v0.3.2

Add Uniform Blending for Classification

Author: fukuball

FukuML/__init__.py

@@ -1,6 +1,6 @@
 #encoding=utf8
 
-__version__ = '0.3.1'
+__version__ = '0.3.2'
 __all__ = [
     'MLBase',
     'PLA',
@@ -49,6 +49,7 @@
     'Utility',
     'Utility.DatasetLoader',
     'Utility.CrossValidator',
+    'Utility.UniformBlendingClassifier',
     'Utility.Kernel',
     'Utility.Serializer'
 ]

README.rst

@@ -127,6 +127,8 @@ Algorithm
 
 - 10 Fold Cross Validation
 
+- Uniform Blending for Classification
+
 Usage
 ============
 

doc/sample_code.rst

@@ -1353,3 +1353,92 @@ Legendre Feature Transform
     >>> avg_errors = cross_validator.excute()
 
     >>> best_model = cross_validator.get_best_model()
+
+Uniform Blending for Classification
+============
+
+.. code-block:: py
+
+    >>> input_train_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/pocket_pla_binary_train.dat')
+
+    >>> input_test_data_file = os.path.join(os.path.join(os.getcwd(), os.path.dirname(__file__)), 'FukuML/dataset/pocket_pla_binary_test.dat')
+
+    >>> uniform_blending_classifier = utility.UniformBlendingClassifier()
+
+    >>> pla_bc = pla.BinaryClassifier()
+
+    >>> pla_bc.load_train_data(input_train_data_file)
+
+    >>> pla_bc.load_test_data(input_test_data_file)
+
+    >>> pla_bc.set_param()
+
+    >>> pla_bc.init_W()
+
+    >>> pla_bc.train()
+
+    >>> print("PLA 平均錯誤值（Eout）：")
+
+    >>> print(pla_bc.calculate_avg_error(pla_bc.test_X, pla_bc.test_Y, pla_bc.W))
+
+    >>> pocket_bc = pocket.BinaryClassifier()
+
+    >>> pocket_bc.load_train_data(input_train_data_file)
+
+    >>> pocket_bc.load_test_data(input_test_data_file)
+
+    >>> pocket_bc.set_param()
+
+    >>> pocket_bc.init_W()
+
+    >>> pocket_bc.train()
+
+    >>> print("Pocket 平均錯誤值（Eout）：")
+
+    >>> print(pocket_bc.calculate_avg_error(pocket_bc.test_X, pocket_bc.test_Y, pocket_bc.W))
+
+    >>> linear_bc = linear_regression.BinaryClassifier()
+
+    >>> linear_bc.load_train_data(input_train_data_file)
+
+    >>> linear_bc.load_test_data(input_test_data_file)
+
+    >>> linear_bc.set_param()
+
+    >>> linear_bc.init_W()
+
+    >>> linear_bc.train()
+
+    >>> print("Linear 平均錯誤值（Eout）：")
+
+    >>> print(linear_bc.calculate_avg_error(linear_bc.test_X, linear_bc.test_Y, linear_bc.W))
+
+    >>> uniform_blending_classifier.add_model(pla_bc)
+
+    >>> uniform_blending_classifier.add_model(pocket_bc)
+
+    >>> uniform_blending_classifier.add_model(linear_bc)
+
+    >>> test_data = '0.32368 0.61439 0.42097 0.025626 -1'
+
+    >>> prediction = uniform_blending_classifier.prediction(test_data)
+
+    >>> print("測試資料 x：")
+
+    >>> print(prediction['input_data_x'])
+
+    >>> print("測試資料 y：")
+
+    >>> print(prediction['input_data_y'])
+
+    >>> print("預測結果：")
+
+    >>> print(prediction['prediction'])
+
+    >>> print("平均錯誤率（Ein）：")
+
+    >>> print(uniform_blending_classifier.calculate_avg_error(input_train_data_file))
+
+    >>> print("平均錯誤率（Eout）：")
+
+    >>> print(uniform_blending_classifier.calculate_avg_error(input_test_data_file))