[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

machine_learning/dimensionality_reduction.py

@@ -6,6 +6,7 @@
 Notes:
  - Each column of the features matrix corresponds to a class item
 """
+
 """
 Implementation of dimensionality reduction algorithms.
 
@@ -15,13 +16,13 @@
 - Locally Linear Embedding (LLE)
 - Multidimensional Scaling (MDS)
 """
 import doctest
 import logging
 
 import numpy as np
 from scipy.linalg import eigh
 from scipy.spatial.distance import cdist
 from sklearn.neighbors import NearestNeighbors
 
 logging.basicConfig(level=logging.INFO, format="%(message)s")
 
@@ -123,7 +124,7 @@
 def principal_component_analysis(features: np.ndarray, dimensions: int) -> np.ndarray:
  """Principal Component Analysis (PCA).
 
- For more details: https://en.wikipedia.org/wiki/Principal_component_analysis 
+ For more details: https://en.wikipedia.org/wiki/Principal_component_analysis
 
  Args:
  features: Input features matrix (n_features x n_samples).
@@ -157,7 +158,7 @@
 ) -> np.ndarray:
  """Linear Discriminant Analysis (LDA).
 
- For more details: https://en.wikipedia.org/wiki/Linear_discriminant_analysis 
+ For more details: https://en.wikipedia.org/wiki/Linear_discriminant_analysis
 
  Args:
  features: Input features matrix (n_features x n_samples).
@@ -198,7 +199,7 @@
 ) -> np.ndarray:
  """Locally Linear Embedding (LLE).
 
- For more details: https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction 
+ For more details: https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction
 
  Args:
  features: Input features matrix (shape: [n_features, n_samples]).
@@ -283,7 +284,7 @@
 ) -> np.ndarray:
  """Multidimensional Scaling (MDS).
 
- For more details: https://en.wikipedia.org/wiki/Multidimensional_scaling 
+ For more details: https://en.wikipedia.org/wiki/Multidimensional_scaling
 
  Args:
  features: Input features matrix (shape: [n_features, n_samples]).
@@ -429,9 +430,7 @@
  """Test function for Principal Component Analysis."""
  features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
  dimensions = 2
- expected_output = np.array(
- [[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]
- )
+ expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
 
  output = principal_component_analysis(features, dimensions)
  if not np.allclose(expected_output, output):