kernel_logisitic_opt_function.py

X = train[['bedrooms','bathrooms']].values
y = (train['sqft_living'] > np.mean(train['sqft_living'])).reshape(len(train),1)
model = linear_model.LogisticRegression()
model.fit(X, y)

X = train[['bedrooms','bathrooms']].values
start_time = time.time()
model = kernel_optimizer(X,y,liklihood_loss,num_param=3)
model.add_intercept()
model.adjust_bias_parameters(n_parameter_updates=100,random_sample_num=100)
model.adjust_variance_parameters(select_n_parameters=100)
model.kernel_optimize_(optimizer=pid_linear_combination)
end_time = time.time()
print("time:",time)