Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

chore: fix typos #11467

Merged
merged 4 commits into from
Jun 17, 2024
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion computer_vision/haralick_descriptors.py
Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down
2 changes: 1 addition & 1 deletion graphs/strongly_connected_components.py
Original file line number Diff line number Diff line change
Expand Up @@ -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]
Expand Down
10 changes: 5 additions & 5 deletions maths/points_are_collinear_3d.py
Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand All @@ -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.
Expand Down
8 changes: 4 additions & 4 deletions neural_network/convolution_neural_network.py
Original file line number Diff line number Diff line change
@@ -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
Expand Down Expand Up @@ -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))
Expand Down Expand Up @@ -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()
Expand Down Expand Up @@ -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
"""