def __init__(self, imgs, lbls, lbda_0=None, proba_0=None):
self.imgs = binarize_imgs(imgs)
self.lbls = lbls
self.n_classes = 10
self.n_imgs = self.lbls.shape[0]
self.img_x = 28
self.img_y = 28
self.rng = UnivariateRNG()
if proba_0 is not None:
assert (proba_0.shape == (self.img_x, self.img_y, self.n_classes))
else:
proba_0 = np.array([
self.rng.rand_uniform(0.25, 0.75)
for i in range(self.img_x * self.img_y * self.n_classes)
]).reshape((self.img_x, self.img_y, self.n_classes))
proba_0[:, :] = proba_0[:, :] / np.sum(proba_0, axis=(0, 1))
# print(np.sum(proba_0, axis=(0,1)))
# proba_0 = np.ones((self.img_x, self.img_y, self.n_classes))
# proba_0 *= 0.4
self.proba = proba_0
if lbda_0 is not None:
assert (lbda_0.shape[0] == self.n_classes)
else:
lbda_0 = [1. / self.n_classes for i in range(self.n_classes)]
# lbda_0 = np.random.random(self.n_classes)
self.lbda = lbda_0
def __init__(self, initial_prior_precision, basis, noise_amplitude, model_parameters): assert(isinstance(initial_prior_precision, np.ndarray)) self.rng = UnivariateRNG() self.n = basis self.initial_precision = initial_prior_precision self.a = np.max(initial_prior_precision) * 1e3 / noise_amplitude self.y_mean = 0 self.step = 0 self.m_prior = 0 self.b_prior = initial_prior_precision self.Y_c = [] self.X_c = [] self.W_mean = np.zeros((n, 1)) self.W_var = np.zeros((3, 3)) self.W_s = np.zeros((3, 3)) self.W_mu = np.zeros(n) self.Y_s = 0 self.Y_mu = 0 self.linear_model = PolyBasisLinearModelRNG(basis, noise_amplitude, model_parameters)
def __init__(self, basis, noise_amplitude, model_parameters): assert(isinstance(model_parameters, np.ndarray)) assert(len(model_parameters.shape) == 1 and model_parameters.shape[0] >= basis) self.n = basis self.a = noise_amplitude self.w = model_parameters self.r = UnivariateRNG()
def __init__(self, mean, var):
self.mean = mean
self.var = var
self.mean_est = 0
self.var_est = 0
self.n = 0
self.M2 = 0
self.r = UnivariateRNG()
def __init__(self, n, mx1, vx1, mx2, vx2, my1, vy1, my2, vy2, basis=3, learning_rate=1e-2): self.n = n self.rng = UnivariateRNG() self.basis = basis self.params = basis * 2 self.lr = learning_rate rand_points = lambda nb, m, v : \ np.array([self.rng.rand_normal(m, v) for i in range(nb)]) self.x1 = rand_points(n, mx1, vx1) self.y1 = rand_points(n, my1, vy1) self.x2 = rand_points(n, mx2, vx2) self.y2 = rand_points(n, my2, vy2) self.phi = self.make_poly_design_matrix((self.x1, self.y1), (self.x2, self.y2)) self.w = np.array([self.rng.rand_normal(0., 0.1) for i in range(self.params)]) self.d = np.array([0. for i in range(n)] + [1. for i in range(n)]) shuf = np.append(self.phi, np.vstack(self.d), axis=1) np.random.shuffle(shuf) self.phi = shuf[:, :self.params] self.y = shuf[:, self.params]
def __init__(self, imgs, lbls):
self.imgs = imgs
self.lbls = lbls
self.rng = UnivariateRNG()
self.n_classes = int(np.amax(lbls) - np.amin(lbls)) + 1
self.n_imgs = self.imgs.shape[2]
self.img_l = self.imgs.shape[0]
self.mu = np.reshape([
self.rng.rand_uniform(0.25, 0.75)
for i in range(self.img_l**2 * self.n_classes)
], (self.img_l, self.img_l, self.n_classes))
self.mu[:, :] /= np.sum(self.mu, axis=(0, 1))
for i in range(10):
print("min %f, max %f, mean %f" %
(np.min(self.mu[:, :, i]), np.max(
self.mu[:, :, i]), np.mean(self.mu[:, :, i])))
plt.imshow(self.mu[:, :, i])
plt.colorbar()
plt.show()
self.pi = np.array([1 / self.n_classes for i in range(self.n_classes)])
for i in range(x.shape[0]): s = 0 for j in range(w.shape[0]): s += w[j] * x[i] ** j y.append(s) return np.array(y) if __name__ == "__main__": n = 4 nsteps = 750 param_range = 10 plot_interval = 1 blocking = False rng = UnivariateRNG() initial_prior_precision = 1e12 * np.eye(n) noise_amplitude = 1e1 model_parameters = np.array([rng.rand_uniform(-param_range, param_range) for i in range(n)]) b = BayesianLinearRegression(initial_prior_precision, n, noise_amplitude, model_parameters) wmu_acc = [] pred_acc = [] mean_error = [] mse = [] plotting = True