Remove unneeded files from template

examples/example.py

@@ -0,0 +1,33 @@
+import time
+from sklearn.model_selection import cross_val_score, StratifiedKFold
+from sklearn.preprocessing import KBinsDiscretizer
+from sklearn.datasets import load_wine
+from bayesclass.clfs import TAN
+import warnings
+
+
+# Warnings are not errors
+warnings.simplefilter("ignore")
+start = time.time()
+random_state = 17
+n_folds = 5
+print(f"Accuracy in {n_folds} folds stratified crossvalidation")
+dataset_start = time.time()
+dataset = load_wine()
+Xc = dataset.data
+enc = KBinsDiscretizer(encode="ordinal")
+X = enc.fit_transform(Xc)
+y = dataset.target
+clf = TAN(random_state=random_state)
+fit_params = dict(features=dataset.feature_names, class_name="class", head=0)
+kfold = StratifiedKFold(
+    n_splits=n_folds, shuffle=True, random_state=random_state
+)
+score = cross_val_score(clf, X, y, cv=kfold, fit_params=fit_params)
+print(
+    f"wine {'.' * 10}{score.mean():9.7f} "
+    f"({time.time()-dataset_start:7.2f} seconds)"
+)
+clf.fit(X, y, **fit_params)
+clf.plot("TAN wine")
+print(f"Took {time.time()-start:.2f} seconds")

examples/plot_classifier.py

@@ -1,44 +0,0 @@
-"""
-============================
-Plotting Template Classifier
-============================
-
-An example plot of :class:`bayesclass.TAN`
-"""
-import numpy as np
-from matplotlib import pyplot as plt
-from bayesclass.bayesclass import TAN
-
-X = [[0, 0], [1, 1]]
-y = [0, 1]
-clf = TAN()
-clf.fit(X, y)
-
-rng = np.random.RandomState(13)
-X_test = rng.randint(2, size=(500, 2))
-y_pred = clf.predict(X_test)
-
-X_0 = X_test[y_pred == 0]
-X_1 = X_test[y_pred == 1]
-
-
-p0 = plt.scatter(0, 0, c="red", s=100)
-p1 = plt.scatter(1, 1, c="blue", s=100)
-
-ax0 = plt.scatter(X_0[:, 0], X_0[:, 1], c="crimson", s=50)
-ax1 = plt.scatter(X_1[:, 0], X_1[:, 1], c="deepskyblue", s=50)
-
-leg = plt.legend(
-    [p0, p1, ax0, ax1],
-    ["Point 0", "Point 1", "Class 0", "Class 1"],
-    loc="upper left",
-    fancybox=True,
-    scatterpoints=1,
-)
-leg.get_frame().set_alpha(0.5)
-
-plt.xlabel("Feature 1")
-plt.ylabel("Feature 2")
-plt.xlim([-0.5, 1.5])
-
-plt.show()