def __init__(self, path="/mhome/kleinaa/data/mnist_npy"):
self.log_reg = LogisticRegression(path)
X_lower = np.concatenate((self.log_reg.original_X_lower, np.array([6.91])))
X_upper = np.concatenate((self.log_reg.original_X_upper, np.array([10.81978])))
self.is_env = np.zeros([self.log_reg.n_dims])
self.is_env = np.concatenate((self.is_env, np.array([1])))
super(EnvLogisticRegression, self).__init__(X_lower, X_upper)
class EnvLogisticRegression(BaseTask):
def __init__(self, path="/mhome/kleinaa/data/mnist_npy"):
self.log_reg = LogisticRegression(path)
X_lower = np.concatenate((self.log_reg.original_X_lower, np.array([6.91])))
X_upper = np.concatenate((self.log_reg.original_X_upper, np.array([10.81978])))
self.is_env = np.zeros([self.log_reg.n_dims])
self.is_env = np.concatenate((self.is_env, np.array([1])))
super(EnvLogisticRegression, self).__init__(X_lower, X_upper)
def objective_function(self, x):
shuffle = np.random.permutation(np.arange(self.log_reg.train.shape[0]))
self.log_reg.train, self.log_reg.train_targets = self.log_reg.train[shuffle], self.log_reg.train_targets[shuffle]
self.log_reg.n_train = int(np.exp(x[0, -1]))
y = self.log_reg.objective_function(x)
return y
def objective_function_test(self, x):
return self.log_reg.objective_function_test(x[:, :-1])