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[pre-commit.ci] pre-commit autoupdate (#9013)

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* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.0.285 → v0.0.286](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.285...v0.0.286)
- [github.com/tox-dev/pyproject-fmt: 0.13.1 → 1.1.0](https://github.com/tox-dev/pyproject-fmt/compare/0.13.1...1.1.0)

* updating DIRECTORY.md

* Fis ruff rules PIE808,PLR1714

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>
This commit is contained in:
pre-commit-ci[bot]
2023-08-29 15:18:10 +02:00
committed by GitHub
parent 0a9438071e
commit 421ace81ed
43 changed files with 70 additions and 71 deletions
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2
machine_learning/gradient_descent.py
View File

@ -110,7 +110,7 @@ def run_gradient_descent():
while True:
j += 1
temp_parameter_vector = [0, 0, 0, 0]
for i in range(0, len(parameter_vector)):
for i in range(len(parameter_vector)):
cost_derivative = get_cost_derivative(i - 1)
temp_parameter_vector[i] = (
parameter_vector[i] - LEARNING_RATE * cost_derivative

4
machine_learning/linear_regression.py
View File

@ -78,7 +78,7 @@ def run_linear_regression(data_x, data_y):
theta = np.zeros((1, no_features))
for i in range(0, iterations):
for i in range(iterations):
theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
error = sum_of_square_error(data_x, data_y, len_data, theta)
print(f"At Iteration {i + 1} - Error is {error:.5f}")
@ -107,7 +107,7 @@ def main():
theta = run_linear_regression(data_x, data_y)
len_result = theta.shape[1]
print("Resultant Feature vector : ")
for i in range(0, len_result):
for i in range(len_result):
print(f"{theta[0, i]:.5f}")

4
machine_learning/lstm/lstm_prediction.py
View File

@ -32,10 +32,10 @@ if __name__ == "__main__":
train_x, train_y = [], []
test_x, test_y = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
for i in range(len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
for i in range(len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
x_train = np.array(train_x)