theta = 0,K = 5000 , mean_trait=0,dev_trait=20,mean_pop=50,dev_pop=20, num_time=2000,replicate=0)
# a node is defined by giving a distribution from scipy.stats together with any arguments (here 0 and 2)
gamma = elfi.Prior(scipy.stats.uniform, 0, 1)
# ELFI also supports giving the scipy.stats distributions as strings
a = elfi.Prior('uniform', 0, 1)
def single_trait_sim(gamma,a):
sim = traitsim(h = 1, num_iteration=1,num_species=10,gamma1=gamma,gamma_K2=gamma,a = a,r = 1,theta = 0,K = 5000
, mean_trait=0,dev_trait=20,mean_pop=50,dev_pop=20, num_time=2000,replicate = 0)
return sim
Y = elfi.Simulator(single_trait_sim,gamma,a,observed=obs)
def summary1(x):
trait = x[0]
return trait
simdata = elfi.Summary(summary1, Y)
d = elfi.Distance('euclidean', simdata, obs[0])
elfi.draw(d)
print(d)
import elfi
from elfi.examples import ma2
model = ma2.get_model()
elfi.draw(model)
# Generate some data (using a fixed seed here)
np.random.seed(20170525)
y0 = simulator(mean0, std0)
print(y0)
mu = elfi.Prior('uniform', -2, 4)
sigma = elfi.Prior('uniform', 1, 4)
# Add the simulator node and observed data to the model
sim = elfi.Simulator(simulator, mu, sigma, observed=y0)
# Add summary statistics to the model
S1 = elfi.Summary(mean, sim)
S2 = elfi.Summary(var, sim)
# Specify distance as euclidean between summary vectors (S1, S2) from simulated and
# observed data
d = elfi.Distance('euclidean', S1, S2)
# Plot the complete model (requires graphviz)
elfi.draw(d)
rej = elfi.Rejection(d, batch_size=10000, seed=30052017)
res = rej.sample(1000, threshold=.5)
print(res)
import matplotlib.pyplot as plt
res.plot_marginals()
plt.show()
def autocov(x, lag=1):
C = np.mean(x[:, lag:] * x[:, :-lag], axis=1)
return C
# %%
S1 = elfi.Summary(autocov, Y)
S2 = elfi.Summary(autocov, Y,
2) # the optional keyword lag is given the value 2
# %%
# Finish the model with the final node that calculates the squared distance (S1_sim-S1_obs)**2 + (S2_sim-S2_obs)**2
d = elfi.Distance('euclidean', S1, S2)
# %%
elfi.draw(d) # just give it a node in the model, or the model itself (d.model)
# %%
# define prior for t1 as in Marin et al., 2012 with t1 in range [-b, b]
class CustomPrior_t1(elfi.Distribution):
def rvs(b, size=1, random_state=None):
u = scipy.stats.uniform.rvs(loc=0,
scale=1,
size=size,
random_state=random_state)
t1 = np.where(u < 0.5,
np.sqrt(2. * u) * b - b, -np.sqrt(2. * (1. - u)) * b + b)
return t1