Linear Regression Study
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Python/인공지능(AI)

Linear Regression Study

by 조훈이 2022. 4. 17.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

perch_length = np.array(
    [8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0,
     21.0, 21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5,
     22.5, 22.7, 23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5,
     27.3, 27.5, 27.5, 27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0,
     36.5, 36.0, 37.0, 37.0, 39.0, 39.0, 39.0, 40.0, 40.0, 40.0,
     40.0, 42.0, 43.0, 43.0, 43.5, 44.0]
     )
perch_weight = np.array(
    [5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0,
     110.0, 115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0,
     130.0, 150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0,
     197.0, 218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0,
     514.0, 556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0,
     820.0, 850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0,
     1000.0, 1000.0]
     )

train_input, test_input, train_target, test_target = train_test_split(
    perch_length, perch_weight, random_state=42)

train_input = train_input.reshape(-1, 1)
test_input = test_input.reshape(-1, 1)

knr = KNeighborsRegressor(n_neighbors=3)
knr.fit(train_input, train_target)

lr = LinearRegression()
lr.fit(train_input, train_target)
print(lr.coef_, lr.intercept_)

train_poly = np.column_stack((train_input ** 2, train_input))
test_poly = np.column_stack((test_input ** 2, test_input))

point = np.arange(15, 50)
plt.scatter(train_input, train_target)
plt.plot(point, 1.01*point**2 - 21.6*point + 116.05)

lr2 = LinearRegression()
lr2.fit(train_poly, train_target)

plt.plot([5, 50], [5*lr.coef_+lr.intercept_, 50*lr.coef_+lr.intercept_])
plt.scatter(perch_length, perch_weight)
plt.scatter(50, lr.predict([[50]]), marker='^')
plt.scatter(50, knr.predict([[50]]), marker='^')
plt.scatter(50, lr2.predict([[50**2, 50]]), marker='^')
plt.xlabel('length')
plt.ylabel('weight')
plt.show()

 

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