chore: fix typos

computer_vision/haralick_descriptors.py

@@ -141,7 +141,7 @@ def transform(
 
     center_x, center_y = (x // 2 for x in kernel.shape)
 
-    # Use padded image when applying convolotion
+    # Use padded image when applying convolution
     # to not go out of bounds of the original the image
     transformed = np.zeros(image.shape, dtype=np.uint8)
     padded = np.pad(image, 1, "constant", constant_values=constant)

graphs/strongly_connected_components.py

@@ -38,7 +38,7 @@ def find_components(
     reversed_graph: dict[int, list[int]], vert: int, visited: list[bool]
 ) -> list[int]:
     """
-    Use depth first search to find strongliy connected
+    Use depth first search to find strongly connected
     vertices. Now graph is reversed
     >>> find_components({0: [1], 1: [2], 2: [0]}, 0, 5 * [False])
     [0, 1, 2]

maths/points_are_collinear_3d.py

@@ -99,7 +99,7 @@ def are_collinear(a: Point3d, b: Point3d, c: Point3d, accuracy: int = 10) -> boo
 
     1- Create tow vectors AB and AC.
     2- Get the cross vector of the tow vectors.
-    3- Calcolate the length of the cross vector.
+    3- Calculate the length of the cross vector.
     4- If the length is zero then the points are collinear, else they are not.
 
     The use of the accuracy parameter is explained in is_zero_vector docstring.

neural_network/convolution_neural_network.py

@@ -135,7 +135,7 @@ def convolute(self, data, convs, w_convs, thre_convs, conv_step):
             )
             data_featuremap.append(featuremap)
 
-        # expanding the data slice to One dimenssion
+        # expanding the data slice to One dimension
         focus1_list = []
         for each_focus in data_focus:
             focus1_list.extend(self.Expand_Mat(each_focus))