[MNT] Del wrong files

examples/workflow_by_code/test_stat/0/__init__.py

@@ -1,20 +0,0 @@
-import os
-import joblib
-import numpy as np
-from learnware.model import BaseModel
-
-
-class SVM(BaseModel):
-    def __init__(self):
-        super(SVM, self).__init__(input_shape=(64,), output_shape=(10,))
-        dir_path = os.path.dirname(os.path.abspath(__file__))
-        self.model = joblib.load(os.path.join(dir_path, "svm.pkl"))
-
-    def fit(self, X: np.ndarray, y: np.ndarray):
-        pass
-
-    def predict(self, X: np.ndarray) -> np.ndarray:
-        return self.model.predict_proba(X)
-
-    def finetune(self, X: np.ndarray, y: np.ndarray):
-        pass

examples/workflow_by_code/test_stat/0/learnware.yaml

@@ -1,8 +0,0 @@
-model:
-  class_name: SVM
-  kwargs: {}
-stat_specifications:
-  - module_path: learnware.specification
-    class_name: RKMEStatSpecification
-    file_name: svm.json
-    kwargs: {}  

examples/workflow_by_code/test_stat/1/__init__.py

@@ -1,20 +0,0 @@
-import os
-import joblib
-import numpy as np
-from learnware.model import BaseModel
-
-
-class SVM(BaseModel):
-    def __init__(self):
-        super(SVM, self).__init__(input_shape=(64,), output_shape=(10,))
-        dir_path = os.path.dirname(os.path.abspath(__file__))
-        self.model = joblib.load(os.path.join(dir_path, "svm.pkl"))
-
-    def fit(self, X: np.ndarray, y: np.ndarray):
-        pass
-
-    def predict(self, X: np.ndarray) -> np.ndarray:
-        return self.model.predict_proba(X)
-
-    def finetune(self, X: np.ndarray, y: np.ndarray):
-        pass

examples/workflow_by_code/test_stat/1/learnware.yaml

@@ -1,8 +0,0 @@
-model:
-  class_name: SVM
-  kwargs: {}
-stat_specifications:
-  - module_path: learnware.specification
-    class_name: RKMEStatSpecification
-    file_name: svm.json
-    kwargs: {}  

examples/workflow_by_code/test_stat/2/__init__.py

@@ -1,20 +0,0 @@
-import os
-import joblib
-import numpy as np
-from learnware.model import BaseModel
-
-
-class SVM(BaseModel):
-    def __init__(self):
-        super(SVM, self).__init__(input_shape=(64,), output_shape=(10,))
-        dir_path = os.path.dirname(os.path.abspath(__file__))
-        self.model = joblib.load(os.path.join(dir_path, "svm.pkl"))
-
-    def fit(self, X: np.ndarray, y: np.ndarray):
-        pass
-
-    def predict(self, X: np.ndarray) -> np.ndarray:
-        return self.model.predict_proba(X)
-
-    def finetune(self, X: np.ndarray, y: np.ndarray):
-        pass

examples/workflow_by_code/test_stat/2/learnware.yaml

@@ -1,8 +0,0 @@
-model:
-  class_name: SVM
-  kwargs: {}
-stat_specifications:
-  - module_path: learnware.specification
-    class_name: RKMEStatSpecification
-    file_name: svm.json
-    kwargs: {}  

examples/workflow_by_code/test_stat/3/__init__.py

@@ -1,20 +0,0 @@
-import os
-import joblib
-import numpy as np
-from learnware.model import BaseModel
-
-
-class SVM(BaseModel):
-    def __init__(self):
-        super(SVM, self).__init__(input_shape=(64,), output_shape=(10,))
-        dir_path = os.path.dirname(os.path.abspath(__file__))
-        self.model = joblib.load(os.path.join(dir_path, "svm.pkl"))
-
-    def fit(self, X: np.ndarray, y: np.ndarray):
-        pass
-
-    def predict(self, X: np.ndarray) -> np.ndarray:
-        return self.model.predict_proba(X)
-
-    def finetune(self, X: np.ndarray, y: np.ndarray):
-        pass

examples/workflow_by_code/test_stat/3/learnware.yaml

@@ -1,8 +0,0 @@
-model:
-  class_name: SVM
-  kwargs: {}
-stat_specifications:
-  - module_path: learnware.specification
-    class_name: RKMEStatSpecification
-    file_name: svm.json
-    kwargs: {}  

examples/workflow_by_code/test_stat/4/__init__.py

@@ -1,20 +0,0 @@
-import os
-import joblib
-import numpy as np
-from learnware.model import BaseModel
-
-
-class SVM(BaseModel):
-    def __init__(self):
-        super(SVM, self).__init__(input_shape=(64,), output_shape=(10,))
-        dir_path = os.path.dirname(os.path.abspath(__file__))
-        self.model = joblib.load(os.path.join(dir_path, "svm.pkl"))
-
-    def fit(self, X: np.ndarray, y: np.ndarray):
-        pass
-
-    def predict(self, X: np.ndarray) -> np.ndarray:
-        return self.model.predict_proba(X)
-
-    def finetune(self, X: np.ndarray, y: np.ndarray):
-        pass

examples/workflow_by_code/test_stat/4/learnware.yaml

@@ -1,8 +0,0 @@
-model:
-  class_name: SVM
-  kwargs: {}
-stat_specifications:
-  - module_path: learnware.specification
-    class_name: RKMEStatSpecification
-    file_name: svm.json
-    kwargs: {}