def calcSummaryStats(Data, SS, LP, **kwargs):
''' Calculate summary statistics for given dataset and local parameters
Returns
--------
SS : SuffStatBag object, with K components.
'''
if not hasattr(Data, 'X_NE'):
Data.X_NE = np.hstack([Data.X, np.ones(Data.nObs)[:, np.newaxis]])
Y_N = Data.Y
X_NE = Data.X_NE
E = X_NE.shape[1]
if 'resp' in LP:
# Dense responsibility calculations
resp = LP['resp']
K = resp.shape[1]
S_yy_K = dotATB(resp, np.square(Y_N)).flatten()
S_yx_KE = dotATB(resp, Y_N * X_NE)
# Expected outer product
S_xxT_KEE = np.zeros((K, E, E))
sqrtResp_k_N = np.sqrt(resp[:, 0])
sqrtR_X_k_NE = sqrtResp_k_N[:, np.newaxis] * X_NE
S_xxT_KEE[0] = dotATA(sqrtR_X_k_NE)
for k in xrange(1, K):
np.sqrt(resp[:, k], out=sqrtResp_k_N)
np.multiply(sqrtResp_k_N[:, np.newaxis], X_NE, out=sqrtR_X_k_NE)
S_xxT_KEE[k] = dotATA(sqrtR_X_k_NE)
else:
raise ValueError("TODO")
spR = LP['spR']
K = spR.shape[1]
if SS is None:
SS = SuffStatBag(K=K, D=Data.dim, E=E)
elif not hasattr(SS, 'E'):
SS._Fields.E = E
SS.setField('xxT_KEE', S_xxT_KEE, dims=('K', 'E', 'E'))
SS.setField('yx_KE', S_yx_KE, dims=('K', 'E'))
SS.setField('yy_K', S_yy_K, dims=('K'))
# Expected count for each k
# Usually computed by allocmodel. But just in case...
if not hasattr(SS, 'N'):
if 'resp' in LP:
SS.setField('N', LP['resp'].sum(axis=0), dims='K')
else:
SS.setField('N', as1D(toCArray(LP['spR'].sum(axis=0))), dims='K')
#SS.setField("N_K", SS.N, dims="K")
return SS
def calcSummaryStats(Data, SS, LP, **kwargs):
''' Calculate summary statistics for given dataset and local parameters
Returns
--------
SS : SuffStatBag object, with K components.
'''
X = Data.X
if 'resp' in LP:
resp = LP['resp']
K = resp.shape[1]
# 1/2: Compute mean statistic
S_x = dotATB(resp, X)
# 2/2: Compute expected outer-product statistic
S_xxT = np.zeros((K, Data.dim, Data.dim))
sqrtResp_k = np.sqrt(resp[:, 0])
sqrtRX_k = sqrtResp_k[:, np.newaxis] * Data.X
S_xxT[0] = dotATA(sqrtRX_k)
for k in xrange(1, K):
np.sqrt(resp[:, k], out=sqrtResp_k)
np.multiply(sqrtResp_k[:, np.newaxis], Data.X, out=sqrtRX_k)
S_xxT[k] = dotATA(sqrtRX_k)
else:
spR = LP['spR']
K = spR.shape[1]
# 1/2: Compute mean statistic
S_x = spR.T * X
# 2/2: Compute expected outer-product statistic
S_xxT = calcSpRXXT(X=X, spR_csr=spR)
if SS is None:
SS = SuffStatBag(K=K, D=Data.dim)
# Expected mean for each state k
SS.setField('x', S_x, dims=('K', 'D'))
# Expected outer-product for each state k
SS.setField('xxT', S_xxT, dims=('K', 'D', 'D'))
# Expected count for each k
# Usually computed by allocmodel. But just in case...
if not hasattr(SS, 'N'):
if 'resp' in LP:
SS.setField('N', LP['resp'].sum(axis=0), dims='K')
else:
SS.setField('N', as1D(toCArray(LP['spR'].sum(axis=0))), dims='K')
return SS
def calcSummaryStatsForContigBlock(self, Data, SS=None, a=0, b=0):
''' Calculate sufficient stats for a single contiguous block of data
'''
D = Data.X.shape[1]
E = Data.Xprev.shape[1]
if SS is None:
SS = SuffStatBag(K=1, D=D, E=E)
elif not hasattr(SS, 'E'):
SS._Fields.E = E
ppT = dotATA(Data.Xprev[a:b])[np.newaxis, :, :]
xxT = dotATA(Data.X[a:b])[np.newaxis, :, :]
pxT = dotATB(Data.Xprev[a:b], Data.X[a:b])[np.newaxis, :, :]
SS.setField('N', (b - a) * np.ones(1), dims='K')
SS.setField('xxT', xxT, dims=('K', 'D', 'D'))
SS.setField('ppT', ppT, dims=('K', 'E', 'E'))
SS.setField('pxT', pxT, dims=('K', 'E', 'D'))
return SS
def calcSummaryStats(Data, SS, LP, **kwargs):
''' Calculate summary statistics for given dataset and local parameters
Returns
--------
SS : SuffStatBag object, with K components.
'''
X = Data.X
if 'resp' in LP:
resp = LP['resp']
K = resp.shape[1]
# 1/2: Compute mean statistic
S_x = dotATB(resp, X)
# 2/2: Compute expected outer-product statistic
S_xx = calcRXX_withDenseResp(resp, X)
else:
spR = LP['spR']
K = spR.shape[1]
# 1/2: Compute mean statistic
S_x = spR.T * X
# 2/2: Compute expected outer-product statistic
S_xx = calcSpRXX(X=X, spR_csr=spR)
if SS is None:
SS = SuffStatBag(K=K, D=Data.dim)
# Expected mean for each state k
SS.setField('x', S_x, dims=('K', 'D'))
# Expected sum-of-squares for each state k
SS.setField('xx', S_xx, dims=('K', 'D'))
# Expected count for each k
# Usually computed by allocmodel. But sometimes not (eg TopicModel)
if not hasattr(SS, 'N'):
if 'resp' in LP:
SS.setField('N', LP['resp'].sum(axis=0), dims='K')
else:
SS.setField('N', as1D(toCArray(LP['spR'].sum(axis=0))), dims='K')
return SS