.181935 -0.047663 1 STANDING \n",
"4 0.185151 -0.043892 1 STANDING \n",
"... ... ... ... ... \n",
"7347 0.238604 0.049819 30 WALKING_UPSTAIRS \n",
"7348 0.252676 0.050053 30 WALKING_UPSTAIRS \n",
"7349 0.249145 0.040811 30 WALKING_UPSTAIRS \n",
"7350 0.246432 0.025339 30 WALKING_UPSTAIRS \n",
"7351 0.246809 0.036695 30 WALKING_UPSTAIRS \n",
"\n",
"[7352 rows x 563 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"remove_subject = data.iloc[:,0:561]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"label = data.iloc[:,562]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"fin_data = pd.concat([remove_subject, label], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>tBodyAcc-mean()-X</th>\n",
" <th>tBodyAcc-mean()-Y</th>\n",
" <th>tBodyAcc-mean()-Z</th>\n",
" <th>tBodyAcc-std()-X</th>\n",
" <th>tBodyAcc-std()-Y</th>\n",
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" <th>tBodyAcc-mad()-Z</th>\n",
" <th>tBodyAcc-max()-X</th>\n",
" <th>...</th>\n",
" <th>fBodyBodyGyroJerkMag-skewness()</th>\n",
" <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
" <th>angle(tBodyAccMean,gravity)</th>\n",
" <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
" <th>angle(tBodyGyroMean,gravityMean)</th>\n",
" <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
" <th>angle(X,gravityMean)</th>\n",
" <th>angle(Y,gravityMean)</th>\n",
" <th>angle(Z,gravityMean)</th>\n",
" <th>Activity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.288585</td>\n",
" <td>-0.020294</td>\n",
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" <td>-0.934724</td>\n",
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" <td>STANDING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.278419</td>\n",
" <td>-0.016411</td>\n",
" <td>-0.123520</td>\n",
" <td>-0.998245</td>\n",
" <td>-0.975300</td>\n",
" <td>-0.960322</td>\n",
" <td>-0.998807</td>\n",
" <td>-0.974914</td>\n",
" <td>-0.957686</td>\n",
" <td>-0.943068</td>\n",
" <td>...</td>\n",
" <td>-0.595051</td>\n",
" <td>-0.861499</td>\n",
" <td>0.053477</td>\n",
" <td>-0.007435</td>\n",
" <td>-0.732626</td>\n",
" <td>0.703511</td>\n",
" <td>-0.844788</td>\n",
" <td>0.180289</td>\n",
" <td>-0.054317</td>\n",
" <td>STANDING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.279653</td>\n",
" <td>-0.019467</td>\n",
" <td>-0.113462</td>\n",
" <td>-0.995380</td>\n",
" <td>-0.967187</td>\n",
" <td>-0.978944</td>\n",
" <td>-0.996520</td>\n",
" <td>-0.963668</td>\n",
" <td>-0.977469</td>\n",
" <td>-0.938692</td>\n",
" <td>...</td>\n",
" <td>-0.390748</td>\n",
" <td>-0.760104</td>\n",
" <td>-0.118559</td>\n",
" <td>0.177899</td>\n",
" <td>0.100699</td>\n",
" <td>0.808529</td>\n",
" <td>-0.848933</td>\n",
" <td>0.180637</td>\n",
" <td>-0.049118</td>\n",
" <td>STANDING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.279174</td>\n",
" <td>-0.026201</td>\n",
" <td>-0.123283</td>\n",
" <td>-0.996091</td>\n",
" <td>-0.983403</td>\n",
" <td>-0.990675</td>\n",
" <td>-0.997099</td>\n",
" <td>-0.982750</td>\n",
" <td>-0.989302</td>\n",
" <td>-0.938692</td>\n",
" <td>...</td>\n",
" <td>-0.117290</td>\n",
" <td>-0.482845</td>\n",
" <td>-0.036788</td>\n",
" <td>-0.012892</td>\n",
" <td>0.640011</td>\n",
" <td>-0.485366</td>\n",
" <td>-0.848649</td>\n",
" <td>0.181935</td>\n",
" <td>-0.047663</td>\n",
" <td>STANDING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.276629</td>\n",
" <td>-0.016570</td>\n",
" <td>-0.115362</td>\n",
" <td>-0.998139</td>\n",
" <td>-0.980817</td>\n",
" <td>-0.990482</td>\n",
" <td>-0.998321</td>\n",
" <td>-0.979672</td>\n",
" <td>-0.990441</td>\n",
" <td>-0.942469</td>\n",
" <td>...</td>\n",
" <td>-0.351471</td>\n",
" <td>-0.699205</td>\n",
" <td>0.123320</td>\n",
" <td>0.122542</td>\n",
" <td>0.693578</td>\n",
" <td>-0.615971</td>\n",
" <td>-0.847865</td>\n",
" <td>0.185151</td>\n",
" <td>-0.043892</td>\n",
" <td>STANDING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7347</th>\n",
" <td>0.299665</td>\n",
" <td>-0.057193</td>\n",
" <td>-0.181233</td>\n",
" <td>-0.195387</td>\n",
" <td>0.039905</td>\n",
" <td>0.077078</td>\n",
" <td>-0.282301</td>\n",
" <td>0.043616</td>\n",
" <td>0.060410</td>\n",
" <td>0.210795</td>\n",
" <td>...</td>\n",
" <td>-0.588433</td>\n",
" <td>-0.880324</td>\n",
" <td>-0.190437</td>\n",
" <td>0.829718</td>\n",
" <td>0.206972</td>\n",
" <td>-0.425619</td>\n",
" <td>-0.791883</td>\n",
" <td>0.238604</td>\n",
" <td>0.049819</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7348</th>\n",
" <td>0.273853</td>\n",
" <td>-0.007749</td>\n",
" <td>-0.147468</td>\n",
" <td>-0.235309</td>\n",
" <td>0.004816</td>\n",
" <td>0.059280</td>\n",
" <td>-0.322552</td>\n",
" <td>-0.029456</td>\n",
" <td>0.080585</td>\n",
" <td>0.117440</td>\n",
" <td>...</td>\n",
" <td>-0.390738</td>\n",
" <td>-0.680744</td>\n",
" <td>0.064907</td>\n",
" <td>0.875679</td>\n",
" <td>-0.879033</td>\n",
" <td>0.400219</td>\n",
" <td>-0.771840</td>\n",
" <td>0.252676</td>\n",
" <td>0.050053</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7349</th>\n",
" <td>0.273387</td>\n",
" <td>-0.017011</td>\n",
" <td>-0.045022</td>\n",
" <td>-0.218218</td>\n",
" <td>-0.103822</td>\n",
" <td>0.274533</td>\n",
" <td>-0.304515</td>\n",
" <td>-0.098913</td>\n",
" <td>0.332584</td>\n",
" <td>0.043999</td>\n",
" <td>...</td>\n",
" <td>0.025145</td>\n",
" <td>-0.304029</td>\n",
" <td>0.052806</td>\n",
" <td>-0.266724</td>\n",
" <td>0.864404</td>\n",
" <td>0.701169</td>\n",
" <td>-0.779133</td>\n",
" <td>0.249145</td>\n",
" <td>0.040811</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7350</th>\n",
" <td>0.289654</td>\n",
" <td>-0.018843</td>\n",
" <td>-0.158281</td>\n",
" <td>-0.219139</td>\n",
" <td>-0.111412</td>\n",
" <td>0.268893</td>\n",
" <td>-0.310487</td>\n",
" <td>-0.068200</td>\n",
" <td>0.319473</td>\n",
" <td>0.101702</td>\n",
" <td>...</td>\n",
" <td>0.063907</td>\n",
" <td>-0.344314</td>\n",
" <td>-0.101360</td>\n",
" <td>0.700740</td>\n",
" <td>0.936674</td>\n",
" <td>-0.589479</td>\n",
" <td>-0.785181</td>\n",
" <td>0.246432</td>\n",
" <td>0.025339</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7351</th>\n",
" <td>0.351503</td>\n",
" <td>-0.012423</td>\n",
" <td>-0.203867</td>\n",
" <td>-0.269270</td>\n",
" <td>-0.087212</td>\n",
" <td>0.177404</td>\n",
" <td>-0.377404</td>\n",
" <td>-0.038678</td>\n",
" <td>0.229430</td>\n",
" <td>0.269013</td>\n",
" <td>...</td>\n",
" <td>-0.387052</td>\n",
" <td>-0.740738</td>\n",
" <td>-0.280088</td>\n",
" <td>-0.007739</td>\n",
" <td>-0.056088</td>\n",
" <td>-0.616956</td>\n",
" <td>-0.783267</td>\n",
" <td>0.246809</td>\n",
" <td>0.036695</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7352 rows × 562 columns</p>\n",
"</div>"
],
"text/plain": [
" tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z \\\n",
"0 0.288585 -0.020294 -0.132905 \n",
"1 0.278419 -0.016411 -0.123520 \n",
"2 0.279653 -0.019467 -0.113462 \n",
"3 0.279174 -0.026201 -0.123283 \n",
"4 0.276629 -0.016570 -0.115362 \n",
"... ... ... ... \n",
"7347 0.299665 -0.057193 -0.181233 \n",
"7348 0.273853 -0.007749 -0.147468 \n",
"7349 0.273387 -0.017011 -0.045022 \n",
"7350 0.289654 -0.018843 -0.158281 \n",
"7351 0.351503 -0.012423 -0.203867 \n",
"\n",
" tBodyAcc-std()-X tBodyAcc-std()-Y tBodyAcc-std()-Z tBodyAcc-mad()-X \\\n",
"0 -0.995279 -0.983111 -0.913526 -0.995112 \n",
"1 -0.998245 -0.975300 -0.960322 -0.998807 \n",
"2 -0.995380 -0.967187 -0.978944 -0.996520 \n",
"3 -0.996091 -0.983403 -0.990675 -0.997099 \n",
"4 -0.998139 -0.980817 -0.990482 -0.998321 \n",
"... ... ... ... ... \n",
"7347 -0.195387 0.039905 0.077078 -0.282301 \n",
"7348 -0.235309 0.004816 0.059280 -0.322552 \n",
"7349 -0.218218 -0.103822 0.274533 -0.304515 \n",
"7350 -0.219139 -0.111412 0.268893 -0.310487 \n",
"7351 -0.269270 -0.087212 0.177404 -0.377404 \n",
"\n",
" tBodyAcc-mad()-Y tBodyAcc-mad()-Z tBodyAcc-max()-X ... \\\n",
"0 -0.983185 -0.923527 -0.934724 ... \n",
"1 -0.974914 -0.957686 -0.943068 ... \n",
"2 -0.963668 -0.977469 -0.938692 ... \n",
"3 -0.982750 -0.989302 -0.938692 ... \n",
"4 -0.979672 -0.990441 -0.942469 ... \n",
"... ... ... ... ... \n",
"7347 0.043616 0.060410 0.210795 ... \n",
"7348 -0.029456 0.080585 0.117440 ... \n",
"7349 -0.098913 0.332584 0.043999 ... \n",
"7350 -0.068200 0.319473 0.101702 ... \n",
"7351 -0.038678 0.229430 0.269013 ... \n",
"\n",
" fBodyBodyGyroJerkMag-skewness() fBodyBodyGyroJerkMag-kurtosis() \\\n",
"0 -0.298676 -0.710304 \n",
"1 -0.595051 -0.861499 \n",
"2 -0.390748 -0.760104 \n",
"3 -0.117290 -0.482845 \n",
"4 -0.351471 -0.699205 \n",
"... ... ... \n",
"7347 -0.588433 -0.880324 \n",
"7348 -0.390738 -0.680744 \n",
"7349 0.025145 -0.304029 \n",
"7350 0.063907 -0.344314 \n",
"7351 -0.387052 -0.740738 \n",
"\n",
" angle(tBodyAccMean,gravity) angle(tBodyAccJerkMean),gravityMean) \\\n",
"0 -0.112754 0.030400 \n",
"1 0.053477 -0.007435 \n",
"2 -0.118559 0.177899 \n",
"3 -0.036788 -0.012892 \n",
"4 0.123320 0.122542 \n",
"... ... ... \n",
"7347 -0.190437 0.829718 \n",
"7348 0.064907 0.875679 \n",
"7349 0.052806 -0.266724 \n",
"7350 -0.101360 0.700740 \n",
"7351 -0.280088 -0.007739 \n",
"\n",
" angle(tBodyGyroMean,gravityMean) angle(tBodyGyroJerkMean,gravityMean) \\\n",
"0 -0.464761 -0.018446 \n",
"1 -0.732626 0.703511 \n",
"2 0.100699 0.808529 \n",
"3 0.640011 -0.485366 \n",
"4 0.693578 -0.615971 \n",
"... ... ... \n",
"7347 0.206972 -0.425619 \n",
"7348 -0.879033 0.400219 \n",
"7349 0.864404 0.701169 \n",
"7350 0.936674 -0.589479 \n",
"7351 -0.056088 -0.616956 \n",
"\n",
" angle(X,gravityMean) angle(Y,gravityMean) angle(Z,gravityMean) \\\n",
"0 -0.841247 0.179941 -0.058627 \n",
"1 -0.844788 0.180289 -0.054317 \n",
"2 -0.848933 0.180637 -0.049118 \n",
"3 -0.848649 0.181935 -0.047663 \n",
"4 -0.847865 0.185151 -0.043892 \n",
"... ... ... ... \n",
"7347 -0.791883 0.238604 0.049819 \n",
"7348 -0.771840 0.252676 0.050053 \n",
"7349 -0.779133 0.249145 0.040811 \n",
"7350 -0.785181 0.246432 0.025339 \n",
"7351 -0.783267 0.246809 0.036695 \n",
"\n",
" Activity \n",
"0 STANDING \n",
"1 STANDING \n",
"2 STANDING \n",
"3 STANDING \n",
"4 STANDING \n",
"... ... \n",
"7347 WALKING_UPSTAIRS \n",
"7348 WALKING_UPSTAIRS \n",
"7349 WALKING_UPSTAIRS \n",
"7350 WALKING_UPSTAIRS \n",
"7351 WALKING_UPSTAIRS \n",
"\n",
"[7352 rows x 562 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fin_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Избор на карактеристики"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ова податочно множество содржи голем број на карактеристики. За полесна обработка и поголема прегледност, се избираат само дел од нив. Ова може да се направи со функцијата `SelectKBest` од scikit-learn. Во овој случај, ќе ги избереме 10-те карактеристики кои најдобро ја објеснуваат варијансата во множеството. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"select_feats = SelectKBest(k=10)\n",
"feats = select_feats.fit_transform(remove_subject, label)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.96339614, 0.89205451, 0.97743631, 0.89946864, -0.81994925,\n",
" -0.86341476, -0.94635692, -1. , -1. , -1. ])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feats[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" 'STANDING']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(fin_data.iloc[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"На следниот начин можеме да видиме кои колони се избрани."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"234"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(fin_data.iloc[0]).index(-0.86341476)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"40"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(fin_data.iloc[0]).index(0.96339614)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'tGravityAcc-mean()-X'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fin_data.columns[40]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'tBodyAccJerkMag-entropy()'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fin_data.columns[234]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"На ист начин се вчитува и множеството за тестирање."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"X_train = feats\n",
"y_train = label.to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"test = pd.read_csv(\"test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>tBodyAcc-mean()-X</th>\n",
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" <th>tBodyAcc-mean()-Z</th>\n",
" <th>tBodyAcc-std()-X</th>\n",
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" <th>angle(Z,gravityMean)</th>\n",
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" <td>0.280083</td>\n",
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" <td>2</td>\n",
" <td>STANDING</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2942</th>\n",
" <td>0.310155</td>\n",
" <td>-0.053391</td>\n",
" <td>-0.099109</td>\n",
" <td>-0.287866</td>\n",
" <td>-0.140589</td>\n",
" <td>-0.215088</td>\n",
" <td>-0.356083</td>\n",
" <td>-0.148775</td>\n",
" <td>-0.232057</td>\n",
" <td>0.185361</td>\n",
" <td>...</td>\n",
" <td>-0.750809</td>\n",
" <td>-0.337422</td>\n",
" <td>0.346295</td>\n",
" <td>0.884904</td>\n",
" <td>-0.698885</td>\n",
" <td>-0.651732</td>\n",
" <td>0.274627</td>\n",
" <td>0.184784</td>\n",
" <td>24</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2943</th>\n",
" <td>0.363385</td>\n",
" <td>-0.039214</td>\n",
" <td>-0.105915</td>\n",
" <td>-0.305388</td>\n",
" <td>0.028148</td>\n",
" <td>-0.196373</td>\n",
" <td>-0.373540</td>\n",
" <td>-0.030036</td>\n",
" <td>-0.270237</td>\n",
" <td>0.185361</td>\n",
" <td>...</td>\n",
" <td>-0.700274</td>\n",
" <td>-0.736701</td>\n",
" <td>-0.372889</td>\n",
" <td>-0.657421</td>\n",
" <td>0.322549</td>\n",
" <td>-0.655181</td>\n",
" <td>0.273578</td>\n",
" <td>0.182412</td>\n",
" <td>24</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2944</th>\n",
" <td>0.349966</td>\n",
" <td>0.030077</td>\n",
" <td>-0.115788</td>\n",
" <td>-0.329638</td>\n",
" <td>-0.042143</td>\n",
" <td>-0.250181</td>\n",
" <td>-0.388017</td>\n",
" <td>-0.133257</td>\n",
" <td>-0.347029</td>\n",
" <td>0.007471</td>\n",
" <td>...</td>\n",
" <td>-0.467179</td>\n",
" <td>-0.181560</td>\n",
" <td>0.088574</td>\n",
" <td>0.696663</td>\n",
" <td>0.363139</td>\n",
" <td>-0.655357</td>\n",
" <td>0.274479</td>\n",
" <td>0.181184</td>\n",
" <td>24</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2945</th>\n",
" <td>0.237594</td>\n",
" <td>0.018467</td>\n",
" <td>-0.096499</td>\n",
" <td>-0.323114</td>\n",
" <td>-0.229775</td>\n",
" <td>-0.207574</td>\n",
" <td>-0.392380</td>\n",
" <td>-0.279610</td>\n",
" <td>-0.289477</td>\n",
" <td>0.007471</td>\n",
" <td>...</td>\n",
" <td>-0.617737</td>\n",
" <td>0.444558</td>\n",
" <td>-0.819188</td>\n",
" <td>0.929294</td>\n",
" <td>-0.008398</td>\n",
" <td>-0.659719</td>\n",
" <td>0.264782</td>\n",
" <td>0.187563</td>\n",
" <td>24</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2946</th>\n",
" <td>0.153627</td>\n",
" <td>-0.018437</td>\n",
" <td>-0.137018</td>\n",
" <td>-0.330046</td>\n",
" <td>-0.195253</td>\n",
" <td>-0.164339</td>\n",
" <td>-0.430974</td>\n",
" <td>-0.218295</td>\n",
" <td>-0.229933</td>\n",
" <td>-0.111527</td>\n",
" <td>...</td>\n",
" <td>-0.436940</td>\n",
" <td>0.598808</td>\n",
" <td>-0.287951</td>\n",
" <td>0.876030</td>\n",
" <td>-0.024965</td>\n",
" <td>-0.660080</td>\n",
" <td>0.263936</td>\n",
" <td>0.188103</td>\n",
" <td>24</td>\n",
" <td>WALKING_UPSTAIRS</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2947 rows × 563 columns</p>\n",
"</div>"
],
"text/plain": [
" tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z \\\n",
"0 0.257178 -0.023285 -0.014654 \n",
"1 0.286027 -0.013163 -0.119083 \n",
"2 0.275485 -0.026050 -0.118152 \n",
"3 0.270298 -0.032614 -0.117520 \n",
"4 0.274833 -0.027848 -0.129527 \n",
"... ... ... ... \n",
"2942 0.310155 -0.053391 -0.099109 \n",
"2943 0.363385 -0.039214 -0.105915 \n",
"2944 0.349966 0.030077 -0.115788 \n",
"2945 0.237594 0.018467 -0.096499 \n",
"2946 0.153627 -0.018437 -0.137018 \n",
"\n",
" tBodyAcc-std()-X tBodyAcc-std()-Y tBodyAcc-std()-Z tBodyAcc-mad()-X \\\n",
"0 -0.938404 -0.920091 -0.667683 -0.952501 \n",
"1 -0.975415 -0.967458 -0.944958 -0.986799 \n",
"2 -0.993819 -0.969926 -0.962748 -0.994403 \n",
"3 -0.994743 -0.973268 -0.967091 -0.995274 \n",
"4 -0.993852 -0.967445 -0.978295 -0.994111 \n",
"... ... ... ... ... \n",
"2942 -0.287866 -0.140589 -0.215088 -0.356083 \n",
"2943 -0.305388 0.028148 -0.196373 -0.373540 \n",
"2944 -0.329638 -0.042143 -0.250181 -0.388017 \n",
"2945 -0.323114 -0.229775 -0.207574 -0.392380 \n",
"2946 -0.330046 -0.195253 -0.164339 -0.430974 \n",
"\n",
" tBodyAcc-mad()-Y tBodyAcc-mad()-Z tBodyAcc-max()-X ... \\\n",
"0 -0.925249 -0.674302 -0.894088 ... \n",
"1 -0.968401 -0.945823 -0.894088 ... \n",
"2 -0.970735 -0.963483 -0.939260 ... \n",
"3 -0.974471 -0.968897 -0.938610 ... \n",
"4 -0.965953 -0.977346 -0.938610 ... \n",
"... ... ... ... ... \n",
"2942 -0.148775 -0.232057 0.185361 ... \n",
"2943 -0.030036 -0.270237 0.185361 ... \n",
"2944 -0.133257 -0.347029 0.007471 ... \n",
"2945 -0.279610 -0.289477 0.007471 ... \n",
"2946 -0.218295 -0.229933 -0.111527 ... \n",
"\n",
" fBodyBodyGyroJerkMag-kurtosis() angle(tBodyAccMean,gravity) \\\n",
"0 -0.705974 0.006462 \n",
"1 -0.594944 -0.083495 \n",
"2 -0.640736 -0.034956 \n",
"3 -0.736124 -0.017067 \n",
"4 -0.846595 -0.002223 \n",
"... ... ... \n",
"2942 -0.750809 -0.337422 \n",
"2943 -0.700274 -0.736701 \n",
"2944 -0.467179 -0.181560 \n",
"2945 -0.617737 0.444558 \n",
"2946 -0.436940 0.598808 \n",
"\n",
" angle(tBodyAccJerkMean),gravityMean) angle(tBodyGyroMean,gravityMean) \\\n",
"0 0.162920 -0.825886 \n",
"1 0.017500 -0.434375 \n",
"2 0.202302 0.064103 \n",
"3 0.154438 0.340134 \n",
"4 -0.040046 0.736715 \n",
"... ... ... \n",
"2942 0.346295 0.884904 \n",
"2943 -0.372889 -0.657421 \n",
"2944 0.088574 0.696663 \n",
"2945 -0.819188 0.929294 \n",
"2946 -0.287951 0.876030 \n",
"\n",
" angle(tBodyGyroJerkMean,gravityMean) angle(X,gravityMean) \\\n",
"0 0.271151 -0.720009 \n",
"1 0.920593 -0.698091 \n",
"2 0.145068 -0.702771 \n",
"3 0.296407 -0.698954 \n",
"4 -0.118545 -0.692245 \n",
"... ... ... \n",
"2942 -0.698885 -0.651732 \n",
"2943 0.322549 -0.655181 \n",
"2944 0.363139 -0.655357 \n",
"2945 -0.008398 -0.659719 \n",
"2946 -0.024965 -0.660080 \n",
"\n",
" angle(Y,gravityMean) angle(Z,gravityMean) subject Activity \n",
"0 0.276801 -0.057978 2 STANDING \n",
"1 0.281343 -0.083898 2 STANDING \n",
"2 0.280083 -0.079346 2 STANDING \n",
"3 0.284114 -0.077108 2 STANDING \n",
"4 0.290722 -0.073857 2 STANDING \n",
"... ... ... ... ... \n",
"2942 0.274627 0.184784 24 WALKING_UPSTAIRS \n",
"2943 0.273578 0.182412 24 WALKING_UPSTAIRS \n",
"2944 0.274479 0.181184 24 WALKING_UPSTAIRS \n",
"2945 0.264782 0.187563 24 WALKING_UPSTAIRS \n",
"2946 0.263936 0.188103 24 WALKING_UPSTAIRS \n",
"\n",
"[2947 rows x 563 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"test_cols = test.iloc[:,0:561]\n",
"test_labels = test.iloc[:,562]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"X_test = select_feats.transform(test_cols)\n",
"y_test = test_labels.to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.93648925"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.iloc[0][40]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.93648925, 0.90608259, 0.9444614 , 0.82929682, -0.08517415,\n",
" -0.12955231, -0.33967327, -0.4706616 , -0.6721718 , -0.48461929])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test[0]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.12955231"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.iloc[0][234]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Методи од машинското учење"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Support Vector Machine"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svmClassifier = svm.SVC()\n",
"svmClassifier.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['STANDING'], dtype=object)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svmClassifier.predict(X_test[:1])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'STANDING'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test[0]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7444859178825924"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svmClassifier.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
" intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
" multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
" verbose=0)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linear_svmClassifier = svm.LinearSVC()\n",
"linear_svmClassifier.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"y_pred_linearSVM = linear_svmClassifier.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7519511367492365"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linear_svmClassifier.score(X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### k Nearest Neightbors"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NearestCentroid(metric='euclidean', shrink_threshold=None)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knnClassifier = NearestCentroid()\n",
"knnClassifier.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"y_pred_kNN = knnClassifier.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6871394638615541"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knnClassifier.score(X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Наивен Баесов класификатор"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GaussianNB(priors=None, var_smoothing=1e-09)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nbClassifier = GaussianNB()\n",
"nbClassifier.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7081778079402783"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nbClassifier.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"y_pred_NB = nbClassifier.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['STANDING', 'STANDING', 'STANDING', ..., 'WALKING_UPSTAIRS',\n",
" 'WALKING_UPSTAIRS', 'WALKING_UPSTAIRS'], dtype='<U18')"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred_NB"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING SITTING\n",
"STANDING STANDING\n",
"STANDING SITTING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_UPSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_UPSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_UPSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_UPSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_UPSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_UPSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_UPSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"STANDING SITTING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"SITTING STANDING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
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},
{
"name": "stdout",
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"text": [
"WALKING WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"LAYING LAYING\n",
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"WALKING WALKING\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
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"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_UPSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"WALKING WALKING_UPSTAIRS\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING_DOWNSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"STANDING STANDING\n",
"SITTING STANDING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING SITTING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"SITTING STANDING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"LAYING LAYING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING\n",
"WALKING WALKING_UPSTAIRS\n",
"WALKING WALKING_UPSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_DOWNSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n",
"WALKING_UPSTAIRS WALKING_UPSTAIRS\n"
]
}
],
"source": [
"for true, pred in zip(y_test, y_pred_NB):\n",
" print(true, pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Дрва на одлука"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Класификација со дрво на одлука. Во првиот пример за оценување на поделбите се користи gini, а во вториот ентропија. "
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort='deprecated',\n",
" random_state=0, splitter='best')"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtClassifier = tree.DecisionTreeClassifier(random_state=0)\n",
"dtClassifier.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7010519172039362"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtClassifier.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6959619952494062"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtClassifierEntropy = tree.DecisionTreeClassifier(criterion='entropy', random_state = 0)\n",
"dtClassifierEntropy.fit(X_train, y_train)\n",
"dtClassifierEntropy.score(X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Класификација со случајна шума - комбинација на повеќе дрва на одлука."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
" criterion='gini', max_depth=None, max_features='auto',\n",
" max_leaf_nodes=None, max_samples=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100,\n",
" n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"randForestClassifier = RandomForestClassifier(random_state=0)\n",
"randForestClassifier.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7468612147947065"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"randForestClassifier.score(X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Класификација со случајна шума со ограничена длабочина на дрво."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5581947743467933"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"randForestClassifierLimited = RandomForestClassifier(max_depth=2, random_state=0)\n",
"randForestClassifierLimited.fit(X_train, y_train)\n",
"randForestClassifierLimited.score(X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Невронски мрежи"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Класификација со повеќеслоен перцептрон и можни вредности на влезните аргументи.\n",
"- hidden_layer_sizes: број на неврони во секој од скриените слоеви. Се внесуваат како подредени торки, при што секој елемент го претставува бројот на неврони во еден слој (default = 100)\n",
"- solver: решавач за оптимизација на тежините. lbfgs е особено погоден за помали множества, а adam е добар кога тренирачкото множество е големо (со илјадници примероци) (default = adam)\n",
"- activation: активациска функција (default = relu)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
" % self.max_iter, ConvergenceWarning)\n"
]
},
{
"data": {
"text/plain": [
"0.7512724804886325"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nnClassifier = MLPClassifier(random_state=0)\n",
"nnClassifier.fit(X_train, y_train)\n",
"nnClassifier.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
" self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
]
},
{
"data": {
"text/plain": [
"0.7648456057007126"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nnClassifier5 = MLPClassifier(random_state=0, hidden_layer_sizes=(5,), solver='lbfgs')\n",
"nnClassifier5.fit(X_train, y_train)\n",
"nnClassifier5.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
" % self.max_iter, ConvergenceWarning)\n"
]
},
{
"data": {
"text/plain": [
"0.7370206990159485"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nnClassifier5 = MLPClassifier(random_state=0, hidden_layer_sizes=(5,), solver='adam')\n",
"nnClassifier5.fit(X_train, y_train)\n",
"nnClassifier5.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
" self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
]
},
{
"data": {
"text/plain": [
"0.6603325415676959"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nnClassifier2 = MLPClassifier(random_state=0, hidden_layer_sizes=(5,5), solver='lbfgs')\n",
"nnClassifier2.fit(X_train, y_train)\n",
"nnClassifier2.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
" self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
]
},
{
"data": {
"text/plain": [
"0.7543264336613505"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nnClassifier20 = MLPClassifier(random_state=0, hidden_layer_sizes=(20,), solver='lbfgs')\n",
"nnClassifier20.fit(X_train, y_train)\n",
"nnClassifier20.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
" self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
]
},
{
"data": {
"text/plain": [
"0.7678995588734306"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nnClassifierTan = MLPClassifier(random_state=0, hidden_layer_sizes=(20,), solver='lbfgs', activation='tanh')\n",
"nnClassifierTan.fit(X_train, y_train)\n",
"nnClassifierTan.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7597556837461825"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nnClassifierTan = MLPClassifier(random_state=0, hidden_layer_sizes=(20,), solver='adam', activation='tanh', max_iter=700)\n",
"nnClassifierTan.fit(X_train, y_train)\n",
"nnClassifierTan.score(X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Tensorflow и Keras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Библиотеките Tensorflow и Keras овозможуваат работа со невронски мрежи од секој тип, односно и додавање на слоеви со различни активациски функции, како и градење на рекурентни и конволуциски невронски мрежи. Конволуциските невронски мрежи се особено соодветни за обработка на слики, што е тип на податоци кој често се појавува и наоѓа примена во областа на AAL."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras.models import Model, Sequential\n",
"from tensorflow.keras.layers import Input, Dense"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"a = Input(shape=(10,))\n",
"x = Dense(64, activation='relu')(a)\n",
"x = Dense(125, activation='relu')(x)\n",
"x = Dense(512, activation='relu')(x)\n",
"b = Dense(6, activation='softmax')(x) #6 classes\n",
"model = Model(inputs=a, outputs=b)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"model.compile(loss='sparse_categorical_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Податоците тука мора да се претворат во нумерички формат. Во овој случај, само е потребно тоа да се направи кај класата."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"classes = label.unique()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"classes_dict = {'STANDING':0, 'SITTING':1, 'LAYING':2, 'WALKING':3, 'WALKING_DOWNSTAIRS':4,\n",
" 'WALKING_UPSTAIRS':5}"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"y_train_new = []\n",
"for sample in y_train:\n",
" y_train_new.append(classes_dict[sample])\n",
"y_train_new = np.array(y_train_new)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"y_test_new = []\n",
"for sample in y_test:\n",
" y_test_new.append(classes_dict[sample])\n",
"y_test_new = np.array(y_test_new)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"230/230 [==============================] - 3s 5ms/step - loss: 0.7170 - accuracy: 0.6289\n",
"Epoch 2/10\n",
"230/230 [==============================] - 1s 4ms/step - loss: 0.5704 - accuracy: 0.7087\n",
"Epoch 3/10\n",
"230/230 [==============================] - 1s 4ms/step - loss: 0.5289 - accuracy: 0.7349\n",
"Epoch 4/10\n",
"230/230 [==============================] - 1s 4ms/step - loss: 0.5143 - accuracy: 0.7486\n",
"Epoch 5/10\n",
"230/230 [==============================] - 1s 4ms/step - loss: 0.5077 - accuracy: 0.7477\n",
"Epoch 6/10\n",
"230/230 [==============================] - 1s 4ms/step - loss: 0.4991 - accuracy: 0.7490\n",
"Epoch 7/10\n",
"230/230 [==============================] - 1s 4ms/step - loss: 0.4949 - accuracy: 0.7549\n",
"Epoch 8/10\n",
"230/230 [==============================] - 1s 4ms/step - loss: 0.4841 - accuracy: 0.7601: 0s - loss:\n",
"Epoch 9/10\n",
"230/230 [==============================] - 1s 4ms/step - loss: 0.4821 - accuracy: 0.7594\n",
"Epoch 10/10\n",
"230/230 [==============================] - 1s 3ms/step - loss: 0.4728 - accuracy: 0.7573\n"
]
},
{
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x289e3758470>"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X_train, y_train_new, \n",
" batch_size=32, epochs=10, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"93/93 [==============================] - 0s 2ms/step - loss: 0.5101 - accuracy: 0.7350\n"
]
},
{
"data": {
"text/plain": [
"[0.5101351141929626, 0.7349847555160522]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.evaluate(X_test, y_test_new, batch_size=32, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"230/230 [==============================] - 1s 2ms/step - loss: 0.9916 - accuracy: 0.5796\n",
"Epoch 2/10\n",
"230/230 [==============================] - 0s 2ms/step - loss: 0.6972 - accuracy: 0.6670\n",
"Epoch 3/10\n",
"230/230 [==============================] - 0s 2ms/step - loss: 0.6510 - accuracy: 0.6892\n",
"Epoch 4/10\n",
"230/230 [==============================] - 0s 2ms/step - loss: 0.6231 - accuracy: 0.6952\n",
"Epoch 5/10\n",
"230/230 [==============================] - 0s 2ms/step - loss: 0.5990 - accuracy: 0.7100\n",
"Epoch 6/10\n",
"230/230 [==============================] - 0s 2ms/step - loss: 0.5809 - accuracy: 0.7210\n",
"Epoch 7/10\n",
"230/230 [==============================] - 0s 2ms/step - loss: 0.5662 - accuracy: 0.7300\n",
"Epoch 8/10\n",
"230/230 [==============================] - 0s 2ms/step - loss: 0.5551 - accuracy: 0.7286\n",
"Epoch 9/10\n",
"230/230 [==============================] - 0s 2ms/step - loss: 0.5445 - accuracy: 0.7380\n",
"Epoch 10/10\n",
"230/230 [==============================] - 0s 2ms/step - loss: 0.5366 - accuracy: 0.7442\n",
"93/93 [==============================] - 0s 1ms/step - loss: 0.5575 - accuracy: 0.7323\n"
]
},
{
"data": {
"text/plain": [
"[0.5574547648429871, 0.7322701215744019]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = Input(shape=(10,))\n",
"x = Dense(64, activation='relu')(a)\n",
"b = Dense(6, activation='softmax')(x) #6 classes\n",
"model = Model(inputs=a, outputs=b)\n",
"model.compile(loss='sparse_categorical_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])\n",
"model.fit(X_train, y_train_new, \n",
" batch_size=32, epochs=10, verbose=1)\n",
"model.evaluate(X_test, y_test_new, batch_size=32, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Conv1D, LSTM, MaxPooling1D"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"230/230 [==============================] - 5s 8ms/step - loss: 7.4888 - accuracy: 0.1523\n",
"Epoch 2/10\n",
"230/230 [==============================] - 2s 9ms/step - loss: 7.4888 - accuracy: 0.1678\n",
"Epoch 3/10\n",
"230/230 [==============================] - 2s 8ms/step - loss: 7.4888 - accuracy: 0.1616\n",
"Epoch 4/10\n",
"230/230 [==============================] - 2s 8ms/step - loss: 7.4888 - accuracy: 0.1657: 0s - loss: 7\n",
"Epoch 5/10\n",
"230/230 [==============================] - 2s 8ms/step - loss: 7.4888 - accuracy: 0.1693\n",
"Epoch 6/10\n",
"230/230 [==============================] - 2s 8ms/step - loss: 7.4888 - accuracy: 0.1654\n",
"Epoch 7/10\n",
"230/230 [==============================] - 2s 7ms/step - loss: 7.4888 - accuracy: 0.1741\n",
"Epoch 8/10\n",
"230/230 [==============================] - 2s 7ms/step - loss: 7.4888 - accuracy: 0.1662\n",
"Epoch 9/10\n",
"230/230 [==============================] - 2s 8ms/step - loss: 7.4888 - accuracy: 0.1677\n",
"Epoch 10/10\n",
"230/230 [==============================] - 2s 9ms/step - loss: 7.4888 - accuracy: 0.1722\n"
]
},
{
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x289e5ccda90>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = Sequential()\n",
"model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu',\n",
" input_shape=(10, 1)))\n",
"model.add(MaxPooling1D(pool_size=4))\n",
"model.add(LSTM(64))\n",
"model.add(Dense(units=10, activation='relu'))\n",
"model.add(Dense(units=6, activation='softmax'))\n",
"\n",
"model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
"\n",
"X_train_conv = tf.expand_dims(X_train, axis=-1)\n",
"\n",
"model.fit(X_train_conv, y_train_new, \n",
" batch_size=32, epochs=10, verbose=1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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