def mk_node(species, node_name, node, parent_idx, effects, path, has_siblings=False):
paths = reduce(lambda x, y: "{}__{}".format(x, y).replace(' ', '_'), path)
if has_siblings:
theta.append(pm.MvNormalCov('theta_{}'.format(paths),
mu=theta[parent_idx],
C=400*np.eye(num_traits),
value=node_means[species]))
sigma.append(pm.WishartCov('sigma_{}'.format(paths),
n=num_traits+1,
C=sigma[parent_idx],
value=node_matrices[species]))
parent_idx = len(theta) - 1
if not node.items():
obs_data = np.array(data.ix[(data['species'] == str(n.taxon)) &
reduce(operator.iand,
map(lambda s: data[s[0]] == s[1],
zip(effects, path[1:]))), 0:num_traits])
data_list.append(pm.MvNormalCov('data_{}'.format(paths),
mu=theta[parent_idx],
C=sigma[parent_idx],
value=obs_data,
observed=True))
# Slow method to simulate n populations from posterior
#ds = []
#for i in xrange(0,obs_data.shape[0]):
# ds.append(pm.MvNormalCov('data_{}_{}'.format(paths, i),
# mu=theta[parent_idx],
# C=sigma[parent_idx]
# ))
#data_sim_list.append(ds)
return
has_siblings = len(node.keys()) > 1
for k in node.keys():
mk_node(species, k, node[k], parent_idx, effects, path + [k], has_siblings)
def getModel():
D = pm.Dirichlet('1-Dirichlet', theta=[3,2,4]); #@UndefinedVariable
C1 = pm.Categorical('2-Cat', D); #@UndefinedVariable
C2 = pm.Categorical('10-Cat', D); #@UndefinedVariable
C3 = pm.Categorical('11-Cat', D); #@UndefinedVariable
W0_0 = pm.WishartCov('4-Wishart0_1', n=5, C=np.eye(2)); #@UndefinedVariable
N0_1 = pm.MvNormalCov('5-Norm0_1', mu=[-20,-20], C=np.eye(2)); #@UndefinedVariable
N0_2 = pm.MvNormalCov('6-Norm0_2', mu=[0,0], C=np.eye(2)); #@UndefinedVariable
N0_3 = pm.MvNormalCov('7-Norm0_3', mu=[20,20], C=np.eye(2)); #@UndefinedVariable
aMu = [N0_1.value, N0_2.value, N0_3.value];
fL1 = lambda n=C1: np.select([n==0, n==1, n==2], aMu);
fL2 = lambda n=C2: np.select([n==0, n==1, n==2], aMu);
fL3 = lambda n=C3: np.select([n==0, n==1, n==2], aMu);
p_N1 = pm.Lambda('p_Norm1', fL1, doc='Pr[Norm|Cat]');
p_N2 = pm.Lambda('p_Norm2', fL2, doc='Pr[Norm|Cat]');
p_N3 = pm.Lambda('p_Norm3', fL3, doc='Pr[Norm|Cat]');
N = pm.MvNormalCov('3-Norm', mu=p_N1, C=W0_0); #@UndefinedVariable
obsN1 = pm.MvNormalCov('8-Norm', mu=p_N2, C=W0_0, observed=True, value=[-20,-20]); #@UndefinedVariable @UnusedVariable
obsN2 = pm.MvNormalCov('9-Norm', mu=p_N3, C=W0_0, observed=True, value=[20,20]); #@UndefinedVariable @UnusedVariable
return pm.Model([D,C1,C2,C3,N,W0_0,N0_1,N0_2,N0_3,N,obsN1,obsN2]);
theta = {
node_name(root):
pm.MvNormalCov(
'theta_0',
mu=np.array(data.ix[:, 0:num_traits].mean()),
#value=node_means[str(root)],
value=np.zeros(num_traits),
#mu=np.zeros(num_traits),
C=np.eye(num_traits) * 100.)
}
sigma = {
node_name(root):
pm.WishartCov(
'sigma_0',
value=node_matrices[node_name(root)],
#value=np.eye(num_traits),
n=num_traits + 1,
C=node_matrices[node_name(root)])
}
#C=np.eye(num_traits)*100.)}
#var_factors = {}
betas = {}
delta_z = {}
for n in t.nodes()[1:]:
parent_idx = node_name(n.parent_node)
#var_factors[str(i)] = pm.Uniform('var_factor_{}'.format(str(i)), lower=0, upper=1000)
betas[node_name(n)] = pm.MvNormalCov('betas_{}'.format(node_name(n)),
t = dendropy.Tree.get_from_string("(B, ((C, E),(A,D)))", "newick")
num_leafs = len(t.leaf_nodes())
num_traits = 4
root = t.seed_node
theta = [
pm.MvNormalCov('theta_0',
mu=np.array(data.ix[:, 0:num_traits].mean()),
C=np.eye(num_traits) * 10.,
value=np.zeros(num_traits))
]
sigma = [
pm.WishartCov('sigma_0',
n=num_traits + 1,
C=np.eye(num_traits) * 10.,
value=np.eye(num_traits))
]
tree_idx = {str(root): 0}
i = 1
for n in t.nodes()[1:]:
parent_idx = tree_idx[str(n.parent_node)]
theta.append(
pm.MvNormalCov('theta_{}'.format(str(i)),
mu=theta[parent_idx],
C=sigma[parent_idx],
value=np.zeros(num_traits)))
for n in t.postorder_node_iter():
if str(n) not in node_matrices:
node_matrices[str(n)], node_sample_size[str(n)], node_means[str(n)] = matrix_mean(n.child_nodes())
# Agora comeca o PyMC
root = t.seed_node
theta = [pm.MvNormalCov('theta_0',
#mu=np.array(data.ix[:, 0:num_traits].mean()),
mu=np.zeros(num_traits),
C=np.eye(num_traits)*10.,
value=node_means[str(root)])]
sigma = [pm.WishartCov('sigma_0',
n=num_traits+1,
C=np.eye(num_traits)*10.,
value=node_matrices[str(root)])]
tree_idx = {str(root): 0}
i = 1
for n in t.nodes()[1:]:
parent_idx = tree_idx[str(n.parent_node)]
theta.append(pm.MvNormalCov('theta_{}'.format(str(i)),
mu=theta[parent_idx],
C=sigma[parent_idx],
value=node_means[str(n)]))
sigma.append(pm.WishartCov('sigma_{}'.format(str(i)),
n=num_traits+1,
root = t.seed_node
theta = [
pm.MvNormalCov(
'theta_0',
#mu=np.array(data.ix[:, 0:num_traits].mean()),
#value=node_means[str(root)],
value=np.zeros(num_traits),
mu=np.zeros(num_traits),
C=np.eye(num_traits) * 100.)
]
sigma = [
pm.WishartCov(
'sigma_0',
#value=node_matrices[str(root)],
value=np.eye(num_traits),
n=num_traits + 1,
C=np.eye(num_traits) * 100.)
]
tree_idx = {str(root): 0}
var_factors = {}
betas = {}
i = 1
for n in t.nodes()[1:]:
parent_idx = tree_idx[str(n.parent_node)]
var_factors[str(i)] = pm.Uniform('var_factor_{}'.format(str(i)),
lower=0,
upper=1000)
import pymc as pm
import numpy as np
# Criando parametros conhecidos
original_sigma = np.array([[1, 0.5], [0.5, 1]])
original_theta = [1, -1]
# Simulando dados com media theta e covariancia sigma
data = np.random.multivariate_normal(original_theta, original_sigma, 100)
# Definindo priors, Wishart pra sigma e normal pra theta
sigma = pm.WishartCov('sigma', n=3, C=np.eye(2), value=np.eye(2))
theta = pm.MvNormalCov('theta', mu=[0.,0.], C=np.eye(2), value=[0.,0.])
# Verossimilhanca gaussiana com media theta e covariancia sigma
x = pm.MvNormalCov('x', theta, sigma, value=data, observed=True)
import pandas as pd
import numpy as np
import pymc as pm
dados = pd.read_csv("../dados/dados5sp.csv")
# Filogenia: (B, ((C, E),(A, D)))
# Hiper parametros do no (B, (CEAD))
theta_B_CEAD = pm.MvNormalCov('theta_B_CEAD',
mu=np.zeros(4),
C=np.eye(4),
value=np.zeros(4))
sigma_B_CEAD = pm.WishartCov('sigma_B_CEAD', n=5, C=np.eye(4), value=np.eye(4))
# Ramos do no (B, (CEAD))
theta_B = pm.MvNormalCov('theta_B',
mu=theta_B_CEAD,
C=sigma_B_CEAD,
value=np.zeros(4))
sigma_B = pm.WishartCov('sigma_B', n=5, C=sigma_B_CEAD, value=np.eye(4))
theta_CEAD = pm.MvNormalCov('theta_CEAD',
mu=theta_B_CEAD,
C=sigma_B_CEAD,
value=np.zeros(4))
original_theta = [1, -1]
# Simulando dados com media theta e covariancia sigma
original_theta_1 = np.random.multivariate_normal(original_theta,
original_sigma)
original_theta_2 = np.random.multivariate_normal(original_theta,
original_sigma)
#original_sigma_1 = pm.distributions.wishart_cov_like(original_sigma,
#C=original_sigma,
#n=3)
#original_sigma_2 = pm.distributions.WishartCov(original_sigma,
#C=original_sigma,
#n=3)
data_1 = np.random.multivariate_normal(original_theta_1, original_sigma, 100)
data_2 = np.random.multivariate_normal(original_theta_2, original_sigma, 100)
# Definindo priors, Wishart pra sigma e normal pra theta
sigma = pm.WishartCov('sigma', n=3, C=np.eye(2), value=np.eye(2))
sigma_1 = pm.WishartCov('sigma_1', n=3, C=sigma, value=np.eye(2))
sigma_2 = pm.WishartCov('sigma_2', n=3, C=sigma, value=np.eye(2))
theta = pm.MvNormalCov('theta', mu=[0., 0.], C=np.eye(2), value=[0., 0.])
theta_1 = pm.MvNormalCov('theta_1', mu=theta, C=sigma, value=[0., 0.])
theta_2 = pm.MvNormalCov('theta_2', mu=theta, C=sigma, value=[0., 0.])
# Verossimilhanca gaussiana com media theta e covariancia sigma
x_1 = pm.MvNormalCov('x_1', theta_1, sigma_1, value=data_1, observed=True)
x_2 = pm.MvNormalCov('x_2', theta_2, sigma_2, value=data_2, observed=True)
