cfuncs module#
cfuncs contains all standard cost functions integrated into mlr-gd and melar.
Each cost function has a name (ex. mse) that returns a cost. Each cost function has a derivative function, indicated by _deriv
- melar.cfuncs.mae(y_predictions: ndarray, y_target: ndarray) float64[source]#
MAE Function.
Calculates the mean absolute error of predictions as compared to the target values.
- Parameters:
y_predictions (np.ndarray) – Predicted values.
y_target (np.ndarray) – Target values.
- Returns:
Mean of the absolute remainder array (y_predictions - y_target)
- Return type:
np.float64
- melar.cfuncs.mae_deriv(x_training: ndarray, y_training: ndarray, y_predict: ndarray) tuple[source]#
Derivative of mae
- Parameters:
x_training (np.ndarray) – Input values.
y_training (np.ndarray) – Target values.
y_predict (np.ndarray) – Predicted values.
- Returns:
Derivative of cost function mae (bias_derivative, weights_derivative)
- Return type:
tuple (np.float64, np.ndarray)
- melar.cfuncs.mse(y_predictions: ndarray, y_target: ndarray) float64[source]#
MSE Function.
Calculates the mean square error of predictions as compared to the target values.
- Parameters:
y_predictions (np.ndarray) – Predicted values.
y_target (np.ndarray) – Target values.
- Returns:
Mean of the squared remainder array (y_predictions - y_target)
- Return type:
np.float64
- melar.cfuncs.mse_deriv(x_training: ndarray, y_training: ndarray, y_predict: ndarray) tuple[source]#
Derivative of mse
- Parameters:
x_training (np.ndarray) – Input values.
y_training (np.ndarray) – Target values.
y_predict (np.ndarray) – Predicted values.
- Returns:
Derivative of cost function mse (bias_derivative, weights_derivative)
- Return type:
tuple (np.float64, np.ndarray)