kernel_poly_opt_function.py

def poly_least_sqs_loss(x,y,w):
    hypothesis = w[0]*x[:,0:1] + w[1]*(x[:,1:2]) + w[2]*(x[:,1:2])**w[3]
    loss = hypothesis-y 
    return np.sum(loss**2)/len(y)
  
start_time = time.time()
X = train[['sqft_living']].values
y = train[["price"]].values
model = kernel_optimizer(X,y,poly_least_sqs_loss,num_param=4)
model.add_intercept()
model.adjust_uniform_random_low_high(0,2)
model.kernel_optimize_(optimizer=pid_linear_combination)    
end_time = time.time()
print("time:",end_time-start_time)