diff --git a/CESmoothedLogisticRegression.py b/CESmoothedLogisticRegression.py
deleted file mode 100644
index 99e576a..0000000
--- a/CESmoothedLogisticRegression.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import random
-import numpy as np
-from math import log
-from netcal.metrics import ECE
-from scipy.optimize import fmin_bfgs
-from scipy.special import expit, xlogy
-from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import accuracy_score, log_loss
-import warnings
-warnings.filterwarnings('once')
-
-def sigmoid(m, b, X):
-    #Z = np.dot(row, self.Ws)
-    return 1 / (1 + np.exp(-np.dot(X, m)-b))
-
-def _sigmoid_calibration(X, y,  T1 = None, tol = 1e-5):
-    if X.ndim == 1:
-        X = X.reshape(-1, 1)
-    prior0 = float(np.sum(y <= 0))
-    prior1 = y.shape[0] - prior0
-    if T1 is None:
-        T = np.zeros(y.shape)
-        T[y <= 0] = (prior1 + 1.) / (prior1 + 2.)
-        T[y > 0] = 1. / (prior0 + 2.)
-        T1 = 1. - T
-    else:
-        T = 1. - T1
-        
-    def objective(AB):
-        tmp = 0
-        for i in range(X.shape[1]):
-            tmp += AB[i] * X[:,i]
-        tmp += AB[X.shape[1]]
-        #P = expit(-(AB[0] * X + AB[1]))
-        P = expit(-(tmp))
-        loss = -(xlogy(T, P) + xlogy(T1, 1. - P))
-        return loss.sum()
-
-    def grad(AB):
-        # gradient of the objective function
-        tmp = 0
-        for i in range(X.shape[1]):
-            tmp += AB[i] * X[:,i]
-        tmp += AB[X.shape[1]]
-        #P = expit(-(AB[0] * X + AB[1]))
-        P = expit(-(tmp))
-        TEP_minus_T1P = T - P
-        dA = np.dot(TEP_minus_T1P, X)
-        dB = np.sum(TEP_minus_T1P)
-        out_grad = np.append(dA, dB)
-        return out_grad
-    
-    AB0 = np.array([0.] * X.shape[1] + [log((prior0 + 1.) / (prior1 + 1.))])
-    AB_ = fmin_bfgs(objective, AB0, fprime=grad, disp=False, gtol = tol, maxiter=10000)
-    return AB_[0:-1], AB_[-1]
-
-class CESmoothedLogisticRegression():
-    def __init__(self, tolerance = 1e-5):
-        self.tolerance = tolerance
-        random.seed(0)
-
-    def fun(self, x):
-        return 2*x - 1
-
-    def smooth_labels(self, X, y):
-        clf2 = LogisticRegression(random_state=0, solver='lbfgs', penalty = 'none', tol=self.tolerance)
-        clf2.fit(X, y)
-        y_pred = clf2.predict_proba(X)
-        y_smoothed = np.zeros(len(y))
-        N0 = np.sum(y == 0); N1 = np.sum(y == 1)
-        platt_neg = 1 / (N0 + 2)
-        platt_pos = 1 / (N1 + 2)
-        for i in range(len(y)):
-            if y[i] > 0:
-                #print(y_pred[i][1])
-                y_smoothed[i] = 1 - platt_pos * self.fun(y_pred[i][1])
-            else:
-                y_smoothed[i] = platt_neg * self.fun(1 - y_pred[i][1])
-        return y_smoothed
-
-    def fit(self, X_train, y_train):
-        if X_train.ndim == 1:
-            X_train = X_train.reshape(-1, 1)
-        y_train_smoothed = self.smooth_labels(X_train, y_train)
-        self.a, self.b = _sigmoid_calibration(np.squeeze(X_train), y_train, y_train_smoothed, tol = self.tolerance)
-	
-    def predict_proba(self, X):
-        #print(self.b, self.b.shape, self.a, self.a.shape)
-        preds_probs = sigmoid(self.a, self.b, X)
-        return preds_probs
-
-    def predict(self, X, threshold = 0.5):
-        return self.predict_proba(X) >= threshold
-
-    def predict_logloss(self, X, y):
-        preds_probs = sigmoid(self.a, self.b, X)
-        return log_loss(y, preds_probs, labels = [0, 1])
-
-    def predict_accuracy(self, X, y, threshold = 0.5):
-        return accuracy_score(y, self.predict(X, threshold = threshold))
-
-    def predict_ece(self, X, y, bins = 10):
-        ece = ECE(bins)
-        calibrated_score = ece.measure(self.predict_proba(X), y)
-        return calibrated_score
-    
-    def predict_ece_logloss(self, X, y, bins = 10):
-        preds_probs = self.predict_proba(X)
-        ece = ECE(bins)
-        calibrated_score = ece.measure(preds_probs, y)
-        return calibrated_score, log_loss(y, preds_probs, labels = [0, 1])
\ No newline at end of file
diff --git a/CustomLogisticRegression.py b/CustomLogisticRegression.py
index 11db6db..2635563 100644
--- a/CustomLogisticRegression.py
+++ b/CustomLogisticRegression.py
@@ -64,7 +64,7 @@ def grad(AB):
     return AB_[0:-1], AB_[-1]
 
 class CustomLogisticRegression():
-	def __init__(self, smoothing_factor_pos = 0, smoothing_factor_neg = 0, tolerance = 1e-1, regularization = 'none', regularization_strength = 0, platt_scaling = False):
+	def __init__(self, smoothing_factor_pos = 0, smoothing_factor_neg = 0, tolerance = 1e-3, regularization = 'none', regularization_strength = 0, platt_scaling = False):
 		self.smoothing_factor_pos = smoothing_factor_pos
 		self.smoothing_factor_neg = smoothing_factor_neg
 		self.platt = platt_scaling