MagP64

class rfbp.MagP64.MagP64(x)

Bases: ReplicatedFocusingBeliefPropagation.rfbp.Mag.BaseMag

__mod__(m)

Clip value in [-1, 1].

Parameters:m (MagP64) – The input value Returns:m – The MagP64 of the operation between the two mags. The clip operation is computed as np.clip( (self.mag + m.mag) / (1. + self.mag * m.mag), -1., 1.) Return type:MagP64

Example

>>> import numpy as np
>>> from ReplicatedFocusingBeliefPropagation import MagP64
>>> x = np.random.uniform(low=0., high=10)
>>> y = np.random.uniform(low=0., high=10)
>>> m1 = MagP64(x)
>>> m2 = MagP64(y)
>>> mx = m1 % m2
>>> my = m2 % m1
>>> assert np.isclose(mx.mag, my.mag)
>>> assert np.isclose(mx.value, my.value)
>>> assert -1. <= mx.mag <= 1.
>>> assert -1. <= my.mag <= 1.
>>> assert -1. <= mx.value <= 1.
>>> assert -1. <= my.value <= 1.

__xor__(m)

Mag product

Parameters:m (MagP64) – The input value Returns:m – The product of mags Return type:MagP64

Example

>>> import numpy as np
>>> from ReplicatedFocusingBeliefPropagation import MagP64
>>> x = np.random.uniform(low=0., high=10)
>>> y = np.random.uniform(low=0., high=10)
>>> m1 = MagP64(x)
>>> m2 = MagP64(y)
>>> mx = m1 ^ m2
>>> my = m2 ^ m1
>>> assert np.isclose(mx.mag, my.mag)
>>> assert np.isclose(mx.value, my.value)

static convert(x)

Convert a float to a mag value (as a constructor)

Parameters:x (float) – The number to convert Returns:m – Convert any-number to a MagP64 type Return type:MagP64

Example

>>> import numpy as np
>>> from ReplicatedFocusingBeliefPropagation import MagP64
>>>
>>> x = np.random.uniform(low=0., high=10)
>>> m1 = MagP64.convert(x)
>>> m2 = MagP64(x)
>>> assert m1.mag == m2.mag
>>> assert m1.value == m2.value

static couple(x1, x2)

Combine two mags as diff / sum

Parameters:

• x1 (float) – The first element of the operation
• x2 (float) – The second element of the operation

Returns:

x – In MagP64 the value is equal to the magnetization since the tanh operation is neglected

Return type:

float

Example

>>> import numpy as np
>>> from ReplicatedFocusingBeliefPropagation import MagP64
>>>
>>> x = np.random.uniform(low=0., high=10)
>>> y = np.random.uniform(low=0., high=10)
>>> mx = MagP64.couple(x, y)
>>> my = MagP64.couple(y, x)
>>> assert np.isclose(abs(mx.mag), abs(my.mag))
>>> assert np.isclose(abs(mx.value), abs(my.value))

magformat

Return the mag description

Returns:plain – The MagP64 type corresponds to a plain operation Return type:str

Example

>>> from ReplicatedFocusingBeliefPropagation import MagP64
>>> m = MagP64(3.14)
>>> m.magformat
  'plain'

static merf(x)

Perform erf on magnetization value (MagP64(erf(x)) in this case)

Parameters:x (float) – The input value Returns:m – The MagP64 version of the erf(x) Return type:MagP64

Example

>>> import numpy as np
>>> from scipy.special import erf
>>> from ReplicatedFocusingBeliefPropagation import MagP64
>>>
>>> x = np.random.uniform(low=0., high=10)
>>> mx = MagP64.merf(x)
>>> assert 0 <= mx.mag <= 1
>>> assert np.isclose(mx.mag, erf(x))

static mtanh(x)

Perform tanh on magnetization value (MagP64(tanh(x)) in this case)

Parameters:x (float) – The input value Returns:m – The MagP64 version of the tanh(x) Return type:MagP64

Example

>>> import numpy as np
>>> from ReplicatedFocusingBeliefPropagation import MagP64
>>>
>>> x = np.random.uniform(low=0., high=10)
>>> mx = MagP64.mtanh(x)
>>> assert 0 <= mx.mag <= 1
>>> assert np.isclose(mx.mag, np.tanh(x))

value

Return the mag value

Returns:x – In MagP64 the value is equal to the magnetization since the tanh operation is neglected Return type:float

Example

>>> from ReplicatedFocusingBeliefPropagation import MagP64
>>> x = np.random.uniform(low=0, high=1.)
>>> m = MagP64(x)
>>> assert np.isclose(m.mag, x)
>>> assert np.isclose(m.value, x)