def test_grad_SEE(self): data_file = 'curvefitting.txt' for order in xrange(10): [X, Y] = hw1.getData(data_file) phi = hw1.designMatrix(X, order) weights = hw1.regressionFit(X, Y, phi) delta = 1 analytic = hw1.computeSEEGrad(X,Y, weights, order).flatten() approx = gd.gradient_approx_SEE(X, Y, weights, order, delta).flatten() for i in xrange(order): self.assertAlmostEqual(analytic[i], approx[i]) delta2 = .1 analytic2 = hw1.computeSEEGrad(X,Y, weights, order).flatten() approx2 = gd.gradient_approx_SEE(X, Y, weights, order, delta2).flatten() for i in xrange(order): self.assertAlmostEqual(analytic2[i], approx2[i]) delta3 = .01 analytic3 = hw1.computeSEEGrad(X,Y, weights, order).flatten() approx3 = gd.gradient_approx_SEE(X, Y, weights, order, delta3).flatten() for i in xrange(order): self.assertAlmostEqual(analytic3[i], approx3[i]) delta4 = .001 analytic4 = hw1.computeSEEGrad(X,Y, weights, order).flatten() approx4 = gd.gradient_approx_SEE(X, Y, weights, order, delta4).flatten() for i in xrange(order): self.assertAlmostEqual(analytic4[i], approx4[i])
def test_compare_derivative(self):
""" Verify the gradient using the numerical derivative code """
""" Part of question 2.2 """
order = 4
[X,Y] = hw1.getData('curvefitting.txt')
Phi_matrix=hw1.designMatrix(X,order)
#print Phi_matrix.shape
weight_vector=hw1.regressionFit(X,Y,Phi_matrix)
SSEG=hw1.computeSEEGrad(X,Y,weight_vector,order)
approx = gd.gradient_approx_SEE(X, Y, weight_vector, order, 1e-10)
true_deriv = hw1.computeSEEGrad(X,Y, weight_vector, order)
print "COMPARE approx", approx
print "TRUE ", true_deriv