def inverse_stoc_hyper_priors_test9():
"""
real nn arch with 16 nn params, two hyperparams, layer granularity,
independent 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 = 'layer'
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,
independent=True)
print "degen dependence lengths"
print tools.get_degen_dependence_lengths(weight_shapes, independent=True)
hyperprior_types = [9, 7]
prior_types = [4]
hyperprior_params = [[1., 2.], [2., 0.]]
prior_hyperparams = [0.]
param_hyperprior_types = [1, 0, 1]
param_prior_types = [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
])
prior(p)
u = [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]
v = [
2., 2., 2., 2., 2., 2., 1.6986436, 1.6986436, 1.6986436, 1.6986436,
1.6986436, 1.6986436, 1.6986436, 1.6986436, 2., 2., 2.
]
print isp.gaussian_prior()(p[3], u[0], v[0])
print isp.gaussian_prior()(p[4], u[1], v[1])
print isp.gaussian_prior()(p[5], u[2], v[2])
print isp.gaussian_prior()(p[6], u[3], v[3])
print isp.gaussian_prior()(p[7], u[4], v[4])
print isp.gaussian_prior()(p[8], u[5], v[5])
print isp.gaussian_prior()(p[9], u[6], v[6])
print isp.gaussian_prior()(p[10], u[7], v[7])
print isp.gaussian_prior()(p[11], u[8], v[8])
print isp.gaussian_prior()(p[12], u[9], v[9])
print isp.gaussian_prior()(p[13], u[10], v[10])
print isp.gaussian_prior()(p[14], u[11], v[11])
print isp.gaussian_prior()(p[15], u[12], v[12])
print isp.gaussian_prior()(p[16], u[13], v[13])
print isp.gaussian_prior()(p[17], u[14], v[14])
print isp.gaussian_prior()(p[18], u[15], v[15])
print isp.gaussian_prior()(p[19], u[16], v[16])
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])