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O‘zbekiston respublikasi axborot texnologiyalari va kommunikatsiyalarini rivojlantirish vazirligi
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| bet | 1/2 | | Sana | 22.05.2024 | | Hajmi | 0,78 Mb. | | #250635 |
Bog'liq Mashinali o`qitish amaliy ish
O‘ZBEKISTON RESPUBLIKASI
AXBOROT TEXNOLOGIYALARI VA
KOMMUNIKATSIYALARINI RIVOJLANTIRISH VAZIRLIGI
MUHAMMAD AL-XORAZMIY NOMIDAGI
TOSHKЕNT AXBOROT TЕXNOLOGIYALARI UNIVЕRSITЕTI
MACHINALI O’QITISHGA KIRISH FANIDAN
AMALIY MASHG’ULOT
Guruh: AX 070-20
Bajardi: Mingboyev.A
Tekshirdi: Qobilov. S
Toshkent 2024
AMALIY MASHG`ULOT.
Python kutubxonalaridan foydalangan holda dataset xosil qilish. Bunda o‘zgaruvchilar soni kamida 10 tani va qatorlar soni 20 tani tashkil etishi lozim. Olingan datasetdan foydalangan holda har bir talaba sklearn kutubxonasi orqali modelini tuzadi.
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# Randomly generate a dataset with at least 10 features and 20 samples
np.random.seed(0)
num_features = 10
num_samples = 20
X = np.random.rand(num_samples, num_features)
y = np.random.rand(num_samples)
# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# Train a linear regression model
model = LinearRegression()
model.fit(X_train, y_train)
# Evaluate the model
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
import pandas as pd
from sklearn.datasets import load_wine
wine_data = load_wine()
# Convert data to pandas dataframe
wine_df = pd.DataFrame(wine_data.data, columns=wine_data.feature_names)
# Add the target label
wine_df["target"] = wine_data.target
# Take a preview
wine_df.head()
N atija:
Chiziqli regressiya tushunchasi. Ikkinchi darajali polynomial regressiya tushunchasi. y=wx+b va y=w1x2+w2x+b funksiyalardagi og`irliklar va bias qiymatlarini topish. Gradient pastlash va Loss grafigi.
Dastur kodi:
import time
import numpy as np
import matplotlib.pyplot as plt
N=int(input("N sonini kiriting =>"))
x=np.zeros(N)
y=np.zeros(N-1)
r=np.zeros(N)
w=np.zeros(N)
w1=np.zeros(N-1)
x[0]=1
for i in range(1,N):
x[i]=x[i-1]+1
y[i-1]=-2*x[i-1]
w[0] = 0
a=0.01
for i in range(1,N):
S=0
for j in range(0,3):
r[j]=pow(w[i-1]*x[j]-y[j],2)
S=S+r[j]
w1[i-1]=S/3
w[i] = w[i-1] + a
print(w1[i-1])
plt.plot(w1)
plt.show()
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