def fit_polynomial(X, Y, M, out_png=None):
'''Problem 2.1'''
ndata = len(X)
nparams = M + 1
Y = np.reshape(Y, (ndata, 1))
phi = gradient_descent.polynomial_design_matrix(X, M)
weights = gradient_descent.analytic_least_squares(phi, Y)
if out_png:
plt.figure(1, figsize=(4, 4))
plt.clf()
plt.plot(X, np.array(Y), 'o', color='blue', label='data')
xp = np.linspace(0, 1, 100)
y_model = np.cos(np.pi * xp) + np.cos(2 * np.pi * xp)
plt.plot(xp, y_model, color='orange', label='true model')
y_regress = np.dot(gradient_descent.polynomial_design_matrix(xp, M),
weights.reshape((nparams, 1)))
plt.plot(xp, y_regress, color='red', label='fitted model')
SSE = gradient_descent.least_squares_objective(
weights, gradient_descent.polynomial_design_matrix(X, M),
Y.reshape((ndata, 1)))
plt.xlabel('x')
plt.ylabel('y')
plt.legend(loc='best')
plt.title('M = {}, SSE = {:.2f}'.format(M, SSE))
plt.tight_layout()
plt.savefig(out_png)
return weights
def R_squared(weights, X, Y, M):
A = np.empty((len(X), M + 1))
for i in range(M + 1):
A[:, i] = X**i
TSS = np.sum((Y - np.mean(Y))**2)
RSS = gradient_descent.least_squares_objective(weights, A, Y)
print TSS, RSS
return (TSS - RSS) / TSS
def main():
X, Y = getData(False)
ndata = len(X)
for M in (0,1,2,3,4,6,8,10):
#print 'M=%i' % M
weights, _ = fit_polynomial(X,Y,M,'sgd_plots/regress_m_%i.png' % M)
#print weights
print 'SSE = {}'.format(gd.least_squares_objective(weights, gd.polynomial_design_matrix(X, M), Y.reshape((ndata,1))))
print 'SSE derivative = {}'.format(gd.least_squares_gradient(weights, gd.polynomial_design_matrix(X, M), Y.reshape((ndata,1))))
print 'M=%i & ' % M, 'w = ', [round(w,3) for w in weights[:,0]], '\\\\'
def main():
X, Y = loadFittingDataP1.getData()
Y = Y.reshape((100, 1))
w_opt = analytic_least_squares(X, Y)
#Problem 1.3.abc, running batch and stochastic gd on a variety of start points, saving num_iters, weights, calcing
#difference, plot both num iters and the difference
#----------------------------------------------------------------------------------------
start_points = [
np.zeros((10, 1)),
np.zeros((10, 1)) + 10,
np.zeros((10, 1)) - 10,
np.zeros((10, 1)) + 100,
np.zeros((10, 1)) - 100,
np.zeros((10, 1)) + 1000,
np.zeros((10, 1)) - 1000, 20 * np.random.random_sample((10, 1)) - 10
]
batch_iterations = []
batch_weights = []
batch_diff = []
batch_f = []
stochastic_iterations = []
stochastic_weights = []
stochastic_diff = []
stochastic_f = []
fig, ax = plt.subplots()
#labels = map(lambda x: str([round(i,2) for i in x]), points)
start_points = [np.zeros((10, 1))]
for point in start_points:
#batch gd
w_batch, d_batch, f_batch, iters_batch = gradient_descent.run_gradient_descent(
func=lambda theta: least_squares_objective(theta, X, Y),
deriv=lambda theta: least_squares_gradient(theta, X, Y),
x0=point,
h=10.**(-6),
tol=0.1)
batch_weights.append(w_batch[-1])
batch_iterations.append(
iters_batch *
100) #since every batch iteration is 1 round of the whole dataset
batch_diff.append(
np.linalg.norm(w_opt - w_batch[-1]) / np.linalg.norm(w_opt))
batch_f.append(f_batch)
plt.plot(range(0, iters_batch + 1),
map(np.log, f_batch[:-1]),
color="k")
#stochastic gd
w_sgd, iters_sgd, err_sgd, f_sgd = gradient_descent.stochastic_gradient_descent(
func=least_squares_objective,
deriv=least_squares_gradient,
X=X,
Y=Y,
weights0=point,
tau=10.**8,
k=.75,
tol=.1,
return_f=True)
stochastic_weights.append(w_sgd)
stochastic_iterations.append(iters_sgd)
stochastic_diff.append(
np.linalg.norm(w_opt - w_sgd) / np.linalg.norm(w_opt))
stochastic_f.append(stochastic_f)
print iters_sgd / 100
print len(f_sgd)
plt.plot(range(0, iters_sgd / 100), map(np.log, f_sgd), color="b")
ax.set_xlabel('Iterations')
ax.set_ylabel('Objective function', color='k')
plt.title("Least Squares Objective stuff")
#plt.legend(labels,shadow=True,fancybox=True)
plt.show()
print "batch iterations"
print batch_iterations
print "batch diff"
print batch_diff
print "stochastic iterations"
print stochastic_iterations
print "stochastic_diff"
print stochastic_diff
