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- builtins.object
-
- PCA
- SVD
class PCA(builtins.object) |
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PCA(n_components)
Principal Component Analysis (PCA) implementation.
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Methods defined here:
- __init__(self, n_components)
- Initializes the PCA model.
Args:
n_components: (int) - Number of principal components to keep.
- fit(self, X)
- Fits the PCA model to the data.
Args:
X: (np.ndarray) - Input data of shape (n_samples, n_features).
Raises:
ValueError: If input data is not a 2D numpy array or if n_components exceeds the number of features.
- fit_transform(self, X)
- Fits the PCA model and applies dimensionality reduction on the input data.
Args:
X: (np.ndarray) - Input data of shape (n_samples, n_features).
Returns:
X_transformed: (np.ndarray) - Data transformed into the principal component space of shape (n_samples, n_components).
- get_components(self)
- Retrieves the principal components.
Returns:
components_: (np.ndarray) - Array of principal components of shape (n_features, n_components).
- get_explained_variance_ratio(self)
- Retrieves the explained variance ratio.
Returns:
explained_variance_ratio_: (np.ndarray) - Array of explained variance ratios for each principal component.
- inverse_transform(self, X_reduced)
- Reconstructs the original data from the reduced data.
Args:
X_reduced: (np.ndarray) - Reduced data of shape (n_samples, n_components).
Returns:
X_original: (np.ndarray) - Reconstructed data of shape (n_samples, n_features).
Raises:
ValueError: If input data is not a 2D numpy array.
- transform(self, X)
- Applies dimensionality reduction on the input data.
Args:
X: (np.ndarray) - Input data of shape (n_samples, n_features).
Returns:
X_transformed: (np.ndarray) - Data transformed into the principal component space of shape (n_samples, n_components).
Raises:
ValueError: If input data is not a 2D numpy array or if its dimensions do not match the fitted data.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class SVD(builtins.object) |
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SVD(n_components)
Singular Value Decomposition (SVD) implementation.
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Methods defined here:
- __init__(self, n_components)
- Initializes the SVD model.
Args:
n_components: (int) - Number of singular values and vectors to keep.
- fit(self, X)
- Fits the SVD model to the data.
Args:
X: (np.ndarray) - Input data of shape (n_samples, n_features).
Raises:
ValueError: If input data is not a 2D numpy array or if n_components exceeds the minimum dimension of the input data.
- fit_transform(self, X)
- Fits the SVD model and applies the transformation on the input data.
Args:
X: (np.ndarray) - Input data of shape (n_samples, n_features).
Returns:
X_transformed: (np.ndarray) - Data transformed into the singular value space of shape (n_samples, n_components).
- get_singular_values(self)
- Retrieves the singular values.
Returns:
S: (np.ndarray) - Array of singular values of shape (n_components,).
- get_singular_vectors(self)
- Retrieves the singular vectors.
Returns:
U: (np.ndarray) - Left singular vectors of shape (n_samples, n_components).
Vt: (np.ndarray) - Right singular vectors of shape (n_components, n_features).
- transform(self, X)
- Applies the SVD transformation on the input data.
Args:
X: (np.ndarray) - Input data of shape (n_samples, n_features).
Returns:
X_transformed: (np.ndarray) - Data transformed into the singular value space of shape (n_samples, n_components).
Raises:
ValueError: If input data is not a 2D numpy array.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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