Metadata-Version: 2.1
Name: nista_learn
Version: 0.0.4
Summary: A small machine learning package
Home-page: https://github.com/wariored/nista_learn
Author: Cheikh Tidjane Konteye
Author-email: cheikh@cheikhkonteye.com
License: UNKNOWN
Description: nista\_learn
        ============
        
        This is a Python ML librabry like scikit-learn
        
        You can create your ML model or use some ML algorithms on your project
        
        Example: Logistic Regression
        ----------------------------
        
        Read csv file and slip data into training and test data
        
        ::
        
            import pandas as pd
            df = pd.read_csv('Data_for_UCI_named.csv', header=0)
            df['stabf'] = df['stabf'].map({'unstable': 0, 'stable': 1})
            Y = df['stabf'].values
            # sometimes it's needed to reshape data
            X = df.drop(['stabf'], axis=1).values
        
            X_train = X[:9000]
            Y_train = Y[:9000]
            X_test = X[9000:]
            Y_test = Y[9000:]
        
        Let's use our library
        
        ::
        
            # call the LogisticRegression class
            from nista_learn.regressions import LinearRegression, LogisticRegression
        
            log_reg = LogisticRegression()
            # fitting data
            log_reg.fit(X_train, Y_train, iterations=200000, learning_rate=0.25, show=True)
            # predict a small dataset
            y_pred = log_reg.predict(X_test[20:28])
            print('--- small value ---')
            print(Y_test[20:28])
            print('--- predicted data ---')
            print(y_pred)
            # plotting the cost function
            log_reg.plot_cost()
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
