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- sega_learn.svm.baseSVM.BaseSVM(builtins.object)
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- GeneralizedSVC
- GeneralizedSVR
class GeneralizedSVC(sega_learn.svm.baseSVM.BaseSVM) |
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GeneralizedSVC(C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, kernel='linear', degree=3, gamma='scale', coef0=0.0)
GeneralizedSVC: A Support Vector Classifier (SVC) model with support for multiple kernels.
This class implements an SVC model using gradient descent for optimization. It supports
linear and non-linear kernels, including polynomial and RBF kernels.
Attributes:
C (float): Regularization parameter. Default is 1.0.
tol (float): Tolerance for stopping criteria. Default is 1e-4.
max_iter (int): Maximum number of iterations for gradient descent. Default is 1000.
learning_rate (float): Learning rate for gradient descent. Default is 0.01.
kernel (str): Kernel type ('linear', 'poly', 'rbf'). Default is 'linear'.
degree (int): Degree of the polynomial kernel function ('poly'). Ignored by other kernels. Default is 3.
gamma (str or float): Kernel coefficient for 'rbf' and 'poly'. Default is 'scale'.
coef0 (float): Independent term in kernel function ('poly'). Default is 0.0.
Methods:
__init__(self, C=1.0, tol=1e-4, max_iter=1000, learning_rate=0.01, kernel="linear", degree=3, gamma="scale", coef0=0.0):
Initialize the GeneralizedSVC model with specified hyperparameters.
_fit(self, X, y):
Fit the GeneralizedSVC model to the training data using gradient descent.
_predict_binary(self, X):
Predict binary class labels for input samples.
_predict_multiclass(self, X):
Predict multi-class labels using one-vs-rest strategy.
decision_function(self, X):
Compute raw decision function values for input samples.
_score_binary(self, X, y):
Compute the accuracy score for binary classification.
_score_multiclass(self, X, y):
Compute the accuracy score for multi-class classification.
Raises:
ValueError: If numerical instability is detected during training.
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- Method resolution order:
- GeneralizedSVC
- sega_learn.svm.baseSVM.BaseSVM
- builtins.object
Methods defined here:
- __init__(self, C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, kernel='linear', degree=3, gamma='scale', coef0=0.0)
- Initializes the GeneralizedSVC model with specified hyperparameters.
Args:
C: (float) - Regularization parameter. Default is 1.0.
tol: (float) - Tolerance for stopping criteria. Default is 1e-4.
max_iter: (int) - Maximum number of iterations for gradient descent. Default is 1000.
learning_rate: (float) - Learning rate for gradient descent. Default is 0.01.
kernel: (str) - Kernel type ('linear', 'poly', 'rbf'). Default is 'linear'.
degree: (int) - Degree of the polynomial kernel function ('poly'). Ignored by other kernels. Default is 3.
gamma: (str or float) - Kernel coefficient for 'rbf' and 'poly'. Default is 'scale'.
coef0: (float) - Independent term in kernel function ('poly'). Default is 0.0.
- decision_function(self, X)
- Compute raw decision function values for input samples.
Methods inherited from sega_learn.svm.baseSVM.BaseSVM:
- __sklearn_is_fitted__(self)
- Checks if the model has been fitted (for sklearn compatibility).
Returns:
fitted: (bool) - True if the model has been fitted, otherwise False.
- fit(self, X, y=None)
- Fits the SVM model to the training data.
Args:
X: (array-like of shape (n_samples, n_features)) - Training vectors.
y: (array-like of shape (n_samples,)) - Target values. Default is None.
Returns:
self: (BaseSVM) - The fitted instance.
- get_params(self, deep=True)
- Retrieves the hyperparameters of the model.
Args:
deep: (bool) - If True, returns parameters of subobjects as well. Default is True.
Returns:
params: (dict) - Dictionary of hyperparameter names and values.
- predict(self, X)
- Predicts class labels for input samples.
Args:
X: (array-like of shape (n_samples, n_features)) - Input samples.
Returns:
predicted_labels: (ndarray of shape (n_samples,)) - Predicted class labels.
- score(self, X, y)
- Computes the mean accuracy of the model on the given test data.
Args:
X: (array-like of shape (n_samples, n_features)) - Test samples.
y: (array-like of shape (n_samples,)) - True class labels.
Returns:
score: (float) - Mean accuracy of predictions.
- set_params(self, **parameters)
- Sets the hyperparameters of the model.
Args:
**parameters: (dict) - Hyperparameter names and values.
Returns:
self: (BaseSVM) - The updated estimator instance.
Data descriptors inherited from sega_learn.svm.baseSVM.BaseSVM:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class GeneralizedSVR(sega_learn.svm.baseSVM.BaseSVM) |
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GeneralizedSVR(C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, epsilon=0.1, kernel='linear', degree=3, gamma='scale', coef0=0.0)
GeneralizedSVR: A Support Vector Regression (SVR) model with support for multiple kernels.
This class implements an SVR model using gradient descent for optimization. It supports
linear and non-linear kernels, including polynomial and RBF kernels.
Attributes:
C (float): Regularization parameter. Default is 1.0.
tol (float): Tolerance for stopping criteria. Default is 1e-4.
max_iter (int): Maximum number of iterations for gradient descent. Default is 1000.
learning_rate (float): Learning rate for gradient descent. Default is 0.01.
epsilon (float): Epsilon parameter for epsilon-insensitive loss. Default is 0.1.
kernel (str): Kernel type ('linear', 'poly', 'rbf'). Default is 'linear'.
degree (int): Degree of the polynomial kernel function ('poly'). Ignored by other kernels. Default is 3.
gamma (str or float): Kernel coefficient for 'rbf' and 'poly'. Default is 'scale'.
coef0 (float): Independent term in kernel function ('poly'). Default is 0.0.
Methods:
__init__(self, C=1.0, tol=1e-4, max_iter=1000, learning_rate=0.01, epsilon=0.1, kernel="linear", degree=3, gamma="scale", coef0=0.0):
Initialize the GeneralizedSVR model with specified hyperparameters.
_fit(self, X, y):
Fit the GeneralizedSVR model to the training data using gradient descent.
predict(self, X):
Predict continuous target values for input samples.
decision_function(self, X):
Compute raw decision function values for input samples.
score(self, X, y):
Compute the coefficient of determination (R² score) for the model's predictions.
Raises:
ValueError: If numerical instability is detected during training.
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- Method resolution order:
- GeneralizedSVR
- sega_learn.svm.baseSVM.BaseSVM
- builtins.object
Methods defined here:
- __init__(self, C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, epsilon=0.1, kernel='linear', degree=3, gamma='scale', coef0=0.0)
- Initializes the GeneralizedSVR model with specified hyperparameters.
Args:
C: (float) - Regularization parameter. Default is 1.0.
tol: (float) - Tolerance for stopping criteria. Default is 1e-4.
max_iter: (int) - Maximum number of iterations for gradient descent. Default is 1000.
learning_rate: (float) - Learning rate for gradient descent. Default is 0.01.
epsilon: (float) - Epsilon parameter for epsilon-insensitive loss. Default is 0.1.
kernel: (str) - Kernel type ('linear', 'poly', 'rbf'). Default is 'linear'.
degree: (int) - Degree of the polynomial kernel function ('poly'). Ignored by other kernels. Default is 3.
gamma: (str or float) - Kernel coefficient for 'rbf' and 'poly'. Default is 'scale'.
coef0: (float) - Independent term in kernel function ('poly'). Default is 0.0.
- decision_function(self, X)
- Compute raw decision function values for input samples.
- predict(self, X)
- Predict continuous target values for input samples.
- score(self, X, y)
- Compute the coefficient of determination (R² score) for the model's predictions.
Methods inherited from sega_learn.svm.baseSVM.BaseSVM:
- __sklearn_is_fitted__(self)
- Checks if the model has been fitted (for sklearn compatibility).
Returns:
fitted: (bool) - True if the model has been fitted, otherwise False.
- fit(self, X, y=None)
- Fits the SVM model to the training data.
Args:
X: (array-like of shape (n_samples, n_features)) - Training vectors.
y: (array-like of shape (n_samples,)) - Target values. Default is None.
Returns:
self: (BaseSVM) - The fitted instance.
- get_params(self, deep=True)
- Retrieves the hyperparameters of the model.
Args:
deep: (bool) - If True, returns parameters of subobjects as well. Default is True.
Returns:
params: (dict) - Dictionary of hyperparameter names and values.
- set_params(self, **parameters)
- Sets the hyperparameters of the model.
Args:
**parameters: (dict) - Hyperparameter names and values.
Returns:
self: (BaseSVM) - The updated estimator instance.
Data descriptors inherited from sega_learn.svm.baseSVM.BaseSVM:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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