zajecia3 i zajecia4
This commit is contained in:
kubapok
parent
bcc0d7b698
commit
5ceed1d6b5
11
README.md
11
README.md
@ -30,13 +30,14 @@ Do nauki można wykorzystać wiele tutoriali internetowych python (w wersji pyth
|
||||
- Zajęcia 2 - Wprowadzenie do python 2/2
|
||||
- Zajęcia 3 - pandas
|
||||
- Zajęcia 4 - numpy
|
||||
- Zajęcia 5 - scikit-learn
|
||||
- Zajęcia 6 - przetwarzanie tekstu w python
|
||||
- Zajęcia 7 - przetwarzanie obrazów w python
|
||||
- Zajęcia 8 - zajęcia z analizy wizualizacji danych
|
||||
- Zajęcia 5 - scikit-learn 1
|
||||
- Zajęcia 6 - scikit-learn 2
|
||||
- Zajęcia 7 - przetwarzanie tekstu w python
|
||||
- Zajęcia 8 - przetwarzanie obrazów w python
|
||||
- Zajęcia 9 - zajęcia z analizy wizualizacji danych
|
||||
- Zajęcia 10 - zajęcia z analizy wizualizacji danych
|
||||
- Zajęcia 11 - Zaliczenie
|
||||
- Zajęcia 11 - zajęcia z analizy wizualizacji danych
|
||||
- Zaliczenie - Zaliczenie przedmiotu 8 luty 14:30-16.45
|
||||
|
||||
|
||||
## Zaliczenie przedmiotu
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Binary file not shown.
2280
zajecia3/1.ipynb
Normal file
2280
zajecia3/1.ipynb
Normal file
File diff suppressed because it is too large
Load Diff
311
zajecia3/1_odpowiedzi.ipynb
Normal file
311
zajecia3/1_odpowiedzi.ipynb
Normal file
@ -0,0 +1,311 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "23ed41a0-7a05-493e-a640-4bfb10c42164",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "fa3799c5-d3a0-4967-98d4-a340d19dbfc6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[10 11 12 13 14 15 16 17 18 19 20]\n",
|
||||
"(11,)\n",
|
||||
"<class 'numpy.ndarray'>\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Zadanie 1.1\n",
|
||||
"# Tworzenie tablicy jednowymiarowej\n",
|
||||
"arr = np.array([10,11,12,13,14,15,16,17,18,19,20])\n",
|
||||
"print(arr)\n",
|
||||
"print(arr.shape)\n",
|
||||
"print(type(arr))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "b6b4fa7d-7ee5-416c-8060-39057b49d77b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[10 20]\n",
|
||||
" [30 40]\n",
|
||||
" [50 60]]\n",
|
||||
"(3, 2)\n",
|
||||
"<class 'numpy.ndarray'>\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Zadanie 1.2\n",
|
||||
"arr = np.array([[10, 20], [30, 40], [50, 60]])\n",
|
||||
"\n",
|
||||
"print(arr)\n",
|
||||
"\n",
|
||||
"print(arr.shape)\n",
|
||||
"\n",
|
||||
"print(type(arr))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "f96f774c-d6cd-440f-b6bf-a2d373404de3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[1 2 3]\n",
|
||||
" [4 5 6]\n",
|
||||
" [7 8 9]]\n",
|
||||
"8\n",
|
||||
"[[7 8 9]]\n",
|
||||
"[3 6 9]\n",
|
||||
"[[5 6]\n",
|
||||
" [8 9]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Zadanie 2\n",
|
||||
"# Tworzenie dwuwymiarowej tablicy\n",
|
||||
"arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
|
||||
"\n",
|
||||
"print(arr)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(arr[2, 1])\n",
|
||||
"\n",
|
||||
"print(arr[2:])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(arr[:,2])\n",
|
||||
"\n",
|
||||
"print(arr[1:,1:])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "43216855-9d5d-4d03-9512-557f4d228571",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[10 20 30 40]\n",
|
||||
"int64\n",
|
||||
"[10. 20. 30. 40.]\n",
|
||||
"float32\n",
|
||||
"['Python' 'NumPy' 'Coding']\n",
|
||||
"<U6\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Zadanie 3\n",
|
||||
"\n",
|
||||
"# Punkt 1\n",
|
||||
"arr = np.array([10, 20, 30, 40])\n",
|
||||
"print(arr)\n",
|
||||
"print(arr.dtype)\n",
|
||||
"\n",
|
||||
"# Punkt 2\n",
|
||||
"arr = arr.astype('float32')\n",
|
||||
"print(arr)\n",
|
||||
"print(arr.dtype)\n",
|
||||
"\n",
|
||||
"# Punkt 3\n",
|
||||
"arr = np.array([\"Python\", \"NumPy\", \"Coding\"])\n",
|
||||
"print(arr)\n",
|
||||
"print(arr.dtype)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "0a700a92-fb6e-498d-bf2f-d5f9758d0147",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[50 2 3 4 5]\n",
|
||||
"[50 2 3 4 5]\n",
|
||||
"[50 2 3 4 5]\n",
|
||||
"[1 2 3 4 5]\n",
|
||||
"[50 2 3 4 5]\n",
|
||||
"[[1 2 3 4]]\n",
|
||||
"[1 2 3 4]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"### Zadanie 4\n",
|
||||
"import copy\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Punkt 1 - Przypisanie do zmiennej\n",
|
||||
"arr = np.array([1, 2, 3, 4, 5])\n",
|
||||
"x = arr\n",
|
||||
"arr[0] = 50\n",
|
||||
"print(arr) # Tablica arr po zmianie\n",
|
||||
"print(x) # Tablica x po zmianie\n",
|
||||
"\n",
|
||||
"# Punkt 2 - Kopia tablicy\n",
|
||||
"arr = np.array([1, 2, 3, 4, 5])\n",
|
||||
"x = arr.copy()\n",
|
||||
"arr[0] = 50\n",
|
||||
"print(arr) # Tablica arr po zmianie\n",
|
||||
"print(x) # Tablica x po kopii\n",
|
||||
"\n",
|
||||
"# Punkt 3 - Głęboka kopia\n",
|
||||
"arr = np.array([1, 2, 3, 4, 5])\n",
|
||||
"x = copy.deepcopy(arr)\n",
|
||||
"arr[0] = 50\n",
|
||||
"print(arr) # Tablica arr po zmianie\n",
|
||||
"\n",
|
||||
"arr2 = np.array([1, 2, 3, 4], ndmin=2)\n",
|
||||
"print(arr2)\n",
|
||||
"\n",
|
||||
"# Zmiana wymiaru na jednowymiarowy\n",
|
||||
"arr_squeezed = arr2.squeeze()\n",
|
||||
"print(arr_squeezed)\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "fbf7f5ee-aace-47f6-9a73-14fdf7595ff4",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[[[ 1],\n",
|
||||
" [ 2]]],\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" [[[ 3],\n",
|
||||
" [ 4]]],\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" [[[ 5],\n",
|
||||
" [ 6]]],\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" [[[ 7],\n",
|
||||
" [ 8]]],\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" [[[ 9],\n",
|
||||
" [10]]]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"## Zadanie 5\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
|
||||
"\n",
|
||||
"arr\n",
|
||||
"\n",
|
||||
"arr.reshape(5,2)\n",
|
||||
"\n",
|
||||
"arr.reshape(10,1)\n",
|
||||
"\n",
|
||||
"arr.reshape(5,-1)\n",
|
||||
"\n",
|
||||
"arr.reshape(5,1,2,1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "5afcad53-ce4a-408d-bc44-f33fd7b8e276",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[53 65 77]\n",
|
||||
"[13 15 17]\n",
|
||||
"[15 25 35]\n",
|
||||
"[[ 3 5 7]\n",
|
||||
" [ 8 10 12]\n",
|
||||
" [10 10 10]]\n",
|
||||
"[119 135 151]\n",
|
||||
"940\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"### Zadanie 6\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"x = np.array([3, 5, 7])\n",
|
||||
"y = np.array([50, 60, 70])\n",
|
||||
"\n",
|
||||
"print(x + y)\n",
|
||||
"print(x + 10)\n",
|
||||
"print(x * 5)\n",
|
||||
"z = np.array([[3, 5, 7], [8, 10, 12], [10,10,10]])\n",
|
||||
"print(z)\n",
|
||||
"\n",
|
||||
"print(x.dot(z))\n",
|
||||
"print(x.dot(y))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.11.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
BIN
zajecia4/KnnClassification.svg.png
Normal file
BIN
zajecia4/KnnClassification.svg.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 5.8 KiB |
176
zajecia4/gapminder.csv
Normal file
176
zajecia4/gapminder.csv
Normal file
@ -0,0 +1,176 @@
|
||||
Country,female_BMI,male_BMI,gdp,population,under5mortality,life_expectancy,fertility
|
||||
Afghanistan,21.07402,20.62058,1311.0,26528741.0,110.4,52.8,6.2
|
||||
Albania,25.65726,26.44657,8644.0,2968026.0,17.9,76.8,1.76
|
||||
Algeria,26.368409999999997,24.5962,12314.0,34811059.0,29.5,75.5,2.73
|
||||
Angola,23.48431,22.25083,7103.0,19842251.0,192.0,56.7,6.43
|
||||
Antigua and Barbuda,27.50545,25.76602,25736.0,85350.0,10.9,75.5,2.16
|
||||
Argentina,27.46523,27.5017,14646.0,40381860.0,15.4,75.4,2.24
|
||||
Armenia,27.1342,25.355420000000002,7383.0,2975029.0,20.0,72.3,1.4
|
||||
Australia,26.87777,27.56373,41312.0,21370348.0,5.2,81.6,1.96
|
||||
Austria,25.09414,26.467409999999997,43952.0,8331465.0,4.6,80.4,1.41
|
||||
Azerbaijan,27.50879,25.65117,14365.0,8868713.0,43.3,69.2,1.99
|
||||
Bahamas,29.13948,27.24594,24373.0,348587.0,14.5,72.2,1.89
|
||||
Bahrain,28.790940000000003,27.83721,42507.0,1115777.0,9.4,77.6,2.23
|
||||
Bangladesh,20.54531,20.39742,2265.0,148252473.0,55.9,68.3,2.38
|
||||
Barbados,29.221690000000002,26.384390000000003,16075.0,277315.0,15.4,75.3,1.83
|
||||
Belarus,26.641859999999998,26.16443,14488.0,9526453.0,7.2,70.0,1.42
|
||||
Belgium,25.1446,26.75915,41641.0,10779155.0,4.7,79.6,1.82
|
||||
Belize,29.81663,27.02255,8293.0,306165.0,20.1,70.7,2.91
|
||||
Benin,23.74026,22.41835,1646.0,8973525.0,116.3,59.7,5.27
|
||||
Bhutan,22.88243,22.8218,5663.0,694990.0,48.1,70.7,2.51
|
||||
Bolivia,26.8633,24.43335,5066.0,9599916.0,52.0,71.2,3.48
|
||||
Bosnia and Herzegovina,26.35874,26.611629999999998,9316.0,3839749.0,8.1,77.5,1.22
|
||||
Botswana,26.09156,22.129839999999998,13858.0,1967866.0,63.8,53.2,2.86
|
||||
Brazil,25.99113,25.78623,13906.0,194769696.0,18.6,73.2,1.9
|
||||
Brunei,22.892310000000002,24.18179,72351.0,380786.0,9.0,76.9,2.1
|
||||
Bulgaria,25.51574,26.542859999999997,15368.0,7513646.0,13.7,73.2,1.43
|
||||
Burkina Faso,21.63031,21.27157,1358.0,14709011.0,130.4,58.0,6.04
|
||||
Burundi,21.27927,21.50291,723.0,8821795.0,108.6,59.1,6.48
|
||||
Cambodia,21.69608,20.80496,2442.0,13933660.0,51.5,66.1,3.05
|
||||
Cameroon,24.9527,23.681729999999998,2571.0,19570418.0,113.8,56.6,5.17
|
||||
Canada,26.698290000000004,27.4521,41468.0,33363256.0,5.8,80.8,1.68
|
||||
Cape Verde,24.96136,23.515220000000003,6031.0,483824.0,28.4,70.4,2.57
|
||||
Chad,21.95424,21.485689999999998,1753.0,11139740.0,168.0,54.3,6.81
|
||||
Chile,27.92807,27.015420000000002,18698.0,16645940.0,8.9,78.5,1.89
|
||||
China,22.91041,22.92176,7880.0,1326690636.0,18.5,73.4,1.53
|
||||
Colombia,26.22529,24.94041,10489.0,44901660.0,19.7,76.2,2.43
|
||||
Comoros,22.444329999999997,22.06131,1440.0,665414.0,91.2,67.1,5.05
|
||||
"Congo, The Democratic Republic of the",21.6677,19.86692,607.0,61809278.0,124.5,57.5,6.45
|
||||
"Congo",23.10824,21.87134,5022.0,3832771.0,72.6,58.8,5.1
|
||||
Costa Rica,27.03497,26.47897,12219.0,4429506.0,10.3,79.8,1.91
|
||||
Ivory Coast,23.82088,22.56469,2854.0,19261647.0,116.9,55.4,4.91
|
||||
Croatia,25.17882,26.596290000000003,21873.0,4344151.0,5.9,76.2,1.43
|
||||
Cuba,26.576140000000002,25.06867,17765.0,11290239.0,6.3,77.6,1.5
|
||||
Cyprus,25.92587,27.41899,35828.0,1077010.0,4.2,80.0,1.49
|
||||
Denmark,25.106270000000002,26.13287,45017.0,5495302.0,4.3,78.9,1.89
|
||||
Djibouti,24.38177,23.38403,2502.0,809639.0,81.0,61.8,3.76
|
||||
Ecuador,27.062690000000003,25.58841,9244.0,14447600.0,26.8,74.7,2.73
|
||||
Egypt,30.099970000000003,26.732429999999997,9974.0,78976122.0,31.4,70.2,2.95
|
||||
El Salvador,27.84092,26.36751,7450.0,6004199.0,21.6,73.7,2.32
|
||||
Equatorial Guinea,24.528370000000002,23.7664,40143.0,686223.0,118.4,57.5,5.31
|
||||
Eritrea,21.082320000000003,20.885089999999998,1088.0,4500638.0,60.4,60.1,5.16
|
||||
Estonia,25.185979999999997,26.264459999999996,24743.0,1339941.0,5.5,74.2,1.62
|
||||
Ethiopia,20.71463,20.247,931.0,83079608.0,86.9,60.0,5.19
|
||||
Fiji,29.339409999999997,26.53078,7129.0,843206.0,24.0,64.9,2.74
|
||||
Finland,25.58418,26.733390000000004,42122.0,5314170.0,3.3,79.6,1.85
|
||||
France,24.82949,25.853289999999998,37505.0,62309529.0,4.3,81.1,1.97
|
||||
Gabon,25.95121,24.0762,15800.0,1473741.0,68.0,61.7,4.28
|
||||
Gambia,24.82101,21.65029,1566.0,1586749.0,87.4,65.7,5.8
|
||||
Georgia,26.45014,25.54942,5900.0,4343290.0,19.3,71.8,1.79
|
||||
Germany,25.73903,27.165090000000003,41199.0,80665906.0,4.4,80.0,1.37
|
||||
Ghana,24.33014,22.842470000000002,2907.0,23115919.0,79.9,62.0,4.19
|
||||
Greece,24.92026,26.33786,32197.0,11161755.0,4.9,80.2,1.46
|
||||
Grenada,27.31948,25.179879999999997,12116.0,103934.0,13.5,70.8,2.28
|
||||
Guatemala,26.84324,25.29947,6960.0,14106687.0,36.9,71.2,4.12
|
||||
Guinea,22.45206,22.52449,1230.0,10427356.0,121.0,57.1,5.34
|
||||
Guinea-Bissau,22.92809,21.64338,1326.0,1561293.0,127.6,53.6,5.25
|
||||
Guyana,26.470190000000002,23.68465,5208.0,748096.0,41.9,65.0,2.74
|
||||
Haiti,23.27785,23.66302,1600.0,9705130.0,83.3,61.0,3.5
|
||||
Honduras,26.73191,25.10872,4391.0,7259470.0,26.5,71.8,3.27
|
||||
"Hong Kong",23.71046,25.057470000000002,46635.0,6910384.0,3.06,82.49,1.04
|
||||
Hungary,25.97839,27.115679999999998,23334.0,10050699.0,7.2,73.9,1.33
|
||||
Iceland,26.02599,27.206870000000002,42294.0,310033.0,2.7,82.4,2.12
|
||||
India,21.31478,20.95956,3901.0,1197070109.0,65.6,64.7,2.64
|
||||
Indonesia,22.986929999999997,21.85576,7856.0,235360765.0,36.2,69.4,2.48
|
||||
Iran,27.236079999999998,25.310029999999998,15955.0,72530693.0,21.4,73.1,1.88
|
||||
Iraq,28.411170000000002,26.71017,11616.0,29163327.0,38.3,66.6,4.34
|
||||
Ireland,26.62176,27.65325,47713.0,4480145.0,4.5,80.1,2.0
|
||||
Israel,27.301920000000003,27.13151,28562.0,7093808.0,4.9,80.6,2.92
|
||||
Italy,24.79289,26.4802,37475.0,59319234.0,4.1,81.5,1.39
|
||||
Jamaica,27.22601,24.00421,8951.0,2717344.0,18.9,75.1,2.39
|
||||
Japan,21.87088,23.50004,34800.0,127317900.0,3.4,82.5,1.34
|
||||
Jordan,29.218009999999996,27.47362,10897.0,6010035.0,22.1,76.9,3.59
|
||||
Kazakhstan,26.65065,26.290779999999998,18797.0,15915966.0,25.9,67.1,2.51
|
||||
Kenya,23.06181,21.592579999999998,2358.0,38244442.0,71.0,60.8,4.76
|
||||
Kiribati,31.30769,29.2384,1803.0,98437.0,64.5,61.5,3.13
|
||||
Kuwait,31.161859999999997,29.172109999999996,91966.0,2705290.0,11.3,77.3,2.68
|
||||
Latvia,25.615129999999997,26.45693,20977.0,2144215.0,10.5,72.4,1.5
|
||||
Lebanon,27.70471,27.20117,14158.0,4109389.0,11.3,77.8,1.57
|
||||
Lesotho,26.780520000000003,21.90157,2041.0,1972194.0,114.2,44.5,3.34
|
||||
Liberia,23.21679,21.89537,588.0,3672782.0,100.9,59.9,5.19
|
||||
Libya,29.19874,26.54164,29853.0,6123022.0,18.8,75.6,2.64
|
||||
Lithuania,26.01424,26.86102,23223.0,3219802.0,8.2,72.1,1.42
|
||||
Luxembourg,26.09326,27.434040000000003,95001.0,485079.0,2.8,81.0,1.63
|
||||
Macao,24.895039999999998,25.713820000000002,80191.0,507274.0,6.72,79.32,0.94
|
||||
Macedonia,25.37646,26.34473,10872.0,2055266.0,11.8,74.5,1.47
|
||||
Madagascar,20.73501,21.403470000000002,1528.0,19926798.0,66.7,62.2,4.79
|
||||
Malawi,22.91455,22.034679999999998,674.0,13904671.0,101.1,52.4,5.78
|
||||
Malaysia,25.448320000000002,24.73069,19968.0,27197419.0,8.0,74.5,2.05
|
||||
Maldives,26.4132,23.219910000000002,12029.0,321026.0,16.0,78.5,2.38
|
||||
Mali,23.07655,21.78881,1602.0,14223403.0,148.3,58.5,6.82
|
||||
Malta,27.04993,27.683609999999998,27872.0,406392.0,6.6,80.7,1.38
|
||||
Mauritania,26.26476,22.62295,3356.0,3414552.0,103.0,67.9,4.94
|
||||
Mauritius,26.09824,25.15669,14615.0,1238013.0,15.8,72.9,1.58
|
||||
Mexico,28.737509999999997,27.42468,15826.0,114972821.0,17.9,75.4,2.35
|
||||
Micronesia,31.28402,28.10315,3197.0,104472.0,43.1,68.0,3.59
|
||||
Moldova,27.05617,24.2369,3890.0,4111168.0,17.6,70.4,1.49
|
||||
Mongolia,25.71375,24.88385,7563.0,2629666.0,34.8,64.8,2.37
|
||||
Montenegro,25.70186,26.55412,14183.0,619740.0,8.1,76.0,1.72
|
||||
Morocco,26.223090000000003,25.63182,6091.0,31350544.0,35.8,73.3,2.44
|
||||
Mozambique,23.317339999999998,21.93536,864.0,22994867.0,114.4,54.0,5.54
|
||||
Myanmar,22.47733,21.44932,2891.0,51030006.0,87.2,59.4,2.05
|
||||
Namibia,25.14988,22.65008,8169.0,2115703.0,62.2,59.1,3.36
|
||||
Nepal,20.72814,20.76344,1866.0,26325183.0,50.7,68.4,2.9
|
||||
Netherlands,25.47269,26.01541,47388.0,16519862.0,4.8,80.3,1.77
|
||||
New Zealand,27.36642,27.768929999999997,32122.0,4285380.0,6.4,80.3,2.12
|
||||
Nicaragua,27.57259,25.77291,4060.0,5594524.0,28.1,77.0,2.72
|
||||
Niger,21.95958,21.21958,843.0,15085130.0,141.3,58.0,7.59
|
||||
Nigeria,23.674020000000002,23.03322,4684.0,151115683.0,140.9,59.2,6.02
|
||||
Norway,25.73772,26.934240000000003,65216.0,4771633.0,3.6,80.8,1.96
|
||||
Oman,26.66535,26.241090000000003,47799.0,2652281.0,11.9,76.2,2.89
|
||||
Pakistan,23.44986,22.299139999999998,4187.0,163096985.0,95.5,64.1,3.58
|
||||
Panama,27.67758,26.26959,14033.0,3498679.0,21.0,77.3,2.61
|
||||
Papua New Guinea,25.77189,25.015060000000002,1982.0,6540267.0,69.7,58.6,4.07
|
||||
Paraguay,25.90523,25.54223,6684.0,6047131.0,25.7,74.0,3.06
|
||||
Peru,25.98511,24.770410000000002,9249.0,28642048.0,23.2,78.2,2.58
|
||||
Philippines,23.4671,22.872629999999997,5332.0,90297115.0,33.4,69.8,3.26
|
||||
Poland,25.918870000000002,26.6738,19996.0,38525752.0,6.7,75.4,1.33
|
||||
Portugal,26.183020000000003,26.68445,27747.0,10577458.0,4.1,79.4,1.36
|
||||
Puerto Rico,30.2212,28.378040000000002,35855.0,3728126.0,8.78,77.0,1.69
|
||||
Qatar,28.912509999999997,28.13138,126076.0,1388962.0,9.5,77.9,2.2
|
||||
Romania,25.22425,25.41069,18032.0,20741669.0,16.1,73.2,1.34
|
||||
Russia,27.21272,26.01131,22506.0,143123163.0,13.5,67.9,1.49
|
||||
Rwanda,22.07156,22.55453,1173.0,9750314.0,78.3,64.1,5.06
|
||||
Samoa,33.659079999999996,30.42475,5731.0,183440.0,18.8,72.3,4.43
|
||||
Sao Tome and Principe,24.88216,23.51233,2673.0,163595.0,61.0,66.0,4.41
|
||||
Saudi Arabia,29.598779999999998,27.884320000000002,44189.0,26742842.0,18.1,78.3,2.97
|
||||
Senegal,24.30968,21.927429999999998,2162.0,12229703.0,75.8,63.5,5.11
|
||||
Serbia,25.669970000000003,26.51495,12522.0,9109535.0,8.0,74.3,1.41
|
||||
Seychelles,27.973740000000003,25.56236,20065.0,91634.0,14.2,72.9,2.28
|
||||
Sierra Leone,23.93364,22.53139,1289.0,5521838.0,179.1,53.6,5.13
|
||||
Singapore,22.86642,23.83996,65991.0,4849641.0,2.8,80.6,1.28
|
||||
Slovak Republic,26.323729999999998,26.92717,24670.0,5396710.0,8.8,74.9,1.31
|
||||
Slovenia,26.582140000000003,27.43983,30816.0,2030599.0,3.7,78.7,1.43
|
||||
Solomon Islands,28.8762,27.159879999999998,1835.0,503410.0,33.1,62.3,4.36
|
||||
Somalia,22.66607,21.969170000000002,615.0,9132589.0,168.5,52.6,7.06
|
||||
South Africa,29.4803,26.85538,12263.0,50348811.0,66.1,53.4,2.54
|
||||
Spain,26.30554,27.49975,34676.0,45817016.0,5.0,81.1,1.42
|
||||
Sri Lanka,23.11717,21.96671,6907.0,19949553.0,11.7,74.0,2.32
|
||||
Sudan,23.16132,22.40484,3246.0,34470138.0,84.7,65.5,4.79
|
||||
Suriname,27.749859999999998,25.49887,13470.0,506657.0,26.4,70.2,2.41
|
||||
Swaziland,28.448859999999996,23.16969,5887.0,1153750.0,112.2,45.1,3.7
|
||||
Sweden,25.1466,26.37629,43421.0,9226333.0,3.2,81.1,1.92
|
||||
Switzerland,24.07242,26.20195,55020.0,7646542.0,4.7,82.0,1.47
|
||||
Syria,28.87418,26.919690000000003,6246.0,20097057.0,16.5,76.1,3.17
|
||||
Tajikistan,23.84799,23.77966,2001.0,7254072.0,56.2,69.6,3.7
|
||||
Tanzania,23.0843,22.47792,2030.0,42844744.0,72.4,60.4,5.54
|
||||
Thailand,24.38577,23.008029999999998,12216.0,66453255.0,15.6,73.9,1.48
|
||||
Togo,22.73858,21.87875,1219.0,6052937.0,96.4,57.5,4.88
|
||||
Tonga,34.25969,30.99563,4748.0,102816.0,17.0,70.3,4.01
|
||||
Trinidad and Tobago,28.27587,26.396690000000003,30875.0,1315372.0,24.9,71.7,1.8
|
||||
Tunisia,27.93706,25.15699,9938.0,10408091.0,19.4,76.8,2.04
|
||||
Turkey,28.247490000000003,26.703709999999997,16454.0,70344357.0,22.2,77.8,2.15
|
||||
Turkmenistan,24.66154,25.24796,8877.0,4917541.0,63.9,67.2,2.48
|
||||
Uganda,22.48126,22.35833,1437.0,31014427.0,89.3,56.0,6.34
|
||||
Ukraine,26.23317,25.42379,8762.0,46028476.0,12.9,67.8,1.38
|
||||
United Arab Emirates,29.614009999999997,28.053590000000003,73029.0,6900142.0,9.1,75.6,1.95
|
||||
United Kingdom,26.944490000000002,27.392490000000002,37739.0,61689620.0,5.6,79.7,1.87
|
||||
United States,28.343590000000003,28.456979999999998,50384.0,304473143.0,7.7,78.3,2.07
|
||||
Uruguay,26.593040000000002,26.39123,15317.0,3350832.0,13.0,76.0,2.11
|
||||
Uzbekistan,25.43432,25.32054,3733.0,26952719.0,49.2,69.6,2.46
|
||||
Vanuatu,28.458759999999998,26.78926,2944.0,225335.0,28.2,63.4,3.61
|
||||
Venezuela,28.134079999999997,27.445,17911.0,28116716.0,17.1,74.2,2.53
|
||||
Vietnam,21.065,20.9163,4085.0,86589342.0,26.2,74.1,1.86
|
||||
Palestine,29.026429999999998,26.5775,3564.0,3854667.0,24.7,74.1,4.38
|
||||
Zambia,23.05436,20.68321,3039.0,13114579.0,94.9,51.1,5.88
|
||||
Zimbabwe,24.645220000000002,22.0266,1286.0,13495462.0,98.3,47.3,3.85
|
||||
|
BIN
zajecia4/logistic.png
Normal file
BIN
zajecia4/logistic.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 28 KiB |
812
zajecia4/sklearn cz. 1-ODPOWIEDZI.ipynb
Normal file
812
zajecia4/sklearn cz. 1-ODPOWIEDZI.ipynb
Normal file
File diff suppressed because one or more lines are too long
430
zajecia4/sklearn cz. 1.ipynb
Normal file
430
zajecia4/sklearn cz. 1.ipynb
Normal file
@ -0,0 +1,430 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Kkolejna część zajęć będzie wprowadzeniem do szeroko używanej biblioteki w Pythonie: `sklearn`. Zajęcia będą miały charaktere case-study poprzeplatane zadaniami do wykonania. Zacznijmy od załadowania odpowiednich bibliotek."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ! pip install matplotlib"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"%matplotlib inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Zacznijmy od załadowania danych. Na dzisiejszych zajęciach będziemy korzystać z danych z portalu [gapminder.org](https://www.gapminder.org/data/)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.read_csv('gapminder.csv', index_col=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Dane zawierają różne informacje z większość państw świata (z roku 2008). Poniżej znajduje się opis kolumn:\n",
|
||||
" * female_BMI - średnie BMI u kobiet\n",
|
||||
" * male_BMI - średnie BMI u mężczyzn\n",
|
||||
" * gdp - PKB na obywatela\n",
|
||||
" * population - wielkość populacji\n",
|
||||
" * under5mortality - wskaźnik śmiertelności dzieni pon. 5 roku życia (na 1000 urodzonych dzieci)\n",
|
||||
" * life_expectancy - średnia długość życia\n",
|
||||
" * fertility - wskaźnik dzietności"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**zad. 1**\n",
|
||||
"Na podstawie danych zawartych w `df` odpowiedz na następujące pytania:\n",
|
||||
" * Jaki był współczynniki dzietności w Polsce w 2018?\n",
|
||||
" * W którym kraju ludzie żyją najdłużej?\n",
|
||||
" * Z ilu krajów zostały zebrane dane?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**zad. 2** Stwórz kolumnę `gdp_log`, która powstanie z kolumny `gdp` poprzez zastowanie funkcji `log` (logarytm). \n",
|
||||
"\n",
|
||||
"Hint 1: Wykorzystaj funkcję `apply` (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.apply.html#pandas.Series.apply).\n",
|
||||
"\n",
|
||||
"Hint 2: Wykorzystaj fukcję `log` z pakietu `np`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Naszym zadaniem będzie oszacowanie długości życia (kolumna `life_expectancy`) na podstawie pozostałych zmiennych. Na samym początku, zastosujemy regresje jednowymiarową na `fertility`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y = df['life_expectancy'].values\n",
|
||||
"X = df['fertility'].values\n",
|
||||
"\n",
|
||||
"print(\"Y shape:\", y.shape)\n",
|
||||
"print(\"X shape:\", X.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Będziemy korzystać z gotowej implementacji regreji liniowej z pakietu sklearn. Żeby móc wykorzystać, musimy napierw zmienić shape na dwuwymiarowy."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y = y.reshape(-1, 1)\n",
|
||||
"X = X.reshape(-1, 1)\n",
|
||||
"\n",
|
||||
"print(\"Y shape:\", y.shape)\n",
|
||||
"print(\"X shape:\", X.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Jeszcze przed właściwą analizą, narysujmy wykres i zobaczny czy istnieje \"wizualny\" związek pomiędzy kolumnami."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.plot.scatter('fertility', 'life_expectancy')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**zad. 3** Zaimportuj `LinearRegression` z pakietu `sklearn.linear_model`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Tworzymy obiekt modelu regresji liniowej."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = LinearRegression()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Trening modelu ogranicza się do wywołania metodu `fit`, która przyjmuje dwa argumenty:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.fit(X, y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Współczynniki modelu:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Wyraz wolny (bias):\", model.intercept_)\n",
|
||||
"print(\"Współczynniki cech:\", model.coef_)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**zad. 4** Wytrenuj nowy model `model2`, który będzie jako X przyjmie kolumnę `gdp_log`. Wyświetl parametry nowego modelu."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Mając wytrenowany model możemy wykorzystać go do predykcji. Wystarczy wywołać metodę `predict`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_test = X[:5,:]\n",
|
||||
"y_test = y[:5,:]\n",
|
||||
"output = model.predict(X_test)\n",
|
||||
"\n",
|
||||
"for i in range(5):\n",
|
||||
" print(\"input: {}\\t predicted: {}\\t expected: {}\".format(X_test[i,0], output[i,0], y_test[i,0]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Sprawdzenie jakości modelu - metryki: $MSE$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Istnieją 3 metryki, które określają jak dobry jest nasz model:\n",
|
||||
" * $MSE$: [błąd średnio-kwadratowy](https://pl.wikipedia.org/wiki/B%C5%82%C4%85d_%C5%9Bredniokwadratowy) \n",
|
||||
" * $RMSE = \\sqrt{MSE}$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.metrics import mean_squared_error\n",
|
||||
"\n",
|
||||
"rmse = np.sqrt(mean_squared_error(y, model.predict(X)))\n",
|
||||
"print(\"Root Mean Squared Error: {}\".format(rmse))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Import necessary modules\n",
|
||||
"from sklearn.linear_model import LinearRegression\n",
|
||||
"from sklearn.metrics import mean_squared_error\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"\n",
|
||||
"# Create training and test sets\n",
|
||||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state=42)\n",
|
||||
"\n",
|
||||
"# Create the regressor: reg_all\n",
|
||||
"reg_all = LinearRegression()\n",
|
||||
"\n",
|
||||
"# Fit the regressor to the training data\n",
|
||||
"reg_all.fit(X_train, y_train)\n",
|
||||
"\n",
|
||||
"# Predict on the test data: y_pred\n",
|
||||
"y_pred = reg_all.predict(X_test)\n",
|
||||
"\n",
|
||||
"# Compute and print R^2 and RMSE\n",
|
||||
"print(\"R^2: {}\".format(reg_all.score(X_test, y_test)))\n",
|
||||
"rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
|
||||
"print(\"Root Mean Squared Error: {}\".format(rmse))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Regresja wielu zmiennych"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Model regresji liniowej wielu zmiennych nie różni się istotnie od modelu jednej zmiennej. Np. chcąc zbudować model oparty o dwie kolumny: `fertility` i `gdp` wystarczy zmienić X (cechy wejściowe):"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X = df[['fertility', 'gdp']]\n",
|
||||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state=42)\n",
|
||||
"\n",
|
||||
"print(X.shape)\n",
|
||||
"\n",
|
||||
"model_mv = LinearRegression()\n",
|
||||
"model_mv.fit(X_train, y_train)\n",
|
||||
"\n",
|
||||
"print(\"Wyraz wolny (bias):\", model_mv.intercept_)\n",
|
||||
"print(\"Współczynniki cech:\", model_mv.coef_)\n",
|
||||
"\n",
|
||||
"y_pred = model_mv.predict(X_test)\n",
|
||||
"\n",
|
||||
"rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
|
||||
"print(\"Root Mean Squared Error: {}\".format(rmse))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**zad. 6** \n",
|
||||
" * Zbuduj model regresji liniowej, która oszacuje wartność kolumny `life_expectancy` na podstawie pozostałych kolumn.\n",
|
||||
" * Wyświetl współczynniki modelu.\n",
|
||||
" * Oblicz wartości metryki rmse na zbiorze trenującym.\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**zad. 7**\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
" Zaimplementuj metrykę $RMSE$ jako fukcję rmse (szablon poniżej). Fukcja rmse przyjmuje dwa parametry typu list i ma zwrócić wartość metryki $RMSE$ ."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def rmse(expected, predicted):\n",
|
||||
" \"\"\"\n",
|
||||
" argumenty:\n",
|
||||
" expected (type: list): poprawne wartości\n",
|
||||
" predicted (type: list): oszacowanie z modelu\n",
|
||||
" \"\"\"\n",
|
||||
" pass\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"y = df['life_expectancy'].values\n",
|
||||
"X = df[['fertility', 'gdp']].values\n",
|
||||
"\n",
|
||||
"test_model = LinearRegression()\n",
|
||||
"test_model.fit(X, y)\n",
|
||||
"\n",
|
||||
"predicted = list(test_model.predict(X))\n",
|
||||
"expected = list(y)\n",
|
||||
"\n",
|
||||
"print(rmse(predicted,expected))\n",
|
||||
"print(np.sqrt(mean_squared_error(predicted, expected)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.11.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user