diff --git a/mini_proj/traditionals.py b/mini_proj/traditionals.py
new file mode 100644
index 0000000..77ede83
--- /dev/null
+++ b/mini_proj/traditionals.py
@@ -0,0 +1,56 @@
+import numpy as np
+import time as t
+from sklearn import svm, ensemble, naive_bayes, neighbors
+from _image_classifier import ImageClassifier
+
+def precision(y_true, y_pred):
+    y_pred = np.round(y_pred)
+    num = np.sum(np.logical_and(y_true, y_pred))
+    den = np.sum(y_pred)
+    return np.divide(num, den)
+
+def recall(y_true, y_pred):
+    y_pred = np.round(y_pred)
+    num = np.sum(np.logical_and(y_true, y_pred))
+    den = np.sum(y_true)
+    return np.divide(num, den)
+
+def f_measure(y_true, y_pred):
+    p = precision(y_true, y_pred)
+    r = recall(y_true, y_pred)
+    return 2 * p * r / (p + r)
+
+def metric_test(iclf, metric, test_X, test_Y):
+    return metric(test_Y, iclf.predict(test_X))
+
+## Open data
+im_train = np.load('Waldo_train_data.npy')
+im_test = np.load('Waldo_test_data.npy')
+
+lbl_train = np.load('Waldo_train_lbl.npy')
+lbl_test = np.load('Waldo_test_lbl.npy')
+
+# lbl_train = to_categorical(lbl_train)       # One hot encoding the labels
+# lbl_test = to_categorical(lbl_test)
+
+my_metric_test = lambda iclf, f: metric_test(iclf, f, im_test, lbl_test)
+
+# ## Define model
+svm_iclf = ImageClassifier(svm.SVC)
+tree_iclf = ImageClassifier(neighbors.KNeighborsClassifier)
+naive_bayes_iclf = ImageClassifier(naive_bayes.GaussianNB)
+ensemble_iclf = ImageClassifier(ensemble.RandomForestClassifier)
+
+classifiers = [
+    svm_iclf,
+    tree_iclf,
+    naive_bayes_iclf,
+    ensemble_iclf,
+]
+
+for clf in classifiers:
+    start = t.time()                            # Records time before training
+    clf.fit(im_train, lbl_train)
+    end = t.time()                              # Records time after tranining
+    print("training time:", end-start)
+    print(clf.score(im_test, lbl_test))