def test_ScaleParameter():
"""Tests probflow.parameters.ScaleParameter"""
# Create the parameter
param = ScaleParameter()
# All samples should be > 0
assert np.all(param.posterior_sample(n=1000) > 0)
# 1D ScaleParameter
param = ScaleParameter(shape=5)
samples = param.posterior_sample(n=10)
assert samples.ndim == 2
assert samples.shape[0] == 10
assert samples.shape[1] == 5
assert np.all(samples > 0)
# 2D ScaleParameter
param = ScaleParameter(shape=[5, 4])
samples = param.posterior_sample(n=10)
assert samples.ndim == 3
assert samples.shape[0] == 10
assert samples.shape[1] == 5
assert samples.shape[2] == 4
assert np.all(samples > 0)
def __init__(self, d: List[int], heteroscedastic: bool = False, **kwargs):
self.heteroscedastic = heteroscedastic
if heteroscedastic:
d[-1] = 2 * d[-1]
self.network = DenseNetwork(d, **kwargs)
else:
self.network = DenseNetwork(d, **kwargs)
self.std = ScaleParameter([1, 1], name='std')
def __init__(self, d: int, heteroscedastic: bool = False):
self.heteroscedastic = heteroscedastic
if heteroscedastic:
self.weights = Parameter([d, 2], name='weights')
self.bias = Parameter([1, 1], name='bias')
else:
self.weights = Parameter([d, 1], name='weights')
self.bias = Parameter([1, 1], name='bias')
self.std = ScaleParameter([1, 1], name='std')
def __init__(self, d: int, d_o: int = 1, heteroscedastic: bool = False):
self.heteroscedastic = heteroscedastic
if heteroscedastic:
self.d_o = d_o
self.weights = Parameter([d, d_o * 2], name="weights")
self.bias = Parameter([1, d_o * 2], name="bias")
else:
self.weights = Parameter([d, d_o], name="weights")
self.bias = Parameter([1, d_o], name="bias")
self.std = ScaleParameter([1, d_o], name="std")
def __init__(self):
self.weight = Parameter(name="Weight")
self.bias = Parameter(name="Bias")
self.std = ScaleParameter(name="Std")
def __init__(self, d_in, d_out):
self.weight = Parameter([d_in, d_out], name="Weight")
self.bias = Parameter([1, d_out], name="Bias")
self.std = ScaleParameter([1, d_out], name="Std")
def __init__(self):
self.module = MyModule()
self.std = ScaleParameter([1, 1],
name="Std",
prior=Gamma(1.0, 1.0))
def __init__(self):
self.mean = Parameter([1], name="Mean")
self.std = ScaleParameter([1], name="Std")
def __init__(self, cols):
self.cols = cols
self.weight = Parameter([len(cols), 1], name="Weight")
self.bias = Parameter([1, 1], name="Bias")
self.std = ScaleParameter([1, 1], name="Std")
def __init__(self, d: int):
self.weights = Parameter([d, 1], name='weights')
self.bias = Parameter(name='bias')
self.std = ScaleParameter(name='std')
def __init__(self, d: List[int]):
self.network = DenseNetwork(d)
self.std = ScaleParameter(name='std')