import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
tou = 1
data=pd.read_csv("tips.csv")
X_train = np.array(data.total_bill)
print(X_train)
X_train = X_train[:,np.newaxis]
print(len(X_train))
y_train = np.array(data.tip)

X_test = np.array([i/10 for i in range(500)])
X_test = X_test[:,np.newaxis]
y_test = []
count = 0
for r in range(len(X_test)):
    wts = np.exp(-np.sum((X_train - X_test[r]) ** 2, axis=1)/(2 * tou ** 2))
    W = np.diag(wts)
    factor1 = np.linalg.inv(X_train.T.dot(W).dot(X_train))
    parameter = factor1.dot(X_train.T).dot(W).dot(y_train)
    prediction = X_test[r].dot(parameter)
    y_test.append(prediction)
    count +=1
   
print(len(y_test))
y_test = np.array(y_test)
plt.plot(X_train.squeeze(), y_train,'o')

plt.plot(X_test.squeeze(), y_test,'o')
plt.show()