human
Can you provide a the Python program you requested given the following:
I am working on the file "Anjum48/Anjum48 Facies Classification.ipynb".
The first task is:
Use a homemade majority voting to use with TensorFlow
Can you write Python program for it?
assistant
#Here is the Python program you requested.

# classifiers = {
#     "SVM": SVC(C=1000, gamma=1),
#     "DecisionTree": tree.DecisionTreeClassifier(max_depth=32),
#     "KNN": neighbors.KNeighborsClassifier(1, weights="distance"),
# #     "DNN": learn.DNNClassifier(feature_columns=feature_columns, hidden_units=[200, 400, 200], n_classes=10),
#     "RandomForest": RandomForestClassifier(n_estimators=10, max_depth=None, min_samples_split=2, random_state=0),
#     "XGBoost": xgb.XGBClassifier()
#               }

# def fit_and_predict(X_train, y_train, X_test, classifiers):
#     predicted_values = {}
#     for key, classifier in classifiers.items():
#         if key == "DNN":
#             classifier.fit(X_train, y_train, steps=5000)
#             list(classifier.predict(X_test, as_iterable=True))
#         else:
#             classifier.fit(X_train, y_train)
#             predicted_values[key] = classifier.predict(X_test)
#     return pd.DataFrame(predicted_values)

# predicted_values = fit_and_predict(X_train, y_train, X_test, classifiers)

# majority_vote = predicted_values.mode(axis=1)[0]

# conf = confusion_matrix(y_test, majority_vote.fillna(2))
# display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)

# display_adj_cm(conf, facies_labels, adjacent_facies, display_metrics=True, hide_zeros=True)