chore: fix typos (TheAlgorithms#11467)

* chore: fix typos Signed-off-by: snoppy <michaleli@foxmail.com> * Apply suggestions from code review Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com> --------- Signed-off-by: snoppy <michaleli@foxmail.com> Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>

Author: 3 people

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

@@ -76,9 +76,9 @@ def get_3d_vectors_cross(ab: Vector3d, ac: Vector3d) -> Vector3d:
 
 def is_zero_vector(vector: Vector3d, accuracy: int) -> bool:
  """
- Check if vector is equal to (0, 0, 0) of not.
+ Check if vector is equal to (0, 0, 0) or not.
 
- Sine the algorithm is very accurate, we will never get a zero vector,
+ Since the algorithm is very accurate, we will never get a zero vector,
  so we need to round the vector axis,
  because we want a result that is either True or False.
  In other applications, we can return a float that represents the collinearity ratio.
@@ -97,9 +97,9 @@ def are_collinear(a: Point3d, b: Point3d, c: Point3d, accuracy: int = 10) -> boo
  """
  Check if three points are collinear or not.
 
- 1- Create tow vectors AB and AC.
- 2- Get the cross vector of the tow vectors.
- 3- Calcolate the length of the cross vector.
+ 1- Create two vectors AB and AC.
+ 2- Get the cross vector of the two vectors.
+ 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

@@ -1,7 +1,7 @@
 """
  - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 Name - - CNN - Convolution Neural Network For Photo Recognizing
-Goal - - Recognize Handing Writing Word Photo
+Goal - - Recognize Handwriting Word Photo
 Detail: Total 5 layers neural network
  * Convolution layer
  * Pooling layer
@@ -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))
@@ -304,7 +304,7 @@ def draw_error():
  plt.grid(True, alpha=0.5)
  plt.show()
 
- print("------------------Training Complished---------------------")
+ print("------------------Training Complete---------------------")
  print((" - - Training epoch: ", rp, f" - - Mse: {mse:.6f}"))
  if draw_e:
  draw_error()
@@ -353,5 +353,5 @@ def convolution(self, data):
 
 if __name__ == "__main__":
  """
- I will put the example on other file
+ I will put the example in another file
  """