LWR-Algo

import matplotlib.pyplot as plt    
import pandas as pd    
import numpy as np       
def kernel(point,xmat, k):        
    m,n = np.shape(xmat)        
    weights = np.mat(np.eye((m))) # eye - identity matrix        
    for j in range(m):            
        diff = point - X[j]            
        weights[j,j] = np.exp(diff*diff.T/(-2.0*k**2))         
    return weights 
def localWeight(point,xmat,ymat,k):        
     wei = kernel(point,xmat,k)        
     W = (X.T*(wei*X)).I*(X.T*(wei*ymat.T))        
     return W
def localWeightRegression(xmat,ymat,k):        
    m,n = np.shape(xmat)        
    ypred = np.zeros(m)        
    for i in range(m):            
        ypred[i] = xmat[i]*localWeight(xmat[i],xmat,ymat,k)        
    return ypred 
def graphPlot(X,ypred):            
    sortindex = X[:,1].argsort(0) #argsort - index of the smallest         
    xsort = X[sortindex][:,0]              
    fig = plt.figure()        
    ax = fig.add_subplot(1,1,1)        
    ax.scatter(bill,tip, color='green')        
    ax.plot(xsort[:,1],ypred[sortindex], color = 'red', linewidth=5)        
    plt.xlabel('Total bill')        
    plt.ylabel('Tip')        
    plt.show(); 
 # load data points    
data = pd.read_csv('P10_data10_tips.csv')    
bill = np.array(data.total_bill) # We use only Bill amount and Tips data    
tip  = np.array(data.tip)        
mbill = np.mat(bill) # .mat will convert nd array is converted in 2D array    
mtip = np.mat(tip)    
m= np.shape(mbill)[1]    
one = np.mat(np.ones(m))    
X = np.hstack((one.T,mbill.T)) # 244 rows, 2 cols        
ypred = localWeightRegression(X,mtip,2) # increase k to get smooth curves    
graphPlot(X,ypred)