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[WIP] implemeting diagonal_Q flag in RTBM #48

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[WIP] implemeting diagonal_Q flag in RTBM #48

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32 changes: 31 additions & 1 deletion theta/mathtools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,6 @@ def rtbm_parts(v, bv, bh, t, w, q, mode=1):
return ( np.sqrt(detT / (2.0 * np.pi) ** (v.shape[0])) * ExpF ), ( vR1 / vR2 * np.exp(uR1-uR2) )



def rtbm_probability(v, bv, bh, t, w, q, mode=1):
"""Implements the RTBM probability"""
detT = np.linalg.det(t)
Expand All @@ -58,6 +57,37 @@ def rtbm_probability(v, bv, bh, t, w, q, mode=1):
return np.sqrt(detT / (2.0 * np.pi) ** (v.shape[0])) * ExpF * vR1 / vR2 * np.exp(uR1-uR2)


def factorized_rtbm_probability(v, bv, bh, t, w, q, mode=1):
"""Implements the RTBM probability"""
detT = np.linalg.det(t)
invT = np.linalg.inv(t)
vT = v.T
vTv = np.dot(np.dot(vT, t), v)
BvT = bv.T
BhT = bh.T
Bvv = np.dot(BvT, v)
BiTB = np.dot(np.dot(BvT, invT), bv)
BtiTW = np.dot(np.dot(BvT, invT), w)
WtiTW = np.dot(np.dot(w.T, invT), w)

ExpF = np.exp(-0.5 * vTv.diagonal() - Bvv - BiTB * np.ones(v.shape[1]))

# factorize Q
vWb = (vT.dot(w) + BhT)
uR1 = np.ones(vWb.shape[0], dtype=complex)
vR1 = np.ones(vWb.shape[0], dtype=complex)

for i in range(vWb.shape[1]):
O = np.matrix([[q[i, i]]], dtype=complex)
tuR1, tvR1 = RiemannTheta.parts_eval(vWb[:, [i]] / (2.0j * np.pi), -O / (2.0j * np.pi), mode, epsilon=RTBM_precision)
uR1 *= tuR1
vR1 *= tvR1

uR2, vR2 = RiemannTheta.parts_eval((BhT - BtiTW) / (2.0j * np.pi), (-q + WtiTW) / (2.0j * np.pi), mode, epsilon=RTBM_precision)

return np.sqrt(detT / (2.0 * np.pi) ** (v.shape[0])) * ExpF * vR1 / vR2 * np.exp(uR1-uR2)


def rtbm_log_probability(v, bv, bh, t, w, q, mode=1):
"""Implements the RTBM probability"""
detT = np.linalg.det(t)
Expand Down
51 changes: 38 additions & 13 deletions theta/rtbm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,11 @@
from __future__ import absolute_import
import numpy as np
from theta.mathtools import rtbm_probability, hidden_expectations, rtbm_log_probability, \
check_normalization_consistency, check_pos_def
check_normalization_consistency, check_pos_def, factorized_rtbm_probability

from theta.riemann_theta.riemann_theta import RiemannTheta


class AssignError(Exception):
pass

Expand All @@ -18,7 +19,7 @@ class Mode:
Expectation = 2

def __init__(self, visible_units, hidden_units, mode=Mode.Probability,
init_max_param_bound=2, random_bound=1, phase=1, diagonal_T=False):
init_max_param_bound=2, random_bound=1, phase=1, diagonal_T=False, diagonal_Q=False):
"""Setup operators for BM based on the number of visible and hidden units

Args:
Expand All @@ -40,14 +41,21 @@ def __init__(self, visible_units, hidden_units, mode=Mode.Probability,
self._w = np.zeros([visible_units, hidden_units])
self._q = np.zeros([hidden_units, hidden_units])
self._diagonal_T = diagonal_T
self._diagonal_Q = diagonal_Q
self._mode = None
self._call = None
self._parameters = None
self._X = None
self._phase = phase
self._check_positivity = True
if diagonal_T:
self._size = 2 * self._Nv + self._Nh + (self._Nh**2+self._Nh)//2 + self._Nv*self._Nh

self._size = self._Nv + self._Nh + self._Nv*self._Nh
if diagonal_T and diagonal_Q:
self._size += self._Nv + self._Nh
elif diagonal_T and not diagonal_Q:
self._size += self._Nv + (self._Nh**2+self._Nh)//2
elif not diagonal_T and diagonal_Q:
self._size += self._Nh + (self._Nv**2+self._Nv)//2
else:
self._size = self._Nv + self._Nh + (self._Nv**2+self._Nv+self._Nh**2+self._Nh)//2+self._Nv*self._Nh

Expand Down Expand Up @@ -119,7 +127,7 @@ def random_init(self, bound):
a_size = (self._Nv+self._Nh)**2

params = np.random.uniform(-bound, bound, a_size+self._Nv+self._Nh)
if self._diagonal_T:
if self._diagonal_T or self._diagonal_Q:
x = np.eye(a_shape[0])
np.fill_diagonal(x, params[:self._Nv+self._Nh])
else:
Expand All @@ -134,10 +142,18 @@ def random_init(self, bound):
self._bh = self._phase * params[-self._Nh:].reshape(self._bh.shape)

# store parameters having in mind that Q and T are symmetric.
if self._diagonal_T:
if self._diagonal_T and not self._diagonal_Q:
self._parameters = np.concatenate([self._bv.flatten(), self._bh.flatten(),
self._w.flatten(), self._t.diagonal(),
self._q[np.triu_indices(self._Nh)]])
elif not self._diagonal_T and self._diagonal_Q:
self._parameters = np.concatenate([self._bv.flatten(), self._bh.flatten(),
self._w.flatten(), self._t[np.triu_indices(self._Nv)],
self._q.diagonal()])
elif self._diagonal_T and self._diagonal_Q:
self._parameters = np.concatenate([self._bv.flatten(), self._bh.flatten(),
self._w.flatten(), self._t.diagonal(),
self._q.diagonal()])
else:
self._parameters = np.concatenate([self._bv.flatten(), self._bh.flatten(),
self._w.flatten(), self._t[np.triu_indices(self._Nv)],
Expand Down Expand Up @@ -176,9 +192,13 @@ def set_parameters(self, params):
self._t[(inds[1], inds[0])] = params[index:index+(self._Nv**2+self._Nv)//2]
index += (self._Nv**2+self._Nv)//2

inds = np.triu_indices_from(self._q)
self._q[inds] = params[index:index+(self._Nh**2+self._Nh)//2]
self._q[(inds[1], inds[0])] = params[index:index+(self._Nh**2+self._Nh)//2]
if self._diagonal_Q:
np.fill_diagonal(self._q, params[index:index+self._Nh])
index += self._Nh
else:
inds = np.triu_indices_from(self._q)
self._q[inds] = params[index:index+(self._Nh**2+self._Nh)//2]
self._q[(inds[1], inds[0])] = params[index:index+(self._Nh**2+self._Nh)//2]

if self._check_positivity:
if not check_normalization_consistency(self._t, self._q, self._w) or \
Expand All @@ -194,12 +214,14 @@ def get_gradients(self):
"""Return flat array with calculated gradients
[Gbh,Gbv,Gw,Gt,Gq]
"""

inds = np.triu_indices_from(self._gradQ)

if(self._diagonal_T):
if self._diagonal_T and not self._diagonal_Q:
return np.real(np.concatenate((self._gradBv.flatten(),self._gradBh.flatten(),self._gradW.flatten(), self._gradT.diagonal(), self._gradQ[inds].flatten() )))

elif not self._diagonal_T and self._diagonal_Q:
return np.real(np.concatenate((self._gradBv.flatten(),self._gradBh.flatten(),self._gradW.flatten(), self._gradT.flatten(), self._gradQ.diagonal() )))
elif self._diagonal_T and self._diagonal_Q:
return np.real(np.concatenate((self._gradBv.flatten(),self._gradBh.flatten(),self._gradW.flatten(), self._gradT.diagonal(), self._gradQ.diagonal() )))
else:
return np.real(np.concatenate((self._gradBv.flatten(),self._gradBh.flatten(),self._gradW.flatten(), self._gradT.flatten(), self._gradQ[inds].flatten() )))

Expand Down Expand Up @@ -240,7 +262,10 @@ def mode(self, value):
mode = 2

if value is self.Mode.Probability:
self._call = lambda data: np.real(rtbm_probability(data, self._bv, self._bh, self._t, self._w, self._q, mode))
if self._diagonal_Q:
self._call = lambda data: np.real(factorized_rtbm_probability(data, self._bv, self._bh, self._t, self._w, self._q, mode))
else:
self._call = lambda data: np.real(rtbm_probability(data, self._bv, self._bh, self._t, self._w, self._q, mode))
elif value is self.Mode.LogProbability:
self._call = lambda data: np.real(rtbm_log_probability(data, self._bv, self._bh, self._t, self._w, self._q, mode))
elif value is self.Mode.Expectation:
Expand Down