18CSL76 Implement the non-parametric Locally Weighted Regression algorithm

 Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs

from numpy import * import operator from os import listdir import matplotlib import matplotlib.pyplot as plt import pandas as pd import numpy as np1 import numpy.linalg as np from scipy.stats.stats import pearsonr def kernel(point,xmat, k): m,n = np1.shape(xmat) weights = np1.mat(np1.eye((m))) for j in range(m): diff = point - X[j] weights[j,j] = np1.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 = np1.shape(xmat) ypred = np1.zeros(m) for i in range(m): ypred[i] = xmat[i]*localWeight(xmat[i],xmat,ymat,k) return ypred # load data points data = pd.read_csv('Non-Parametric.csv') bill = np1.array(data.total_bill) tip = np1.array(data.tip) #preparing and add 1 in bill mbill = np1.mat(bill) mtip = np1.mat(tip) m= np1.shape(mbill)[1] one = np1.mat(np1.ones(m)) X= np1.hstack((one.T,mbill.T)) #set k here ypred = localWeightRegression(X,mtip,2) SortIndex = X[:,1].argsort(0) 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();