1472 lines
2.9 MiB
Plaintext
1472 lines
2.9 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Загрузка данных"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 129,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[-2.32401186e+00 7.05074756e-01 4.48535225e-01 7.70090520e-01\n",
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" -1.16617999e+00]\n",
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" [-1.63988490e+00 -2.21973399e+00 -1.04704769e+00 -1.58925732e+00\n",
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" -1.51443302e+00]\n",
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" [-1.13037684e+00 -2.22988519e+00 -1.94326817e+00 -1.42554953e-01\n",
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" 2.10224064e+00]\n",
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" [ 8.89767155e-01 -1.99946166e-02 -5.92572962e-01 8.44169856e-01\n",
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" 2.59653162e+00]\n",
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" [ 2.52863614e+00 -5.86695065e-01 3.37245492e-01 -1.96432890e+00\n",
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" -1.49138457e+00]\n",
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" [-9.47696580e-01 7.07797661e-01 -9.33993509e-02 1.48477196e+00\n",
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" 1.49224073e+00]\n",
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" [-2.44791559e+00 -1.22007013e+00 -6.33679067e-01 -5.20124031e-01\n",
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" -1.26493805e+00]\n",
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" [-2.60738014e-01 -6.97849748e-01 -9.82892815e-02 -9.39816888e-01\n",
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" -1.25008567e+00]\n",
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" [ 2.75252550e+00 -9.43712510e-03 5.24284409e-01 -1.37074995e+00\n",
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" -8.57686558e-01]\n",
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" [-3.67259514e-01 7.63618140e-01 7.07417207e-01 9.98208513e-02\n",
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" -1.19947859e+00]\n",
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" [ 3.36701785e+00 8.88176188e-01 1.10099581e+00 -9.84728456e-01\n",
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" -8.51415934e-01]\n",
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" [ 6.64819182e-01 3.03843068e+00 1.80593569e+00 1.79060735e+00\n",
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" -1.93589802e-02]\n",
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" [ 4.61145798e-01 1.01080810e+00 7.82473809e-01 2.42854908e-01\n",
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" -5.98775837e-01]\n",
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" [ 1.94269425e+00 -3.01272578e+00 -1.28541457e+00 -3.07312169e+00\n",
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" -8.09046024e-01]\n",
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" [ 7.53183627e-01 -1.38299848e+00 -4.94669889e-01 -1.55270510e+00\n",
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" -7.94455067e-01]\n",
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" [ 1.66835300e+00 -2.24532878e+00 -5.78017577e-01 -2.98222798e+00\n",
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" -1.96090718e+00]\n",
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" [-3.23311391e-01 -1.92013284e+00 -8.07199059e-01 -1.72397519e+00\n",
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" -1.22268471e+00]\n",
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" [ 3.87002898e-01 -1.72388946e+00 -1.45822798e+00 -3.92560220e-01\n",
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" 1.99062094e+00]\n",
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" [ 1.29180739e+00 -6.66698890e-01 -4.89292656e-01 -4.67528892e-01\n",
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" 9.68194767e-01]\n",
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" [ 3.00903015e+00 -5.62690331e-01 4.05331998e-01 -2.12079964e+00\n",
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" -1.49594730e+00]\n",
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" [-1.05578756e+00 -1.52753087e+00 -7.01896645e-01 -1.13772773e+00\n",
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" -1.08257843e+00]\n",
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" [ 3.85933835e-02 2.72219392e+00 1.73929616e+00 1.48597851e+00\n",
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" -7.16527769e-01]\n",
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" [ 1.65725033e+00 2.68970754e-01 -5.13933472e-02 2.61643933e-01\n",
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" 1.48183567e+00]\n",
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|
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" [-1.11207161e+00 -2.30290435e+00 -7.26655967e-01 -2.36588668e+00\n",
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" -2.69526342e+00]\n",
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|||
|
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" [ 5.61837545e-01 2.66700802e-01 7.30978713e-01 -9.13221264e-01\n",
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" -1.97501003e+00]\n",
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" [-1.35492363e-01 7.02465614e-01 4.36856228e-01 4.27915114e-01\n",
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" -2.00752796e-01]\n",
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" [ 2.47328934e+00 -3.47444499e+00 -1.45007006e+00 -3.63801255e+00\n",
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" -9.57984984e-01]\n",
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" [-1.34159831e+00 -6.26203386e-01 -9.21340952e-01 7.75816671e-01\n",
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" 1.59145461e+00]\n",
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" [-7.99516309e-01 -1.91004018e-01 -1.34443068e-01 4.74966951e-02\n",
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" -2.39666613e-01]\n",
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" [-1.67702569e+00 5.89636027e-01 1.12756226e-01 1.05319626e+00\n",
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" 1.39555930e-01]\n",
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|
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" [-4.71637541e-01 1.06921441e+00 1.27433073e-01 1.62243064e+00\n",
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" 1.63500568e+00]\n",
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|
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" [-1.59204136e+00 -1.62432795e+00 -1.79256496e+00 6.72608794e-01\n",
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|
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" 2.64593841e+00]\n",
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" [-9.41729071e-02 -1.28770715e-01 -1.02190057e+00 1.56654050e+00\n",
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|||
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" 3.58359742e+00]\n",
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|||
|
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" [-1.44225275e+00 8.90625263e-01 2.03200368e-01 1.35560587e+00\n",
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" 5.46984283e-01]\n",
|
|||
|
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" [ 3.62538500e-01 -1.78842111e+00 -1.46613316e+00 -4.81452484e-01\n",
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|||
|
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" 1.87035172e+00]\n",
|
|||
|
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" [ 1.54884285e+00 -9.65672411e-02 -9.79541287e-02 -2.43744164e-01\n",
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|||
|
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" 8.20152259e-01]\n",
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|||
|
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" [-2.49857121e+00 -2.26679455e+00 -1.13773822e+00 -1.36995184e+00\n",
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|||
|
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" -1.63373925e+00]\n",
|
|||
|
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" [ 3.83263661e-01 5.57447514e-01 -1.94520234e-01 1.19334466e+00\n",
|
|||
|
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" 2.11548115e+00]\n",
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|||
|
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" [-1.02259420e-01 1.68088463e+00 7.11452032e-01 1.56386210e+00\n",
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|
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" 8.88784737e-01]\n",
|
|||
|
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" [-2.40919500e+00 2.58034721e-04 -5.83804388e-01 1.40215680e+00\n",
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|||
|
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" 1.21043305e+00]\n",
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|||
|
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" [-9.16088134e-01 -1.25825735e+00 -1.13167611e+00 2.57433578e-02\n",
|
|||
|
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" 1.20268179e+00]\n",
|
|||
|
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" [-6.72912927e-01 -5.13438246e-01 -1.74146645e-01 -4.37010449e-01\n",
|
|||
|
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" -7.34468017e-01]\n",
|
|||
|
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" [-1.24940354e+00 -8.33029422e-01 -1.05365534e+00 6.47641045e-01\n",
|
|||
|
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" 1.68680226e+00]\n",
|
|||
|
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" [ 1.08781107e+00 -6.59236358e-01 3.54211521e-01 -1.87766488e+00\n",
|
|||
|
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" -2.32363703e+00]\n",
|
|||
|
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" [ 1.24169036e+00 -7.60775616e-01 -3.73392153e-01 -8.14681743e-01\n",
|
|||
|
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" 3.00057482e-01]\n",
|
|||
|
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" [-2.98242427e+00 -7.31481416e-02 8.39818910e-03 3.52643415e-01\n",
|
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|
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" -1.45346481e+00]\n",
|
|||
|
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" [-1.49955191e+00 -4.52175218e-01 -1.01024935e+00 1.24241729e+00\n",
|
|||
|
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" 2.24199765e+00]\n",
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|
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" [-1.64372400e+00 1.21437485e+00 8.25868474e-01 8.48376946e-01\n",
|
|||
|
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" -1.21362382e+00]\n",
|
|||
|
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" [ 4.82815115e-01 -1.60125809e+00 -6.97300730e-01 -1.51908316e+00\n",
|
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|
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" -6.08851929e-01]\n",
|
|||
|
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" [ 3.69196732e-01 1.39287818e+00 1.26603432e+00 5.40827949e-02\n",
|
|||
|
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" -1.65509196e+00]\n",
|
|||
|
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" [-2.33136489e+00 -6.21017288e-01 -2.15423676e-01 -2.72066365e-01\n",
|
|||
|
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" -1.51246813e+00]\n",
|
|||
|
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" [-1.55290874e+00 -6.55141194e-01 -5.45462670e-01 1.13871401e-01\n",
|
|||
|
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" 3.54445022e-03]\n",
|
|||
|
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" [-1.70867065e+00 -2.09139706e+00 -2.30818816e+00 8.10511888e-01\n",
|
|||
|
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" 3.55187277e+00]\n",
|
|||
|
|
" [ 1.58229686e+00 -7.12483411e-01 3.91380708e-02 -1.50267249e+00\n",
|
|||
|
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" -1.02651792e+00]\n",
|
|||
|
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" [ 5.73921945e-01 -9.73172493e-01 -1.97728693e-01 -1.35927833e+00\n",
|
|||
|
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" -1.11470025e+00]\n",
|
|||
|
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" [ 1.65983137e+00 5.47758784e-01 1.98195614e-01 2.90712698e-01\n",
|
|||
|
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" 1.13493019e+00]\n",
|
|||
|
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" [ 9.21364254e-01 -1.06342762e+00 -1.04442434e+00 -1.03786828e-01\n",
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|||
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" 2.07029521e+00]\n",
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|||
|
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" [ 3.27190962e+00 1.84630675e-01 9.57879270e-01 -1.88367583e+00\n",
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" -1.87253732e+00]\n",
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|
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" [ 1.17618176e+00 -1.23443978e+00 -3.15780257e-01 -1.68539285e+00\n",
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" -9.76095704e-01]\n",
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" [ 5.48339224e-01 2.63682216e+00 1.70610234e+00 1.31893406e+00\n",
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" -5.59304052e-01]\n",
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" [ 1.08449640e+00 -1.11130225e+00 -9.45159546e-01 -3.80836414e-01\n",
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" 1.65628285e+00]\n",
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|||
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" [-1.65570361e-01 -2.65300684e+00 -2.19185538e+00 -5.69896795e-01\n",
|
|||
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" 2.54148813e+00]\n",
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|||
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" [-3.05246009e+00 -6.96843213e-01 -5.34818987e-01 2.71968474e-01\n",
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|||
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" -7.62466037e-01]\n",
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|||
|
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" [-1.40237494e+00 1.12322945e+00 7.74338118e-01 7.47355739e-01\n",
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|||
|
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" -1.11252365e+00]\n",
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|||
|
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" [-8.93686047e-01 -5.47012333e-01 -4.09857502e-01 -4.95024138e-02\n",
|
|||
|
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" -5.50505067e-04]\n",
|
|||
|
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" [ 3.30465394e+00 -8.32078650e-01 -5.48925999e-01 -9.66925184e-01\n",
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|
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" 1.68901590e+00]\n",
|
|||
|
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" [ 1.37030563e+00 -5.56716982e-01 -3.49002855e-01 -5.40926713e-01\n",
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|||
|
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" 7.04387324e-01]\n",
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|||
|
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" [ 8.66418291e-01 9.31841174e-01 3.04543386e-01 8.71059388e-01\n",
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|
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" 1.22674989e+00]\n",
|
|||
|
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" [ 2.07959188e+00 -3.11611817e-02 4.99785502e-01 -1.25424504e+00\n",
|
|||
|
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" -1.09615990e+00]\n",
|
|||
|
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" [ 4.38734763e-01 1.01911030e+00 2.50845305e-01 1.17778079e+00\n",
|
|||
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" 1.44026007e+00]\n",
|
|||
|
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" [-7.84260783e-01 4.64261795e-01 -6.74854194e-01 2.05952052e+00\n",
|
|||
|
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" 3.25349034e+00]\n",
|
|||
|
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" [ 1.13823897e+00 -2.23127395e+00 -6.13983375e-01 -2.81022722e+00\n",
|
|||
|
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" -2.01744288e+00]\n",
|
|||
|
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" [ 1.09352824e+00 -1.81798150e-01 3.84529148e-01 -1.14270174e+00\n",
|
|||
|
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" -1.40043942e+00]\n",
|
|||
|
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" [ 7.93459861e-01 -1.31465500e+00 -8.04000422e-01 -9.12580041e-01\n",
|
|||
|
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" 5.52431795e-01]\n",
|
|||
|
|
" [ 5.73059507e-01 -4.79265988e+00 -2.46694576e+00 -3.74839519e+00\n",
|
|||
|
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" -7.40571087e-01]\n",
|
|||
|
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" [-2.66876373e-01 2.81947710e+00 1.59694025e+00 1.94225646e+00\n",
|
|||
|
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" -9.09190168e-02]\n",
|
|||
|
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" [-1.42090057e+00 1.30261908e-01 -3.93148823e-01 1.12605075e+00\n",
|
|||
|
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" 1.18288094e+00]\n",
|
|||
|
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" [ 5.80935274e-01 -1.66944383e+00 -1.18161788e+00 -8.11808714e-01\n",
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" 1.13444490e+00]\n",
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|||
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" [-1.91694566e+00 5.59871952e-02 1.50956034e-01 1.45489974e-01\n",
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" -1.26652345e+00]\n",
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" [-2.45626305e-01 -1.79881951e-01 -3.03596982e-02 -2.04394049e-01\n",
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|||
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" -3.78571542e-01]\n",
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|||
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" [-7.61341420e-01 4.56966196e-01 3.75352565e-01 2.31143996e-01\n",
|
|||
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" -7.63799261e-01]\n",
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|||
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" [ 1.87532049e+00 -4.84389287e-02 2.73092483e-01 -8.58096335e-01\n",
|
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" -3.54318040e-01]\n",
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|||
|
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" [-1.02560771e+00 1.59381412e+00 3.04852423e-01 2.27292824e+00\n",
|
|||
|
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" 1.86181950e+00]\n",
|
|||
|
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" [ 6.55133420e-01 -1.45394216e+00 -1.28615744e+00 -2.87737560e-01\n",
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|||
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" 2.03301200e+00]\n",
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|||
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" [-1.09764135e+00 9.54341127e-01 5.98205807e-01 7.22670118e-01\n",
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" -6.77475144e-01]\n",
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|||
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" [-2.06545779e+00 1.02300229e+00 2.82286533e-01 1.53960758e+00\n",
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" 2.68501701e-01]\n",
|
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" [ 5.98430922e-01 -1.25715913e+00 -1.15810243e+00 -1.74597264e-01\n",
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" 1.94715336e+00]\n",
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" [ 2.19317944e+00 6.53583615e-01 4.88711783e-01 -1.23238237e-01\n",
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" 4.80879262e-01]\n",
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" [-1.99112180e+00 1.39225582e+00 4.50467983e-01 1.84679500e+00\n",
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" 4.59310992e-01]\n",
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" [-2.28506035e+00 4.29493275e-01 1.86820620e-01 7.60449188e-01\n",
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" -7.48417786e-01]\n",
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|||
|
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" [ 1.64928678e-01 3.17378349e+00 1.77659211e+00 2.14649065e+00\n",
|
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" 1.74958852e-01]\n",
|
|||
|
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" [-1.26063140e+00 -1.58256204e+00 -1.56003412e+00 2.86014172e-01\n",
|
|||
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" 1.98878889e+00]\n",
|
|||
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" [-6.51202394e-01 -1.17946939e+00 -2.34913275e-01 -1.43526440e+00\n",
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" -1.93921423e+00]\n",
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|
|
" [ 4.07206333e-01 -5.86604679e-01 -2.83093246e-01 -5.46469347e-01\n",
|
|||
|
|
" -1.99542756e-02]\n",
|
|||
|
|
" [ 6.26050241e-01 -5.81468747e-01 -2.88410427e-01 -5.64614305e-01\n",
|
|||
|
|
" 1.04149941e-01]\n",
|
|||
|
|
" [-3.02807572e+00 -1.80180468e+00 -8.11852158e-01 -1.07811225e+00\n",
|
|||
|
|
" -2.09304062e+00]\n",
|
|||
|
|
" [-6.22496112e-01 8.20149679e-01 8.27459556e-01 2.77215026e-02\n",
|
|||
|
|
" -1.64328274e+00]\n",
|
|||
|
|
" [-5.62435594e-01 2.86710137e+00 1.42271503e+00 2.36993314e+00\n",
|
|||
|
|
" 5.52791599e-01]\n",
|
|||
|
|
" [ 1.59443272e+00 -2.72456995e-02 5.16717405e-02 -3.94999873e-01\n",
|
|||
|
|
" 4.18477064e-01]\n",
|
|||
|
|
" [-2.59329155e+00 2.41577012e+00 5.67006074e-01 3.43393527e+00\n",
|
|||
|
|
" 1.98144945e+00]\n",
|
|||
|
|
" [-1.19257193e+00 3.31165872e-02 -5.62117555e-01 1.21994436e+00\n",
|
|||
|
|
" 1.71398590e+00]\n",
|
|||
|
|
" [-9.12678493e-01 8.76975437e-01 7.90712908e-01 2.32431456e-01\n",
|
|||
|
|
" -1.50240317e+00]\n",
|
|||
|
|
" [-3.80285801e-01 1.01492530e+00 -8.92773445e-02 1.89188465e+00\n",
|
|||
|
|
" 2.38351295e+00]\n",
|
|||
|
|
" [ 9.80994864e-01 3.98631172e-01 6.22629648e-01 -5.76979510e-01\n",
|
|||
|
|
" -1.09730105e+00]\n",
|
|||
|
|
" [ 1.12293931e+00 -5.96546154e-02 -5.36985599e-01 6.44608332e-01\n",
|
|||
|
|
" 2.39682611e+00]\n",
|
|||
|
|
" [-1.68336645e+00 -1.34947106e-01 -5.77738556e-01 1.04973131e+00\n",
|
|||
|
|
" 1.20099103e+00]\n",
|
|||
|
|
" [ 3.44644261e+00 -9.06047771e-02 1.01155175e+00 -2.45926174e+00\n",
|
|||
|
|
" -2.60121428e+00]\n",
|
|||
|
|
" [ 1.55635768e+00 -2.42226510e-01 5.53666297e-02 -7.50069826e-01\n",
|
|||
|
|
" -7.85014197e-02]\n",
|
|||
|
|
" [ 9.34177543e-02 8.43197721e-01 7.77867381e-01 3.42471378e-02\n",
|
|||
|
|
" -1.10075669e+00]\n",
|
|||
|
|
" [ 1.84076164e+00 -1.28653158e+00 -3.05988484e-02 -2.37231664e+00\n",
|
|||
|
|
" -1.89707040e+00]\n",
|
|||
|
|
" [-8.24426464e-01 4.95525305e-01 -2.35201187e-01 1.35889573e+00\n",
|
|||
|
|
" 1.62515550e+00]\n",
|
|||
|
|
" [-1.37337974e+00 -4.27759235e-01 7.03784303e-02 -6.03001032e-01\n",
|
|||
|
|
" -1.77892144e+00]\n",
|
|||
|
|
" [-1.80630266e-01 9.06001500e-01 9.80680415e-01 -1.67276018e-01\n",
|
|||
|
|
" -1.85504051e+00]\n",
|
|||
|
|
" [-6.81671337e-01 6.53554194e-01 7.22599881e-01 -5.66575952e-02\n",
|
|||
|
|
" -1.62953117e+00]\n",
|
|||
|
|
" [ 3.34476851e-01 3.13051414e-01 -4.47382987e-01 1.23426435e+00\n",
|
|||
|
|
" 2.52999859e+00]\n",
|
|||
|
|
" [-3.03854933e-01 8.00584586e-01 1.13519922e+00 -5.87843504e-01\n",
|
|||
|
|
" -2.72624640e+00]\n",
|
|||
|
|
" [ 1.51574132e+00 1.40061832e+00 9.69059527e-01 3.91852912e-01\n",
|
|||
|
|
" -1.86468780e-02]\n",
|
|||
|
|
" [-2.31468266e-01 1.61622069e+00 1.02342668e+00 9.39796900e-01\n",
|
|||
|
|
" -4.97860979e-01]\n",
|
|||
|
|
" [-4.56684606e-01 1.01440959e+00 1.06868065e+00 -9.50219712e-02\n",
|
|||
|
|
" -2.07264636e+00]\n",
|
|||
|
|
" [-1.46692019e+00 -1.20243214e+00 -1.59523052e+00 1.00808170e+00\n",
|
|||
|
|
" 2.86119265e+00]\n",
|
|||
|
|
" [ 6.05369271e-02 1.85555246e+00 5.48226964e-01 2.10731187e+00\n",
|
|||
|
|
" 1.96034708e+00]\n",
|
|||
|
|
" [-7.69696067e-01 8.68764996e-01 7.21703517e-01 3.14590958e-01\n",
|
|||
|
|
" -1.19612494e+00]\n",
|
|||
|
|
" [-3.54774167e-01 6.71641610e-01 6.20182736e-01 9.64906137e-02\n",
|
|||
|
|
" -1.06070173e+00]\n",
|
|||
|
|
" [-1.10309302e+00 -5.69665127e-01 -7.77939549e-01 5.82639965e-01\n",
|
|||
|
|
" 1.26746774e+00]\n",
|
|||
|
|
" [ 2.24413787e+00 -2.24917026e+00 -5.48991553e-01 -3.13286090e+00\n",
|
|||
|
|
" -1.83635720e+00]\n",
|
|||
|
|
" [-1.85867893e+00 5.28156290e-01 1.10377843e-01 9.85518550e-01\n",
|
|||
|
|
" -6.17404072e-02]\n",
|
|||
|
|
" [-8.35303619e-01 8.94105746e-01 4.00333659e-01 9.21821654e-01\n",
|
|||
|
|
" 5.85563526e-02]\n",
|
|||
|
|
" [-6.80563819e-01 2.26687673e+00 1.44779726e+00 1.35502965e+00\n",
|
|||
|
|
" -8.96170990e-01]\n",
|
|||
|
|
" [-7.67910130e-01 9.72109045e-01 1.52462414e-01 1.46738401e+00\n",
|
|||
|
|
" 1.20315838e+00]\n",
|
|||
|
|
" [-2.86833951e-01 6.86835495e-01 3.17994439e-01 6.32015985e-01\n",
|
|||
|
|
" 1.55223606e-01]\n",
|
|||
|
|
" [-5.64907969e-01 -1.19546231e+00 -6.54276407e-01 -7.51993892e-01\n",
|
|||
|
|
" -3.35692143e-01]\n",
|
|||
|
|
" [ 4.13141684e+00 -7.47917442e-01 6.82256144e-01 -3.08816482e+00\n",
|
|||
|
|
" -2.48062866e+00]\n",
|
|||
|
|
" [ 1.58725355e+00 2.17687628e+00 1.56422235e+00 6.34504829e-01\n",
|
|||
|
|
" -5.76190261e-01]\n",
|
|||
|
|
" [ 1.10869323e+00 2.64110459e+00 8.24962970e-01 2.75511863e+00\n",
|
|||
|
|
" 3.05263101e+00]\n",
|
|||
|
|
" [-1.49702457e+00 5.24036105e-01 -9.39573409e-02 1.27222053e+00\n",
|
|||
|
|
" 8.62852973e-01]\n",
|
|||
|
|
" [ 2.09568939e+00 -1.66461385e+00 -2.12654949e-01 -2.72399819e+00\n",
|
|||
|
|
" -1.91466502e+00]\n",
|
|||
|
|
" [ 1.87232229e-01 6.88821300e-01 -5.27238705e-01 2.01655338e+00\n",
|
|||
|
|
" 3.58850346e+00]\n",
|
|||
|
|
" [ 2.73593477e+00 -4.25241487e-01 3.99117606e-01 -1.83847717e+00\n",
|
|||
|
|
" -1.28940701e+00]\n",
|
|||
|
|
" [-5.85530900e-02 1.64278513e+00 9.33853402e-01 1.10996287e+00\n",
|
|||
|
|
" -2.48693018e-02]\n",
|
|||
|
|
" [ 1.70380541e+00 1.80920644e-01 1.72343023e-01 -2.77491184e-01\n",
|
|||
|
|
" 4.55840310e-01]\n",
|
|||
|
|
" [-1.82481260e+00 -1.39620162e+00 -4.86021218e-01 -1.16768939e+00\n",
|
|||
|
|
" -1.94751913e+00]\n",
|
|||
|
|
" [ 5.22411828e-02 1.84085437e+00 5.19920027e-01 2.13325900e+00\n",
|
|||
|
|
" 2.03303927e+00]\n",
|
|||
|
|
" [ 6.53251865e-01 -1.47619962e+00 -1.06995257e+00 -6.97329890e-01\n",
|
|||
|
|
" 1.15812059e+00]\n",
|
|||
|
|
" [-1.59092913e+00 -2.72335754e+00 -2.66660440e+00 3.66518950e-01\n",
|
|||
|
|
" 3.59859238e+00]]\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
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|
|||
|
|
"data": {
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|
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|
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|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>X1</th>\n",
|
|||
|
|
" <th>X2</th>\n",
|
|||
|
|
" <th>X3</th>\n",
|
|||
|
|
" <th>X4</th>\n",
|
|||
|
|
" <th>X5</th>\n",
|
|||
|
|
" <th>y</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>-2.324012</td>\n",
|
|||
|
|
" <td>0.705075</td>\n",
|
|||
|
|
" <td>0.448535</td>\n",
|
|||
|
|
" <td>0.770091</td>\n",
|
|||
|
|
" <td>-1.166180</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>-1.639885</td>\n",
|
|||
|
|
" <td>-2.219734</td>\n",
|
|||
|
|
" <td>-1.047048</td>\n",
|
|||
|
|
" <td>-1.589257</td>\n",
|
|||
|
|
" <td>-1.514433</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>-1.130377</td>\n",
|
|||
|
|
" <td>-2.229885</td>\n",
|
|||
|
|
" <td>-1.943268</td>\n",
|
|||
|
|
" <td>-0.142555</td>\n",
|
|||
|
|
" <td>2.102241</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>0.889767</td>\n",
|
|||
|
|
" <td>-0.019995</td>\n",
|
|||
|
|
" <td>-0.592573</td>\n",
|
|||
|
|
" <td>0.844170</td>\n",
|
|||
|
|
" <td>2.596532</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>2.528636</td>\n",
|
|||
|
|
" <td>-0.586695</td>\n",
|
|||
|
|
" <td>0.337245</td>\n",
|
|||
|
|
" <td>-1.964329</td>\n",
|
|||
|
|
" <td>-1.491385</td>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" X1 X2 X3 X4 X5 y\n",
|
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|
|
"0 -2.324012 0.705075 0.448535 0.770091 -1.166180 1\n",
|
|||
|
|
"1 -1.639885 -2.219734 -1.047048 -1.589257 -1.514433 0\n",
|
|||
|
|
"2 -1.130377 -2.229885 -1.943268 -0.142555 2.102241 1\n",
|
|||
|
|
"3 0.889767 -0.019995 -0.592573 0.844170 2.596532 3\n",
|
|||
|
|
"4 2.528636 -0.586695 0.337245 -1.964329 -1.491385 2"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
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|
|
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|
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|
|
"data": {
|
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|
|
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"</style>\n",
|
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|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
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|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>X1</th>\n",
|
|||
|
|
" <th>X2</th>\n",
|
|||
|
|
" <th>X3</th>\n",
|
|||
|
|
" <th>X4</th>\n",
|
|||
|
|
" <th>X5</th>\n",
|
|||
|
|
" <th>y</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>139</th>\n",
|
|||
|
|
" <td>1.703805</td>\n",
|
|||
|
|
" <td>0.180921</td>\n",
|
|||
|
|
" <td>0.172343</td>\n",
|
|||
|
|
" <td>-0.277491</td>\n",
|
|||
|
|
" <td>0.455840</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>140</th>\n",
|
|||
|
|
" <td>-1.824813</td>\n",
|
|||
|
|
" <td>-1.396202</td>\n",
|
|||
|
|
" <td>-0.486021</td>\n",
|
|||
|
|
" <td>-1.167689</td>\n",
|
|||
|
|
" <td>-1.947519</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>141</th>\n",
|
|||
|
|
" <td>0.052241</td>\n",
|
|||
|
|
" <td>1.840854</td>\n",
|
|||
|
|
" <td>0.519920</td>\n",
|
|||
|
|
" <td>2.133259</td>\n",
|
|||
|
|
" <td>2.033039</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>142</th>\n",
|
|||
|
|
" <td>0.653252</td>\n",
|
|||
|
|
" <td>-1.476200</td>\n",
|
|||
|
|
" <td>-1.069953</td>\n",
|
|||
|
|
" <td>-0.697330</td>\n",
|
|||
|
|
" <td>1.158121</td>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>143</th>\n",
|
|||
|
|
" <td>-1.590929</td>\n",
|
|||
|
|
" <td>-2.723358</td>\n",
|
|||
|
|
" <td>-2.666604</td>\n",
|
|||
|
|
" <td>0.366519</td>\n",
|
|||
|
|
" <td>3.598592</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" X1 X2 X3 X4 X5 y\n",
|
|||
|
|
"139 1.703805 0.180921 0.172343 -0.277491 0.455840 3\n",
|
|||
|
|
"140 -1.824813 -1.396202 -0.486021 -1.167689 -1.947519 0\n",
|
|||
|
|
"141 0.052241 1.840854 0.519920 2.133259 2.033039 1\n",
|
|||
|
|
"142 0.653252 -1.476200 -1.069953 -0.697330 1.158121 3\n",
|
|||
|
|
"143 -1.590929 -2.723358 -2.666604 0.366519 3.598592 1"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"array([1, 0, 1, 3, 2, 1, 0, 1, 2, 0, 3, 2, 2, 2, 2, 0, 0, 3, 3, 2, 0, 3,\n",
|
|||
|
|
" 3, 0, 0, 0, 2, 1, 1, 1, 1, 1, 3, 0, 3, 3, 0, 3, 3, 0, 1, 1, 1, 2,\n",
|
|||
|
|
" 3, 0, 1, 0, 2, 2, 0, 0, 1, 2, 2, 3, 0, 2, 2, 2, 3, 3, 1, 0, 1, 3,\n",
|
|||
|
|
" 3, 3, 2, 3, 3, 2, 2, 3, 2, 2, 2, 3, 0, 2, 1, 3, 3, 3, 0, 0, 3, 3,\n",
|
|||
|
|
" 0, 0, 1, 1, 0, 2, 2, 0, 0, 2, 3, 3, 1, 0, 1, 2, 3, 1, 2, 3, 0, 2,\n",
|
|||
|
|
" 1, 0, 0, 1, 3, 0, 3, 2, 0, 1, 1, 0, 0, 1, 2, 1, 0, 2, 1, 0, 1, 2,\n",
|
|||
|
|
" 2, 1, 1, 2, 1, 2, 2, 3, 0, 1, 3, 1])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn import datasets\n",
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"X, y = datasets.make_classification(\n",
|
|||
|
|
" n_samples=144, \n",
|
|||
|
|
" n_features=5,\n",
|
|||
|
|
" n_informative=3, \n",
|
|||
|
|
" n_classes=4,\n",
|
|||
|
|
")\n",
|
|||
|
|
"print(X)\n",
|
|||
|
|
"df = pd.DataFrame(X) # type: ignore\n",
|
|||
|
|
"df.columns = [\"X1\", \"X2\", \"X3\", \"X4\", \"X5\"]\n",
|
|||
|
|
"df['y'] = y\n",
|
|||
|
|
"\n",
|
|||
|
|
"y_names = ['class' + str(i) for i in range(5)]\n",
|
|||
|
|
"display(df.head())\n",
|
|||
|
|
"display(df.tail())\n",
|
|||
|
|
"\n",
|
|||
|
|
"display(y)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Визуализация данных с учетом понимания из особенностей"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 130,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABR4AAAPbCAYAAAA6qpd6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hU1dbA4d8+U9IrHULvRXpHEQRBUIq99y6WK9d7FfVTsV/b1WuvWBGxIKAC0nvvvfce0tu0s78/BgIhM5NCkknCep8nD2TOnnNW2sw56+y9ltJaa4QQQgghhBBCCCGEEKIEGcEOQAghhBBCCCGEEEIIUflI4lEIIYQQQgghhBBCCFHiJPEohBBCCCGEEEIIIYQocZJ4FEIIIYQQQgghhBBClDhJPAohhBBCCCGEEEIIIUqcJB6FEEIIIYQQQgghhBAlThKPQgghhBBCCCGEEEKIEieJRyGEEEIIIYQQQgghRImzBjuAsmSaJocOHSIqKgqlVLDDEUIIIYQoEq016enp1K5dG8OQ+8cVkZyPCiGEEKKiK8o56XmVeDx06BB169YNdhhCCCGEEOdk//79JCQkBDsMUQxyPiqEEEKIyqIw56TnVeIxKioK8H5joqOjgxyNEEIIIUTRpKWlUbdu3dxzGlHxyPmoEEIIISq6opyTnleJx1PLWaKjo+VETwghhBAVlizRrbjkfFQIIYQQlUVhzkmlOJAQQgghhBBCCCGEEKLESeJRCCGEEEIIIYQQQghR4iTxKIQQQgghhBBCCCGEKHGSeBRCCCGEEEIIIYQQQpS486q5jBDi/LL3aDI/z1vL+l2HsdksXNimIcN6tiEuMizYoQkhhBBCCCFEPtrMgOzf0Y6/QWeBtRUq/AaUrVWwQxOiWCTxKISolH6Zv47XfpyJoRQeUwOweschvpyyjA8euZJ2jWoHOUIhhBBCCCGEOE27d6CTbgPzxKlHwLURnT0OIh9DRY4IanxCFIcstRZCVDqrdxzk1bEz0ZrcpCOA1ppsh4tHPphAelZOECMUQgghhBBCiNO0dqGT7gYzGdAnPwA83u0Z76FzpgUrPCGKTRKPQohK5/uZK7EYyuc2U2sys51MXrKpjKMSQgghhBBCCD9ypoN5mFOJxvwMdMbnZRmRECVClloLISqdxZv25pnp6MuSzXu56ZKOZRSR19GcVKYeWsOxnFTiQyK5rFZ76oTHl2kMQgghhBBCiPJHOxfiTdG4/Ywwwb0ObWahjPAyjEyIcyOJRyFEpWMWkHTUgNtjlk0weJd4f7ZjBmN2zgHAUApTaz7bPoNr6/Xg8ZaXY1EyAV0IIYQQQojzl8np5dUFjROi4qgwV7off/wxbdu2JTo6mujoaHr06MGUKVOCHZYQohxq26gWhp+l1uBN/LVvXKfM4hm7ZyFf7pyNicZE49YmJhoNjN+3mM+2zyizWIQQQgghhBDlj7K1w/8yawAFlgagIsooIiFKRoVJPCYkJPD666+zcuVKVqxYwSWXXMKwYcPYuHFjsEMTQpQzN13S0e+sRwUYhuLKXm3KJBan6WbMztkBx4zds4AMtzS7EUIIIYQQ4rwVOgRUFP7TNBoVcSdK+Z9gIUR5VGESj0OGDGHw4ME0bdqUZs2a8corrxAZGcmSJUuCHZoQopy5uG0jbu3fCSBPkxmLoTAMxat3DaJabGSZxLImeQ9p7uyAYxymmyWJ28skHiGEEEIIIUT5o4wIVOxHgA2wnLHlZNomdCiEXR+EyIQ4NxWyxqPH4+Hnn38mMzOTHj16+B3ncDhwOBy5n6elpZVFeEJUaJluB1vTDgHQPLo2EdaQIEdUdEopHr+6N12b1+XHOWvYsPswVouF3m0bcWPfDjStU7XMYsl2O0t0nBBCCCGEEKJyUiHdoOof6KzvIGcKaAdYm6MiboGQgSipCy8qoAqVeFy/fj09evQgJyeHyMhIJkyYQKtWrfyOf+211xg9enQZRihExZXjcfHRtmlM2L8ch+kCIMSwcWXdLjzUbCChFluQIyy6Xm0a0qtNw6DG0CCiWuHGRRZunBBCCCGEEKLyUtb6qOhnIfrZYIciRIlQWuvCtE0qF5xOJ/v27SM1NZVffvmFL774grlz5/pNPvqa8Vi3bl1SU1OJjo4uq7CFKPfcpodHV4xhVdJuzLM6qRkoOsY35H+d78RqWPzswb9ERzoT9y9nUeJWPKamXVx9rq7XjXoRZTfrMNjuW/oZ65L35vvegvf7Wz+iGuMufEzqtQghCpSWlkZMTIycy1Rg8jMUQgghREVXlPOZCjXj0W6306RJEwA6derE8uXLee+99/j00099jg8JCSEkpOItExWirM06uoEVSbt8bjPRrEjaxeyjG7m0Vtsi7XfFiZ2MXPUtTo87N+m2Nf0QP+1dxDNtrmJIQqdzjr0iGNV6OHcv+YRsjxOPNnMftygDm7LwfNtrJOkohBBCCCGEEKLSqdAFAkzTzDOjUQhRPL/vX46B/8SXgWLC/mVF2ucJRzojV32L44ykI4BHm5hoXt7wG5tSDxQ75oqkYWR1vu05ggG12mI5WZfFQNGneivG9HiIVjEJQY5QCCGEEEIIIYQoeRVmxuOoUaMYNGgQ9erVIz09nbFjxzJnzhymTZsW7NCEqPAOZSf7XAZ8ionmYHZykfY56cBKHB432s9+DaX4cc9CXmpXvM5s2r0bnfUjuNaAsqNC+kLY1Sgjtlj7K20J4VUY3fY6nmo1nBRXJtG28ArZuEcIIYQQQgghhCisCpN4PHbsGLfddhuHDx8mJiaGtm3bMm3aNC699NJghyZEhRdnj+BwdrLf1KMC4u0RRdrnksRtfpOO4J35uCRxW5H2eYrO+hGd9gLeSdse72PO5ZDxEcR9hbK3K9Z+y0KY1U6Y1R7sMIQQQgghhBBCiFJXYZZaf/nll+zZsweHw8GxY8eYMWOGJB2FKCGX1+kYcLsuxJiznVnL0P+Yove20s7l6LTnT0blOXML6Ex08t1oM6PI+y0pKc5MjmSn4DLdQYvBH5fHw/rdh1mxbT9JaVnBDkcIISqkjz/+mLZt2xIdHU10dDQ9evRgypQpwQ5LCCGEEKJcqjAzHoUQpefy2h0Zt2cRB7OT8iUMLcqgTlg8g2t3KNI+28c1YEPKfr9LuC3KoH1cgyLHqjO/AizkTTqeYoJOh+wJEHFrkfd9LhYe38qXO2axIXU/AJHWUK6s25W7GvcN+pJqrTVjZ61mzLRlJKVnA2AYiv4dmvLEtX2oGlO02axCCHE+S0hI4PXXX6dp06Zorfnmm28YNmwYq1evpnXr1sEOTwghhBCiXKkwMx6FEKUnzGrnk6730C6uPuBdWn2q1Uy7uPp80vUewouYPLuqbreAnZo92uSG+j2LHqxjAb6Tjqdp58Ki7/cc/L5/OY+v/CZPs5wMdw4/7J7P/Us/I8sd3CZY702Yz9u/zM1NOgKYpmbm6u3c8eY4kjOyAzxbCCHEmYYMGcLgwYNp2rQpzZo145VXXiEyMpIlS5YEOzQhhBBCiHJHZjwKIQCoGhrNJ13vZWf6EVYn7wGgQ1wDGkfVLNb+aofHMbrttTy3djxKqdyZlBZl4NEm9zfpT9eqTYqx54KWcGvQgROTJSnJkcEbmyYC5JvdaaLZkX6E73bP4/6mwSkNsfdoMt9OX+lzm8fUHElO59vpK3jsyovKODIhhKj4PB4PP//8M5mZmfTo0cPnGIfDgcNx+gZUWlpaWYUnhBBCCBF0kngUQuTROKpmsZONZxtQqx0NIqrz095FLDq+FY82aRdXn+vr96RzlcbF26mtHbhW4T8BaaDsRVsWfi7+PLQqYK1KE82v+5Zyb5N+GKrsJ5lPWrwRi6HwmL5jNE3NbwvW8+jwCwPOUBVCCHHa+vXr6dGjBzk5OURGRjJhwgRatWrlc+xrr73G6NGjyzhCIYQQQojyQRKPQohS1Sy6Fv93wdUltj8VcTs6ZYW/rYAFwq4tseMVZE/GcQylAiYfU1xZpLtyiLGHl1lcpxw6kUZBPXzSsxzkON2EhdjKJighhKjgmjdvzpo1a0hNTeWXX37h9ttvZ+7cuT6Tj6N
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1600x1200 with 6 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from visual import draw_data_2d\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(16, 12))\n",
|
|||
|
|
"draw_data_2d(df, 0, 1, y.tolist(), y_names, plt.subplot(3, 2, 1))\n",
|
|||
|
|
"draw_data_2d(df, 2, 3, y.tolist(), y_names, plt.subplot(3, 2, 2))\n",
|
|||
|
|
"draw_data_2d(df, 0, 2, y.tolist(), y_names, plt.subplot(3, 2, 3))\n",
|
|||
|
|
"draw_data_2d(df, 1, 3, y.tolist(), y_names, plt.subplot(3, 2, 4))\n",
|
|||
|
|
"draw_data_2d(df, 1, 2, y.tolist(), y_names, plt.subplot(3, 2, 5))\n",
|
|||
|
|
"draw_data_2d(df, 0, 3, y.tolist(), y_names, plt.subplot(3, 2, 6))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Визуализация данных без понимания их особенностей"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 131,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1600x1200 with 6 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(16, 12))\n",
|
|||
|
|
"draw_data_2d(df, 0, 1, subplot=plt.subplot(3, 2, 1))\n",
|
|||
|
|
"draw_data_2d(df, 2, 3, subplot=plt.subplot(3, 2, 2))\n",
|
|||
|
|
"draw_data_2d(df, 0, 2, subplot=plt.subplot(3, 2, 3))\n",
|
|||
|
|
"draw_data_2d(df, 1, 3, subplot=plt.subplot(3, 2, 4))\n",
|
|||
|
|
"draw_data_2d(df, 1, 2, subplot=plt.subplot(3, 2, 5))\n",
|
|||
|
|
"draw_data_2d(df, 0, 3, subplot=plt.subplot(3, 2, 6))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Иерархическая агломеративная кластеризация"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Также формируется дендрограмма"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 132,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from utils_clusters import get_linkage_matrix, run_agglomerative\n",
|
|||
|
|
"from visual import draw_dendrogram\n",
|
|||
|
|
"from scipy.cluster import hierarchy\n",
|
|||
|
|
"\n",
|
|||
|
|
"tree = run_agglomerative(df)\n",
|
|||
|
|
"linkage_matrix = get_linkage_matrix(tree)\n",
|
|||
|
|
"draw_dendrogram(linkage_matrix)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Получение результатов иерархической кластеризации"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Также производится сравнение с реальным разбиением\n",
|
|||
|
|
"\n",
|
|||
|
|
"https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 133,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"array([4, 4, 6, 7, 2, 7, 4, 4, 2, 3, 2, 5, 5, 1, 2, 1, 4, 6, 2, 2, 4, 5,\n",
|
|||
|
|
" 2, 4, 3, 3, 1, 7, 4, 7, 7, 6, 7, 7, 6, 2, 4, 7, 5, 7, 7, 4, 7, 2,\n",
|
|||
|
|
" 2, 4, 7, 3, 2, 5, 4, 4, 6, 2, 2, 2, 7, 2, 2, 5, 6, 6, 4, 3, 4, 2,\n",
|
|||
|
|
" 2, 5, 2, 5, 7, 1, 2, 6, 1, 5, 7, 6, 4, 2, 3, 2, 7, 6, 3, 7, 6, 2,\n",
|
|||
|
|
" 7, 4, 5, 6, 4, 2, 2, 4, 3, 5, 2, 7, 7, 3, 7, 2, 7, 7, 2, 2, 3, 1,\n",
|
|||
|
|
" 7, 4, 3, 3, 7, 3, 5, 5, 3, 6, 7, 3, 3, 7, 1, 7, 3, 5, 7, 3, 4, 2,\n",
|
|||
|
|
" 5, 7, 7, 1, 7, 2, 5, 2, 4, 7, 6, 6], dtype=int32)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"array([1, 0, 1, 3, 2, 1, 0, 1, 2, 0, 3, 2, 2, 2, 2, 0, 0, 3, 3, 2, 0, 3,\n",
|
|||
|
|
" 3, 0, 0, 0, 2, 1, 1, 1, 1, 1, 3, 0, 3, 3, 0, 3, 3, 0, 1, 1, 1, 2,\n",
|
|||
|
|
" 3, 0, 1, 0, 2, 2, 0, 0, 1, 2, 2, 3, 0, 2, 2, 2, 3, 3, 1, 0, 1, 3,\n",
|
|||
|
|
" 3, 3, 2, 3, 3, 2, 2, 3, 2, 2, 2, 3, 0, 2, 1, 3, 3, 3, 0, 0, 3, 3,\n",
|
|||
|
|
" 0, 0, 1, 1, 0, 2, 2, 0, 0, 2, 3, 3, 1, 0, 1, 2, 3, 1, 2, 3, 0, 2,\n",
|
|||
|
|
" 1, 0, 0, 1, 3, 0, 3, 2, 0, 1, 1, 0, 0, 1, 2, 1, 0, 2, 1, 0, 1, 2,\n",
|
|||
|
|
" 2, 1, 1, 2, 1, 2, 2, 3, 0, 1, 3, 1])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1600x2400 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"result = hierarchy.fcluster(linkage_matrix, 10, criterion=\"distance\")\n",
|
|||
|
|
"display(result)\n",
|
|||
|
|
"display(y)\n",
|
|||
|
|
"# result = [0 if val == 1 else 1 if val == 3 else 2 for val in result]\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(16, 24))\n",
|
|||
|
|
"draw_data_2d(df, 0, 1, result, y_names, plt.subplot(4, 2, 1))\n",
|
|||
|
|
"draw_data_2d(df, 0, 1, y.tolist(), y_names, plt.subplot(4, 2, 2))\n",
|
|||
|
|
"draw_data_2d(df, 2, 3, result, y_names, plt.subplot(4, 2, 3))\n",
|
|||
|
|
"draw_data_2d(df, 2, 3, y.tolist(), y_names, plt.subplot(4, 2, 4))\n",
|
|||
|
|
"draw_data_2d(df, 0, 2, result, y_names, plt.subplot(4, 2, 5))\n",
|
|||
|
|
"draw_data_2d(df, 0, 2, y.tolist(), y_names, plt.subplot(4, 2, 6))\n",
|
|||
|
|
"draw_data_2d(df, 1, 3, result, y_names, plt.subplot(4, 2, 7))\n",
|
|||
|
|
"draw_data_2d(df, 1, 3, y.tolist(), y_names, plt.subplot(4, 2, 8))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Неиерархическая четка кластеризация (k-means)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 134,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Cluster 1 (57):\n",
|
|||
|
|
"1, 4, 7, 8, 10, 13, 14, 15, 16, 17, 18, 19, 20, 23, 24, 26, 34, 35, 36, 43, 44, 48, 53, 54, 57, 58, 60, 61, 65, 66, 68, 71, 72, 73, 74, 77, 81, 83, 86, 87, 92, 93, 94, 95, 98, 103, 106, 107, 109, 124, 130, 131, 135, 137, 139, 140, 142\n",
|
|||
|
|
"\n",
|
|||
|
|
"--------\n",
|
|||
|
|
"Cluster 2 (87):\n",
|
|||
|
|
"0, 2, 3, 5, 6, 9, 11, 12, 21, 22, 25, 27, 28, 29, 30, 31, 32, 33, 37, 38, 39, 40, 41, 42, 45, 46, 47, 49, 50, 51, 52, 55, 56, 59, 62, 63, 64, 67, 69, 70, 75, 76, 78, 79, 80, 82, 84, 85, 88, 89, 90, 91, 96, 97, 99, 100, 101, 102, 104, 105, 108, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 125, 126, 127, 128, 129, 132, 133, 134, 136, 138, 141, 143\n",
|
|||
|
|
"\n",
|
|||
|
|
"--------\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"array([[ 1.05781172, -1.10417484, -0.35961761, -1.37530793, -0.57595436,\n",
|
|||
|
|
" 1.96491228],\n",
|
|||
|
|
" [-0.69486012, 0.55944142, 0.10909332, 0.84899378, 0.50377892,\n",
|
|||
|
|
" 1.17241379]])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"array([1, 0, 1, 3, 2, 1, 0, 1, 2, 0, 3, 2, 2, 2, 2, 0, 0, 3, 3, 2, 0, 3,\n",
|
|||
|
|
" 3, 0, 0, 0, 2, 1, 1, 1, 1, 1, 3, 0, 3, 3, 0, 3, 3, 0, 1, 1, 1, 2,\n",
|
|||
|
|
" 3, 0, 1, 0, 2, 2, 0, 0, 1, 2, 2, 3, 0, 2, 2, 2, 3, 3, 1, 0, 1, 3,\n",
|
|||
|
|
" 3, 3, 2, 3, 3, 2, 2, 3, 2, 2, 2, 3, 0, 2, 1, 3, 3, 3, 0, 0, 3, 3,\n",
|
|||
|
|
" 0, 0, 1, 1, 0, 2, 2, 0, 0, 2, 3, 3, 1, 0, 1, 2, 3, 1, 2, 3, 0, 2,\n",
|
|||
|
|
" 1, 0, 0, 1, 3, 0, 3, 2, 0, 1, 1, 0, 0, 1, 2, 1, 0, 2, 1, 0, 1, 2,\n",
|
|||
|
|
" 2, 1, 1, 2, 1, 2, 2, 3, 0, 1, 3, 1])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from utils_clusters import print_cluster_result, run_kmeans\n",
|
|||
|
|
"\n",
|
|||
|
|
"random_state = 9\n",
|
|||
|
|
"\n",
|
|||
|
|
"labels, centers = run_kmeans(df, 2, random_state)\n",
|
|||
|
|
"print_cluster_result(df, 2, labels)\n",
|
|||
|
|
"display(centers)\n",
|
|||
|
|
"display(y)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Визуализация результатов кластеризации"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 135,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1600x1200 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from visual import draw_cluster_results\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(16, 12))\n",
|
|||
|
|
"draw_cluster_results(df, 0, 1, labels, centers, plt.subplot(2, 2, 1))\n",
|
|||
|
|
"draw_cluster_results(df, 2, 3, labels, centers, plt.subplot(2, 2, 2))\n",
|
|||
|
|
"draw_cluster_results(df, 0, 2, labels, centers, plt.subplot(2, 2, 3))\n",
|
|||
|
|
"draw_cluster_results(df, 1, 3, labels, centers, plt.subplot(2, 2, 4))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Разбиение на 4 кластера и сравнение с реальным разбиением"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 136,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1600x2400 with 8 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"labels, centers = run_kmeans(df, 4, random_state)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(16, 24))\n",
|
|||
|
|
"draw_data_2d(df, 0, 1, labels.tolist(), y_names, plt.subplot(4, 2, 1))\n",
|
|||
|
|
"draw_data_2d(df, 0, 1, y.tolist(), y_names, plt.subplot(4, 2, 2))\n",
|
|||
|
|
"draw_data_2d(df, 2, 3, labels.tolist(), y_names, plt.subplot(4, 2, 3))\n",
|
|||
|
|
"draw_data_2d(df, 2, 3, y.tolist(), y_names, plt.subplot(4, 2, 4))\n",
|
|||
|
|
"draw_data_2d(df, 0, 2, labels.tolist(), y_names, plt.subplot(4, 2, 5))\n",
|
|||
|
|
"draw_data_2d(df, 0, 2, y.tolist(), y_names, plt.subplot(4, 2, 6))\n",
|
|||
|
|
"draw_data_2d(df, 1, 3, labels.tolist(), y_names, plt.subplot(4, 2, 7))\n",
|
|||
|
|
"draw_data_2d(df, 1, 3, y.tolist(), y_names, plt.subplot(4, 2, 8))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Понижение размерности до n=2"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 137,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"array([[ 1.32593942e+00, -2.18749592e+00],\n",
|
|||
|
|
" [-1.97826650e+00, -1.10701677e+00],\n",
|
|||
|
|
" [ 2.71639984e-01, 2.31820884e+00],\n",
|
|||
|
|
" [ 1.25744596e+00, 2.81369781e+00],\n",
|
|||
|
|
" [-3.22217535e+00, -1.60031442e-01],\n",
|
|||
|
|
" [ 2.40214727e+00, 2.46806378e-01],\n",
|
|||
|
|
" [-3.69674519e-01, -1.65368049e+00],\n",
|
|||
|
|
" [-1.32384707e+00, -9.90035169e-01],\n",
|
|||
|
|
" [-2.37151218e+00, 7.58074265e-02],\n",
|
|||
|
|
" [ 1.81753342e-01, -2.21238708e+00],\n",
|
|||
|
|
" [-1.99480240e+00, 1.54103339e-01],\n",
|
|||
|
|
" [ 2.35227119e+00, -1.44073541e+00],\n",
|
|||
|
|
" [ 1.99596367e-01, -8.24024797e-01],\n",
|
|||
|
|
" [-4.57633500e+00, 1.59002642e+00],\n",
|
|||
|
|
" [-2.32120217e+00, 4.17385129e-01],\n",
|
|||
|
|
" [-4.41726824e+00, -6.68615754e-01],\n",
|
|||
|
|
" [-2.32198366e+00, -7.31514607e-01],\n",
|
|||
|
|
" [-4.29087530e-01, 3.15934054e+00],\n",
|
|||
|
|
" [-7.79068193e-01, 1.97363655e+00],\n",
|
|||
|
|
" [-3.50689785e+00, -6.38732621e-02],\n",
|
|||
|
|
" [-1.39728127e+00, -1.01048460e+00],\n",
|
|||
|
|
" [ 1.88621795e+00, -1.53988950e+00],\n",
|
|||
|
|
" [ 2.22856117e-01, 1.94858265e+00],\n",
|
|||
|
|
" [-3.25080635e+00, -1.87463074e+00],\n",
|
|||
|
|
" [-1.42709881e+00, -2.33401710e+00],\n",
|
|||
|
|
" [ 7.09268385e-01, -1.33053092e+00],\n",
|
|||
|
|
" [-5.44332864e+00, 1.85279709e+00],\n",
|
|||
|
|
" [ 1.50112132e+00, 9.72993095e-01],\n",
|
|||
|
|
" [ 2.19271277e-01, -5.95430684e-01],\n",
|
|||
|
|
" [ 1.76586839e+00, -9.33884019e-01],\n",
|
|||
|
|
" [ 2.53699446e+00, 2.71779314e-01],\n",
|
|||
|
|
" [ 1.51540328e+00, 2.30545107e+00],\n",
|
|||
|
|
" [ 2.50055734e+00, 3.42649564e+00],\n",
|
|||
|
|
" [ 2.25340896e+00, -1.14859403e+00],\n",
|
|||
|
|
" [-5.60663777e-01, 3.09384448e+00],\n",
|
|||
|
|
" [-5.27198952e-01, 1.60023774e+00],\n",
|
|||
|
|
" [-1.56629594e+00, -1.37784438e+00],\n",
|
|||
|
|
" [ 1.75483269e+00, 1.99194756e+00],\n",
|
|||
|
|
" [ 2.19429274e+00, 2.79893301e-01],\n",
|
|||
|
|
" [ 2.53312842e+00, -3.55236486e-01],\n",
|
|||
|
|
" [ 3.69138767e-01, 1.11500796e+00],\n",
|
|||
|
|
" [-5.17740459e-01, -7.79793582e-01],\n",
|
|||
|
|
" [ 1.32307501e+00, 1.18518882e+00],\n",
|
|||
|
|
" [-2.98696830e+00, -1.12012356e+00],\n",
|
|||
|
|
" [-1.32192214e+00, 1.48328012e+00],\n",
|
|||
|
|
" [ 8.80981444e-01, -2.56560118e+00],\n",
|
|||
|
|
" [ 2.23577569e+00, 1.35602887e+00],\n",
|
|||
|
|
" [ 1.39055086e+00, -2.77422805e+00],\n",
|
|||
|
|
" [-2.21479277e+00, 6.22261556e-01],\n",
|
|||
|
|
" [-1.64262467e-01, -1.89675831e+00],\n",
|
|||
|
|
" [-7.52957875e-02, -2.15523014e+00],\n",
|
|||
|
|
" [ 5.09791723e-01, -7.48741253e-01],\n",
|
|||
|
|
" [ 1.82319793e+00, 3.25677676e+00],\n",
|
|||
|
|
" [-2.40004461e+00, 5.62354370e-02],\n",
|
|||
|
|
" [-2.06653462e+00, -1.06587490e-01],\n",
|
|||
|
|
" [ 2.23134983e-01, 1.51894414e+00],\n",
|
|||
|
|
" [ 8.35958260e-02, 1.70207914e+00],\n",
|
|||
|
|
" [-3.26340013e+00, -7.15754258e-01],\n",
|
|||
|
|
" [-2.58496993e+00, 2.90260937e-01],\n",
|
|||
|
|
" [ 1.66323595e+00, -1.67426284e+00],\n",
|
|||
|
|
" [-5.52632938e-01, 2.71809702e+00],\n",
|
|||
|
|
" [-5.28838999e-01, 3.98232108e+00],\n",
|
|||
|
|
" [ 7.96501888e-01, -1.26050462e+00],\n",
|
|||
|
|
" [ 1.22850749e+00, -2.58652708e+00],\n",
|
|||
|
|
" [ 1.27301782e-01, -2.30138121e-01],\n",
|
|||
|
|
" [-1.67496052e+00, 3.11188314e+00],\n",
|
|||
|
|
" [-9.20672211e-01, 1.72153704e+00],\n",
|
|||
|
|
" [ 1.14322455e+00, 1.19199664e+00],\n",
|
|||
|
|
" [-2.14072759e+00, -2.60008050e-01],\n",
|
|||
|
|
" [ 1.64932337e+00, 1.21249551e+00],\n",
|
|||
|
|
" [ 3.23848407e+00, 2.67277957e+00],\n",
|
|||
|
|
" [-4.23543458e+00, 7.15700353e-03],\n",
|
|||
|
|
" [-1.87840480e+00, -6.50297891e-01],\n",
|
|||
|
|
" [-1.36190448e+00, 1.88552701e+00],\n",
|
|||
|
|
" [-5.29221524e+00, 2.30948117e+00],\n",
|
|||
|
|
" [ 2.68774500e+00, -1.59545623e+00],\n",
|
|||
|
|
" [ 1.88346626e+00, 6.36714570e-01],\n",
|
|||
|
|
" [-1.12740736e+00, 2.49466713e+00],\n",
|
|||
|
|
" [ 4.63048602e-01, -2.23789570e+00],\n",
|
|||
|
|
" [-2.84107113e-01, -1.54896111e-01],\n",
|
|||
|
|
" [ 4.05813974e-01, -1.36229837e+00],\n",
|
|||
|
|
" [-1.54763720e+00, 7.17429763e-01],\n",
|
|||
|
|
" [ 3.40877222e+00, 8.83768163e-01],\n",
|
|||
|
|
" [-3.20095470e-01, 3.10637348e+00],\n",
|
|||
|
|
" [ 1.19926669e+00, -2.07549088e+00],\n",
|
|||
|
|
" [ 2.56474512e+00, -1.58998023e+00],\n",
|
|||
|
|
" [-1.66730583e-01, 2.91564356e+00],\n",
|
|||
|
|
" [-4.94736393e-01, 1.06623807e+00],\n",
|
|||
|
|
" [ 2.99558541e+00, -1.62576887e+00],\n",
|
|||
|
|
" [ 1.41640869e+00, -2.12021519e+00],\n",
|
|||
|
|
" [ 2.99625518e+00, -1.90580317e+00],\n",
|
|||
|
|
" [ 8.64102148e-01, 1.83828088e+00],\n",
|
|||
|
|
" [-1.95994093e+00, -1.78882831e+00],\n",
|
|||
|
|
" [-8.06960737e-01, 5.09330916e-01],\n",
|
|||
|
|
" [-8.49760003e-01, 6.56589855e-01],\n",
|
|||
|
|
" [-1.14152403e+00, -2.12534375e+00],\n",
|
|||
|
|
" [ 7.03879551e-02, -2.65638914e+00],\n",
|
|||
|
|
" [ 3.38848855e+00, -1.18011529e+00],\n",
|
|||
|
|
" [-7.86036044e-01, 1.25343042e+00],\n",
|
|||
|
|
" [ 5.23936833e+00, 1.53688725e-01],\n",
|
|||
|
|
" [ 2.10066904e+00, 7.39620232e-01],\n",
|
|||
|
|
" [ 4.08674973e-01, -2.64432373e+00],\n",
|
|||
|
|
" [ 2.97660341e+00, 9.18001854e-01],\n",
|
|||
|
|
" [-1.05396335e+00, -7.58011870e-01],\n",
|
|||
|
|
" [ 9.27898191e-01, 2.73195679e+00],\n",
|
|||
|
|
" [ 1.88170910e+00, 3.09803354e-01],\n",
|
|||
|
|
" [-4.15836121e+00, -1.10080396e+00],\n",
|
|||
|
|
" [-1.32218416e+00, 9.68951593e-01],\n",
|
|||
|
|
" [ 3.58888407e-02, -2.06965739e+00],\n",
|
|||
|
|
" [-3.72549122e+00, -2.55973339e-01],\n",
|
|||
|
|
" [ 2.22694149e+00, 4.99182288e-01],\n",
|
|||
|
|
" [-6.97951691e-01, -2.24857318e+00],\n",
|
|||
|
|
" [-2.84762293e-01, -2.76820542e+00],\n",
|
|||
|
|
" [-9.70900037e-02, -2.14032524e+00],\n",
|
|||
|
|
" [ 1.86466006e+00, 2.44533611e+00],\n",
|
|||
|
|
" [-9.40249095e-01, -3.43079482e+00],\n",
|
|||
|
|
" [ 2.52489015e-01, 9.54766411e-02],\n",
|
|||
|
|
" [ 1.26547237e+00, -1.24281113e+00],\n",
|
|||
|
|
" [-1.70132052e-01, -3.06620379e+00],\n",
|
|||
|
|
" [ 1.98027096e+00, 2.27183117e+00],\n",
|
|||
|
|
" [ 3.15917813e+00, 2.19249127e-01],\n",
|
|||
|
|
" [ 5.34696684e-01, -2.36280599e+00],\n",
|
|||
|
|
" [ 1.91018239e-01, -2.04817767e+00],\n",
|
|||
|
|
" [ 1.16551796e+00, 7.40410345e-01],\n",
|
|||
|
|
" [-4.81789328e+00, 4.21301137e-01],\n",
|
|||
|
|
" [ 1.67713941e+00, -1.10244667e+00],\n",
|
|||
|
|
" [ 1.51632690e+00, -1.39562399e+00],\n",
|
|||
|
|
" [ 1.85415809e+00, -2.02593818e+00],\n",
|
|||
|
|
" [ 2.31951123e+00, -8.71471407e-02],\n",
|
|||
|
|
" [ 1.05068741e+00, -1.07471134e+00],\n",
|
|||
|
|
" [-9.19418638e-01, -5.91973296e-02],\n",
|
|||
|
|
" [-5.10508391e+00, -4.79067414e-01],\n",
|
|||
|
|
" [ 5.73359235e-01, -1.18790144e+00],\n",
|
|||
|
|
" [ 4.01530110e+00, 8.97874118e-01],\n",
|
|||
|
|
" [ 2.12070426e+00, -2.80541551e-01],\n",
|
|||
|
|
" [-4.24576720e+00, -1.69481555e-04],\n",
|
|||
|
|
" [ 3.20006458e+00, 2.18975297e+00],\n",
|
|||
|
|
" [-3.05754194e+00, -4.03559153e-02],\n",
|
|||
|
|
" [ 1.52629544e+00, -8.41106533e-01],\n",
|
|||
|
|
" [-6.37066079e-01, 1.19336895e+00],\n",
|
|||
|
|
" [-1.42342604e+00, -1.95148797e+00],\n",
|
|||
|
|
" [ 3.20407657e+00, 2.83195472e-01],\n",
|
|||
|
|
" [-9.78711507e-01, 2.42328725e+00],\n",
|
|||
|
|
" [ 1.20394346e+00, 3.67222510e+00]])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 137,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.decomposition import PCA\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"reduced_data = PCA(n_components=2).fit_transform(df)\n",
|
|||
|
|
"reduced_data"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Визуализация данных после понижения размерности"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 138,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABR0AAAINCAYAAACzjdj/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeXzU1fX/8df9TPZtCGvCIiCuARFQUdw3FBfcqrVWrNalLVVra/ur2tZSaltrF792sda61FbaWnfFBeuKG4iKCAiiIIpAWAPZ1/nc3x/DICEzySSZmc8s7+fjQW1mbmZOJpnJzZl7zjHWWouIiIiIiIiIiIhIjDheByAiIiIiIiIiIiLpRUlHERERERERERERiSklHUVERERERERERCSmlHQUERERERERERGRmFLSUURERERERERERGJKSUcRERERERERERGJKSUdRUREREREREREJKaUdBQREREREREREZGYyvI6gERyXZf169dTXFyMMcbrcERERES6xVpLbW0tgwcPxnH03nEq0n5UREREUl20e9KMSjquX7+eYcOGeR2GiIiISK98/vnnDB061OswpAe0HxUREZF00dWeNKOSjsXFxUDwQSkpKfE4GhEREZHuqampYdiwYTv3NJJ6tB8VERGRVBftnjSjko6hEpaSkhJt8kRERCRlqSw3dWk/KiIiIumiqz2pmgGJiIiIiIiIiIhITCnpKCIiIiIiIiIiIjGlpKOIiIiIiIiIiIjElJKOIiIiIiIiIiIiElNKOoqIiIiIiIiIiEhMKekoIiIiIiIiIiIiMaWko4iIiIiIiIiIiMSUko4iIiIiIiIiIiISU0o6ioiIiIiIiIiISEwp6SgiIiIiIiIiIiIxpaSjiIiIiIiIiIiIxJSSjiIiIiIiIiIiIhJTKZt0/PWvf40xhu9+97tehyIiIiIiGUj7URHpLevWYdvWYt06r0MREYm5LK8D6Im3336bO++8k7Fjx3odioikqIBrWbC6ik21TQwszmPiyL74HON1WCIikiK0HxWR3rBtK7G1f4Dm5wEX8GFzT8YUX4PJGul1eCIiMZFyJx3r6uq48MILueuuuygtLfU6HBFJQXOWVnLkLS9xwV3zueaBRVxw13yOvOUl5iyt9Do0ERFJAdqPikhv2NYPsFu/BM0vEEw4AgSg+Tns1i9hWz/yMjwRkZhJuaTjlVdeyWmnncaJJ57odSgikoLmLK1k+qyFVFY3tbt8Q3UT02ctVOJRRES6pP2oiPSUtRZbfT3YZiCw27UBsA3Ymh97EZqISMylVHn1Aw88wMKFC3n77bejWt/c3Exzc/POj2tqauIVmoikgIBrmTl7GTbMdRYwwMzZy5hcUaZSaxERCUv7URHplbal0LaikwUutL6Pbf0Ik71PwsISEYmHlDnp+Pnnn3PNNdfwr3/9i7y8vKg+5+abb8bv9+/8N2zYsDhHKSLJbMHqqg4nHHdlgcrqJhasrkpcUCIikjK0HxWRXmv7JLp1gSjXiYgkMWOtDXfoJ+k8/vjjnH322fh8vp2XBQIBjDE4jkNzc3O76yD8O8vDhg2jurqakpKShMUuIsnhiUXruOaBRV2u+8NXxnHmuCHxD0hEpJtqamrw+/3ay3hE+1ER6S3b9Dx2+5VdrjOld2Nyj05ARCIi3RftnjRlyqtPOOEElixZ0u6yr3/96+y3335cd911HTZ4ALm5ueTm5iYqRBFJcgOLozuVEu06ERHJLNqPikiv5RwOJh9sY+Q1pghyDk1cTCIicZIyScfi4mLGjBnT7rLCwkL69evX4XIRkXAmjuxLuT+PDdVNYfs6GqDMn8fEkX0THZqIiKQA7UdFpLeMUwiFV2Dr/hh5TeF0jNGbFSKS+lKmp6OISG/5HMOMqRVAMMG4q9DHM6ZWaIiMiIiIiMRP4beh4FKCO1CH4FkgJ/iv8JtQeLmn4YmIxErK9HSMBfVBEhGAOUsrmTl7WbuhMuX+PGZMrWDKmHIPIxMR6Zz2MqlP30MRCbGB9dD4BNbdgnEGQv6ZGF+Z12GJiHQp7Xo6iojEypQx5UyuKGPB6io21TYxsDhYUq0TjiIiIiKSKMY3GIqmd6jAERFJF0o6ikhG8jmGSaP6eR2GiIiIiIiISFpST0cRERERERERERGJKSUdRUREREREREREJKaUdBQREREREREREZGYUtJRREREREREREREYkpJRxEREREREREREYkpTa8WEZFeCbiWBaur2FTbxMDiPCaO7IvPMV6HJSIiIiIZYltdI88uWM76rTX0Kcrn5IP3ZdiAPl6HJZLxlHQUEZEem7O0kpmzl1FZ3bTzsnJ/HjOmVjBlTLmHkYmIiIhIJpj14kL++NhrBFyLzzG41vKXJ9/k7CPGcMMFJ5DlU4GniFf07BMRkR6Zs7SS6bMWtks4AmyobmL6rIXMWVrpUWQiIiIikglmz/uAWx+eS1vAxVpLW8DFdS0Aj7+xlFsfnutxhCKZTUlHERHptoBrmTl7GTbMdaHLZs5eRsANt0JEREREpHdc1/LXp+ZFvN4CD736PlU1DYkLSkTaUdJRRES6bcHqqg4nHHdlgcrqJhasrkpcUCIiIiKSMVas3URlVW2nawKuZe7iVQmKSER2p6SjiIh026bayAnHnqwTEREREemOhqbWLtc4xlDf3PU6EYkPJR1FRKTbBhbnxXSdiIiIiEh3DBvYB2M6X+Nay4hBpYkJSEQ6UNJRRES6beLIvpT784i0zzMEp1hPHNk3kWGJiIiISIYY2KeII8eMxOeE35E6xjDAX8ikiuEJjkxEQpR0FBGRbvM5hhlTKwA6JB5DH8+YWhFxEygiIiIi0lv/77xjKS7I67DndIzBcQw/v2QKPkdpDxGv6NknIiI9MmVMOXdMm0CZv30JdZk/jzumTWDKmHKPIhMRERGRTDB0QB9mXf9VphyyH1m+YHrDAIftvwf3/uB8Dt1vD28DFMlwxlprvQ4iUWpqavD7/VRXV1NSUuJ1OCIiaSHgWhasrmJTbRMDi4Ml1TrhKBIf2sukPn0PRUTio6GphS01DfgL8/AXqq+4SDxFu5/JSmBMIiKShnyOYdKofl6HISIiIiIZrCAvhz3ycrwOQ0R2ofJqERERERERERERiSklHUVERERERERERCSmVF4tIiLSA+plKSIiIiJesm4VND6GbV0BJg+TdwLkHIUxOl8myUFJRxERkW6as7SSmbOXUVndtPOycn8eM6ZWaGq3iIiIiMSdbXwSW30D0EawiNVgGx+ArH2g9F6Mb6DHEYqovFpERKRb5iytZPqshe0SjgAbqpuYPmshc5ZWehSZiIiIiGQC2/I2tvr/Aa2ABQIEk49A2yrstsuw1vUuQJEdlHQUERGJUsC1zJy9DBvmutBlM2cvI+CGWyEiIiIi0nu27q9ETucEoG0FtLyWyJBEwlLSUUREJEoLVld1OOG4KwtUVjexYHVV4oISERERkYxhbQu0vE7wdGMkWdimFxMVkkhESjqKiIhEaVNt5IRjT9aJiIiIiHSLbYGwdTftFoHVflS8p6SjiIhIlAYW58V0nYiIiIhIt5hCcMq6WORisvdNSDginVHSUUREJEoTR/al3J+HiXC9ITjFeuLIvokMS0REREQyhDEGUzANIu5IAbIg/+xEhSQSkZKOIiIiUfI5hhlTK4CO27zQxzOmVuBzOtsEioiIiIj0QuHFkH0wHXekPsBg/L/COHoTXLynpKOIiEg3TBlTzh3TJlDmb19CXebP445pE5gyptyjyEREREQk0ay1tAY6G+oSe8bkYvreiyn6/i6l1gZyDsf0vR+Tf2ZC4xGJJMvrAERERHoi4FoWrK5iU20TA4uDJc2JOmE4ZUw5kyvKPLt/EREREfHWynVbuO9/7/DCwo9oaQswqLSI848Zx/nHjSM/Jzvu929MLhR9AwqvAFsPJjt4mUgSUdJRRERSzpyllcycvYzK6i+m8pX785gxtSJhJw19jmHSqH4JuS8RERERSR4LPlzD1bc/juu6BNzgJOmN2+r48xNv8OKij/nbd88jPzf+iUcI9njEFCXkvkS6S+XVIiKSUuY
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1600x600 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(16, 6))\n",
|
|||
|
|
"draw_data_2d(\n",
|
|||
|
|
" pd.DataFrame({\"Column1\": reduced_data[:, 0], \"Column2\": reduced_data[:, 1]}),\n",
|
|||
|
|
" 0,\n",
|
|||
|
|
" 1,\n",
|
|||
|
|
" subplot=plt.subplot(1, 2, 1),\n",
|
|||
|
|
")\n",
|
|||
|
|
"draw_data_2d(\n",
|
|||
|
|
" pd.DataFrame({\"Column1\": reduced_data[:, 0], \"Column2\": reduced_data[:, 1]}),\n",
|
|||
|
|
" 0,\n",
|
|||
|
|
" 1,\n",
|
|||
|
|
" y.tolist(),\n",
|
|||
|
|
" y_names,\n",
|
|||
|
|
" plt.subplot(1, 2, 2),\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Визуализация результатов неиерархической кластеризации для двух кластеров с учетом понижения размерности"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 139,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from utils_clusters import fit_kmeans\n",
|
|||
|
|
"from visual import draw_clusters\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"kmeans = fit_kmeans(reduced_data, 2, random_state)\n",
|
|||
|
|
"draw_clusters(reduced_data, kmeans)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Визуализация результатов неиерархической кластеризации для четырех кластеров с учетом понижения размерности"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 140,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"kmeans = fit_kmeans(reduced_data, 4, random_state)\n",
|
|||
|
|
"draw_clusters(reduced_data, kmeans)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Сравнение результатов кластеризации с реальным разбиением с учетом понижения размерности"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 141,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1600x1200 with 3 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"labels = [2 if val == 1 else 1 if val == 2 else val for val in kmeans.labels_]\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(16, 12))\n",
|
|||
|
|
"draw_data_2d(\n",
|
|||
|
|
" pd.DataFrame({\"Column1\": reduced_data[:, 0], \"Column2\": reduced_data[:, 1]}),\n",
|
|||
|
|
" 0,\n",
|
|||
|
|
" 1,\n",
|
|||
|
|
" labels,\n",
|
|||
|
|
" y_names,\n",
|
|||
|
|
" plt.subplot(2, 2, 1),\n",
|
|||
|
|
")\n",
|
|||
|
|
"draw_data_2d(\n",
|
|||
|
|
" pd.DataFrame({\"Column1\": reduced_data[:, 0], \"Column2\": reduced_data[:, 1]}),\n",
|
|||
|
|
" 0,\n",
|
|||
|
|
" 1,\n",
|
|||
|
|
" result,\n",
|
|||
|
|
" y_names,\n",
|
|||
|
|
" plt.subplot(2, 2, 2),\n",
|
|||
|
|
")\n",
|
|||
|
|
"draw_data_2d(\n",
|
|||
|
|
" pd.DataFrame({\"Column1\": reduced_data[:, 0], \"Column2\": reduced_data[:, 1]}),\n",
|
|||
|
|
" 0,\n",
|
|||
|
|
" 1,\n",
|
|||
|
|
" y.tolist(),\n",
|
|||
|
|
" y_names,\n",
|
|||
|
|
" plt.subplot(2, 2, 3),\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Выбор количества кластеров на основе инерции\n",
|
|||
|
|
"\n",
|
|||
|
|
"Инерция -- сумма квадратов расстояний выборок до ближайшего центра кластера, взвешенная по весам выборок, если таковые имеются."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 142,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"range(2, 13)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"[1095.0175884953487,\n",
|
|||
|
|
" 840.435967632246,\n",
|
|||
|
|
" 690.9568840077667,\n",
|
|||
|
|
" 569.8045875559183,\n",
|
|||
|
|
" 489.9561312217587,\n",
|
|||
|
|
" 420.18478011974236,\n",
|
|||
|
|
" 399.30137712803855,\n",
|
|||
|
|
" 343.2316060605482,\n",
|
|||
|
|
" 312.7806842989379,\n",
|
|||
|
|
" 284.2906232030758,\n",
|
|||
|
|
" 268.29293041255244]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x500 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from utils_clusters import get_clusters_inertia\n",
|
|||
|
|
"from visual import draw_elbow_diagram\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"inertias, clusters_range = get_clusters_inertia(df, random_state)\n",
|
|||
|
|
"display(clusters_range)\n",
|
|||
|
|
"display(inertias)\n",
|
|||
|
|
"draw_elbow_diagram(inertias, clusters_range)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Выбор количества кластеров на основе коэффициента силуэта\n",
|
|||
|
|
"\n",
|
|||
|
|
"Коэффициент силуэта рассчитывается с использованием среднего расстояния внутри кластера (а) и среднего расстояния до ближайшего кластера (b) для каждого образца. Коэффициент силуэта для образца равен (b - a) / max(a, b). Для пояснения: b — это расстояние между образцом и ближайшим кластером, частью которого образец не является. Обратите внимание, что коэффициент силуэта определяется только в том случае, если количество меток равно 2 <= n_labels <= n_samples - 1.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Эта функция возвращает средний коэффициент силуэта по всем образцам. \n",
|
|||
|
|
"\n",
|
|||
|
|
"Лучшее значение — 1, худшее — -1. Значения около 0 указывают на перекрывающиеся кластеры. Отрицательные значения обычно указывают на то, что образец был отнесен к неправильному кластеру."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 143,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"range(2, 13)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"[0.27024619065262834,\n",
|
|||
|
|
" 0.2704234654274893,\n",
|
|||
|
|
" 0.2619942652399716,\n",
|
|||
|
|
" 0.2946089738043157,\n",
|
|||
|
|
" 0.29442550478815743,\n",
|
|||
|
|
" 0.3239316140412729,\n",
|
|||
|
|
" 0.2955578664142471,\n",
|
|||
|
|
" 0.3109257191360407,\n",
|
|||
|
|
" 0.31515029418107016,\n",
|
|||
|
|
" 0.3282942559953307,\n",
|
|||
|
|
" 0.31900504693790716]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x500 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from utils_clusters import get_clusters_silhouette_scores\n",
|
|||
|
|
"from visual import draw_silhouettes_diagram\n",
|
|||
|
|
"\n",
|
|||
|
|
"silhouette_scores, clusters_range = get_clusters_silhouette_scores(df, random_state)\n",
|
|||
|
|
"display(clusters_range)\n",
|
|||
|
|
"display(silhouette_scores)\n",
|
|||
|
|
"draw_silhouettes_diagram(silhouette_scores, clusters_range)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Пример анализа силуэтов для разбиения от 2 до 12 кластеров\n",
|
|||
|
|
"\n",
|
|||
|
|
"max_clusters = int(math.sqrt(len(df)))\n",
|
|||
|
|
"\n",
|
|||
|
|
"https://scikit-learn.org/1.5/auto_examples/cluster/plot_kmeans_silhouette_analysis.html"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 144,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1800x700 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABZoAAAKgCAYAAAAS1si3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gUVdsG8Hu2ZJNseoMUSEJCCc1QpQdEuiBFeo0iSAeRV7FRFFCwgCKI5QMFC0WKiID0KojSO0hooYWWkJ7dPd8fIWM22SQbUibJ3r/rypXdmTMzz8zOzJ559swZSQghQERERERERERERET0hFRKB0BEREREREREREREpRsTzURERERERERERERUIEw0ExEREREREREREVGBMNFMRERERERERERERAXCRDMRERERERERERERFQgTzURERERERERERERUIEw0ExEREREREREREVGBMNFMRERERERERERERAXCRDMRERERERERERERFQgTzURU4ixZsgSSJMl/mbVs2VIePmTIEHn45cuXzabZuXNn8QZNJdLUqVPlfSIoKKhYlz1kyBB52S1btiyy5dy6dQuRkZHw9/eHRqORl7l27doiWyaVfjmdS21Fbt8zZLtKyn5RUuIoCQq6Lcr6tlSynkNERGQJE81EVOR+/vlntGvXDuXKlYNWq4WrqyuCg4PRsmVLjBs3Dps3b1Y6xBLN2iS6rSeObJEQAi+88AKWLFmCGzduwGg0Kh0SAGDnzp1m++ySJUvMxj948AANGzaUx6tUKixcuNDitJIkoUuXLhaXs3nz5mxlue+XDmU9+UNUkmQ9r16+fFnpkAoFzyPKY0MP61y8eBFjxoxBkyZNEBAQAAcHB9jb28Pf3x8dOnTAkiVLSkwdjoiooDRKB0BEZdugQYOwdOlSs2FxcXGIi4vD5cuXsWvXLly5cgXt2rWTxzdo0ABz5swp7lCJClWfPn1Qs2ZNAECFChWKZBlXr17Fvn375PfPPfccmjdvDpVKJS+7pImJiUGbNm1w7NgxAIBKpcI333yDyMjIHKfZsGEDLl26hEqVKpkNnzdvXpHGSmUXv2eISgceq1QWHD16FPPnz882/MaNG7hx4wY2bdqE9evX45dfflEgOiKiwsVEMxEVmU2bNpklmevVq4d27drByckJMTExOHz4MP78889s09WoUQM1atQozlCJCl379u3Rvn37Il3GlStXzN7PnTsXISEhRbrM1NRUCCGg0+nyPe2tW7fQunVrnD59GgCgVqvx/fffo1+/frlOZzKZMH/+fHzyySfysPPnz2PTpk35joFsW1xcHFxcXPg9Q1RK8FglSwpSF1GCSqVCWFgYnn76afj5+UGv1yMqKgrLly/Ho0ePAACrV6/GgQMH0KhRI4WjJSIqGHadQURF5o8//pBfh4aG4uDBg5gxYwYmT56MTz75BDt37kRMTAwmTZpkNl1h3Qq5Zs0aNG7cGI6OjnB3d0fPnj1x7do1i2X/+ecfDBo0CMHBwbC3t4eTkxNq1qyJiRMn4vr169nKBwUFyfFNnTrVbFxe/eXFxcVh1qxZePrpp+Hq6go7OztUrFgRQ4YMwalTp7ItJzg42GxYq1atzPr+zVjerl275DLfffddjrfJpqSkYP78+WjRogU8PDxgZ2cHX19f9OzZ02LiPzdHjx7FyJEj8fTTT8Pf31++FTAwMBC9e/fG3r17s02TdfvExsZi0qRJCAwMhJ2dHSpVqoSZM2dCCFHgZVkSFxcHZ2dnOYavvvoqW5mePXvK4zt06CAP37NnD7p16wZ/f3/Y2dnByckJQUFB6NChA6ZOnYrY2Fi5bG59NJ84cQIDBgxAUFAQdDodHBwcULFiRTzzzDOYPHkyoqOj81wPSZIQERFhNiw0NNTicZPf/TtrNywnT55E165d4enpCZ1OhzNnzuQZX1bXr19HRESEnGTWarVYvnx5nklmlSq9qvJ///d/SEhIkId//vnn8j6iVqtzncft27fx5ptvIjw8HM7OzrC3t0doaChGjRqFq1evZitfnPt1QkICpk+fjrp168LZ2RlarRY+Pj4IDw/Hyy+/nO9k+qFDhxAZGYnQ0FA4OjrCyckJVapUQWRkJP799988p8/rFvvczn2//vor2rdvL3eT5OLigpCQEHTt2hWzZs2CyWSSb7PO2oI98zKzznfPnj3o06cPKlasCJ1OBxcXFzRu3BhffPEF0tLSsq1D1m5b1q1bhyZNmsDJyQkVK1YEkL9nAVy4cAF9+/aFl5cX7O3tUbduXaxbt87i9tuzZw9atmwJvV4PDw8P9OrVC1FRUQXqs72gx29+4s9Jfs59hXH83Lx5E4MHD4aXlxdcXFzQuXNnnD9/HgBw+PBhtG/fHs7Ozjl+t2fdjy9duoS5c+eievXq8u3qr776qpzgsVZhfn8C6T8W9u3bFx4eHtDr9WjRogW2bt2a6zQ7d+7ESy+9hLp168LX1xc6nQ6Ojo4IDQ1FZGQkTpw4YVZekiS0atXKbFhwcHC27oYMBgPeeecddOzYESEhIXBzc4NWq4WnpyeaN2+Ozz//3OLxZsmxY8fMtn/m8+ybb74pD3/11Vfl4bdv3zab5uDBgwAsH6tPch7JkJaWhtmzZ6NatWrQ6XQICAjAa6+9hpSUFKvWDSiaYyyr69ev4/XXX0edOnXg4uICe3t7VKxYEV27dsWWLVusmkduXVgVRh3FmjpqZpcuXcLYsWMRFhYGvV4PBwcHVK9eHW+88Qbu3r2bLX5r6yL5OT8ppXv37jh9+jQWL16MGTNm4M0338TXX38tdxuWIWsDAiKiUkkQERWRMWPGCAACgPDy8hIXL160arrFixfL02U9TUVERMjDBw8eLA+Piooym6Zdu3Zm7zP+KleuLJKSkszm+emnnwqVSmWxPADh6uoqduzYYTZNYGCgPH7KlClm46ZMmSKPCwwMNBt3/vx5ERQUlOOydDqdWLFihcXlWPqLiIgwW15Of1FRUUIIIe7cuSPCw8NzLKdSqcTcuXOt+pyEEOLzzz/PdbmSJInFixfnuH08PT1FWFiYxWnfeeedQl1W5s9i1KhR8vAGDRqYTRMfHy8cHR3l8Rmfx9atW4Varc41hjNnzsjzGTx4sNnnlOHUqVNm87f0t3Hjxjy3fV6feYYn2b8zH2d16tQRer3ebJojR47kGtuOHTvMyk+ZMkUEBweb7ee//vqrVdN27dpVfv3FF18IIYSIjY0Vzs7OcnyZj5PM5wUhhNi/f7/w8vLKdf13795tNk1x7tctW7bMdVm9e/fOdVtnNm3aNCFJUo7zWrNmjVw2p3Np1u2fce7IkNO5L+t529JfUlJStnO1pb/M833zzTdzLdu8eXMRHx9vFmPW8Vk/b0vxZpZ529SuXVve17LuA1u3bjWbbv369UKj0WQr6+npKZo0aWLxfJCXgh6/+Yk/J/k99xX0+PHw8LD4Pent7S3WrFkjdDpdtnFZv9uz7sfPPPOMxVgaNGhgNl1u+0Vhf39GRUWJ8uXLW9w+HTt2zDGOiRMn5rp97ezsxJYtW+TyeR1vGcf/o0eP8iz77LPPCoPBkOe6mUwm4enpKU/3ww8/yOOaNWsmD69fv748fOXKlfJwFxcXeTmWPpP8nEeyTp9T/XDgwIFWf3aFfYxltWHDBovzzPgbN26cXDa3OmfmabIec4VRR7Gmjpph7dq1uc7X399fnD59OsftnFNdJL/np9xY8z2W+S/r9s6P5ORkcebMGfH888+bzfPEiRNPPE8iopKCXWcQUZGpW7eu/Pru3buoUqUKwsPD0aBBA9SrVw+tWrVCaGhokSx78+bNaNCgAdq1a4cdO3bI/dheuHABa9euRZ8+fQAAu3fvxquvviq3MqxYsSL69u2L+Ph4LF68GImJiYiNjUWPHj1w8eJFuLu7P3FMRqMR3bp1k1sIent7o1+/fvDw8MDmzZuxf/9+pKSkYNCgQahXrx4qVaqEt956C5cvX8bMmTPl+bzyyity9wgVKlRAhQoV4OTkhIULF+LSpUsAgPr166N3797yNB4eHgCAgQMH4ujRowAAZ2dn9OvXDwE
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1800x700 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1800x700 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
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{
|
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|
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1800x700 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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"<Figure size 1800x700 with 2 Axes>"
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]
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},
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"metadata": {},
|
|||
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"output_type": "display_data"
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},
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1800x700 with 2 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
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|||
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{
|
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"data": {
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|
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"text/plain": [
|
|||
|
|
"<Figure size 1800x700 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1800x700 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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|||
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|
"<Figure size 1800x700 with 2 Axes>"
|
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]
|
|||
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|
},
|
|||
|
|
"metadata": {},
|
|||
|
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"output_type": "display_data"
|
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABZoAAAKgCAYAAAAS1si3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gUVdsG8Hu2ZrPpCYEQIISEXgxVqaEXkS4dgWgEKVLlVWyAUhQ+ERVBsQBSlCJFRIh0pEoLvUrvPSG97Pn+2Oywm90kG1I2Iffvuvba6fPM7MzkzJMzZyQhhAARERERERERERER0TNSODoAIiIiIiIiIiIiIircmGgmIiIiIiIiIiIiohxhopmIiIiIiIiIiIiIcoSJZiIiIiIiIiIiIiLKESaaiYiIiIiIiIiIiChHmGgmIiIiIiIiIiIiohxhopmIiIiIiIiIiIiIcoSJZiIiIiIiIiIiIiLKESaaiYiIiIiIiIiIiChHmGgmIodZsGABJEmSP+aaNm0qDx84cKA8/PLlyxbzbN++PX+DpgJp4sSJ8jFRtmzZfF33wIED5XU3bdo0z9Zz+/ZthIWFwd/fHyqVSl7nmjVr8mydVPhldC0tKjL7O0NFV0E5LgpKHAVBTvfF874vHVnOISIiyg4mmoko1/z2229o06YNihcvDrVaDXd3dwQGBqJp06YYOXIkIiIiHB1igWZvEr2oJ46KIiEEXn31VSxYsAA3b95Eamqqo0MCAGzfvt3imF2wYIHF+EePHqFevXryeIVCgblz59qcV5IkdOzY0eZ6IiIirKblsV84PO/JH6KCJP119fLly44OKVfwOuJ4rOhhn+TkZHz55Zfo27cvKlasCIVCYVdlhNOnT+PDDz9E27Zt4ePjk2nZioiooFM5OgAiej70798fixYtshgWHR2N6OhoXL58GTt27MCVK1fQpk0beXzdunUxY8aM/A6VKFf16tUL1apVAwCULl06T9Zx9epV7N69W+5/5ZVX0LhxYygUCnndBc29e/fQqlUrHD16FACgUCjw448/IiwsLMN51q9fj4sXL6JcuXIWw7/66qs8jZWeX/w7Q1Q48Fyl50FsbCzGjBmT7fkiIiIwZcqUPIiIiCj/MdFMRDm2ceNGiyRz7dq10aZNG7i4uODevXs4fPgw9u7dazVf1apVUbVq1fwMlSjXtW3bFm3bts3TdVy5csWif9asWQgKCsrTdSYlJUEIAa1Wm+15b9++jRYtWuDUqVMAAKVSiV9++QV9+vTJdD6DwYDZs2dj5syZ8rBz585h48aN2Y6Birbo6Gi4ubnx7wxRIcFzlWzJSVnEUdRqNapWrYo6depgy5YtuHTpkl3zeXp6olatWggKCsK8efPyOEoiorzDpjOIKMf+/vtvuTs4OBj79+/HlClTMH78eMycORPbt2/HvXv3MG7cOIv5cutRyNWrV6N+/fpwdnaGp6cnunfvjmvXrtmc9tChQ+jfvz8CAwPh5OQEFxcXVKtWDWPHjsX169etpi9btqwc38SJEy3GZdVeXnR0NKZNm4YXX3wR7u7u0Gg0KFOmDAYOHIiTJ09arScwMNBiWLNmzSwetzOtb8eOHfI0CxcuzPAx2cTERMyePRtNmjSBl5cXNBoN/Pz80L17d5uJ/8xERkZi6NChePHFF+Hv7w+dTgcnJycEBASgZ8+e2LVrl9U86fdPVFQUxo0bh4CAAGg0GpQrVw5Tp06FECLH67IlOjoarq6ucgy2Cu3du3eXx7dr104e/s8//6BLly7w9/eHRqOBi4sLypYti3bt2mHixImIioqSp82sjebjx4+jX79+KFu2LLRaLXQ6HcqUKYPmzZtj/PjxuHHjRpbbIUkSQkNDLYYFBwfbPG+ye3ynb4blxIkT6Ny5M7y9vaHVanH69Oks40vv+vXrCA0NlZPMarUay5YtyzLJrFAYiyQ///wzYmNj5eHffPONfIwolcpMl3Hnzh28//77CAkJgaurK5ycnBAcHIxhw4bh6tWrVtPn53EdGxuLTz75BLVq1YKrqyvUajV8fX0REhKCN998M9vJ9AMHDiAsLAzBwcFwdnaGi4sLKlSogLCwMPz3339Zzp/VI/aZXfv++OMPtG3bVm4myc3NDUFBQejcuTOmTZsGg8EgP2advga7+TrTL/eff/5Br169UKZMGWi1Wri5uaF+/fr49ttvkZycbLUN6R8tXrt2LRo0aAAXFxeUKVMGQPbeBXD+/Hn07t0bPj4+cHJyQq1atbB27Vqb+++ff/5B06ZNodfr4eXlhR49euDSpUs5arM9p+dvduLPSHaufblx/ty6dQsDBgyAj48P3Nzc0KFDB5w7dw4AcPjwYbRt2xaurq4Z/m1PfxxfvHgRs2bNQpUqVeDk5AR/f3+MGTMGT548ydZ+yM2/n4Dxn4W9e/eGl5cX9Ho9mjRpgs2bN2c6z/bt2/HGG2+gVq1a8PPzg1arhbOzM4KDgxEWFobjx49bTC9JEpo1a2YxLDAw0Kq5oZSUFHz00Ud4+eWXERQUBA8PD6jVanh7e6Nx48b45ptvbJ5vthw9etRi/5tfZ99//315uHkNzzt37ljMs3//fgC2z9VnuY6YJCcnY/r06ahUqRK0Wi1KlSqFd955B4mJiXZtG5A351h6169fx7vvvouaNWvCzc0NTk5OKFOmDDp37oxNmzbZtYzMmlnIjTKKPWVUcxcvXsSIESNQuXJl6PV66HQ6VKlSBe+99x7u379vFb+9ZZHsXJ8cxd3dHU+ePMGRI0fwww8/yH+LsjJ48GA8fPgQmzdvxvjx4/M4SiKiPCaIiHLo7bffFgAEAOHj4yMuXLhg13zz58+X50t/OQoNDZWHDxgwQB5+6dIli3natGlj0W/6lC9fXsTHx1ss88svvxQKhcLm9ACEu7u72LZtm8U8AQEB8vgJEyZYjJswYYI8LiAgwGLcuXPnRNmyZTNcl1arFcuXL7e5Hluf0NBQi/Vl9Ll06ZIQQoi7d++KkJCQDKdTKBRi1qxZdv1OQgjxzTffZLpeSZLE/PnzM9w/3t7eonLlyjbn/eijj3J1Xea/xbBhw+ThdevWtZgnJiZGODs7y+NNv8fmzZuFUqnMNIbTp0/LyxkwYIDF72Ry8uRJi+Xb+mzYsCHLfZ/Vb27yLMe3+XlWs2ZNodfrLeY5cuRIprFt27bNYvoJEyaIwMBAi+P8jz/+sGvezp07y93ffvutEEKIqKgo4erqKsdnfp6YXxeEEGLPnj3Cx8cn0+3fuXOnxTz5eVw3bdo003X17Nkz031tbtKkSUKSpAyXtXr1annajK6l6fe/6dphktG1L/1129YnPj7e6lpt62O+3Pfffz/TaRs3bixiYmIsYkw/Pv3vbStec+b7pkaNGvKxlv4Y2Lx5s8V869atEyqVympab29v0aBBA5vXg6zk9PzNTvwZye61L6fnj5eXl82/k8WKFROrV68WWq3Walz6v+3pj+PmzZvbjKVu3boW82V2XOT2389Lly6JEiVK2Nw/L7/8coZxjB07NtP9q9FoxKZNm+TpszrfTOf/kydPspy2ZcuWIiUlJcttMxgMwtvbW55vyZIl8rhGjRrJw+vUqSMPX7FihTzczc1NXo+t3yQ715H082dUPnzttdfs/u1y+xxLb/369TaXafqMHDlSnjazMqf5POnPudwoo9hTRjVZs2ZNpsv19/cXp06dynA/Z1QWye71KTP2/B0z/6Tf39lhvm32/k1If9yn/02JiAo6Np1BRDlWq1Ytufv+/fuoUKECQkJCULduXdSuXRvNmjVDcHBwnqw7IiICdevWRZs2bbBt2za5Hdvz589jzZo16NWrFwBg586dGDNmjFzLsEyZMujduzdiYmIwf/58xMXFISoqCt26dcOFCxfg6en5zDGlpqaiS5cucg3BYsWKoU+fPvDy8kJERAT27NmDxMRE9O/fH7Vr10a5cuXwwQcf4PLly5g6daq8nLfeektuHqF06dIoXbo0XFxcMHfuXFy8eBEAUKdOHfTs2VOex8vLCwDw2muvITIyEgDg6uqKPn36oFSpUti9ezc2btwIg8GA0aN
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1800x700 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1800x700 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from utils_clusters import get_clusters_silhouettes\n",
|
|||
|
|
"from visual import draw_silhouettes\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"silhouettes = get_clusters_silhouettes(reduced_data, random_state)\n",
|
|||
|
|
"draw_silhouettes(reduced_data, silhouettes)"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": ".venv",
|
|||
|
|
"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": "3.12.4"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|