def _cumulative_hazard(self, params, T, *Xs):
alpha_params = params[self._LOOKUP_SLICE["alpha_"]]
alpha_ = np.exp(np.dot(Xs[0], alpha_params))
beta_params = params[self._LOOKUP_SLICE["beta_"]]
beta_ = np.exp(np.dot(Xs[1], beta_params))
return np.log1p((T / alpha_) ** beta_)
def _get_responsibilities(self, pi, g, beta, mu_ivp, alpha):
""" Gets the posterior responsibilities for each comp. of the mixture.
"""
probs = [[]]*len(self.N_data)
for i, ifx in enumerate(self._ifix):
zM = self._forward(g, beta, mu_ivp[i], ifx)
for q, yq in enumerate(self.Y_train_):
logprob = norm.logpdf(
yq, zM[self.data_inds[q], :, q], scale=1/np.sqrt(alpha))
# sum over the dimension component
logprob = logprob.sum(-1)
if probs[q] == []:
probs[q] = logprob
else:
probs[q] = np.column_stack((probs[q], logprob))
probs = [lp - pi for lp in probs]
# subtract the maxmium for exponential normalize
probs = [p - np.atleast_1d(p.max(axis=-1))[:, None]
for p in probs]
probs = [np.exp(p) / np.exp(p).sum(-1)[:, None] for p in probs]
return probs
def setUp(self):
self.X = None
self.cost = lambda X: np.exp(np.sum(X**2))
n1 = self.n1 = 3
n2 = self.n2 = 4
n3 = self.n3 = 5
Y = self.Y = rnd.randn(n1, n2, n3)
A = self.A = rnd.randn(n1, n2, n3)
# Calculate correct cost and grad...
self.correct_cost = np.exp(np.sum(Y ** 2))
self.correct_grad = correct_grad = 2 * Y * np.exp(np.sum(Y ** 2))
# ... and hess
# First form hessian tensor H (6th order)
Y1 = Y.reshape(n1, n2, n3, 1, 1, 1)
Y2 = Y.reshape(1, 1, 1, n1, n2, n3)
# Create an n1 x n2 x n3 x n1 x n2 x n3 diagonal tensor
diag = np.eye(n1 * n2 * n3).reshape(n1, n2, n3, n1, n2, n3)
H = np.exp(np.sum(Y ** 2)) * (4 * Y1 * Y2 + 2 * diag)
# Then 'right multiply' H by A
Atensor = A.reshape(1, 1, 1, n1, n2, n3)
self.correct_hess = np.sum(H * Atensor, axis=(3, 4, 5))
self.backend = AutogradBackend()
def ackley(x):
a, b, c = 20.0, -0.2, 2.0*np.pi
len_recip = 1.0/len(x)
sum_sqrs = sum(x*x)
sum_cos = sum(np.cos(c*x))
return (-a * np.exp(b*np.sqrt(len_recip*sum_sqrs)) -
np.exp(len_recip*sum_cos) + a + np.e)
def setUp(self):
self.X = None
self.cost = lambda X: np.exp(np.sum(X**2))
m = self.m = 10
n = self.n = 15
Y = self.Y = rnd.randn(m, n)
A = self.A = rnd.randn(m, n)
# Calculate correct cost and grad...
self.correct_cost = np.exp(np.sum(Y ** 2))
self.correct_grad = correct_grad = 2 * Y * np.exp(np.sum(Y ** 2))
# ... and hess
# First form hessian tensor H (4th order)
Y1 = Y.reshape(m, n, 1, 1)
Y2 = Y.reshape(1, 1, m, n)
# Create an m x n x m x n array with diag[i,j,k,l] == 1 iff
# (i == k and j == l), this is a 'diagonal' tensor.
diag = np.eye(m * n).reshape(m, n, m, n)
H = np.exp(np.sum(Y ** 2)) * (4 * Y1 * Y2 + 2 * diag)
# Then 'right multiply' H by A
Atensor = A.reshape(1, 1, m, n)
self.correct_hess = np.sum(H * Atensor, axis=(2, 3))
self.backend = AutogradBackend()
def setUp(self):
self.X = None
self.cost = lambda X: np.exp(np.sum(X**2))
n = self.n = 15
Y = self.Y = rnd.randn(1, n)
A = self.A = rnd.randn(1, n)
# Calculate correct cost and grad...
self.correct_cost = np.exp(np.sum(Y ** 2))
self.correct_grad = correct_grad = 2 * Y * np.exp(np.sum(Y ** 2))
# ... and hess
# First form hessian matrix H
# Convert Y and A into matrices (row vectors)
Ymat = np.matrix(Y)
Amat = np.matrix(A)
diag = np.eye(n)
H = np.exp(np.sum(Y ** 2)) * (4 * Ymat.T.dot(Ymat) + 2 * diag)
# Then 'left multiply' H by A
self.correct_hess = np.array(Amat.dot(H))
self.backend = AutogradBackend()
def callback(params, t, g):
# log_weights = params[:10] - logsumexp(params[:10])
print("Iteration {} lower bound {}".format(t, -objective(params, t)))
# print (np.exp(log_weights))
# mean = params[0]
# log_std = params[1]
# norm_flow_params = params[2]
# print (len(params[2][0][0]))
# print ('u', params[2][0])
print ('u0', params[2][0][0])
print ('u1', params[2][1][0])
print ('w0', params[2][0][1])
print ('w1', params[2][1][1])
print ('b0', params[2][0][2])
print ('b1', params[2][1][2])
# print ('b', params[2][2])
plt.cla()
target_distribution = lambda x: np.exp(log_density(x))
var_distribution = lambda x: np.exp(variational_log_density(params, x))
plot_isocontours(ax, target_distribution)
plot_isocontours(ax, var_distribution, cmap=plt.cm.bone)
ax.set_autoscale_on(False)
# rs = npr.RandomState(0)
# samples = variational_sampler(params, num_plotting_samples, rs)
# plt.plot(samples[:, 0], samples[:, 1], 'x')
plt.draw()
plt.pause(1.0/30.0)
def scalar_log_lik(theta_1, theta_2, x):
arg = (x - theta_1)
lik1 = 1.0 / np.sqrt(2 * SIGMA_x ** 2 * np.pi) * np.exp(- np.dot(arg, arg) / (2 * SIGMA_x ** 2))
arg = (x - theta_1 - theta_2)
lik2 = 1.0 / np.sqrt(2 * SIGMA_x ** 2 * np.pi) * np.exp(- np.dot(arg, arg) / (2 * SIGMA_x ** 2))
return np.log(0.5 * lik1 + 0.5 * lik2)
def test_multi_scalar(self):
"""
Tests functions with multiple scalar output
"""
def f1(x):
# two scalar input
return x**3, np.exp(3*x)
df = jacobian(f1)(0.5)
self.assertAlmostEqual(3*0.5**2, df[0])
self.assertAlmostEqual(3*np.exp(3*0.5), df[1])
def f2(params):
# one list, one numpy array input
x,y = params[0]
A = params[1]
return np.sum(A**2) + np.cos(x) + np.exp(0.5*y)
df = jacobian(f2)
A = np.array([[1.0, 2.0],[3.0, 4.0]])
params = [[0.5, np.pi], A]
diff = df(params)
self.assertAlmostEqual(diff[0][0], -np.sin(0.5))
self.assertAlmostEqual(diff[0][1], 0.5*np.exp(0.5*np.pi))
self.assertTrue(np.linalg.norm(2*A - diff[1]) < 1e-10)
def log_density(x):
x_, y_ = x[:, 0], x[:, 1]
sigma_density = norm.logpdf(y_, 0, 1.35)
mu_density = norm.logpdf(x_, -0.5, np.exp(y_))
sigma_density2 = norm.logpdf(y_, 0.1, 1.35)
mu_density2 = norm.logpdf(x_, 0.5, np.exp(y_))
return np.logaddexp(sigma_density + mu_density, sigma_density2 + mu_density2)
def normalizing_flows(z_0, norm_flow_params):
'''
z_0: [n_samples, D]
u: [D,1]
w: [D,1]
b: [1]
'''
current_z = z_0
all_zs = []
all_zs.append(z_0)
for params_k in norm_flow_params:
u = params_k[0]
w = params_k[1]
b = params_k[2]
# Appendix equations
m_x = -1. + np.log(1.+np.exp(np.dot(w.T,u)))
u_k = u + (-1. + np.log(1.+np.exp(np.dot(w.T,u))) - np.dot(w.T,u)) * (w/np.linalg.norm(w))
# u_k = u
# [D,1]
term1 = np.tanh(np.dot(current_z,w)+b)
# [n_samples, D]
term1 = np.dot(term1,u_k.T)
# [n_samples, D]
current_z = current_z + term1
all_zs.append(current_z)
return current_z, all_zs
def predict(self, x): if self.prob_func_ == "sigmoid": prob = (1.0 / (1.0 + np.exp(-np.dot(x, self.coef_) - self.intercept_)))[:,np.newaxis] prob = np.concatenate((1.0-prob, prob), axis=1) else: # self.prob_func_ == "softmax" prob = np.exp(np.dot(x, self.coef_.T) + self.intercept_) prob /= np.sum(prob, axis=1)[:,np.newaxis] return np.array([self.classes_[i] for i in np.argmax(prob, axis=1)])
def gradient_check():
params = np.array([2,2])
h = np.array([1e-5,0])
print (np.exp(log_variational(params+h,0.5))-np.exp(log_variational(params,0.5)))/h[0]
h = np.array([0,1e-5])
print (np.exp(log_variational(params+h,0.5))-np.exp(log_variational(params,0.5)))/h[1]
print gradient_log_variational(params,0.5,0)
print gradient_log_variational(params,0.5,1)
def log_density(x, t):
mu, log_sigma = x[:, 0], x[:, 1]
sigma_density = norm.logpdf(log_sigma, 0, 1.35)
mu_density = norm.logpdf(mu, -0.5, np.exp(log_sigma))
sigma_density2 = norm.logpdf(log_sigma, 0.1, 1.35)
mu_density2 = norm.logpdf(mu, 0.5, np.exp(log_sigma))
return np.logaddexp(sigma_density + mu_density,
sigma_density2 + mu_density2)
def softmax(xs): """ Initialization of weight tensors. Used when outputs must sum to 1. """ n = np.exp(xs) d = np.sum(np.exp(xs)) return n/d
def callback(params, t, g):
# log_weights = params[:10] - logsumexp(params[:10])
print("Iteration {} lower bound {}".format(t, -objective(params, t)))
# print (np.exp(log_weights))
mean = params[0]
log_std = params[1]
print ('mean', mean)
print ('std', np.exp(log_std))
# print ('u0', params[2][0][0])
# print ('u1', params[2][1][0])
# print ('w0', params[2][0][1])
# print ('w1', params[2][1][1])
# print ('b0', params[2][0][2])
# print ('b1', params[2][1][2])
# x_inverse =
plt.cla()
target_distribution = lambda x: np.exp(log_density(x))
var_distribution = lambda x: np.exp(variational_log_density(params, x))
plot_isocontours(ax, target_distribution)
plot_isocontours(ax, var_distribution, cmap=plt.cm.bone)
ax.set_autoscale_on(False)
#PLot the z0 density
var_distribution0 = lambda x: np.exp(diag_gaussian_log_density(x, mean, log_std))
plot_isocontours(ax, var_distribution0)
for transform in params[2]:
xlimits=[-6, 6]
w = transform[1]
b = transform[2]
x = np.linspace(*xlimits, num=101)
plt.plot(x, (-w[0]*x - b)/w[1], '-')
u = transform[0]
plt.plot(x, (-u[0]*x)/u[1], '-')
#PLot variational samples
samples = variational_sampler(params)
plt.plot(samples[:, 0], samples[:, 1], 'x')
# #Plot q0 variational samples
# rs = npr.RandomState(0)
# samples = sample_diag_gaussian(mean, log_std, n_samples, rs)
# plt.plot(samples[:, 0], samples[:, 1], 'x')
plt.draw()
plt.pause(1.0/30.0)
def sample(self, n_samples=2000, observed_states=None, random_state=None):
"""Generate random samples from the self.
Parameters
----------
n : int
Number of samples to generate.
observed_states : array
If provided, states are not sampled.
random_state: RandomState or an int seed
A random number generator instance. If None is given, the
object's random_state is used
Returns
-------
samples : array_like, length (``n_samples``)
List of samples
states : array_like, shape (``n_samples``)
List of hidden states (accounting for tied states by giving
them the same index)
"""
if random_state is None:
random_state = self.random_state
random_state = check_random_state(random_state)
samples = np.zeros(n_samples)
states = np.zeros(n_samples)
if observed_states is None:
startprob_pdf = np.exp(np.copy(self._log_startprob))
startdist = stats.rv_discrete(name='custm',
values=(np.arange(startprob_pdf.shape[0]),
startprob_pdf),
seed=random_state)
states[0] = startdist.rvs(size=1)[0]
transmat_pdf = np.exp(np.copy(self._log_transmat))
transmat_cdf = np.cumsum(transmat_pdf, 1)
nrand = random_state.rand(n_samples)
for idx in range(1,n_samples):
newstate = (transmat_cdf[states[idx-1]] > nrand[idx-1]).argmax()
states[idx] = newstate
else:
states = observed_states
mu = np.copy(self._mu_)
precision = np.copy(self._precision_)
for idx in range(n_samples):
mean_ = self._mu_[states[idx]]
var_ = np.sqrt(1/precision[states[idx]])
samples[idx] = norm.rvs(loc=mean_, scale=var_, size=1,
random_state=random_state)
states = self._process_sequence(states)
return samples, states
def logloss(ys, ys_hat, ws=None):
#print 'ws',ws.shape, 'ys',ys.shape, 'xs',xs.shape, 'B',B.shape
if ws is None:
return np.sum(np.log(1 + np.exp(-ys * ys_hat))) / float(len(ys)) #+ (0.5 * reg * np.dot(B, B)) #/ float(len(ys))
else:
try:
return np.sum(ws * np.log(1 + np.exp(-ys * ys_hat))) / float(len(ys)) #+ (0.5 * reg * np.dot(B, B)) #/ float(len(ys))
except:
pdb.set_trace()
def _log_1m_sf(self, params, T, *Xs):
alpha_params = params[self._LOOKUP_SLICE["alpha_"]]
log_alpha_ = np.dot(Xs[0], alpha_params)
alpha_ = np.exp(log_alpha_)
beta_params = params[self._LOOKUP_SLICE["beta_"]]
log_beta_ = np.dot(Xs[1], beta_params)
beta_ = np.exp(log_beta_)
return -np.log1p((T / alpha_) ** -beta_)
def devec_ackley(x):
a, b, c = 20.0, -0.2, 2.0*np.pi
len_recip = 1.0/len(x)
sum_sqrs, sum_cos = 0.0, 0.0
for i in x:
sum_cos += np.cos(c*i)
sum_sqrs += i*i
return (-a * np.exp(b*np.sqrt(len_recip*sum_sqrs)) -
np.exp(len_recip*sum_cos) + a + np.e)
def softmax_grads(Ks, beta, i, j):
"""
return the grad of the ith element of weighting w.r.t. j-th element of Ks
"""
if j == i:
num = beta*np.exp(Ks[i]*beta) * (np.sum(np.exp(Ks*beta)) - np.exp(Ks[i]*beta))
else:
num = -beta*np.exp(Ks[i]*beta + Ks[j]*beta)
den1 = np.sum(np.exp(Ks*beta))
return num / (den1 * den1)
def beta_grads(Ks, beta, i): Karr = np.array(Ks) anum = Ks[i] * np.exp(Ks[i] * beta) aden = np.sum(np.exp(beta * Karr)) a = anum / aden bnum = np.exp(Ks[i] * beta) * (np.sum(np.multiply(Karr, np.exp(Karr * beta)))) bden = aden * aden b = bnum / bden return a - b
def _log_hazard(self, params, T, *Xs):
alpha_params = params[self._LOOKUP_SLICE["alpha_"]]
log_alpha_ = np.dot(Xs[0], alpha_params)
alpha_ = np.exp(log_alpha_)
beta_params = params[self._LOOKUP_SLICE["beta_"]]
log_beta_ = np.dot(Xs[1], beta_params)
beta_ = np.exp(log_beta_)
return log_beta_ - log_alpha_ + np.expm1(log_beta_) * (np.log(T) - log_alpha_) - np.log1p((T / alpha_) ** beta_)
def compute_modfeat(self, w):
mod_feat = self.conv_data_fea.copy()
mod_dem = np.ones(mod_feat.shape) ## need to change to accommodate non-binary features
ws = np.array([w[k*self.F:(k+1)*self.F] for k in range(self.K)])
w_tiled = np.array([ws for i in range(mod_feat.shape[0])])
mod_feat = mod_feat * w_tiled
mod_dem = mod_dem * w_tiled
mod_feat = np.sum(np.exp(mod_feat), axis=2)
mod_dem = np.sum(np.exp(mod_dem), axis=2)
return mod_feat / mod_dem
def log_density(x):
'''
x: [n_samples, D]
return: [n_samples]
'''
x_, y_ = x[:, 0], x[:, 1]
sigma_density = norm.logpdf(y_, 0, 1.35)
mu_density = norm.logpdf(x_, -2.2, np.exp(y_))
sigma_density2 = norm.logpdf(y_, 0.1, 1.35)
mu_density2 = norm.logpdf(x_, 2.2, np.exp(y_))
return np.logaddexp(sigma_density + mu_density, sigma_density2 + mu_density2)
def callback(params, t, g):
print("Iteration {} lower bound {}".format(t, -objective(params, t)))
plt.cla()
target_distribution = lambda x : np.exp(log_posterior(x, t))
plot_isocontours(ax, target_distribution)
mean, log_std = unpack_params(params)
variational_contour = lambda x: mvn.pdf(x, mean, np.diag(np.exp(2*log_std)))
plot_isocontours(ax, variational_contour)
plt.draw()
plt.pause(1.0/30.0)
def potassium_dynamics(V, n, t):
# Use resting potential of zero
V_ref = V+60
# Compute the alpha and beta as a function of V
an1 = 0.01*(V_ref+55.) /(1-np.exp(-(V_ref+55.)/10.))
bn1 = 0.125 * np.exp(-(V_ref+65.)/80.)
# Compute the channel state updates
dndt = an1 * (1.0-n) - bn1*n
return dndt
def PyLQR_TrajCtrl_GeneralTest():
#build RBF basis
rbf_basis = np.array([
[-1.0, -1.0],
[-1.0, 1.0],
[1.0, -1.0],
[1.0, 1.0]
])
gamma = 1
T = 100
R = 1e-5
# rbf_funcs = [lambda x, u, t, aux: np.exp(-gamma*np.linalg.norm(x[0:2]-basis)**2) + .01*np.linalg.norm(u)**2 for basis in rbf_basis]
rbf_funcs = [
lambda x, u, t, aux: -np.exp(-gamma*np.linalg.norm(x[0:2]-rbf_basis[0])**2) + R*np.linalg.norm(u)**2,
lambda x, u, t, aux: -np.exp(-gamma*np.linalg.norm(x[0:2]-rbf_basis[1])**2) + R*np.linalg.norm(u)**2,
lambda x, u, t, aux: -np.exp(-gamma*np.linalg.norm(x[0:2]-rbf_basis[2])**2) + R*np.linalg.norm(u)**2,
lambda x, u, t, aux: -np.exp(-gamma*np.linalg.norm(x[0:2]-rbf_basis[3])**2) + R*np.linalg.norm(u)**2
]
weights = np.array([.75, .5, .25, 1.])
weights = weights / (np.sum(weights) + 1e-6)
cost_func = lambda x, u, t, aux: np.sum(weights * np.array([basis_func(x, u, t, aux) for basis_func in rbf_funcs]))
lqr_traj_ctrl = PyLQR_TrajCtrl(use_autograd=True)
lqr_traj_ctrl.build_ilqr_general_solver(cost_func, n_dims=rbf_basis.shape[1], T=T)
n_eval_pnts = 50
coords = np.linspace(-2.5, 2.5, n_eval_pnts)
xv, yv = np.meshgrid(coords, coords)
z = [[cost_func(np.array([xv[i, j], yv[i, j]]), np.zeros(2), None, None) for j in range(yv.shape[1])] for i in range(len(xv))]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hold(True)
ax.contour(xv, yv, z)
n_queries = 5
u_array = np.random.rand(2, T-1).T * 2 - 1
for i in range(n_queries):
#start from a perturbed point
x0 = np.random.rand(2) * 4 - 2
syn_traj = lqr_traj_ctrl.synthesize_trajectory(x0, u_array)
#plot it
ax.plot([x0[0]], [x0[1]], 'k*', markersize=12.0)
ax.plot(syn_traj[:, 0], syn_traj[:, 1], linewidth=3.5)
plt.show()
return
def do_transform(self, item):
if type(item) is not tuple:
item = (item,)
def printer(*args):
if self.__verbosity__ > 0:
print(*args)
printer('Attempting to get {}'.format(item))
A_t, B_t = self.operators
A_bar_t, B_bar_t = self.operators_bar
A_1, A_2, A_3, B = self.__op_cache__.operators
A_1_bar, A_2_bar, A_3_bar, B_bar = self.__op_cache__.operators_bar
exp_kappa_int = np.exp(self.__kappa_int__).reshape((len(self.__kappa_int__), 1))
exp_kappa_bdy = np.exp(self.__kappa_bdy__).reshape((len(self.__kappa_bdy__), 1))
grad_kappa_x = self.__grad_kappa_x__.reshape((len(self.__grad_kappa_x__), 1))
grad_kappa_y = self.__grad_kappa_y__.reshape((len(self.__grad_kappa_y__), 1))
all_things = [()]
printer(exp_kappa_int.shape, exp_kappa_bdy.shape, grad_kappa_x.shape, grad_kappa_y.shape)
# first explode out the objects required
for i in item:
if i == A_t:
all_things = sum([[a + (A_1,), a + (A_2,), a + (A_3,)] for a in all_things], [])
elif i == A_bar_t:
all_things = sum([[a + (A_1_bar,), a + (A_2_bar,), a + (A_3_bar,)] for a in all_things], [])
elif i == B_t:
all_things = [a + (B,) for a in all_things]
elif i == B_bar_t:
all_things = [a + (B_bar,) for a in all_things]
else:
all_things = [a + (i,) for a in all_things]
printer('Mapped {} to {}'.format(item, all_things))
def __ret(x, y, fun_args=None):
return self.calc_result(
x,
y,
fun_args,
all_things,
exp_kappa_int,
exp_kappa_bdy,
grad_kappa_x,
grad_kappa_y
)
return __ret
def f2(params):
# mixed inputs
x = params[0] # float
A = params[1] # 2d array
B = params[2] # 1d array
return np.exp(B**2)/x * A
def sigmoid(z):
return 1/(1 + np.exp(-z))
def log_likelihoods(self, data, input, mask, tag, x):
mus = self.forward(x, input, tag)
etas = np.exp(self.inv_etas)
lls = -0.5 * np.log(2 * np.pi * etas) - 0.5 * (data[:, None, :] - mus)**2 / etas
return np.sum(lls * mask[:, None, :], axis=2)
def self_weighted_logit(x):
return 1.0 / (1.0 + np.exp(-np.dot(x, x)))
def sig(x): return 1 / (1 + np.exp(-1 * x))
n = kerns[0].shape[0]
def sigmoid(x):
return 1. / (1. + np.exp(-x))
def mixture_of_gaussian_em(data,
Q,
init_params=None,
weights=None,
num_iters=100):
"""
Use expectation-maximization (EM) to compute the maximum likelihood
estimate of the parameters of a Gaussian mixture model. The datapoints
x_i are assumed to come from the following model:
z_i ~ Cate(pi)
x_i | z_i ~ N(mu_{z_i}, Sigma_{z_i})
the parameters are {pi_q, mu_q, Sigma_q} for q = 1...Q
Assume:
- data x_i are vectors in R^M
- covariance is diagonal S_q = diag([S_{q1}, .., S_{qm}])
"""
N, M = data.shape ### concatenate all marks; N = # of spikes, M = # of mark dim
if init_params is not None:
pi, mus, inv_sigmas = init_params
assert pi.shape == (Q, )
assert np.all(pi >= 0) and np.allclose(pi.sum(), 1)
assert mus.shape == (M, Q)
assert inv_sigmas.shape == (M, Q)
else:
pi = np.ones(Q) / Q
mus = npr.randn(M, Q)
inv_sigmas = -2 + npr.randn(M, Q)
if weights is not None:
assert weights.shape == (N, ) and np.all(weights >= 0)
else:
weights = np.ones(N)
for itr in range(num_iters):
## E-step:
## output: number of spikes by number of mixture
## attribute spikes to each Q element
sigmas = np.exp(inv_sigmas)
responsibilities = np.zeros((N, Q))
responsibilities += np.log(pi)
for q in range(Q):
responsibilities[:, q] = np.sum(
-0.5 * (data - mus[None, :, q])**2 / sigmas[None, :, q] -
0.5 * np.log(2 * np.pi * sigmas[None, :, q]),
axis=1)
# norm.logpdf(...)
responsibilities -= logsumexp(responsibilities, axis=1, keepdims=True)
responsibilities = np.exp(responsibilities)
## M-step:
## take in responsibilities (output of e-step)
## compute MLE of Gaussian parameters
## mean/std is weighted means/std of mix
for q in range(Q):
pi[q] = np.average(responsibilities[:, q])
mus[:, q] = np.average(data,
weights=responsibilities[:, q] * weights,
axis=0)
sqerr = (data - mus[None, :, q])**2
inv_sigmas[:, q] = np.log(1e-8 + np.average(
sqerr, weights=responsibilities[:, q] * weights, axis=0))
return mus, inv_sigmas, pi
def logreg_loss(x, D, z, lbda):
res = - x * np.dot(D, z)
return np.mean(np.log1p(np.exp(res))) + .5 * lbda * np.sum(z ** 2)
def test_logsumexp5():
combo_check(autograd.scipy.misc.logsumexp, [0])([R(2, 3, 4)],
b=[np.exp(R(2, 3, 4))],
axis=[None, 0, 1],
keepdims=[True, False])
def G(self, t):
'''
Separation of variables: t-dependent
'''
return np.exp(-self.ll * t)
def NegELBO(param, prior, X, S, Ncon, G, M, K):
"""
Parameters
----------
param: length (2M + 2M + MG + 2G + GNK + GDK + GDK + GK + GK)
variational parameters, including:
1) tau_a1: len(M), first parameter of q(alpha_m)
2) tau_a2: len(M), second parameter of q(alpha_m)
3) tau_b1: len(M), first parameter of q(beta_m)
4) tau_b2: len(M), second parameter of q(beta_m)
5) phi: shape(M, G), phi[m,:] is the paramter vector of q(c_m)
6) tau_v1: len(G), first parameter of q(nu_g)
7) tau_v2: len(G), second parameter of q(nu_g)
8) mu_w: shape(G, D, K), mu_w[g,d,k] is the mean parameter of
q(W^g_{dk})
9) sigma_w: shape(G, D, K), sigma_w[g,d,k] is the std parameter of
q(W^g_{dk})
10) mu_b: shape(G, K), mu_b[g,k] is the mean parameter of q(b^g_k)
11) sigma_b: shape(G, K), sigma_b[g,k] is the std parameter of q(b^g_k)
prior: dictionary
the naming of keys follow those in param
{'tau_a1':val1, ...}
X: shape(N, D)
each row represents a sample and each column represents a feature
S: shape(n_con, 4)
each row represents a observed constrain (expert_id, sample1_id,
sample2_id, constraint_type), where
1) expert_id: varies between [0, M-1]
2) sample1 id: varies between [0, N-1]
3) sample2 id: varies between [0, N-1]
4) constraint_type: 1 means must-link and 0 means cannot-link
Ncon: shape(M, 1)
number of constraints provided by each expert
G: int
number of local consensus in the posterior truncated Dirichlet Process
M: int
number of experts
K: int
maximal number of clusters among different solutions, due to the use of
discriminative clustering, some local solution might have empty
clusters
Returns
-------
"""
eps = 1e-12
# get sample size and feature size
[N, D] = np.shape(X)
# unpack the input parameter vector
[tau_a1, tau_a2, tau_b1, tau_b2, phi, tau_v1, tau_v2, mu_w, sigma_w,\
mu_b, sigma_b] = unpackParam(param, N, D, G, M, K)
# compute eta given mu_w and mu_b
eta = np.zeros((0, K))
for g in np.arange(G):
t1 = np.exp(np.dot(X, mu_w[g]) + mu_b[g])
t2 = np.transpose(np.tile(np.sum(t1, axis=1), (K, 1)))
eta = np.vstack((eta, t1 / t2))
eta = np.reshape(eta, (G, N, K))
# compute the expectation terms to be used later
E_log_Alpha = digamma(tau_a1) - digamma(tau_a1 + tau_a2) # len(M)
E_log_OneMinusAlpha = digamma(tau_a2) - digamma(tau_a1 + tau_a2) # len(M)
E_log_Beta = digamma(tau_b1) - digamma(tau_b1 + tau_b2) # len(M)
E_log_OneMinusBeta = digamma(tau_b2) - digamma(tau_b1 + tau_b2) # len(M)
E_log_Nu = digamma(tau_v1) - digamma(tau_v1 + tau_v2) # len(G)
E_log_OneMinusNu = digamma(tau_v2) - digamma(tau_v1 + tau_v2) # len(G)
E_C = phi # shape(M, G)
E_W = mu_w # shape(G, D, K)
E_WMinusMuSqd = sigma_w**2 + (mu_w - prior['mu_w'])**2 # shape(G, D, K)
E_BMinusMuSqd = sigma_b**2 + (mu_b - prior['mu_b'])**2 # shape(G, K)
E_ExpB = np.exp(mu_b + 0.5 * sigma_b**2) # shape(G, K)
E_logP_Alpha = (prior['tau_a1']-1) * E_log_Alpha + \
(prior['tau_a2']-1) * E_log_OneMinusAlpha - \
gammaln(prior['tau_a1']+eps) - \
gammaln(prior['tau_a2']+eps) + \
gammaln(prior['tau_a1']+prior['tau_a2']+eps)
E_logP_Beta = (prior['tau_b1']-1) * E_log_Beta + \
(prior['tau_b2']-1) * E_log_OneMinusBeta - \
gammaln(prior['tau_b1']+eps) - \
gammaln(prior['tau_b2']+eps) + \
gammaln(prior['tau_b1']+prior['tau_b2']+eps)
E_logQ_Alpha = (tau_a1-1)*E_log_Alpha + (tau_a2-1)*E_log_OneMinusAlpha - \
gammaln(tau_a1 + eps) - gammaln(tau_a2 + eps) + \
gammaln(tau_a1+tau_a2 + eps)
E_logQ_Beta = (tau_b1-1)*E_log_Beta + (tau_b2-1)*E_log_OneMinusBeta - \
gammaln(tau_b1 + eps) - gammaln(tau_b2 + eps) + \
gammaln(tau_b1+tau_b2 + eps)
E_logQ_C = np.sum(phi * np.log(phi + eps), axis=1)
eta_N_GK = np.reshape(np.transpose(eta, (1, 0, 2)), (N, G * K))
# compute three terms and then add them up
L_1, L_2, L_3 = [0., 0., 0.]
# the first term and part of the second term
for m in np.arange(M):
idx_S = range(sum(Ncon[:m]), sum(Ncon[:m]) + Ncon[m])
tp_con = S[idx_S, 3]
phi_rep = np.reshape(np.transpose(np.tile(phi[m], (K, 1))), G * K)
E_A = np.dot(eta_N_GK, np.transpose(eta_N_GK * phi_rep))
E_A_use = E_A[S[idx_S, 1], S[idx_S, 2]]
tp_Asum = np.sum(E_A_use)
tp_AdotS = np.sum(E_A_use * tp_con)
L_1 = L_1 + Ncon[m]*E_log_Beta[m] + np.sum(tp_con)*\
(E_log_OneMinusBeta[m]-E_log_Beta[m]) + \
tp_AdotS * (E_log_Alpha[m] + E_log_Beta[m] - \
E_log_OneMinusAlpha[m] - E_log_OneMinusBeta[m]) + \
tp_Asum * (E_log_OneMinusAlpha[m] - E_log_Beta[m])
fg = lambda g: phi[m, g] * np.sum(E_log_OneMinusNu[0:g - 1])
L_2 = L_2 + E_logP_Alpha[m] + E_logP_Beta[m] + \
np.dot(phi[m],E_log_Nu) + np.sum(map(fg, np.arange(G)))
# the second term
for g in np.arange(G):
tp_Nug = (prior['gamma']-1)*E_log_OneMinusNu[g] + \
np.log(prior['gamma']+eps)
t1 = np.dot(X, mu_w[g])
t2 = 0.5 * np.dot(X**2, sigma_w[g]**2)
t3 = np.sum(eta[g], axis=1)
t_mat_i = logsumexp(np.add(mu_b[g] + 0.5 * sigma_b[g]**2, t1 + t2),
axis=1)
tp_Zg = np.sum(eta[g] * np.add(t1, mu_b[g])) - np.dot(t3, t_mat_i)
t5 = -np.log(np.sqrt(2*np.pi)*prior['sigma_w']) - \
0.5/(prior['sigma_w']**2) * (sigma_w[g]**2 + \
(mu_w[g]-prior['mu_w'])**2)
tp_Wg = np.sum(t5)
t6 = -np.log(np.sqrt(2*np.pi)*prior['sigma_b']+eps) - \
0.5/(prior['sigma_b']**2) * (sigma_b[g]**2 + \
(mu_b[g]-prior['mu_b'])**2)
tp_bg = np.sum(t6)
L_2 = L_2 + tp_Nug + tp_Zg + tp_Wg + tp_bg
# the third term
L_3 = np.sum(E_logQ_Alpha + E_logQ_Beta + E_logQ_C)
for g in np.arange(G):
tp_Nug3 = (tau_v1[g]-1)*E_log_Nu[g]+(tau_v2[g]-1)*E_log_OneMinusNu[g] -\
np.log(gamma(tau_v1[g])+eps) - np.log(gamma(tau_v2[g])+eps) + \
np.log(gamma(tau_v1[g]+tau_v2[g])+eps)
tp_Zg3 = np.sum(eta[g] * np.log(eta[g] + eps))
tp_Wg3 = np.sum(-np.log(np.sqrt(2 * np.pi) * sigma_w[g] + eps) - 0.5)
tp_bg3 = np.sum(-np.log(np.sqrt(2 * np.pi) * sigma_b[g] + eps) - 0.5)
L_3 = L_3 + tp_Nug3 + tp_Zg3 + tp_Wg3 + tp_bg3
# Note the third term should have a minus sign before it
ELBO = L_1 + L_2 - L_3
#ELBO = L_1 + L_2
return -ELBO
def ELBO_terms(param, prior, X, S, Ncon, G, M, K):
eps = 1e-12
# get sample size and feature size
[N, D] = np.shape(X)
# unpack the input parameter vector
[tau_a1, tau_a2, tau_b1, tau_b2, phi, tau_v1, tau_v2, mu_w, sigma_w,\
mu_b, sigma_b] = unpackParam(param, N, D, G, M, K)
# compute eta given mu_w and mu_b
eta = np.zeros((0, K))
for g in np.arange(G):
t1 = np.exp(np.dot(X, mu_w[g]) + mu_b[g])
t2 = np.transpose(np.tile(np.sum(t1, axis=1), (K, 1)))
eta = np.vstack((eta, t1 / t2))
eta = np.reshape(eta, (G, N, K))
# compute the expectation terms to be used later
E_log_Alpha = digamma(tau_a1) - digamma(tau_a1 + tau_a2) # len(M)
E_log_OneMinusAlpha = digamma(tau_a2) - digamma(tau_a1 + tau_a2) # len(M)
E_log_Beta = digamma(tau_b1) - digamma(tau_b1 + tau_b2) # len(M)
E_log_OneMinusBeta = digamma(tau_b2) - digamma(tau_b1 + tau_b2) # len(M)
E_log_Nu = digamma(tau_v1) - digamma(tau_v1 + tau_v2) # len(G)
E_log_OneMinusNu = digamma(tau_v2) - digamma(tau_v1 + tau_v2) # len(G)
E_C = phi # shape(M, G)
E_W = mu_w # shape(G, D, K)
E_WMinusMuSqd = sigma_w**2 + (mu_w - prior['mu_w'])**2 # shape(G, D, K)
E_BMinusMuSqd = sigma_b**2 + (mu_b - prior['mu_b'])**2 # shape(G, K)
E_ExpB = np.exp(mu_b + 0.5 * sigma_b**2) # shape(G, K)
E_logP_Alpha = (prior['tau_a1']-1) * E_log_Alpha + \
(prior['tau_a2']-1) * E_log_OneMinusAlpha - \
gammaln(prior['tau_a1']+eps) - \
gammaln(prior['tau_a2']+eps) + \
gammaln(prior['tau_a1']+prior['tau_a2']+eps)
E_logP_Beta = (prior['tau_b1']-1) * E_log_Beta + \
(prior['tau_b2']-1) * E_log_OneMinusBeta - \
gammaln(prior['tau_b1']+eps) - \
gammaln(prior['tau_b2']+eps) + \
gammaln(prior['tau_b1']+prior['tau_b2']+eps)
E_logQ_Alpha = (tau_a1-1)*E_log_Alpha + (tau_a2-1)*E_log_OneMinusAlpha - \
gammaln(tau_a1 + eps) - gammaln(tau_a2 + eps) + \
gammaln(tau_a1+tau_a2 + eps)
E_logQ_Beta = (tau_b1-1)*E_log_Beta + (tau_b2-1)*E_log_OneMinusBeta - \
gammaln(tau_b1 + eps) - gammaln(tau_b2 + eps) + \
gammaln(tau_b1+tau_b2 + eps)
E_logQ_C = np.sum(phi * np.log(phi + eps), axis=1)
eta_N_GK = np.reshape(np.transpose(eta, (1, 0, 2)), (N, G * K))
# compute three terms and then add them up
L_1, L_2, L_3 = [0., 0., 0.]
# the first term and part of the second term
for m in np.arange(M):
idx_S = range(sum(Ncon[:m]), sum(Ncon[:m]) + Ncon[m])
tp_con = S[idx_S, 3]
phi_rep = np.reshape(np.transpose(np.tile(phi[m], (K, 1))), G * K)
E_A = np.dot(eta_N_GK, np.transpose(eta_N_GK * phi_rep))
E_A_use = E_A[S[idx_S, 1], S[idx_S, 2]]
tp_Asum = np.sum(E_A_use)
tp_AdotS = np.sum(E_A_use * tp_con)
L_1 = L_1 + Ncon[m]*E_log_Beta[m] + np.sum(tp_con)*\
(E_log_OneMinusBeta[m]-E_log_Beta[m]) + \
tp_AdotS * (E_log_Alpha[m] + E_log_Beta[m] - \
E_log_OneMinusAlpha[m] - E_log_OneMinusBeta[m]) + \
tp_Asum * (E_log_OneMinusAlpha[m] - E_log_Beta[m])
fg = lambda g: phi[m, g] * np.sum(E_log_OneMinusNu[0:g - 1])
L_2 = L_2 + E_logP_Alpha[m] + E_logP_Beta[m] + \
np.dot(phi[m],E_log_Nu) + np.sum(map(fg, np.arange(G)))
# the second term
for g in np.arange(G):
tp_Nug = (prior['gamma']-1)*E_log_OneMinusNu[g] + \
np.log(prior['gamma']+eps)
t1 = np.dot(X, mu_w[g])
t2 = 0.5 * np.dot(X**2, sigma_w[g]**2)
t3 = np.sum(eta[g], axis=1)
t_mat_i = logsumexp(np.add(mu_b[g] + 0.5 * sigma_b[g]**2, t1 + t2),
axis=1)
tp_Zg = np.sum(eta[g] * np.add(t1, mu_b[g])) - np.dot(t3, t_mat_i)
t5 = -np.log(np.sqrt(2*np.pi)*prior['sigma_w']) - \
0.5/(prior['sigma_w']**2) * (sigma_w[g]**2 + \
(mu_w[g]-prior['mu_w'])**2)
tp_Wg = np.sum(t5)
t6 = -np.log(np.sqrt(2*np.pi)*prior['sigma_b']+eps) - \
0.5/(prior['sigma_b']**2) * (sigma_b[g]**2 + \
(mu_b[g]-prior['mu_b'])**2)
tp_bg = np.sum(t6)
L_2 = L_2 + tp_Nug + tp_Zg + tp_Wg + tp_bg
# the third term
L_3 = np.sum(E_logQ_Alpha + E_logQ_Beta + E_logQ_C)
for g in np.arange(G):
tp_Nug3 = (tau_v1[g]-1)*E_log_Nu[g]+(tau_v2[g]-1)*E_log_OneMinusNu[g] -\
np.log(gamma(tau_v1[g])+eps) - np.log(gamma(tau_v2[g])+eps) + \
np.log(gamma(tau_v1[g]+tau_v2[g])+eps)
tp_Zg3 = np.sum(eta[g] * np.log(eta[g] + eps))
tp_Wg3 = np.sum(-np.log(np.sqrt(2 * np.pi) * sigma_w[g] + eps) - 0.5)
tp_bg3 = np.sum(-np.log(np.sqrt(2 * np.pi) * sigma_b[g] + eps) - 0.5)
L_3 = L_3 + tp_Nug3 + tp_Zg3 + tp_Wg3 + tp_bg3
return (L_1, L_2, L_3)
def _evaluate(self, x, out, *args, **kwargs):
part1 = -1. * self.a * anp.exp(-1. * self.b * anp.sqrt((1. / self.n_var) * anp.sum(x * x, axis=1)))
part2 = -1. * anp.exp((1. / self.n_var) * anp.sum(anp.cos(self.c * x), axis=1))
out["F"] = part1 + part2 + self.a + anp.exp(1)
def rbf_covariance(cov_params, x, xp):
output_scale = np.exp(cov_params[0])
lengthscales = np.exp(cov_params[1:])
diffs = np.expand_dims(x /lengthscales, 1)\
- np.expand_dims(xp/lengthscales, 0)
return output_scale * np.exp(-0.5 * np.sum(diffs**2, axis=2))
def g_analytic(x, gamma = 2, g0 = 10):
return g0*np.exp(-gamma*x)
def classical(p):
"Classical node, requires autograd.numpy functions."
return anp.exp(anp.sum(quantum(p[0], anp.log(p[1]))))
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def grad_analytic(x, D, lbda, step, n_iter):
n, p = D.shape
z = gradient_descent(x, D, lbda, step, n_iter)
return -np.dot(D, z) / (1. + np.exp(x * np.dot(D, z))) / n
def f(x):
return np.sum([
np.log(
np.exp(-y_train[i] * np.dot(scale_parameter * A_train[i], x)) + 1)
for i in range(len(A_train))
])
def softmax(X, axis=0):
return np.exp(X - logsumexp(X, axis=axis, keepdims=True))
def test_logsumexp3():
combo_check(autograd.scipy.misc.logsumexp, [0],
modes=['fwd', 'rev'])([R(4)],
b=[np.exp(R(4))],
axis=[None, 0],
keepdims=[True, False])
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum()
def fun1(B, Bdims):
if Bdims: Bdims = list(range(len(Bdims)))
return np.einsum(np.exp(B**2), Bdims, np.transpose(B), Bdims[::-1], [])
def build_toy_dataset(n_data=80, noise_std=0.1, D=1):
rs = npr.RandomState(0)
inputs = np.concatenate(
[np.linspace(0, 3, num=n_data / 2),
np.linspace(6, 8, num=n_data / 2)])
targets = np.cos(inputs) + rs.randn(n_data) * noise_std
inputs = (inputs - 4.0) / 2.0
inputs = inputs.reshape((len(inputs), D))
targets = targets.reshape((len(targets), D)) / 2.0
return inputs, targets
if __name__ == '__main__':
# Specify inference problem by its unnormalized log-posterior.
rbf = lambda x: np.exp(-x**2)
relu = lambda x: np.maximum(x, 0.0)
# Implement a 3-hidden layer neural network.
num_weights, predictions, logprob = \
make_nn_funs(layer_sizes=[1, 20, 20, 1], nonlinearity=rbf)
inputs, targets = build_toy_dataset()
objective = lambda weights, t: -logprob(weights, inputs, targets)
# Set up figure.
fig = plt.figure(figsize=(12, 8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.show(block=False)
def callback(params, t, g):
def fun0(B, Bdims):
return einsum2.einsum2(np.exp(B**2), Bdims, np.transpose(B),
Bdims[::-1], [])
def sample(self, z, x, input=None, tag=None):
T = z.shape[0]
z = np.zeros_like(z, dtype=int) if self.single_subspace else z
mus = self.forward(x, input, tag)
etas = np.exp(self.inv_etas)
return mus[np.arange(T), z, :] + np.sqrt(etas[z]) * npr.randn(T, self.N)
def test_exp():
fun = lambda x : 3.0 * np.exp(x)
d_fun = grad(fun)
check_grads(fun, npr.randn())
check_grads(d_fun, npr.randn())
from autograd import grad
task_params = {
'target_name': 'measured log solubility in mols per litre',
'data_file': 'delaney.csv'
}
N_train = 800
N_val = 20
N_test = 20
model_params = dict(
fp_length=50, # Usually neural fps need far fewer dimensions than morgan.
fp_depth=4, # The depth of the network equals the fingerprint(radius.)
conv_width=20, # Only the neural fps need this parameter.
h1_size=100, # Size of hidden layer of network on top of fps.
L2_reg=np.exp(-2))
train_params = dict(num_iters=100,
batch_size=100,
init_scale=np.exp(-4),
step_size=np.exp(-6))
# Define the architecture of the network that sits on top of the fingerprints.
vanilla_net_params = dict(
layer_sizes=[model_params['fp_length'],
model_params['h1_size']], # One hidden layer.
normalize=True,
L2_reg=model_params['L2_reg'],
nll_func=rmse)
def train_nn(pred_fun,
def main():
#====== Setup =======
n_iters, n_samples = 2500, 500
init_vals = np.random.randn(n_samples, 1)
allsamps = []
logprob = bimodal_logprob
#====== Tests =======
t = dt.datetime.now()
print('running 1d tests ...')
samps = langevin(logprob,
copy(init_vals),
num_iters=n_iters,
num_samples=n_samples,
step_size=0.05)
print('done langevin in', dt.datetime.now() - t, '\n')
allsamps.append(samps)
samps = MALA(logprob,
copy(init_vals),
num_iters=n_iters,
num_samples=n_samples,
step_size=0.05)
print('done MALA in', dt.datetime.now() - t, '\n')
allsamps.append(samps)
samps = RK_langevin(logprob,
copy(init_vals),
num_iters=n_iters,
num_samples=n_samples,
step_size=0.01)
print('done langevin_RK in', dt.datetime.now() - t, '\n')
allsamps.append(samps)
t = dt.datetime.now()
samps = RWMH(logprob,
copy(init_vals),
num_iters=n_iters,
num_samples=n_samples,
sigma=0.5)
print('done RW MH in', dt.datetime.now() - t, '\n')
allsamps.append(samps)
t = dt.datetime.now()
samps = HMC(logprob,
copy(init_vals),
num_iters=n_iters // 5,
num_samples=n_samples,
step_size=0.05,
num_leap_iters=5)
print('done HMC in', dt.datetime.now() - t, '\n')
allsamps.append(samps)
#====== Plotting =======
lims = [-5, 5]
names = ['langevin', 'MALA', 'langevin_RK', 'RW MH', 'HMC']
f, axes = plt.subplots(len(names), sharex=True)
for i, (name, samps) in enumerate(zip(names, allsamps)):
sns.distplot(samps, bins=1000, kde=False, ax=axes[i])
axb = axes[i].twinx()
axb.scatter(samps,
np.ones(len(samps)),
alpha=0.1,
marker='x',
color='red')
axb.set_yticks([])
zs = np.linspace(*lims, num=250)
axes[i].twinx().plot(zs, np.exp(bimodal_logprob(zs)), color='orange')
axes[i].set_xlim(*lims)
title = name
axes[i].set_title(title)
plt.show()
def diag_gaussian_log_density(x, mu, log_std):
return np.sum(norm.logpdf(x, mu, np.exp(log_std)), axis=-1)
def values(self, param, pos):
b0 = param[0]
pos = pos.T
v = np.exp(-b0 * (pos[0])**2) * np.exp(-b0 * (pos[1])**2)
return v
