def test_arguments(): """ args passing during initialization. """ l = msd.Cauchy() assert isinstance(l, msd.Distribution) l = msd.Cauchy([3.0], [4.0], dtype=dtype.float32) assert isinstance(l, msd.Distribution)
def __init__(self, shape, seed=0): super(Sampling, self).__init__() self.c = msd.Cauchy(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32) self.shape = shape
def __init__(self): super(KL, self).__init__() self.c = msd.Cauchy(np.array([3.]), np.array([4.]), dtype=dtype.float32)
def __init__(self): super(LogProb, self).__init__() self.c = msd.Cauchy(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
def __init__(self): super(CrossEntropy, self).__init__() self.c = msd.Cauchy(np.array([3.]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
def __init__(self): super(Basics, self).__init__() self.c = msd.Cauchy(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
def __init__(self): super(CauchyProb1, self).__init__() self.cauchy = msd.Cauchy()
def __init__(self): super(CauchyProb, self).__init__() self.cauchy = msd.Cauchy(3.0, 4.0, dtype=dtype.float32)
def test_scale(): with pytest.raises(ValueError): msd.Cauchy(0., 0.) with pytest.raises(ValueError): msd.Cauchy(0., -1.)
def test_name(): with pytest.raises(TypeError): msd.Cauchy(0., 1., name=1.0)
def test_seed(): with pytest.raises(TypeError): msd.Cauchy(0., 1., seed='seed')
def test_type(): with pytest.raises(TypeError): msd.Cauchy(0., 1., dtype=dtype.int32)
def test_cauchy_shape_errpr(): """ Invalid shapes. """ with pytest.raises(ValueError): msd.Cauchy([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
def __init__(self): super(CauchyConstruct, self).__init__() self.cauchy = msd.Cauchy(3.0, 4.0) self.cauchy1 = msd.Cauchy()
def __init__(self): super(KL, self).__init__() self.cauchy = msd.Cauchy(3.0, 4.0) self.cauchy1 = msd.Cauchy()
def __init__(self, loc, scale, seed=10, dtype=ms.float32, name='Cauchy'): super().__init__() self.b = msd.Cauchy(loc, scale, seed, dtype, name)