def model(home_id, away_id, score1_obs=None, score2_obs=None):
# priors
alpha = pyro.sample("alpha", dist.Normal(0.0, 1.0))
sd_att = pyro.sample(
"sd_att",
dist.TransformedDistribution(
dist.StudentT(3.0, 0.0, 2.5),
FoldedTransform(),
),
)
sd_def = pyro.sample(
"sd_def",
dist.TransformedDistribution(
dist.StudentT(3.0, 0.0, 2.5),
FoldedTransform(),
),
)
home = pyro.sample("home", dist.Normal(0.0, 1.0)) # home advantage
nt = len(np.unique(home_id))
# team-specific model parameters
with pyro.plate("plate_teams", nt):
attack = pyro.sample("attack", dist.Normal(0, sd_att))
defend = pyro.sample("defend", dist.Normal(0, sd_def))
# likelihood
theta1 = torch.exp(alpha + home + attack[home_id] - defend[away_id])
theta2 = torch.exp(alpha + attack[away_id] - defend[home_id])
with pyro.plate("data", len(home_id)):
pyro.sample("s1", dist.Poisson(theta1), obs=score1_obs)
pyro.sample("s2", dist.Poisson(theta2), obs=score2_obs)
def gen_publications(self):
multiplier = pyro.sample(self.namegen("pub_multiplier"),
pyd.Poisson(15)).item()
return pyro.sample(
self.namegen("number_publication"),
pyd.Poisson((multiplier + 1) * self.iq / 100.0),
).item()
def model(self, home_team, away_team):
sigma_a = pyro.sample("sigma_a", dist.HalfNormal(1.0))
sigma_b = pyro.sample("sigma_b", dist.HalfNormal(1.0))
mu_b = pyro.sample("mu_b", dist.Normal(0.0, 1.0))
rho_raw = pyro.sample("rho_raw", dist.Beta(2, 2))
rho = pyro.deterministic("rho", 2.0 * rho_raw - 1.0)
log_gamma = pyro.sample("log_gamma", dist.Normal(0, 1))
with pyro.plate("teams", self.n_teams):
abilities = pyro.sample(
"abilities",
dist.MultivariateNormal(
torch.tensor([0.0, mu_b]),
covariance_matrix=torch.tensor(
[
[sigma_a ** 2.0, rho * sigma_a * sigma_b],
[rho * sigma_a * sigma_b, sigma_b ** 2.0],
]
),
),
)
log_a = abilities[:, 0]
log_b = abilities[:, 1]
home_inds = torch.tensor([self.team_to_index[team] for team in home_team])
away_inds = torch.tensor([self.team_to_index[team] for team in away_team])
home_rate = torch.exp(log_a[home_inds] + log_b[away_inds] + log_gamma)
away_rate = torch.exp(log_a[away_inds] + log_b[home_inds])
pyro.sample("home_goals", dist.Poisson(home_rate))
pyro.sample("away_goals", dist.Poisson(away_rate))
def setUp(self):
self.lam = Variable(torch.Tensor([2, 4.5, 3., 5.1]))
self.dim = 4
self.batch_lam = Variable(
torch.Tensor([[2, 4.5, 3., 5.1], [6, 3.2, 1, 4]]))
self.test_data = Variable(torch.Tensor([0, 1, 2, 4]))
self.batch_test_data = Variable(
torch.Tensor([[0, 1, 2, 4], [4, 1, 2, 4]]))
self.g = dist.Poisson(self.lam)
self.batch_g = dist.Poisson(self.batch_lam)
def test_gamma_poisson(hyperpriors):
def model(data):
with pyro.plate("latent_dim", data.shape[1]):
alpha = (
pyro.sample("alpha", dist.HalfCauchy(1.0))
if hyperpriors
else torch.tensor([1.0, 1.0])
)
beta = (
pyro.sample("beta", dist.HalfCauchy(1.0))
if hyperpriors
else torch.tensor([1.0, 1.0])
)
gamma_poisson = GammaPoissonPair()
rate = pyro.sample("rate", gamma_poisson.latent(alpha, beta))
with pyro.plate("data", data.shape[0]):
pyro.sample("obs", gamma_poisson.conditional(rate), obs=data)
true_rate = torch.tensor([3.0, 10.0])
num_samples = 100
data = dist.Poisson(rate=true_rate).sample(sample_shape=(torch.Size((100,))))
hmc_kernel = NUTS(
collapse_conjugate(model), jit_compile=True, ignore_jit_warnings=True
)
mcmc = MCMC(hmc_kernel, num_samples=num_samples, warmup_steps=50)
mcmc.run(data)
samples = mcmc.get_samples()
posterior = posterior_replay(model, samples, data, num_samples=num_samples)
assert_equal(posterior["rate"].mean(0), true_rate, prec=0.3)
def model(self, data, demand):
coef = {}
for s in self.features['station']['names']:
coef[s] = pyro.sample(s, dist.Normal(0, 1))
for h in self.features['hour']['names']:
for d in self.features['daytype']['names']:
name = h + '_' + d
coef[name] = pyro.sample(name, dist.Normal(0, 1))
log_lmbda = 0
for i in range(len(self.features['station']['names'])):
name = self.features['station']['names'][i]
index = self.features['station']['index'][i]
log_lmbda += coef[name] * data[:, index]
for h in range(len(self.features['hour']['names'])):
for d in range(len(self.features['daytype']['names'])):
h_name = self.features['hour']['names'][h]
h_index = self.features['hour']['index'][h]
d_name = self.features['daytype']['names'][d]
d_index = self.features['daytype']['index'][d]
log_lmbda += coef[h_name + '_' + d_name] * \
data[:, h_index] * data[:, d_index]
lmbda = log_lmbda.exp()
with pyro.plate("data", len(data)):
pyro.sample("obs", dist.Poisson(lmbda), obs=demand)
return lmbda
def model(self, *args, **kwargs):
I, N = self._data['data'].shape
batch = N if self._params['batch_size'] else self._params['batch_size']
weights = pyro.sample(
'mixture_weights',
dist.Dirichlet(
(1 / self._params['K']) * torch.ones(self._params['K'])))
with pyro.plate('segments', I):
with pyro.plate('components', self._params['K']):
cnv_probs = pyro.sample(
"cnv_probs",
dist.Dirichlet(self._params['probs'] * 1 /
torch.ones(self._params['hidden_dim'])))
with pyro.plate("data2", N, batch):
theta = pyro.sample(
'norm_factor',
dist.Gamma(self._params['theta_scale'],
self._params['theta_rate']))
with pyro.plate('data', N, batch):
assignment = pyro.sample('assignment',
dist.Categorical(weights),
infer={"enumerate": "parallel"})
for i in pyro.plate('segments2', I):
cc = pyro.sample('copy_number_{}'.format(i),
dist.Categorical(
Vindex(cnv_probs)[assignment, i, :]),
infer={"enumerate": "parallel"})
pyro.sample('obs_{}'.format(i),
dist.Poisson((cc * theta * self._data['mu'][i]) +
1e-8),
obs=self._data['data'][i, :])
def test_reparam_stable():
data = dist.Poisson(torch.randn(8).exp()).sample()
@poutine.reparam(config={
"dz": LatentStableReparam(),
"y": LatentStableReparam()
})
def model():
stability = pyro.sample("stability", dist.Uniform(1.0, 2.0))
trans_skew = pyro.sample("trans_skew", dist.Uniform(-1.0, 1.0))
obs_skew = pyro.sample("obs_skew", dist.Uniform(-1.0, 1.0))
scale = pyro.sample("scale", dist.Gamma(3, 1))
# We use separate plates because the .cumsum() op breaks independence.
with pyro.plate("time1", len(data)):
dz = pyro.sample("dz", dist.Stable(stability, trans_skew))
z = dz.cumsum(-1)
with pyro.plate("time2", len(data)):
y = pyro.sample("y", dist.Stable(stability, obs_skew, scale, z))
pyro.sample("x", dist.Poisson(y.abs()), obs=data)
guide = AutoDelta(model)
svi = SVI(model, guide, optim.Adam({"lr": 0.01}), Trace_ELBO())
for step in range(100):
loss = svi.step()
if step % 20 == 0:
logger.info("step {} loss = {:0.4g}".format(step, loss))
def model(data, params):
# initialize data
N = data["N"]
y = data["y"]
roach1 = data["roach1"]
treatment = data["treatment"]
senior = data["senior"]
log_expo = data["log_expo"]
# init parameters
beta = params["beta"]
# model block
# Tau is the global precision
# tau = pyro.sample('tau', dist.Gamma(concentration=0.001, rate=0.001))
# sigma = tau.sqrt().reciprocal()
# NOTE: Had to made change from Stan model here!
sigma = pyro.sample('sigma', dist.HalfCauchy(2.5))
with pyro.plate("data", N):
# NOTE: the zeros_like is here since tau might be in plate
lambdah = pyro.sample(
'lambda', dist.Normal(loc=torch.zeros_like(sigma), scale=sigma))
log_rate = lambdah + log_expo + beta[..., 0] + beta[
..., 1] * roach1 + beta[..., 2] * senior + beta[..., 3] * treatment
# NOTE: Do we have to worry about under-/overflow here due to exp?
pyro.sample('y', dist.Poisson(rate=log_rate.exp() + 1e-8), obs=y)
def log_linear_mm_model(num_sites,
num_days,
num_predictors,
num_clusters,
predictors,
data=None):
weight = pyro.sample("weights", dist.Dirichlet(torch.ones(num_clusters)))
with plate("beta_components", num_predictors):
with plate("beta_clusters", num_clusters):
betas = pyro.sample(
"betas",
dist.Normal(0 * torch.ones(num_clusters, num_predictors), 2),
)
log_thetas = predictors @ betas.T
with plate("epsilons", num_sites):
epsilon = pyro.sample("epsilon", dist.Normal(0, 5))
epsilon = (epsilon.unsqueeze(-1) *
torch.ones(num_sites, num_days)).unsqueeze(-1)
thetas = torch.exp(log_thetas + epsilon)
with plate("sites", num_sites):
assignments = pyro.sample("assignments", dist.Categorical(weight))
# selection = (assignments * torch.ones(num_days,1)).T.long()
a = (torch.arange(num_sites).expand(num_days,
num_sites).permute(1, 0).long())
b = torch.arange(num_days).expand(num_sites, num_days).long()
select = Vindex(thetas)[a, b, assignments.unsqueeze(-1)]
accidents = pyro.sample("accidents",
dist.Poisson(select).to_event(1),
obs=data)
return accidents
def matrix_factorization_poisson(anime_matrix_train, k=k):
m = anime_matrix_train.shape[0]
n = anime_matrix_train.shape[1]
u_mean = Variable(torch.zeros([m, k]))
u_sigma = Variable(torch.ones([m, k]) * sigma_u)
v_mean = Variable(torch.zeros([n, k]))
v_sigma = Variable(torch.ones([n, k]) * sigma_v)
u = pyro.sample("u", dist.Normal(
loc=u_mean, scale=u_sigma).to_event(2))
v = pyro.sample("v", dist.Normal(
loc=v_mean, scale=v_sigma).to_event(2))
expectation = torch.mm(u, v.t())
# softly make all values positive for poisson
expectation[expectation <= 0] = 0.01
is_observed = (~np.isnan(anime_matrix_train))
is_observed = torch.tensor(is_observed)
valid_matrix = torch.tensor(anime_matrix_train).clone()
valid_matrix[~is_observed] = 0 # ensure all values are valid
# round all observed values to positive integers
valid_matrix = np.around(valid_matrix)
with pyro.plate("user", m, dim=-2):
with pyro.plate("anime", n, dim=-3):
with pyro.poutine.mask(mask=is_observed):
pyro.sample("obs", dist.Poisson(expectation),
obs=valid_matrix)
def model(self, *args, **kwargs):
I, N = self._data['data'].shape
batch = N if self._params['batch_size'] else self._params['batch_size']
weights = pyro.sample('mixture_weights',
dist.Dirichlet(torch.ones(self._params['K'])))
with pyro.plate('components', self._params['K']):
probs_z = pyro.sample(
"cnv_probs",
dist.Dirichlet(self._params['t'] *
torch.eye(self._params['hidden_dim']) +
(1 - self._params['t'])).to_event(1))
with pyro.plate("data2", N, batch):
theta = pyro.sample(
'norm_factor',
dist.Gamma(self._params['theta_scale'],
self._params['theta_rate']))
with pyro.plate('data', N, batch):
z = 0
assignment = pyro.sample('assignment',
dist.Categorical(weights),
infer={"enumerate": "parallel"})
for i in pyro.markov(range(I)):
z = pyro.sample("z_{}".format(i),
dist.Categorical(
Vindex(probs_z)[assignment, z]),
infer={"enumerate": "parallel"})
pyro.sample('obs_{}'.format(i),
dist.Poisson((z * theta * self._data['mu'][i]) +
1e-8),
obs=self._data['data'][i, :])
def model(self, *args, **kwargs):
I, N = self.params['data'].shape
weights = pyro.sample('mixture_weights',
dist.Dirichlet(self.params['mixture']))
with pyro.plate('segments', I):
mu = pyro.sample(
'gene_basal',
dist.Gamma(self.params['theta_scale'],
self.params['theta_rate']))
with pyro.plate('components', self.params['K']):
cc = pyro.sample(
'cnv_probs',
dist.LogNormal(np.log(self.params['cnv_mean']),
self.params['cnv_var']))
with pyro.plate('data', N, self.params['batch_size']):
assignment = pyro.sample('assignment',
dist.Categorical(weights),
infer={"enumerate": "parallel"})
theta = pyro.sample(
'norm_factor',
dist.Gamma(self.params['theta_scale'],
self.params['theta_rate']))
for i in pyro.plate('segments2', I):
pyro.sample(
'obs_{}'.format(i),
dist.Poisson((Vindex(cc)[assignment, i] * theta * mu[i]) +
1e-8),
obs=self.params['data'][i, :])
def _true_counts_from_params(self, data: torch.Tensor,
mu_est: torch.Tensor,
lambda_est: torch.Tensor,
alpha_est: torch.Tensor) -> torch.Tensor:
"""Calculate a single sample estimate of mu, the mean of the true count
matrix, and lambda, the rate parameter of the Poisson background counts.
Args:
data: Dense tensor minibatch of cell by gene count data.
mu_est: Dense tensor of Negative Binomial means for true counts.
lambda_est: Dense tensor of Poisson rate params for noise counts.
alpha_est: Dense tensor of Dirichlet concentration params that
inform the overdispersion of the Negative Binomial.
Returns:
dense_counts_torch: Dense matrix of true de-noised counts.
"""
# Estimate a reasonable low-end to begin the Poisson summation.
n = min(100., data.max().item()) # No need to exceed the max value
poisson_values_low = (lambda_est.detach() - n / 2).int()
poisson_values_low = torch.clamp(torch.min(poisson_values_low,
(data - n + 1).int()),
min=0).float()
# Construct a big tensor of possible noise counts per cell per gene,
# shape (batch_cells, n_genes, max_noise_counts)
noise_count_tensor = torch.arange(start=0, end=n) \
.expand([data.shape[0], data.shape[1], -1]) \
.float().to(device=data.device)
noise_count_tensor = noise_count_tensor + poisson_values_low.unsqueeze(
-1)
# Compute probabilities of each number of noise counts.
# NOTE: some values will be outside the support (negative values for NB).
# The resulting NaNs are ignored by torch.argmax().
logits = (mu_est.log() - alpha_est.log()).unsqueeze(-1)
log_prob_tensor = (
dist.Poisson(lambda_est.unsqueeze(-1)).log_prob(noise_count_tensor)
+ dist.NegativeBinomial(
total_count=alpha_est.unsqueeze(-1), logits=logits).log_prob(
data.unsqueeze(-1) - noise_count_tensor))
log_prob_tensor = torch.where(
noise_count_tensor <= data.unsqueeze(-1), log_prob_tensor,
torch.ones_like(log_prob_tensor) * -np.inf)
# Find the most probable number of noise counts per cell per gene.
noise_count_map = torch.argmax(log_prob_tensor, dim=-1,
keepdim=False).float()
# Handle the cases where y = 0 (no cell): all counts are noise.
noise_count_map = torch.where(mu_est == 0, data, noise_count_map)
# Compute the number of true counts.
dense_counts_torch = torch.clamp(data - noise_count_map, min=0.)
return dense_counts_torch
def model():
alpha_p_log = pyro.param("alpha_p_log", self.alpha_p_log_0.clone())
beta_p_log = pyro.param("beta_p_log", self.beta_p_log_0.clone())
alpha_p, beta_p = torch.exp(alpha_p_log), torch.exp(beta_p_log)
lambda_latent = pyro.sample("lambda_latent",
dist.Gamma(alpha_p, beta_p))
pyro.sample("obs", dist.Poisson(lambda_latent), obs=self.data)
return lambda_latent
def model(data):
idk = torch.tensor(4.0) # where is my neural network?
idkb = torch.tensor(4.0)
genelambdas = dist.Gamma(idka, idkb, batch_size=19795)
for celltype in range(data.size(0)): # this one's 56 right?
with iarange('observe_{}'.format(celltype)):
pyro.sample('indiv', dist.Poisson(genelambdas), obs=data[celltype])
def forward(self, f_loc, f_var, y=None):
f_res = self.response_function(
f_loc + torch.randn(f_loc.dim(), device=f_loc.device) * f_var)
y_dist = dist.Poisson(f_res)
self.y_dist = y_dist
if y is not None:
y_dist = y_dist.expand_by(y.shape[:-f_res.dim()]).to_event(y.dim())
return pyro.sample("y", y_dist, obs=y)
def base_model(num_sites, num_days, data=None):
with plate("sites", size=num_sites, dim=-2):
epsilon = pyro.sample("epsilon", dist.Normal(-5, 15))
with plate("days", size=num_days, dim=-1):
accidents = pyro.sample("accidents",
dist.Poisson(torch.exp(epsilon)),
obs=data)
return accidents
def post_model(
p_data,
t_data,
s_data,
r_data,
y,
p_types,
p_stories,
p_subreddits,
zero_inflated,
):
coef_scale_prior = 0.1
num_posts, num_p_indeps = p_data.shape
# shared prior
gamma_loc = torch.zeros((num_p_indeps, 1), dtype=torch.float64)
if zero_inflated:
gamma_gate_loc = torch.zeros((num_p_indeps, 1), dtype=torch.float64)
with pyro.plate("p_indep", num_p_indeps, dim=-2):
gamma = pyro.sample("gamma", dist.Normal(gamma_loc, coef_scale_prior))
if zero_inflated:
gamma_gate = pyro.sample(
"gamma_gate", dist.Normal(gamma_gate_loc, coef_scale_prior)
)
# for each post,
# use the correct set of coefficients to run our post-level regression
with pyro.plate("post", num_posts, dim=-1) as p:
# indep vars for this post
indeps = p_data[p, :]
mu = torch.matmul(
indeps, gamma
).flatten() # ( num_posts, num_p_indeps) x (num_p_indeps, 1)
# defining response dist
if zero_inflated:
gate = torch.nn.Sigmoid()(
torch.matmul(indeps, gamma_gate).flatten()
) # ( num_posts, num_p_indeps) x (num_p_indeps, 1)
response_dist = dist.ZeroInflatedPoisson(
rate=torch.exp(mu), gate=gate
)
else:
response_dist = dist.Poisson(rate=torch.exp(mu))
# sample
if y is None:
pyro.sample("obs", response_dist, obs=y)
else:
pyro.sample("obs", response_dist, obs=y[p])
def model(data):
alpha = 1 / torch.mean(data)
lambda1 = pyro.sample("lambda1", dist.Exponential(alpha))
lambda2 = pyro.sample("lambda2", dist.Exponential(alpha))
tau = pyro.sample("tau", dist.Uniform(0, 1))
idx = torch.arange(len(data)).float()
lambda_ = torch.where(idx.lt(tau * len(data)), lambda1, lambda2)
with pyro.plate("data", len(data)):
pyro.sample("obs", dist.Poisson(lambda_), obs=data)
def model(self, data, ratings):
with pyro.plate("betas", data.shape[1]):
betas = pyro.sample("beta", dist.Normal(0, 1))
lambda_ = torch.exp(torch.sum(betas * data, axis=1))
with pyro.plate("ratings", data.shape[0]):
y = pyro.sample("obs", dist.Poisson(lambda_), obs=ratings)
return y
def model(data, params):
# initialize data
N = data["N"]
x = data["x"]
t = data["t"]
alpha = pyro.sample("alpha", dist.Exponential(1.0))
beta = pyro.sample("beta", dist.Gamma(0.1, 1.0))
with pyro.plate('data', N):
theta = pyro.sample("theta", dist.Gamma(alpha, beta))
x = pyro.sample("x", dist.Poisson(theta * t), obs=x)
def test_gamma_poisson_log_prob(shape):
gamma_conc = torch.randn(shape).exp()
gamma_rate = torch.randn(shape).exp()
value = torch.arange(20.)
num_samples = 300000
poisson_rate = dist.Gamma(gamma_conc, gamma_rate).sample((num_samples, ))
log_probs = dist.Poisson(poisson_rate).log_prob(value)
expected = log_probs.logsumexp(0) - math.log(num_samples)
actual = GammaPoisson(gamma_conc, gamma_rate).log_prob(value)
assert_close(actual, expected, rtol=0.05)
def test_gamma_poisson(sample_shape, batch_shape):
concentration = torch.randn(batch_shape).exp()
rate = torch.randn(batch_shape).exp()
nobs = 5
obs = dist.Poisson(10.).sample((nobs,) + sample_shape + batch_shape).sum(0)
f = dist.Gamma(concentration, rate)
g = dist.Gamma(1 + obs, nobs)
fg, log_normalizer = f.conjugate_update(g)
x = fg.sample(sample_shape)
assert_close(f.log_prob(x) + g.log_prob(x), fg.log_prob(x) + log_normalizer)
def model():
stability = pyro.sample("stability", dist.Uniform(1.0, 2.0))
trans_skew = pyro.sample("trans_skew", dist.Uniform(-1.0, 1.0))
obs_skew = pyro.sample("obs_skew", dist.Uniform(-1.0, 1.0))
scale = pyro.sample("scale", dist.Gamma(3, 1))
# We use separate plates because the .cumsum() op breaks independence.
with pyro.plate("time1", len(data)):
dz = pyro.sample("dz", dist.Stable(stability, trans_skew))
z = dz.cumsum(-1)
with pyro.plate("time2", len(data)):
y = pyro.sample("y", dist.Stable(stability, obs_skew, scale, z))
pyro.sample("x", dist.Poisson(y.abs()), obs=data)
def work_on_projects(self):
def productivity(rep_j,rep_u,comp_prod):
return (sum(rep_j)+sum(rep_u) + comp_prod)
lab_students = [agent for agent in self.model.schedule.agents if (agent.category == 'U' and agent.affliation == self.affliation)]
rep_u = []
u_agents = []
for agent in lab_students:
if not agent.booked:
if self.own_lab_agent.capacity_s_initial >= self.own_lab_agent.capacity_s:
rep_u.append(agent.reputation_points)
agent.booked = True
self.own_lab_agent.capacity_s += 1
u_agents.append(agent)
j_agents = [agent for agent in self.model.schedule.agents if (agent.category == 'J' and agent.affliation == self.unique_id)]
rep_j = [agent.reputation_points for agent in j_agents]
self.project_productivity = productivity(rep_j,rep_u,self.compute_productivity)*self.reputation_points # This includes all the agents and compute
d = self.difficulty_selected
chance = self.chance_of_project_success()
self.is_successful = pyro.sample(self.namegen("Proj_success"),pyd.Bernoulli(chance)).item()
if self.is_successful == 1:
print("Chance of project by",self.unique_id,"is",round(chance,5),"with difficulty",round(d,3),"with bid of",round(self.bid_value,4),"topic was",self.topic_interested,".....it was a SUCCESS")
vision = 4
self.landscape.reduce_novelty([self.pos_y,self.pos_x],1)
publication_generated = pyro.sample(self.namegen("publication_gen"),pyd.Poisson(self.project_productivity)).item() + 1
citations_generated = publication_generated*pyro.sample(self.namegen("citation_gen"),pyd.Poisson(self.landscape.matrix[self.pos_y,self.pos_x])).item() # Significance result to citations
self.publications+= publication_generated
self.citations+= citations_generated
for students in u_agents:
students.publications+= publication_generated
students.citations+= citations_generated
for juniors in j_agents:
juniors.publications+= publication_generated
juniors.citations+= citations_generated
else:
print("Chance of project by",self.unique_id,"is",round(chance,5),"with difficulty",round(d,3),"with bid of",round(self.bid_value,4),"topic was",self.topic_interested,".....it was a FAILURE")
vision = 2
self.landscape.reduce_novelty([self.pos_y,self.pos_x],chance)
max_size = self.model.elsize
for xi in range(-vision , vision + 1):
for yi in range(-vision,vision + 1):
if (xi**2 + yi**2 <= (vision+0.5)**2):
self.landscape.x_arr.append((self.pos_x+xi)%max_size)
self.landscape.y_arr.append((self.pos_y+yi)%max_size)
self.landscape.explored[(self.pos_y+yi)%max_size,(self.pos_x+xi)%max_size] = 1
self.landscape.visible_x = self.landscape.x_arr
self.landscape.visible_y = self.landscape.y_arr
def likelihood(self, inf_params):
lk = torch.zeros(self._params['K'], self._data['data'].shape[1],
self._data['data'].shape[0])
if self._params['K'] == 1:
norm_f = torch.sum(inf_params["cnv_probs"] *
self._data['mu']) / torch.sum(self._data['mu'])
for i in range(self._data['data'].shape[0]):
lamb = (inf_params["cnv_probs"][0, i] * self._data['mu'][i] *
inf_params["norm_factor"]) / norm_f
lk[0, :, i] = torch.log(
inf_params["mixture_weights"]) + dist.Poisson(
lamb).log_prob(self._data['data'][i, :])
return lk
for k in range(self._params['K']):
norm_f = torch.sum(inf_params["cnv_probs"][k, :] *
self._data['mu']) / torch.sum(self._data['mu'])
for i in range(self._data['data'].shape[0]):
lamb = (inf_params["cnv_probs"][k, i] * self._data['mu'][i] *
inf_params["norm_factor"]) / norm_f
lk[k, :,
i] = dist.Poisson(lamb).log_prob(self._data['data'][i, :])
return lk
def model(self, home_team, away_team, gameweek):
n_gameweeks = max(gameweek) + 1
gamma = pyro.sample("gamma", dist.LogNormal(0, 1))
mu_b = pyro.sample("mu_b", dist.Normal(0, 1))
with pyro.plate("teams", self.n_teams):
log_a0 = pyro.sample("log_a0", dist.Normal(0, 1))
log_b0 = pyro.sample("log_b0", dist.Normal(mu_b, 1))
sigma_rw = pyro.sample("sigma_rw", dist.HalfNormal(0.1))
with pyro.plate("random_walk", n_gameweeks - 1):
diffs_a = pyro.sample("diff_a", dist.Normal(0, sigma_rw))
diffs_b = pyro.sample("diff_b", dist.Normal(0, sigma_rw))
log_a0_t = log_a0 if log_a0.dim() == 2 else log_a0[None, :]
diffs_a = torch.cat((log_a0_t, diffs_a), axis=0)
log_a = torch.cumsum(diffs_a, axis=0)
log_b0_t = log_b0 if log_b0.dim() == 2 else log_b0[None, :]
diffs_b = torch.cat((log_b0_t, diffs_b), axis=0)
log_b = torch.cumsum(diffs_b, axis=0)
pyro.sample("log_a", dist.Delta(log_a), obs=log_a)
pyro.sample("log_b", dist.Delta(log_b), obs=log_b)
home_inds = torch.tensor(
[self.team_to_index[team] for team in home_team])
away_inds = torch.tensor(
[self.team_to_index[team] for team in away_team])
home_rate = torch.clamp(
log_a[gameweek, home_inds] - log_b[gameweek, away_inds] + gamma,
-7, 2)
away_rate = torch.clamp(
log_a[gameweek, away_inds] - log_b[gameweek, home_inds], -7, 2)
pyro.sample("home_goals", dist.Poisson(torch.exp(home_rate)))
pyro.sample("away_goals", dist.Poisson(torch.exp(away_rate)))
def model(self):
# Sample Poissonian parameters and compute expected contribution
mu_poiss = torch.zeros(torch.sum(~self.mask), dtype=torch.float64)
for i_temp in torch.arange(self.n_poiss):
norms_poiss = pyro.sample(self.labels_poiss[i_temp],
self.poiss_priors[i_temp])
if self.poiss_log_priors[i_temp]:
norms_poiss = 10**norms_poiss.clone()
mu_poiss += norms_poiss * self.poiss_temps[i_temp]
# Samples non-Poissonian parameters
thetas = []
for i_ps in torch.arange(self.n_ps):
theta_temp = [
pyro.sample(
self.labels_ps_params[i_np_param] + "_" +
self.labels_ps[i_ps], self.ps_priors[i_ps][i_np_param])
for i_np_param in torch.arange(self.n_ps_params)
]
for i_p in torch.arange(len(theta_temp)):
if self.ps_log_priors[i_ps][i_p]:
theta_temp[i_p] = 10**theta_temp[i_p]
thetas.append(theta_temp)
# Mark each pixel as conditionally independent
with pyro.plate("data_plate",
len(self.data),
dim=-1,
subsample_size=self.subsample_size) as idx:
# Use either the non-Poissonian (if there is at least one NP model) or Poissonian likelihood
if self.n_ps != 0:
log_likelihood = log_like_np(mu_poiss[idx], thetas,
self.ps_temps[:, idx],
self.data[idx], self.f_ary,
self.df_rho_div_f_ary)
pyro.factor("obs", log_likelihood)
else:
pyro.sample("obs",
dist.Poisson(mu_poiss[idx]),
obs=self.data[idx])
def forward(self, data):
N_pop = 300
p1 = self.ode_params1.view((-1, ))
p2 = self.ode_params2.view((-1, ))
p3 = self.ode_params3.view((-1, ))
R0 = pyro.deterministic('R0', torch.zeros_like(p1))
ode_params = torch.stack([p1, p2, p3, 1 - p3, R0], dim=1)
SIR_sim = self._ode_op.apply(ode_params, (self._ode_model, ))
for i in range(len(data)):
pyro.sample("obs_{}".format(i),
dist.Poisson(SIR_sim[..., i, 1] * N_pop),
obs=data[i])
return SIR_sim