v0.3.21

Joao-Paulo-Silva · web-flow · commit 370f03af4803 · 2025-09-20T14:08:03.000-03:00
Adjusts cloning function by range and related notebooks
diff --git a/aisp/base/mutation.py b/aisp/base/mutation.py
@@ -122,8 +122,8 @@ def clone_and_mutate_ranged(
         position_mutations = np.random.permutation(n_features)[:n_mutations]
         for j in range(n_mutations):
             idx = position_mutations[j]
-            min_limit = bounds[idx, 0]
-            max_limit = bounds[idx, 1]
+            min_limit = bounds[0][idx]
+            max_limit = bounds[1][idx]
             clone[idx] = np.random.uniform(min_limit, max_limit)
         clone_set[i] = clone
 
diff --git a/aisp/csa/_ai_recognition_sys.py b/aisp/csa/_ai_recognition_sys.py
@@ -111,13 +111,13 @@ class AIRS(BaseAIRS):
         Way to calculate the distance between the detector and the sample:
 
         * ``'Euclidean'`` ➜ The calculation of the distance is given by the expression:
-            √( (x₁ – x₂)² + (y₁ – y₂)² + ... + (yn – yn)²).
+            √( (x₁ - x₂)² + (y₁ - y₂)² + ... + (yn - yn)²).
 
         * ``'minkowski'`` ➜ The calculation of the distance is given by the expression:
-            ( |X₁ – Y₁|p + |X₂ – Y₂|p + ... + |Xn – Yn|p) ¹/ₚ.
+            ( |X₁ - Y₁|p + |X₂ - Y₂|p + ... + |Xn - Yn|p) ¹/ₚ.
 
         * ``'manhattan'`` ➜ The calculation of the distance is given by the expression:
-            ( |x₁ – x₂| + |y₁ – y₂| + ... + |yn – yn|).
+            ( |x₁ - x₂| + |y₁ - y₂| + ... + |yn - yn|).
 
     seed : int
         Seed for the random generation of detector values. Defaults to None.
@@ -139,7 +139,7 @@ class AIRS(BaseAIRS):
 
     References
     ----------
-    .. [1] Brabazon, A., O’Neill, M., & McGarraghy, S. (2015). Natural Computing Algorithms. In
+    .. [1] Brabazon, A., O'Neill, M., & McGarraghy, S. (2015). Natural Computing Algorithms. In
         Natural Computing Series. Springer Berlin Heidelberg.
         https://doi.org/10.1007/978-3-662-43631-8
 
@@ -195,6 +195,7 @@ def __init__(
         self.affinity_threshold = 0.0
         self.classes = []
         self._bounds: Optional[npt.NDArray[np.float64]] = None
+        self._n_features: Optional[int] = None
 
     @property
     def cells_memory(self) -> Optional[Dict[str, list[Cell]]]:
@@ -235,6 +236,7 @@ def fit(self, X: npt.NDArray, y: npt.NDArray, verbose: bool = True) -> AIRS:
                 self._bounds = np.vstack([np.min(X, axis=0), np.max(X, axis=0)])
 
         self.classes = np.unique(y)
+        self._n_features = X.shape[1]
         sample_index = self._slice_index_list_by_class(y)
         progress = tqdm(
             total=len(y),
@@ -330,11 +332,11 @@ def predict(self, X: npt.NDArray) -> Optional[npt.NDArray]:
             An ndarray of the form ``C`` [``N samples``], containing the predicted classes for
             ``X``. or ``None``: If there are no detectors for the prediction.
         """
-        if self._all_class_cell_vectors is None:
+        if self._all_class_cell_vectors is None or self._n_features is None:
             return None
 
         super()._check_and_raise_exceptions_predict(
-            X, len(self._cells_memory[self.classes[0]][0].vector), self._feature_type
+            X, self._n_features, self._feature_type
         )
 
         c: list = []
@@ -380,7 +382,7 @@ def _refinement_arb(
 
         References
         ----------
-        .. [1] Brabazon, A., O’Neill, M., & McGarraghy, S. (2015).
+        .. [1] Brabazon, A., O'Neill, M., & McGarraghy, S. (2015).
                 Natural Computing Algorithms. Natural Computing Series.
                 Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-43631-8
         """
@@ -441,7 +443,7 @@ def _cells_affinity_threshold(self, antigens_list: npt.NDArray):
             distances = pdist(antigens_list, metric="hamming")
         else:
             metric_kwargs = {'p': self.p} if self.metric == 'minkowski' else {}
-            distances = pdist(antigens_list, metric=self.metric, **metric_kwargs)
+            distances = pdist(antigens_list, metric=self.metric, **metric_kwargs) # type: ignore
 
         n = antigens_list.shape[0]
         sum_affinity = np.sum(1.0 - (distances / (1.0 + distances)))
diff --git a/aisp/csa/tests/test_ai_recognition_sys.py b/aisp/csa/tests/test_ai_recognition_sys.py
@@ -1,5 +1,5 @@
 # pylint: disable=redefined-outer-name
-"""Tests for classes implementing negative selection."""
+"""Tests for classes implementing Artificial Immune Recognition System."""
 
 import numpy as np
 import pytest
diff --git a/aisp/ina/_ai_network.py b/aisp/ina/_ai_network.py
@@ -29,7 +29,7 @@ class AiNet(BaseAiNet):
     clustering and data compression tasks. The aiNet algorithm uses principles from immune
     network theory, clonal selection, and affinity maturation to compress high-dimensional
     datasets. [1]_
-    For clustering, the class uses SciPy’s implementation of the **Minimum Spanning Tree**
+    For clustering, the class uses SciPy's implementation of the **Minimum Spanning Tree**
     (MST) to remove the most distant nodes and separate the groups. [2]_
 
     Parameters
@@ -58,13 +58,13 @@ class AiNet(BaseAiNet):
         Way to calculate the distance between the detector and the sample:
 
         * ``'Euclidean'`` ➜ The calculation of the distance is given by the expression:
-            √( (x₁ – x₂)² + (y₁ – y₂)² + ... + (yn – yn)²).
+            √( (x₁ - x₂)² + (y₁ - y₂)² + ... + (yn - yn)²).
 
         * ``'minkowski'`` ➜ The calculation of the distance is given by the expression:
-            ( |X₁ – Y₁|p + |X₂ – Y₂|p + ... + |Xn – Yn|p) ¹/ₚ.
+            ( |X₁ - Y₁|p + |X₂ - Y₂|p + ... + |Xn - Yn|p) ¹/ₚ.
 
         * ``'manhattan'`` ➜ The calculation of the distance is given by the expression:
-            ( |x₁ – x₂| + |y₁ – y₂| + ... + |yn – yn|).
+            ( |x₁ - x₂| + |y₁ - y₂| + ... + |yn - yn|).
 
     seed : Optional[int]
         Seed for the random generation of detector values. Defaults to None.
@@ -455,7 +455,7 @@ def _calculate_affinities(self, u: npt.NDArray, v: npt.NDArray) -> npt.NDArray:
         """
         u = np.reshape(u, (1, -1))
         v = np.atleast_2d(v)
-        distances = cdist(u, v, metric=self.metric, **self._metric_params)[0]
+        distances = cdist(u, v, metric=self.metric, **self._metric_params)[0] # type: ignore
 
         return 1 - (distances / (1 + distances))
 
@@ -523,6 +523,8 @@ def update_clusters(self, mst_inconsistency_factor: Optional[float] = None):
         ------
         ValueError
             If the Minimum Spanning Tree (MST) has not yet been created
+            If Population of antibodies is empty
+            If MST statistics (mean or std) are not available.
 
         Updates
         -------
@@ -534,6 +536,12 @@ def update_clusters(self, mst_inconsistency_factor: Optional[float] = None):
         if self._mst_structure is None:
             raise ValueError("The Minimum Spanning Tree (MST) has not yet been created.")
 
+        if self._population_antibodies is None or len(self._population_antibodies) == 0:
+            raise ValueError("Population of antibodies is empty")
+
+        if self._mst_mean_distance is None or self._mst_std_distance is None:
+            raise ValueError("MST statistics (mean or std) are not available.")
+
         if mst_inconsistency_factor is not None:
             self.mst_inconsistency_factor = mst_inconsistency_factor
 
diff --git a/aisp/nsa/_binary_negative_selection.py b/aisp/nsa/_binary_negative_selection.py
@@ -237,4 +237,4 @@ def __assign_class_to_non_self_sample(self, line: npt.NDArray, c: list):
             else:
                 class_differences[_class_] = distances.sum() / self.N
 
-        c.append(max(class_differences, key=class_differences.get))
+        c.append(max(class_differences, key=class_differences.get)) # type: ignore
diff --git a/aisp/nsa/_negative_selection.py b/aisp/nsa/_negative_selection.py
@@ -40,11 +40,11 @@ class RNSA(BaseNSA):
         Way to calculate the distance between the detector and the sample:
 
         + ``'Euclidean'`` ➜ The calculation of the distance is given by the expression:
-            √( (x₁ – x₂)² + (y₁ – y₂)² + ... + (yn – yn)²).
+            √( (x₁ - x₂)² + (y₁ - y₂)² + ... + (yn - yn)²).
         + ``'minkowski'`` ➜ The calculation of the distance is given by the expression:
-            ( |X₁ – Y₁|p + |X₂ – Y₂|p + ... + |Xn – Yn|p) ¹/ₚ.
+            ( |X₁ - Y₁|p + |X₂ - Y₂|p + ... + |Xn - Yn|p) ¹/ₚ.
         + ``'manhattan'`` ➜ The calculation of the distance is given by the expression:
-            ( |x₁ – x₂| + |y₁ – y₂| + ... + |yn – yn|) .
+            ( |x₁ - x₂| + |y₁ - y₂| + ... + |yn - yn|) .
     max_discards : int, default=1000
         This parameter indicates the maximum number of consecutive detector discards, aimed at
         preventing a possible infinite loop in case a radius is defined that cannot generate
@@ -260,7 +260,7 @@ def predict(self, X: npt.NDArray) -> Optional[npt.NDArray]:
                     average_distance[_class_] = np.average(
                         [self.__distance(detector, line) for detector in detectores]
                     )
-                c.append(max(average_distance, key=average_distance.get))
+                c.append(max(average_distance, key=average_distance.get)) # type: ignore
         return np.array(c)
 
     def __checks_valid_detector(
diff --git a/aisp/utils/distance.py b/aisp/utils/distance.py
@@ -40,7 +40,7 @@ def hamming(u: npt.NDArray[np.bool_], v: npt.NDArray[np.bool_]) -> float64:
 def euclidean(u: npt.NDArray[np.float64], v: npt.NDArray[np.float64]) -> float64:
     """Calculate the normalized Euclidean distance between two points.
     
-    √( (x₁ – x₂)² + (y₁ – y₂)² + ... + (yn – yn)²)
+    √( (x₁ - x₂)² + (y₁ - y₂)² + ... + (yn - yn)²)
 
     Parameters
     ----------
@@ -61,7 +61,7 @@ def euclidean(u: npt.NDArray[np.float64], v: npt.NDArray[np.float64]) -> float64
 def cityblock(u: npt.NDArray[float64], v: npt.NDArray[float64]) -> float64:
     """Calculate the normalized Manhattan distance between two points.
     
-    (|x₁ – x₂| + |y₁ – y₂| + ... + |yn – yn|) / n
+    (|x₁ - x₂| + |y₁ - y₂| + ... + |yn - yn|) / n
 
     Parameters
     ----------
@@ -86,7 +86,7 @@ def cityblock(u: npt.NDArray[float64], v: npt.NDArray[float64]) -> float64:
 def minkowski(u: npt.NDArray[float64], v: npt.NDArray[float64], p: float = 2.0) -> float64:
     """Calculate the normalized Minkowski distance between two points.
     
-    (( |X₁ – Y₁|p + |X₂ – Y₂|p + ... + |Xn – Yn|p) ¹/ₚ.) / n
+    (( |X₁ - Y₁|p + |X₂ - Y₂|p + ... + |Xn - Yn|p) ¹/ₚ.) / n
 
     Parameters
     ----------
@@ -124,7 +124,7 @@ def compute_metric_distance(
     u: npt.NDArray[float64],
     v: npt.NDArray[float64],
     metric: int,
-    p: float64 = 2.0
+    p: float = 2.0
 ) -> float64:
     """Calculate the distance between two points by the chosen metric.
 
diff --git a/aisp/utils/types.py b/aisp/utils/types.py
@@ -12,11 +12,11 @@
 MetricType : Literal["manhattan", "minkowski", "euclidean"]
     Specifies the distance metric to use for calculations. Possible values:
     - "manhattan": The calculation of the distance is given by the expression:
-            √( (x₁ – x₂)² + (y₁ – y₂)² + ... + (yn – yn)²).
+            √( (x₁ - x₂)² + (y₁ - y₂)² + ... + (yn - yn)²).
     - "minkowski": The calculation of the distance is given by the expression:
-            ( |X₁ – Y₁|p + |X₂ – Y₂|p + ... + |Xn – Yn|p) ¹/ₚ.
+            ( |X₁ - Y₁|p + |X₂ - Y₂|p + ... + |Xn - Yn|p) ¹/ₚ.
     - "euclidean": The calculation of the distance is given by the expression:
-            ( |x₁ – x₂| + |y₁ – y₂| + ... + |yn – yn|).
+            ( |x₁ - x₂| + |y₁ - y₂| + ... + |yn - yn|).
 """
 
 
diff --git a/docs/en/advanced-guides/utils.md b/docs/en/advanced-guides/utils.md
@@ -136,7 +136,7 @@ def euclidean(u: npt.NDArray[np.float64], v: npt.NDArray[np.float64]) -> np.floa
 
 Function to calculate the normalized Euclidean distance between two points.
 
-$√( (x₁ – x₂)² + (y₁ – y₂)² + ... + (yn – yn)²)$
+$√( (x₁ - x₂)² + (y₁ - y₂)² + ... + (yn - yn)²)$
 
 
 
@@ -157,7 +157,7 @@ def cityblock(u: npt.NDArray[np.float64], v: npt.NDArray[np.float64]) -> np.floa
 
 Function to calculate the normalized Manhattan distance between two points.
     
-$(|x₁ – x₂| + |y₁ – y₂| + ... + |yn – yn|) / n$
+$(|x₁ - x₂| + |y₁ - y₂| + ... + |yn - yn|) / n$
 
 
 **Parameters:**
@@ -177,7 +177,7 @@ def minkowski(u: npt.NDArray[np.float64], v: npt.NDArray[np.float64], p: float =
 
 Function to calculate the normalized Minkowski distance between two points.
     
-$(( |X₁ – Y₁|p + |X₂ – Y₂|p + ... + |Xn – Yn|p) ¹/ₚ) / n$
+$(( |X₁ - Y₁|p + |X₂ - Y₂|p + ... + |Xn - Yn|p) ¹/ₚ) / n$
 
 
 **Parameters:**
diff --git a/docs/en/classes/Clonal Selection Algorithms/AIRS.md b/docs/en/classes/Clonal Selection Algorithms/AIRS.md
@@ -23,11 +23,11 @@ Related and noteworthy works: access here [2](#ref2).
 * **resource_amplified** (``float``): Resource consumption amplifier is multiplied with the incentive to subtract resources. Defaults to 1.0 without amplification.
 * **metric** (Literal["manhattan", "minkowski", "euclidean"]): Way to calculate the distance between the detector and the sample:
     * ``'Euclidean'`` ➜ The calculation of the distance is given by the expression:  
-    √( (x₁ – x₂)² + (y₁ – y₂)² + ... + (yn – yn)²).
+    √( (x₁ - x₂)² + (y₁ - y₂)² + ... + (yn - yn)²).
     * ``'minkowski'`` ➜ The calculation of the distance is given by the expression:  
-    ( |X₁ – Y₁|p + |X₂ – Y₂|p + ... + |Xn – Yn|p) ¹/ₚ.
+    ( |X₁ - Y₁|p + |X₂ - Y₂|p + ... + |Xn - Yn|p) ¹/ₚ.
     * ``'manhattan'`` ➜ The calculation of the distance is given by the expression:  
-    ( |x₁ – x₂| + |y₁ – y₂| + ... + |yn – yn|).  
+    ( |x₁ - x₂| + |y₁ - y₂| + ... + |yn - yn|).  
     Defaults to "Euclidean".
 
 * **seed** (int): Seed for the random generation of detector values. Defaults to None.
diff --git a/docs/en/classes/Negative Selection/README.md b/docs/en/classes/Negative Selection/README.md
@@ -21,6 +21,6 @@
 > S. Forrest, A. S. Perelson, L. Allen and R. Cherukuri, "Self-nonself discrimination in a computer," Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, CA, USA, 1994, pp. 202-212, doi: http://dx.doi.org/10.1109/RISP.1994.296580.
 
 ##### 3
-> JI, Zhou; DASGUPTA, Dipankar. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors. Genetic and Evolutionary Computation – GECCO 2004. [S. l.]: Springer Berlin Heidelberg, 2004. DOI 10.1007/978-3-540-24854-5_30. Disponível em: http://dx.doi.org/10.1007/978-3-540-24854-5_30.
+> JI, Zhou; DASGUPTA, Dipankar. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors. Genetic and Evolutionary Computation - GECCO 2004. [S. l.]: Springer Berlin Heidelberg, 2004. DOI 10.1007/978-3-540-24854-5_30. Disponível em: http://dx.doi.org/10.1007/978-3-540-24854-5_30.
 
 ---
diff --git a/docs/en/classes/Negative Selection/RNSA.md b/docs/en/classes/Negative Selection/RNSA.md
@@ -15,10 +15,10 @@ The ``RNSA`` class has the purpose of classifying and identifying anomalies thro
 * *k* (``int``): Number of neighbors near the randomly generated detectors to perform the distance average calculation. Defaults to ``1``.
 * *metric* (``str``): Way to calculate the distance between the detector and the sample:
 
-    * ``'Euclidean'`` ➜ The calculation of the distance is given by the expression: √( (X₁ – X₂)² + (Y₁ – Y₂)² + ... + (Yn – Yn)²).
+    * ``'Euclidean'`` ➜ The calculation of the distance is given by the expression: √( (X₁ - X₂)² + (Y₁ - Y₂)² + ... + (Yn - Yn)²).
 
-    * ``'minkowski'`` ➜ The calculation of the distance is given by the expression: ( |X₁ – Y₁|p + |X₂ – Y₂|p + ... |Xn – Yn|p) ¹/ₚ , In this project ``p == 2``.
-    * ``'manhattan'`` ➜ The calculation of the distance is given by the expression: ( |X₁ – X₂| + |Y₁ – Y₂| + ...+ |Yn – Yn₂|) .
+    * ``'minkowski'`` ➜ The calculation of the distance is given by the expression: ( |X₁ - Y₁|p + |X₂ - Y₂|p + ... |Xn - Yn|p) ¹/ₚ , In this project ``p == 2``.
+    * ``'manhattan'`` ➜ The calculation of the distance is given by the expression: ( |X₁ - X₂| + |Y₁ - Y₂| + ...+ |Yn - Yn₂|) .
 
     Defaults to ``'euclidean'``.
 
diff --git a/docs/en/classes/Network Theory Algorithms/AiNet.md b/docs/en/classes/Network Theory Algorithms/AiNet.md
@@ -21,11 +21,11 @@ For clustering, it optionally uses a **Minimum Spanning Tree (MST)** to separate
 * **k** (``int``): Number of nearest neighbors used for label prediction. Defaults to 3.
 * **metric** (Literal["manhattan", "minkowski", "euclidean"]): Way to calculate the distance between the detector and the sample:
     * ``'Euclidean'`` ➜ The calculation of the distance is given by the expression:
-    √( (x₁ – x₂)² + (y₁ – y₂)² + ... + (yn – yn)²).
+    √( (x₁ - x₂)² + (y₁ - y₂)² + ... + (yn - yn)²).
     * ``'minkowski'`` ➜ The calculation of the distance is given by the expression:
-    ( |X₁ – Y₁|p + |X₂ – Y₂|p + ... + |Xn – Yn|p) ¹/ₚ.
+    ( |X₁ - Y₁|p + |X₂ - Y₂|p + ... + |Xn - Yn|p) ¹/ₚ.
     * ``'manhattan'`` ➜ The calculation of the distance is given by the expression:
-    ( |x₁ – x₂| + |y₁ – y₂| + ... + |yn – yn|).
+    ( |x₁ - x₂| + |y₁ - y₂| + ... + |yn - yn|).
     Defaults to "Euclidean".
 
 * **seed** (``Optional[int]``): Seed for random number generation. Defaults to None.
diff --git a/docs/pt-br/README.md b/docs/pt-br/README.md
@@ -14,7 +14,7 @@
 
 O **AISP** é um pacote python que implementa as técnicas dos sistemas imunológicos artificiais, distribuído sob a licença GNU Lesser General Public License v3.0 (LGPLv3).
 
-O pacote foi iniciado no ano de 2022, como parte de um projeto de pesquisa desenvolvido no Instituto Federal do Norte de Minas Gerais – Campus Salinas (IFNMG – Salinas).
+O pacote foi iniciado no ano de 2022, como parte de um projeto de pesquisa desenvolvido no Instituto Federal do Norte de Minas Gerais - Campus Salinas (IFNMG - Salinas).
 
 Os sistemas imunológicos artificiais (SIA) inspiram-se no sistema imunológico dos vertebrados, criando metáforas que aplicam a capacidade de reconhecer e catalogar os patógenos, entre outras características desse sistema.
 
diff --git a/docs/pt-br/advanced-guides/utils.md b/docs/pt-br/advanced-guides/utils.md
@@ -132,7 +132,7 @@ def euclidean(u: npt.NDArray[np.float64], v: npt.NDArray[np.float64]) -> np.floa
 
 Função para calcular a distância euclidiana normalizada entre dois pontos.
 
-$√( (x₁ – x₂)² + (y₁ – y₂)² + ... + (yn – yn)²)$
+$√( (x₁ - x₂)² + (y₁ - y₂)² + ... + (yn - yn)²)$
 
 
 
@@ -153,7 +153,7 @@ def cityblock(u: npt.NDArray[np.float64], v: npt.NDArray[np.float64]) -> np.floa
 
 Função para calcular a distância Manhattan normalizada entre dois pontos.
     
-$(|x₁ – x₂| + |y₁ – y₂| + ... + |yn – yn|) / n$
+$(|x₁ - x₂| + |y₁ - y₂| + ... + |yn - yn|) / n$
 
 
 **Parameters:**
@@ -173,7 +173,7 @@ def minkowski(u: npt.NDArray[np.float64], v: npt.NDArray[np.float64], p: float =
 
 Função para calcular a distância de Minkowski normalizada entre dois pontos.
     
-$(( |X₁ – Y₁|p + |X₂ – Y₂|p + ... + |Xn – Yn|p) ¹/ₚ) / n$
+$(( |X₁ - Y₁|p + |X₂ - Y₂|p + ... + |Xn - Yn|p) ¹/ₚ) / n$
 
 
 **Parameters:**
diff --git a/docs/pt-br/classes/Clonal Selection Algorithms/AIRS.md b/docs/pt-br/classes/Clonal Selection Algorithms/AIRS.md
@@ -31,11 +31,11 @@ Estudos relacionados de destaque: [2](#ref2).
 * **metric** (Literal["manhattan", "minkowski", "euclidean"]): Forma de calcular a distância entre o detector e a amostra:
 
   * `'euclidean'` ➜ O cálculo da distância é dado pela expressão:
-    √( (x₁ – x₂)² + (y₁ – y₂)² + ... + (nₙ – nₙ)² ).
+    √( (x₁ - x₂)² + (y₁ - y₂)² + ... + (nₙ - nₙ)² ).
   * `'minkowski'` ➜ O cálculo da distância é dado pela expressão:
-    ( |X₁ – Y₁|ᵖ + |X₂ – Y₂|ᵖ + ... + |Xₙ – Yₙ|ᵖ )¹/ᵖ.
+    ( |X₁ - Y₁|ᵖ + |X₂ - Y₂|ᵖ + ... + |Xₙ - Yₙ|ᵖ )¹/ᵖ.
   * `'manhattan'` ➜ O cálculo da distância é dado pela expressão:
-    ( |x₁ – x₂| + |y₁ – y₂| + ... + |nₙ – nₙ| ).
+    ( |x₁ - x₂| + |y₁ - y₂| + ... + |nₙ - nₙ| ).
     O padrão é "euclidean".
 
 * **seed** (int): Semente para geração aleatória de valores dos detectores. O padrão é None.
diff --git a/docs/pt-br/classes/Negative Selection/README.md b/docs/pt-br/classes/Negative Selection/README.md
diff --git a/docs/pt-br/classes/Negative Selection/RNSA.md b/docs/pt-br/classes/Negative Selection/RNSA.md
diff --git a/docs/pt-br/classes/Network Theory Algorithms/AiNet.md b/docs/pt-br/classes/Network Theory Algorithms/AiNet.md
diff --git a/examples/en/clustering/AiNet/geyser_dataBase_example.ipynb b/examples/en/clustering/AiNet/geyser_dataBase_example.ipynb
diff --git a/examples/pt-br/classification/AIRS/iris_dataBase_example_pt-br.ipynb b/examples/pt-br/classification/AIRS/iris_dataBase_example_pt-br.ipynb
diff --git a/examples/pt-br/clustering/AiNet/geyser_dataBase_example.ipynb b/examples/pt-br/clustering/AiNet/geyser_dataBase_example.ipynb
diff --git a/pyproject.toml b/pyproject.toml
