def main():
X, Y = hw1.bishopCurveData()
M = 1
function = lambda x, y: math.pow(x,y)
for i in range(-1, 2, 1):
for j in range(-1, 2, 1):
print "Theta_1 = {}, theta_2: {}".format(i, j)
print "approx: {}".format(approxGradient(SSE, X, Y, M, function, 1)(np.array([i, j])))
print "analytical: {}".format(SSEgrad(X, Y, M, np.array([[i],[j]]), function))
def test_bishop_curve():
X, Y = hw1.bishopCurveData()
# X, Y = hw1.regressTrainData()
# X, Y = hw1.regressTestData()
# X, Y = hw1.regressValidateData()
#print "X: {}".format(X)
#print "Y: {}".format(Y)
'''
for M in [0, 1, 3, 9]:
for lambd in [math.pow(2, x) for x in range(-10,0,2)]:
theta = ridgeRegression(Y, _phi(X, M, basis_function_poly), lambd)
sse = SSE(X, Y, M, theta, basis_function_poly)
print "{}, {}, {}".format(M, lambd, sse[0][0])
regressionPlot(X,Y,M, theta, lambd, basis_function_poly)
'''
theta = ridgeRegression(Y, _phi(X, 9, basis_function_poly), math.pow(2, -10))
regressionPlot(X,Y, 9, theta, math.pow(2, -10), basis_function_poly)
theta = ridgeRegression(Y, _phi(X, 9, basis_function_poly), math.pow(2, -2))
regressionPlot(X,Y, 9, theta, math.pow(2, -2), basis_function_poly)
import numpy as np
import scipy as sp
from scipy.optimize import minimize
import pylab as pl
from scipy.optimize import fmin_bfgs
from gradientDescent import gradMinimize
from regression import _phi, SSE, SSEgrad, max_likelihood_weight
import homework1 as hw1
import math
X, Y = hw1.bishopCurveData()
basis_function = lambda x, y: math.pow(x,y)
def gradMinimize(threshold, guess, step, objectiveFcn, gradientFcn, M):
difference = threshold;
previousGuess = guess;
objPrev = objectiveFcn(X, Y, M, previousGuess, basis_function);
count = 0;
while (difference >= threshold):
nextGuess = previousGuess - step*gradientFcn(X, Y, M, previousGuess, basis_function)
objNext = objectiveFcn(X, Y, M, nextGuess, basis_function)
difference = objPrev - objNext
#print difference
previousGuess = nextGuess
objPrev = objNext
count += 1;
# return "gradMinimize soln: {}, nfev: {}, njev: {}".format(previousGuess, count+1, count)
#sse = np.dot((previousGuess - quadSoln).T, (previousGuess - quadSoln))
#return "soln: {}, sse: {}, step: {}, guess: {}, threshold: {}".format(previousGuess, sse, step, guess, threshold)
return previousGuess, count
#!/usr/bin/env python
__author__ = 'Shaun Rong'
__version__ = '0.1'
__maintainer__ = 'Shaun Rong'
__email__ = '*****@*****.**'
from homework1 import regressionPlot, bishopCurveData
if __name__ == '__main__':
X, Y = bishopCurveData()
regressionPlot(X, Y, 0)