def inverse_stoc_var_hyper_priors_test3():
"""
4 hyperparam, 2 var param, 6 params, mix of gamma, delta hyper, delta and gamma var, mix of gauss, laplace prior
"""
hyperprior_types = [9, 7, 9, 7]
var_prior_types = [10, 7]
prior_types = [4, 5, 4]
hyperprior_params = [[1., 2.], [1., 1.], [1., 5.], [3., 1.]]
var_prior_params = [[1., 2.], [3., 3.]]
prior_hyperparams = [0., 1., 2.]
hyper_dependence_lengths = [1, 3, 1, 1]
var_dependence_lengths = [1, 1]
dependence_lengths = [1, 3, 2]
param_hyperprior_types = [1, 0, 3, 2]
var_param_prior_types = [1, 0]
param_prior_types = [1, 2, 0]
n_stoc = 4
n_stoc_var = 2
n_dims = 6
prior = isvp.inverse_stoc_var_hyper_prior(
hyperprior_types, var_prior_types, prior_types, hyperprior_params,
var_prior_params, prior_hyperparams, hyper_dependence_lengths,
var_dependence_lengths, dependence_lengths, param_hyperprior_types,
var_param_prior_types, param_prior_types, n_stoc, n_stoc_var, n_dims)
p = np.array([0.1, 0.5, 0.6, 0.7, 0.9, 0.5, 0.8, 0.9, 0.4, 0.2, 0.1, 0.5])
prior(p)
v = [1., 1.6986436, 1.6986436, 1.6986436, 3., 2.03787088]
print isp.recip_gamma_prior()(p[-7], 1., 2.)
print isp.laplace_prior()(p[-6], 1., v[0])
print isp.gaussian_prior()(p[-5], 2., v[1])
print isp.gaussian_prior()(p[-4], 2., v[2])
print isp.gaussian_prior()(p[-3], 2., v[3])
print isp.gaussian_prior()(p[-2], 0., v[4])
print isp.gaussian_prior()(p[-1], 0., v[5])
def inverse_stoc_hyper_priors_test4():
"""
3 hyperparam, 4 params, gamma, delta, gamma hyper, gauss, laplace prior
"""
hyperprior_types = [9, 7, 9]
prior_types = [4, 5]
hyperprior_params = [[1., 2.], [1., 1.], [1., 5.]]
prior_hyperparams = [0., 1.]
hyper_dependence_lengths = [1, 2, 1]
dependence_lengths = [1, 3]
param_hyperprior_types = [0, 1, 2]
param_prior_types = [0, 1]
n_stoc = 3
n_dims = 4
prior = isp.inverse_stoc_hyper_prior(hyperprior_types, prior_types,
hyperprior_params, prior_hyperparams,
hyper_dependence_lengths,
dependence_lengths,
param_hyperprior_types,
param_prior_types, n_stoc, n_dims)
p = np.array([0.1, 0.5, 0.6, 0.7, 0.8, 0.9, 0.4])
prior(p)
v = [4.35688457, 1., 1., 2.33597589]
print isp.gaussian_prior()(p[-4], 0., v[0])
print isp.laplace_prior()(p[-3], 1., v[1])
print isp.laplace_prior()(p[-2], 1., v[2])
print isp.laplace_prior()(p[-1], 1., v[3])
def stoc_prior_functions_test():
p = np.array([0.1, 0.6, 0.8])
prior_hyperparam1, prior_hyperparam2 = 1., 4.
u = isp.uniform_prior()
plu = isp.pos_log_uniform_prior()
nlu = isp.neg_log_uniform_prior()
lu = isp.log_uniform_prior()
g = isp.gaussian_prior()
l = isp.laplace_prior()
c = isp.cauchy_prior()
d = isp.delta_prior()
ga = isp.gamma_prior()
srga = isp.sqrt_recip_gamma_prior()
su = isp.sorted_uniform_prior()
splu = isp.sorted_pos_log_uniform_prior()
snlu = isp.sorted_neg_log_uniform_prior()
slu = isp.sorted_log_uniform_prior()
sg = isp.sorted_gaussian_prior()
sl = isp.sorted_laplace_prior()
sc = isp.sorted_cauchy_prior()
sd = isp.sorted_delta_prior()
sga = isp.sorted_gamma_prior()
ssrga = isp.sorted_sqrt_rec_gam_prior()
print u(p, prior_hyperparam1, prior_hyperparam2)
print plu(p, prior_hyperparam1, prior_hyperparam2)
print nlu(p, prior_hyperparam1, prior_hyperparam2)
print lu(p, prior_hyperparam1, prior_hyperparam2)
print g(p, prior_hyperparam1, prior_hyperparam2)
print l(p, prior_hyperparam1, prior_hyperparam2)
print c(p, prior_hyperparam1, prior_hyperparam2)
print d(p, prior_hyperparam1, prior_hyperparam2)
print ga(p, prior_hyperparam1, prior_hyperparam2)
print srga(p, prior_hyperparam1, prior_hyperparam2)
print su(p, prior_hyperparam1, prior_hyperparam2)
print splu(p, prior_hyperparam1, prior_hyperparam2)
print snlu(p, prior_hyperparam1, prior_hyperparam2)
print slu(p, prior_hyperparam1, prior_hyperparam2)
print sg(p, prior_hyperparam1, prior_hyperparam2)
print sl(p, prior_hyperparam1, prior_hyperparam2)
print sc(p, prior_hyperparam1, prior_hyperparam2)
print sd(p, prior_hyperparam1, prior_hyperparam2)
print sga(p, prior_hyperparam1, prior_hyperparam2)
print ssrga(p, prior_hyperparam1, prior_hyperparam2)
def inverse_stoc_hyper_priors_test14():
"""
real nn arch with 16 nn params, two hyperparams, input_size granularity,
degen dependent params
"""
num_inputs = 1
layer_sizes = [3, 2]
num_outputs = 1
print "num weights"
n_dims = tools.calc_num_weights(num_inputs, layer_sizes, num_outputs)
print tools.calc_num_weights(num_inputs, layer_sizes, num_outputs)
weight_shapes = tools.get_weight_shapes(num_inputs, layer_sizes,
num_outputs)
print "weight shapes"
print tools.get_weight_shapes(num_inputs, layer_sizes, num_outputs)
granularity = 'input_size'
hyper_dependence_lengths = tools.get_hyper_dependence_lengths(
weight_shapes, granularity)
n_stoc = len(hyper_dependence_lengths)
print "n_stoc"
print n_stoc
print "granularity"
print tools.get_hyper_dependence_lengths(weight_shapes, granularity)
print "number of weights per layer"
print tools.calc_num_weights_layers(weight_shapes)
dependence_lengths = tools.get_degen_dependence_lengths(weight_shapes)
print "degen dependence lengths"
print tools.get_degen_dependence_lengths(weight_shapes)
hyperprior_types = [9, 7]
prior_types = [4, 5]
hyperprior_params = [[1., 2.], [2., 0.]]
prior_hyperparams = [0., 1.]
param_hyperprior_types = [0, 1, 0, 0, 1, 1, 1, 0, 0]
param_prior_types = [0, 1, 0, 1, 0, 0, 1, 1, 0]
prior = isp.inverse_stoc_hyper_prior(hyperprior_types, prior_types,
hyperprior_params, prior_hyperparams,
hyper_dependence_lengths,
dependence_lengths,
param_hyperprior_types,
param_prior_types, n_stoc, n_dims)
p = np.array([
0.1, 0.5, 0.6, 0.7, 0.8, 0.9, 0.4, 0.2, 0.1, 0.5, 0.7, 0.8, 0.9, 0.4,
0.2, 0.1, 0.5, 0.9, 0.2, 0.7, 0.4, 0.9, 0.3, 0.5, 0.6, 0.8
])
prior(p)
u = [0., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1., 0.]
v = [
4.35688457, 4.35688457, 4.35688457, 2., 2., 2., 1.47740087, 1.47740087,
1.28886271, 1.28886271, 2., 2., 2., 2., 2., 2.9938003, 4.35688457
]
print isp.gaussian_prior()(p[9], u[0], v[0])
print isp.gaussian_prior()(p[10], u[1], v[1])
print isp.gaussian_prior()(p[11], u[2], v[2])
print isp.laplace_prior()(p[12], u[3], v[3])
print isp.laplace_prior()(p[13], u[4], v[4])
print isp.laplace_prior()(p[14], u[5], v[5])
print isp.gaussian_prior()(p[15], u[6], v[6])
print isp.gaussian_prior()(p[16], u[7], v[7])
print isp.laplace_prior()(p[17], u[8], v[8])
print isp.laplace_prior()(p[18], u[9], v[9])
print isp.gaussian_prior()(p[19], u[10], v[10])
print isp.gaussian_prior()(p[20], u[11], v[11])
print isp.gaussian_prior()(p[21], u[12], v[12])
print isp.gaussian_prior()(p[22], u[13], v[13])
print isp.laplace_prior()(p[23], u[14], v[14])
print isp.laplace_prior()(p[24], u[15], v[15])
print isp.gaussian_prior()(p[25], u[16], v[16])
def inverse_stoc_hyper_priors_test7():
"""
real nn arch with 16 nn params, one hyperparam, single granularity,
degen dependent params (2 priors)
"""
num_inputs = 1
layer_sizes = [3, 2]
num_outputs = 1
print "num weights"
n_dims = tools.calc_num_weights(num_inputs, layer_sizes, num_outputs)
print tools.calc_num_weights(num_inputs, layer_sizes, num_outputs)
weight_shapes = tools.get_weight_shapes(num_inputs, layer_sizes,
num_outputs)
print "weight shapes"
print tools.get_weight_shapes(num_inputs, layer_sizes, num_outputs)
granularity = 'single'
hyper_dependence_lengths = tools.get_hyper_dependence_lengths(
weight_shapes, granularity)
n_stoc = len(hyper_dependence_lengths)
print "number of weights per layer"
print tools.calc_num_weights_layers(weight_shapes)
dependence_lengths = tools.get_degen_dependence_lengths(weight_shapes)
print "degen dependence lengths"
print tools.get_degen_dependence_lengths(weight_shapes)
hyperprior_types = [9]
prior_types = [4, 5]
hyperprior_params = [[1., 2.]]
prior_hyperparams = [0., 1.]
param_hyperprior_types = [0]
param_prior_types = [0, 1, 0, 1, 0, 0, 1, 1, 0]
prior = isp.inverse_stoc_hyper_prior(hyperprior_types, prior_types,
hyperprior_params, prior_hyperparams,
hyper_dependence_lengths,
dependence_lengths,
param_hyperprior_types,
param_prior_types, n_stoc, n_dims)
p = np.array([
0.1, 0.5, 0.6, 0.7, 0.8, 0.9, 0.4, 0.2, 0.1, 0.5, 0.7, 0.8, 0.9, 0.4,
0.2, 0.1, 0.5, 0.9
])
prior(p)
u = [0., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1., 0.]
v = [
4.35688457, 4.35688457, 4.35688457, 4.35688457, 4.35688457, 4.35688457,
4.35688457, 4.35688457, 4.35688457, 4.35688457, 4.35688457, 4.35688457,
4.35688457, 4.35688457, 4.35688457, 4.35688457, 4.35688457
]
print isp.gaussian_prior()(p[1], u[0], v[0])
print isp.gaussian_prior()(p[2], u[1], v[1])
print isp.gaussian_prior()(p[3], u[2], v[2])
print isp.laplace_prior()(p[4], u[3], v[3])
print isp.laplace_prior()(p[5], u[4], v[4])
print isp.laplace_prior()(p[6], u[5], v[5])
print isp.gaussian_prior()(p[7], u[6], v[6])
print isp.gaussian_prior()(p[8], u[7], v[7])
print isp.laplace_prior()(p[9], u[8], v[8])
print isp.laplace_prior()(p[10], u[9], v[9])
print isp.gaussian_prior()(p[11], u[10], v[10])
print isp.gaussian_prior()(p[12], u[11], v[11])
print isp.gaussian_prior()(p[13], u[12], v[12])
print isp.gaussian_prior()(p[14], u[13], v[13])
print isp.laplace_prior()(p[15], u[14], v[14])
print isp.laplace_prior()(p[16], u[15], v[15])
print isp.gaussian_prior()(p[17], u[16], v[16])