!pip install jovian --upgrade --quiet

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('/content/tips_10.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

# increase k to get smooth curves
ypred = localWeightRegression(X,mtip,3) 
graphPlot(X,ypred)

jovian.commit()