reorganize directories

artlib/__init__.py

@@ -0,0 +1,47 @@
+from artlib.common.BaseART import BaseART
+from artlib.common.BaseARTMAP import BaseARTMAP
+from artlib.common.utils import normalize, compliment_code
+
+from artlib.elementary.ART1 import ART1
+from artlib.elementary.ART2 import ART2A
+from artlib.elementary.BayesianART import BayesianART
+from artlib.elementary.DualVigilanceART import DualVigilanceART
+from artlib.elementary.EllipsoidART import EllipsoidART
+from artlib.elementary.GaussianART import GaussianART
+from artlib.elementary.FuzzyART import FuzzyART
+from artlib.elementary.HypersphereART import HypersphereART
+from artlib.elementary.QuadraticNeuronART import QuadraticNeuronART
+
+from artlib.supervised.ARTMAP import ARTMAP, SimpleARTMAP
+
+from artlib.hierarchical.SMART import SMART
+from artlib.hierarchical.DeepARTMAP import DeepARTMAP
+
+from artlib.fusion.FusionART import FusionART
+
+from artlib.biclustering.BARTMAP import BARTMAP
+
+from artlib.topological.TopoART import TopoART
+
+__all__ = [
+    "BaseART",
+    "BaseARTMAP",
+    "normalize",
+    "compliment_code",
+    "ART1",
+    "ART2A",
+    "BayesianART",
+    "GaussianART",
+    "EllipsoidART",
+    "HypersphereART",
+    "QuadraticNeuronART",
+    "FuzzyART",
+    "TopoART",
+    "DualVigilanceART",
+    "ARTMAP",
+    "SimpleARTMAP",
+    "DeepARTMAP",
+    "SMART",
+    "FusionART",
+    "BARTMAP",
+]

biclustering/BARTMAP.py → artlib/biclustering/BARTMAP.py

@@ -14,7 +14,7 @@
 from typing import Optional
 from collections import defaultdict
 from matplotlib.colors import Colormap
-from common.BaseART import BaseART
+from artlib.common.BaseART import BaseART
 from sklearn.base import BaseEstimator, BiclusterMixin
 from scipy.stats import pearsonr
 

common/BaseART.py → artlib/common/BaseART.py

@@ -5,7 +5,7 @@
 from warnings import warn
 from sklearn.base import BaseEstimator, ClusterMixin
 from sklearn.utils.validation import check_is_fitted
-from common.utils import normalize
+from artlib.common.utils import normalize
 
 
 class BaseART(BaseEstimator, ClusterMixin):

artlib/common/utils.py

@@ -0,0 +1,65 @@
+import numpy as np
+
+def normalize(data: np.ndarray) -> np.ndarray:
+    """
+    normalize data betweeon 0 and 1
+
+    Parameters:
+    - data: data set
+
+    Returns:
+        normalized data
+    """
+    normalized = (data-np.min(data))/(np.max(data)-np.min(data))
+    return normalized
+
+def compliment_code(data: np.ndarray) -> np.ndarray:
+    """
+    compliment code data
+
+    Parameters:
+    - data: data set
+
+    Returns:
+        compliment coded data
+    """
+    cc_data = np.hstack([data, 1.0-data])
+    return cc_data
+
+def l1norm(x: np.ndarray) -> float:
+    """
+    get l1 norm of a vector
+
+    Parameters:
+    - x: some vector
+
+    Returns:
+        l1 norm
+    """
+    return float(np.sum(np.absolute(x)))
+
+def l2norm2(data: np.ndarray) -> float:
+    """
+    get (l2 norm)^2 of a vector
+
+    Parameters:
+    - x: some vector
+
+    Returns:
+        (l2 norm)^2
+    """
+    return float(np.matmul(data, data))
+
+def fuzzy_and(x: np.ndarray, y: np.ndarray) -> np.ndarray:
+    """
+    get the fuzzy AND operation between two vectors
+
+    Parameters:
+    - a: some vector
+    - b: some vector
+
+    Returns:
+        Fuzzy AND result
+
+    """
+    return np.minimum(x, y)

common/utils.py → artlib/common/visualization.py

@@ -1,70 +1,6 @@
 import numpy as np
 from matplotlib.axes import Axes
 
-def normalize(data: np.ndarray) -> np.ndarray:
-    """
-    normalize data betweeon 0 and 1
-
-    Parameters:
-    - data: data set
-
-    Returns:
-        normalized data
-    """
-    normalized = (data-np.min(data))/(np.max(data)-np.min(data))
-    return normalized
-
-def compliment_code(data: np.ndarray) -> np.ndarray:
-    """
-    compliment code data
-
-    Parameters:
-    - data: data set
-
-    Returns:
-        compliment coded data
-    """
-    cc_data = np.hstack([data, 1.0-data])
-    return cc_data
-
-def l1norm(x: np.ndarray) -> float:
-    """
-    get l1 norm of a vector
-
-    Parameters:
-    - x: some vector
-
-    Returns:
-        l1 norm
-    """
-    return float(np.sum(np.absolute(x)))
-
-def l2norm2(data: np.ndarray) -> float:
-    """
-    get (l2 norm)^2 of a vector
-
-    Parameters:
-    - x: some vector
-
-    Returns:
-        (l2 norm)^2
-    """
-    return float(np.matmul(data, data))
-
-def fuzzy_and(x: np.ndarray, y: np.ndarray) -> np.ndarray:
-    """
-    get the fuzzy AND operation between two vectors
-
-    Parameters:
-    - a: some vector
-    - b: some vector
-
-    Returns:
-        Fuzzy AND result
-
-    """
-    return np.minimum(x, y)
-
 def plot_gaussian_contours_fading(
         ax: Axes,
         mean: np.ndarray,

elementary/ART1.py → artlib/elementary/ART1.py

@@ -6,8 +6,8 @@
 
 import numpy as np
 from typing import Optional
-from common.BaseART import BaseART
-from common.utils import normalize, l1norm
+from artlib.common.BaseART import BaseART
+from artlib.common.utils import normalize, l1norm
 
 def prepare_data(data: np.ndarray) -> np.ndarray:
     normalized = normalize(data)

elementary/ART2.py → artlib/elementary/ART2.py

@@ -20,8 +20,7 @@
 import numpy as np
 from typing import Optional
 from warnings import warn
-from common.BaseART import BaseART
-from common.utils import normalize
+from artlib.common.BaseART import BaseART
 
 
 class ART2A(BaseART):

elementary/BayesianART.py → artlib/elementary/BayesianART.py

@@ -6,9 +6,9 @@
 import numpy as np
 from typing import Optional, Iterable
 from matplotlib.axes import Axes
-from common.BaseART import BaseART
-from common.utils import normalize
-from common.utils import plot_gaussian_contours_covariance
+from artlib.common.BaseART import BaseART
+from artlib.common.utils import normalize
+from artlib.common.visualization import plot_gaussian_contours_covariance
 
 def prepare_data(data: np.ndarray) -> np.ndarray:
     normalized = normalize(data)

elementary/DualVigilanceART.py → artlib/elementary/DualVigilanceART.py

@@ -7,7 +7,7 @@
 from typing import Optional, Callable, Iterable
 from warnings import warn
 from matplotlib.axes import Axes
-from common.BaseART import BaseART
+from artlib.common.BaseART import BaseART
 
 
 class DualVigilanceART(BaseART):

elementary/EllipsoidART.py → artlib/elementary/EllipsoidART.py

@@ -12,8 +12,8 @@
 import numpy as np
 from typing import Optional, Iterable
 from matplotlib.axes import Axes
-from common.BaseART import BaseART
-from common.utils import l2norm2
+from artlib.common.BaseART import BaseART
+from artlib.common.utils import l2norm2
 
 class EllipsoidART(BaseART):
     # implementation of EllipsoidART

elementary/FuzzyART.py → artlib/elementary/FuzzyART.py

@@ -6,22 +6,8 @@
 import numpy as np
 from typing import Optional, Iterable
 from matplotlib.axes import Axes
-from common.BaseART import BaseART
-from common.utils import normalize, compliment_code, l1norm, fuzzy_and
-
-def prepare_data(data: np.ndarray) -> np.ndarray:
-    """
-    prepare data for clustering
-
-    Parameters:
-    - X: data set
-
-    Returns:
-        normalized and compliment coded data
-    """
-    normalized = normalize(data)
-    cc_data = compliment_code(normalized)
-    return cc_data
+from artlib.common.BaseART import BaseART
+from artlib.common.utils import normalize, compliment_code, l1norm, fuzzy_and
 
 
 def get_bounding_box(w: np.ndarray, n: Optional[int] = None) -> tuple[list[int], list[int]]:
@@ -81,7 +67,9 @@ def prepare_data(X: np.ndarray) -> np.ndarray:
         Returns:
             normalized and compliment coded data
         """
-        return prepare_data(X)
+        normalized = normalize(X)
+        cc_data = compliment_code(normalized)
+        return cc_data
 
     @staticmethod
     def validate_params(params: dict):

elementary/GaussianART.py → artlib/elementary/GaussianART.py

@@ -7,8 +7,8 @@
 import numpy as np
 from typing import Optional, Iterable
 from matplotlib.axes import Axes
-from common.BaseART import BaseART
-from common.utils import plot_gaussian_contours_fading
+from artlib.common.BaseART import BaseART
+from artlib.common.visualization import plot_gaussian_contours_fading
 
 
 class GaussianART(BaseART):

elementary/HypersphereART.py → artlib/elementary/HypersphereART.py

@@ -7,8 +7,8 @@
 import numpy as np
 from typing import Optional, Iterable
 from matplotlib.axes import Axes
-from common.BaseART import BaseART
-from common.utils import l2norm2
+from artlib.common.BaseART import BaseART
+from artlib.common.utils import l2norm2
 
 class HypersphereART(BaseART):
     # implementation of HypersphereART

elementary/QuadraticNeuronART.py → artlib/elementary/QuadraticNeuronART.py

@@ -11,8 +11,9 @@
 import numpy as np
 from typing import Optional, Iterable
 from matplotlib.axes import Axes
-from common.BaseART import BaseART
-from common.utils import normalize, l2norm2, plot_weight_matrix_as_ellipse
+from artlib.common.BaseART import BaseART
+from artlib.common.utils import normalize, l2norm2
+from artlib.common.visualization import plot_weight_matrix_as_ellipse
 
 def prepare_data(data: np.ndarray) -> np.ndarray:
     normalized = normalize(data)
@@ -187,7 +188,6 @@ def plot_cluster_bounds(self, ax: Axes, colors: Iterable, linewidth: int = 1):
 
         """
         # kinda works
-        from matplotlib.patches import Rectangle
         for w, col in zip(self.W, colors):
             dim2 = self.dim_ * self.dim_
             w_ = w[:dim2].reshape((self.dim_, self.dim_))

fusion/FusionART.py → artlib/fusion/FusionART.py

@@ -8,7 +8,7 @@
 """
 import numpy as np
 from typing import Optional, Union
-from common.BaseART import BaseART
+from artlib.common.BaseART import BaseART
 
 def get_channel_position_tuples(channel_dims: list[int]) -> list[tuple[int, int]]:
     positions = []