normalization and prediction

normalize_and_predict.py

# returns the dependent variable (y axis) value which the model assigns to a certain independent variable (x axis) value
def predict_output(feature_matrix, coefficients):
    ''' 
    inputs: 
        * feature_matrix: two-dimensions array of the data points, where each columns is a feature and a row a point
        * coefficients: one-dimension array of estimated feature coefficients
        
    output: 
        * one-dimension array of predictions
    '''
    predictions = np.dot(feature_matrix, coefficients)
    return predictions

 # normalize features and returns predictions for the normalized features
def normalize_and_predict(feature_values, train_mu, train_sigma, coefficients):
    X = [feature_values]
    H = pd.DataFrame({'X': X})
    # for a single point:
    n = 1
    
    feature_to_predict = np.zeros(n*2)
    feature_to_predict.shape = (n, 2)
    feature_to_predict[:,0] = 1
    feature_to_predict[:,1] = H['X']
    
    #normalize test feature with the same values as in the training set:
    feature_to_predict = (feature_to_predict - train_mu) / train_sigma
    
    return predict_output(feature_to_predict, coefficients)