Merge pull request 'lipatov_ilya_lab_4' (#102) from lipatov_ilya_lab_4 into main
Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/102
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lipatov_ilya_lab_4/README.md
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lipatov_ilya_lab_4/README.md
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## Лабораторная работа №4
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### Кластеризация
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## Выполнил студент группы ПИбд-41 Липатов Илья
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### Как запустить лабораторную работу:
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* установить python, numpy, matplotlib, sklearn
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* запустить проект (стартовая точка класс lab4)
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### Какие технологии использовались:
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* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
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* Среда разработки `PyCharm`
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### Что делает лабораторная работа:
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* Кластеризирует данные о домах в Бостоне исходя из уровня преступности на душу населения в разбивке по городам и процента более низкого статуса населения. Ожидаем, что разбиение домов будет на три кластера.
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### Примеры работы:
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#### Результаты:
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* Кластеризация разбила наши дома в Бостоне на три большие группы, как мы этого и ожидали, значит алгоритм с задачей справился.
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![Result](result.png)
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lipatov_ilya_lab_4/boston.csv
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lipatov_ilya_lab_4/boston.csv
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CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV
|
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0.00632,18.00,2.310,0,0.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,4.98,24.00
|
||||
0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14,21.60
|
||||
0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03,34.70
|
||||
0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94,33.40
|
||||
0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33,36.20
|
||||
0.02985,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,18.70,394.12,5.21,28.70
|
||||
0.08829,12.50,7.870,0,0.5240,6.0120,66.60,5.5605,5,311.0,15.20,395.60,12.43,22.90
|
||||
0.14455,12.50,7.870,0,0.5240,6.1720,96.10,5.9505,5,311.0,15.20,396.90,19.15,27.10
|
||||
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||||
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||||
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||||
0.11747,12.50,7.870,0,0.5240,6.0090,82.90,6.2267,5,311.0,15.20,396.90,13.27,18.90
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||||
0.09378,12.50,7.870,0,0.5240,5.8890,39.00,5.4509,5,311.0,15.20,390.50,15.71,21.70
|
||||
0.62976,0.00,8.140,0,0.5380,5.9490,61.80,4.7075,4,307.0,21.00,396.90,8.26,20.40
|
||||
0.63796,0.00,8.140,0,0.5380,6.0960,84.50,4.4619,4,307.0,21.00,380.02,10.26,18.20
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||||
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|
||||
1.05393,0.00,8.140,0,0.5380,5.9350,29.30,4.4986,4,307.0,21.00,386.85,6.58,23.10
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||||
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|
||||
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1.15172,0.00,8.140,0,0.5380,5.7010,95.00,3.7872,4,307.0,21.00,358.77,18.35,13.10
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1.61282,0.00,8.140,0,0.5380,6.0960,96.90,3.7598,4,307.0,21.00,248.31,20.34,13.50
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0.02763,75.00,2.950,0,0.4280,6.5950,21.80,5.4011,3,252.0,18.30,395.63,4.32,30.80
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0.03359,75.00,2.950,0,0.4280,7.0240,15.80,5.4011,3,252.0,18.30,395.62,1.98,34.90
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0.17142,0.00,6.910,0,0.4480,5.6820,33.80,5.1004,3,233.0,17.90,396.90,10.21,19.30
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0.18836,0.00,6.910,0,0.4480,5.7860,33.30,5.1004,3,233.0,17.90,396.90,14.15,20.00
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0.22927,0.00,6.910,0,0.4480,6.0300,85.50,5.6894,3,233.0,17.90,392.74,18.80,16.60
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0.17899,0.00,9.690,0,0.5850,5.6700,28.80,2.7986,6,391.0,19.20,393.29,17.60,23.10
|
||||
0.28960,0.00,9.690,0,0.5850,5.3900,72.90,2.7986,6,391.0,19.20,396.90,21.14,19.70
|
||||
0.26838,0.00,9.690,0,0.5850,5.7940,70.60,2.8927,6,391.0,19.20,396.90,14.10,18.30
|
||||
0.23912,0.00,9.690,0,0.5850,6.0190,65.30,2.4091,6,391.0,19.20,396.90,12.92,21.20
|
||||
0.17783,0.00,9.690,0,0.5850,5.5690,73.50,2.3999,6,391.0,19.20,395.77,15.10,17.50
|
||||
0.22438,0.00,9.690,0,0.5850,6.0270,79.70,2.4982,6,391.0,19.20,396.90,14.33,16.80
|
||||
0.06263,0.00,11.930,0,0.5730,6.5930,69.10,2.4786,1,273.0,21.00,391.99,9.67,22.40
|
||||
0.04527,0.00,11.930,0,0.5730,6.1200,76.70,2.2875,1,273.0,21.00,396.90,9.08,20.60
|
||||
0.06076,0.00,11.930,0,0.5730,6.9760,91.00,2.1675,1,273.0,21.00,396.90,5.64,23.90
|
||||
0.10959,0.00,11.930,0,0.5730,6.7940,89.30,2.3889,1,273.0,21.00,393.45,6.48,22.00
|
||||
0.04741,0.00,11.930,0,0.5730,6.0300,80.80,2.5050,1,273.0,21.00,396.90,7.88,11.90
|
|
36
lipatov_ilya_lab_4/lab4.py
Normal file
36
lipatov_ilya_lab_4/lab4.py
Normal file
@ -0,0 +1,36 @@
|
||||
from sklearn.cluster import AgglomerativeClustering
|
||||
from scipy.cluster.hierarchy import dendrogram
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
FILE_PATH = "boston.csv"
|
||||
FEATURES = ['LSTAT', 'CRIM']
|
||||
|
||||
def plot_dendrogram(model, **kwargs):
|
||||
counts = np.zeros(model.children_.shape[0])
|
||||
n_samples = len(model.labels_)
|
||||
for i, merge in enumerate(model.children_):
|
||||
current_count = 0
|
||||
for child_idx in merge:
|
||||
if child_idx < n_samples:
|
||||
current_count += 1
|
||||
else:
|
||||
current_count += counts[child_idx - n_samples]
|
||||
counts[i] = current_count
|
||||
|
||||
linkage_matrix = np.column_stack(
|
||||
[model.children_, model.distances_, counts]
|
||||
).astype(float)
|
||||
|
||||
dendrogram(linkage_matrix, **kwargs)
|
||||
|
||||
|
||||
data = pd.read_csv(FILE_PATH)
|
||||
X = data[FEATURES]
|
||||
model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)
|
||||
model = model.fit(X)
|
||||
plt.title("Hierarchical Clustering Dendrogram for Boston House Prices")
|
||||
|
||||
plot_dendrogram(model, truncate_mode="level", p=2)
|
||||
plt.show()
|
BIN
lipatov_ilya_lab_4/result.png
Normal file
BIN
lipatov_ilya_lab_4/result.png
Normal file
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Reference in New Issue
Block a user