Ridge.py

## author: Mingyu Liu
## author: Shi He

import numpy as np
import string
from Dataset import *


def main():
    d = Dataset("rec.sport.hockey.txt", "rec.sport.baseball.txt", cutoff=1000)
    (Xtrain, Ytrain, Xtest, Ytest) = d.getTrainAndTestSets(0.8, seed=100)

    lam = 100
    print lam
    w = trainRidge(Xtrain, Ytrain, lam)

    trainError = computeError(Xtrain, Ytrain, w)
    print 'Train error rate is ' + str(trainError)
    testError = computeError(Xtest, Ytest, w)
    print 'Test error rate is ' + str(testError)

def trainRidge(Xtrain, Ytrain, lam):
    Xmatrix = np.asmatrix(Xtrain)
    Ymatrix = np.asmatrix(Ytrain)
    return np.linalg.inv(Xmatrix.T * Xmatrix + lam * np.eye(Xmatrix.shape[1])) * Xmatrix.T * Ymatrix

def computeError(Xtest, Ytest, w):
    correctCount = 0
    incorrectCount = 0
    for testIndex in range(Ytest.size):
        xtest = Xtest[testIndex]
        xtestw = xtest * w

##      if sign(xtestw) > 0, expected = 1; if sign(xtestw) <= 0, expected = -1        
        expected = -1
        if xtestw > 0:
            expected = 1
            
        if expected == Ytest[testIndex]:
            correctCount += 1
        else:
            incorrectCount += 1
    return incorrectCount * 1.0 / (correctCount + incorrectCount)
                
if __name__ == "__main__":
    main()