“bioinjiniring tizimlari” fanidan

Amaliy qism
Ushbu amaliy qism doirasida biz biosignallarga mashinaviy o‘qitishning Rando Forest algoritmi orqali ishlov berish jarayonini ko‘rib chiqamiz.
# Kerakli kutubxonalarni import qilish
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

# Biosignal ma'lumotlar to‘plamini yuklang (buni ma'lumotlarni yuklash kodingiz bilan almashtiring)

# Faraz qiling, X biosignal xususiyatlarni o‘z ichiga oladi va y yorliqlarni o‘z ichiga oladi (normal uchun 0, anormal uchun 1)
# Buni haqiqiy ma'lumotlarni yuklash mexanizmingizga ko‘ra o‘zgartiring

# Namoyish maqsadida, keling, soxta ma'lumotlar to‘plamini yarataylik

np.random.seed(42)
X = np.random.rand(100, 10) # 100 samples, 10 features
y = np.random.randint(2, size=100) # Binary labels

# Ma'lumotlar to‘plamini mashq va sinov to‘plamlariga ajratish

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Ma'lumotlarni standartlashtirish

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Mashinaviy o‘qitish modeli (Random Forest Classifier algoritmi)

model = RandomForestClassifier(n_estimators=100, random_state=42)

# Modelni o‘qitish

model.fit(X_train_scaled, y_train)

# Sinov to‘plami orqali modelni testlash

y_pred = model.predict(X_test_scaled)

# Natijalarni e'lon qilish

accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")

# Ekranga chiqarish

print("Classification Report:\n", classification_report(y_test, y_pred))

Quyida modelimiz orqali olingan natijani ko‘rishingiz mumkin.

Accuracy: 0.45
Classification Report:
precision recall f1-score support


0 0.44 0.78 0.56 9
1 0.50 0.18 0.27 11


accuracy 0.45 20
macro avg 0.47 0.48 0.41 20
weighted avg 0.47 0.45 0.40 20

Xulosa
Xulosa qilib aytganda, bugungi kunda Sun’iy intellektning mashinaviy o‘qitish va chuqur o‘qitish kabi yo‘nalishlari orqali turli xildagi bioinjineriyada qo‘llanilishi mumkin bo‘lgan Sun’iy intellekt modellarini ishlab chiqishimiz mumkin bo‘ladi.