First functional with 100% coverage

examples/plot_classifier.py

@@ -3,19 +3,19 @@
 Plotting Template Classifier
 ============================
 
-An example plot of :class:`bayesclass.template.TemplateClassifier`
+An example plot of :class:`bayesclass.TAN`
 """
 import numpy as np
 from matplotlib import pyplot as plt
-from bayesclass import TemplateClassifier
+from bayesclass import TAN
 
 X = [[0, 0], [1, 1]]
 y = [0, 1]
-clf = TemplateClassifier()
+clf = TAN()
 clf.fit(X, y)
 
 rng = np.random.RandomState(13)
-X_test = rng.rand(500, 2)
+X_test = rng.randint(2, size=(500, 2))
 y_pred = clf.predict(X_test)
 
 X_0 = X_test[y_pred == 0]

examples/plot_template.py

@@ -1,17 +0,0 @@
-"""
-===========================
-Plotting Template Estimator
-===========================
-
-An example plot of :class:`bayesclass.template.TemplateEstimator`
-"""
-import numpy as np
-from matplotlib import pyplot as plt
-from bayesclass import TemplateEstimator
-
-X = np.arange(100).reshape(100, 1)
-y = np.zeros((100,))
-estimator = TemplateEstimator()
-estimator.fit(X, y)
-plt.plot(estimator.predict(X))
-plt.show()

examples/plot_transformer.py

@@ -1,26 +0,0 @@
-"""
-=============================
-Plotting Template Transformer
-=============================
-
-An example plot of :class:`bayesclass.template.TemplateTransformer`
-"""
-import numpy as np
-from matplotlib import pyplot as plt
-from bayesclass import TemplateTransformer
-
-X = np.arange(50, dtype=np.float).reshape(-1, 1)
-X /= 50
-estimator = TemplateTransformer()
-X_transformed = estimator.fit_transform(X)
-
-plt.plot(X.flatten(), label="Original Data")
-plt.plot(X_transformed.flatten(), label="Transformed Data")
-plt.title("Plots of original and transformed data")
-
-plt.legend(loc="best")
-plt.grid(True)
-plt.xlabel("Index")
-plt.ylabel("Value of Data")
-
-plt.show()