# "n": np.array([1183, 1510, 1597, 1924, 1178, 1324, 2173, 845]),
# "N": 8}
# Kobe Bryant's whole field goal record from NBA records:
data = {"y": np.array([176, 391, 362, 554, 701, 749, 868, 516, 573, 978, 813, 775, 800, 716, 740]),
"n": np.array([422, 913, 779, 1183, 1510, 1597, 1924, 1178, 1324, 2173, 1757, 1690, 1712, 1569, 1639]),
"N": 15}
# Create the model
model = bd.Model()
# Prior p(m)
model.add_node(bd.Node("p", bd.Uniform(0, 1)))
# Likelihood p(D|n,p) place holder.
for i in range(0, data["N"]):
suc = "y{index}".format(index=i)
atp = "n{index}".format(index=i)
model.add_node(bd.Node(suc, bd.Binomial(atp, "p"), observed=True))
# Create the C++ code
bd.generate_h(model, data)
bd.generate_cpp(model, data)
# take1 log(Z) = -39.2245367341 with Ntz original data
# take2 log(Z) = -39.223614467 with Ntz original data
# Analytical log(Z) = -39.2308
# take1 log(Z) = -69.0177594483 with whole record
# Analytical log(Z) = -69.0231
formula += "beta_{j}_5 * SW{i} * PL{i}"
formula = formula.format(i=i, j=j)
model.add_node(bd.Node(name, bd.Delta(formula)))
# Normalising constant
name = "Z_{i}".format(i=i)
formula = ""
formula += "exp(p_0_{i}) + exp(p_1_{i}) + exp(p_2_{i})"
formula = formula.format(i=i)
model.add_node(bd.Node(name, bd.Delta(formula)))
# Probability of the data point.
name = "prob_{i}".format(i=i)
formula = "exp(p_{Species}_{i}) / Z_{i}".format(i=i,
Species=data["Species"][i])
model.add_node(bd.Node(name, bd.Delta(formula)))
# One Trick - this is like the "zeroes trick" in JAGS
name = "ones{i}".format(i=i)
distribution = bd.Binomial(1, "prob_{i}".format(i=i))
node = bd.Node(name, distribution, observed=True)
model.add_node(node)
# Create the C++ code
bd.generate_h(model, data)
bd.generate_cpp(model, data)
# Compile the C++ code so it's ready to go
import os
os.system("make")
# Create the model
model = bd.Model()
# Coefficients
model.add_node(bd.Node("beta_0", bd.Normal(0, 10)))
model.add_node(bd.Node("beta_1", bd.Normal(0, 10)))
# Sampling Distribution
for i in range(0, data["N"]):
name = "CHD{i}".format(i=i)
#prob = "exp(beta_0 + beta_1 * Age{i})/(1.0 + exp(beta_0 + beta_1 * Age{i}))"
prob = "exp(beta_0 + beta_1 * Age{i})"
prob += "/(1.0 + exp(beta_0 + beta_1 * Age{i}))"
prob = prob.format(i=i)
distribution = bd.Binomial(1, prob)
node = bd.Node(name, distribution, observed=True)
model.add_node(node)
# Extra node for prediction
name = "CHDnew"
prob = "exp(beta_0 + beta_1 * 35)/(1.0 + exp(beta_0 + beta_1 * 35))"
distribution = bd.Delta(prob)
node = bd.Node(name, distribution)
model.add_node(node)
# Create the C++ code
bd.generate_h(model, data)
bd.generate_cpp(model, data)
# Compile the C++ code so it's ready to go