Metadata-Version: 2.1
Name: logregnumpy
Version: 0.1.2
Summary: Logistic Regression Classifier
Home-page: UNKNOWN
Author-email: kir.klyukvin@gmail.com
License: UNKNOWN
Description: # logregnumpy
        
        
        Pet Project. Logistic Regressor Classifier.
            
        Performs a gradient descent method for a loss minimizing.
            
        Works with binary and multiclass targets. 
            
        Parameters
        ----------
        **lr : float, default=1e-3**  
        Learning rate (size) for each step of an gradient descent.
                
        **l2_reg : float, default=0.2**     
        Degree of L2 penalty.
                
        **epochs : int, default=100**   
        Number of gradient descent iterations.
            
        Examples
        --------
        ```
        >>> from sklearn.datasets import load_iris  
        >>> from logregnumpy import LogRegNumpy  
        >>> X, y = load_iris(return_X_y=True)  
        >>> model = LogRegNumpy(l2_reg=0.1, epochs=1000)  
        >>> model.fit(X, y)  
        >>> model.predict(X)[:3]  
        
        array([0, 0, 0])  
        
        >>> model.predict_proba(X)[:3]
        
        array([[9.69584306e-01, 3.04018742e-02, 1.38198704e-05],  
               [9.32753885e-01, 6.71844981e-02, 6.16165599e-05],  
               [9.57931295e-01, 4.20313028e-02, 3.74027136e-05]])  
        ```
        
        Methods
        -------
        
        **fit(X, y, verbose=False, plot=False)**  
        Fit the model according to the given training data. May return a loss value graph. 
        
        Parameters
        
        *X : array-like of shape (n_samples, n_features)*  
        Training vector, where n_samples is the number of samples and  
        n_features is the number of features.  
        
        *y : array-like of shape (n_samples,)*   
        Target vector relative to X.  
        
        *verbose : bool, default=False*    
        If true, returns array with loss values on each iteration.  
        
        *plot : bool, default=False*  
        If true, returns a loss value graph.          
        
        **predict(X)**  
        Predict class labels for samples in X.
          
        **predict_proba(X)**  
        Probability estimates.  
        
        
        Notes
        -----
        
        To successfully uninstall the package from Jupyter notebook, use the following code:
        ```
        pip uninstall logregnumpy --yes
        ```
Platform: UNKNOWN
Description-Content-Type: text/markdown
