linearRegresion.py 文件源码

def linear_model_manual(prediction_value):
    data = pd.read_csv('E://Spyder/LinearRegression/data/data.csv')
    X_tem = []
    Y_tem = []
    for X_data ,Y_data in zip(data['x'],data['y']):
        X_tem.append(int(X_data))
        Y_tem.append(float(Y_data))
    X_parameters = np.array(X_tem)
    Y_parameters = np.array(Y_tem)
    xy = X_parameters*Y_parameters
    xy_avg = xy.mean()
    x_avg = X_parameters.mean()
    y_avg = Y_parameters.mean()
    x_square = X_parameters*X_parameters
    x_square_avg = x_square.mean()
    predictions = {}
    #Method of least squares
    predictions['coefficient'] = (xy_avg - x_avg*y_avg) / (x_square_avg - x_avg*x_avg)
    predictions['intercept'] = y_avg - predictions['coefficient']*x_avg
    #prediction_result
    predictions['predictions_result'] = predictions['intercept'] + predictions['coefficient']*prediction_value    
    return predictions