--- a
+++ b/test_mixep.py
@@ -0,0 +1,41 @@
+from neural_network import NeuralNetwork, NeuralLogistic
+from mix_of_exp import MixtureOfExperts
+from neural_network import NeuralNetwork, NeuralLogistic
+from sklearn.linear_model import LogisticRegression, LinearRegression, SGDClassifier, SGDRegressor
+import random
+import numpy as np
+import matplotlib.colors as mcolors
+import matplotlib.pyplot as plt
+from sklearn.ensemble import AdaBoostClassifier
+from utils import generate_data, plot_predictions, prediction_accuracy, sparsify_data
+from sklearn.pipeline import FeatureUnion, Pipeline
+from model_builder import build_model, regex_baseline
+from model_tester import execute_test
+import sys
+import profile
+
+
+logistic = NeuralLogistic(restarts = 2, regularization = 0.00)
+
+nn = NeuralNetwork([(8, 'logistic'), (8, 'tanh'), (None, 'softmax')], 'maxent', regularization = 1e0, restarts = 30, max_iter = 10000, init_size = 1, step_size = 1e-2)
+
+#nn = NeuralNetwork([(None, 'softmax')], 'maxent', include_offset = True, max_iter = 5000)
+
+me = MixtureOfExperts([NeuralLogistic(regularization = 1e-1), NeuralLogistic(regularization = 1e-1), NeuralLogistic(regularization = 1e-1)], 
+					   NeuralLogistic(regularization = 1e-1), max_iter = 25)
+					   #NeuralNetwork([(3, 'tanh'), (None, 'softmax')], 'maxent'))
+
+adaboost = AdaBoostClassifier(n_estimators = 500)
+
+mix_ex_args = {'experts' :[NeuralLogistic(regularization = 1e-1), NeuralLogistic(regularization = 1e-1), NeuralLogistic(regularization = 1e-1)], 
+               'gate'    : NeuralLogistic(regularization = 1e-1),
+               'max_iter': 15}
+
+model = build_model(regex_baseline, method = 'me', model_args = mix_ex_args)
+
+print execute_test(model, 25, 5)
+
+
+
+
+