import numpy as np
from sklearn.linear_model import LinearRegression
 
X = np.array([[0.5, 25], [0.7, 30], [0.9, 35], [1.1, 40]])
 
y = np.array([10, 15, 20, 25])
 
model = LinearRegression()
model.fit(X, y)
 
def predict_energy(solar_radiation, temperature):
    return model.predict(np.array([[solar_radiation, temperature]]))[0]
 
def gradient_descent_optimization(initial_solar_radiation, initial_temperature, target_energy, learning_rate, iterations):
    solar_radiation = initial_solar_radiation
    temperature = initial_temperature
    for i in range(iterations):
        energy = predict_energy(solar_radiation, temperature)
        gradient_solar = 2 * (energy - target_energy) * model.coef_[0]
        gradient_temp = 2 * (energy - target_energy) * model.coef_[1]
        solar_radiation -= learning_rate * gradient_solar
        temperature -= learning_rate * gradient_temp
    return solar_radiation, temperature
 
target_energy = 30  
initial_solar_radiation = 1.2
initial_temperature = 45
learning_rate = 0.01
iterations = 1000
 
optimized_solar_radiation, optimized_temperature = gradient_descent_optimization(initial_solar_radiation, initial_temperature, target_energy, learning_rate, iterations)
print("Optimized solar radiation:", optimized_solar_radiation)
print("Optimized temperature:", optimized_temperature)
print("Predicted energy output:", predict_energy(optimized_solar_radiation, optimized_temperature))

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