def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = observation[self.outputs[0]]
self.N += 1.
self.sum_x += x
self.sum_x_sq += x * x
self.data[rowid] = x
def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = int(observation[self.outputs[0]])
self.N += 1
if x not in self.counts:
self.counts[x] = 0
self.counts[x] += 1
self.data[rowid] = x
def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = observation[self.outputs[0]]
if x < 0:
raise ValueError('Invalid Exponential: %s' % str(x))
self.N += 1
self.sum_x += x
self.data[rowid] = x
def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = observation[self.outputs[0]]
if x not in [0, 1]:
raise ValueError('Invalid Bernoulli: %s' % str(x))
self.N += 1
self.x_sum += x
self.data[rowid] = x
def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = observation[self.outputs[0]]
if not (x % 1 == 0 and x >= 0):
raise ValueError('Invalid Geometric: %s') % str(x)
self.N += 1
self.sum_x += x
self.data[rowid] = x
def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = observation[self.outputs[0]]
if not (0 <= x <= 2 * pi):
raise ValueError('Invalid Vonmises: %s' % str(x))
self.N += 1
self.sum_sin_x += sin(x)
self.sum_cos_x += cos(x)
self.data[rowid] = x
def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = observation[self.outputs[0]]
if x <= 0:
raise ValueError('Invalid Lognormal: %s' % str(x))
self.N += 1
self.sum_log_x += log(x)
self.sum_log_x_sq += log(x) * log(x)
self.data[rowid] = x
def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = observation[self.outputs[0]]
if not (x % 1 == 0 and x >= 0):
raise ValueError('Invalid Poisson: %s' % str(x))
self.N += 1
self.sum_x += x
self.sum_log_fact_x += gammaln(x + 1)
self.data[rowid] = x
def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = observation[self.outputs[0]]
if not (x % 1 == 0 and 0 <= x < self.k):
raise ValueError('Invalid Categorical(%d): %s' % (self.k, x))
x = int(x)
self.N += 1
self.counts[x] += 1
self.data[rowid] = x
def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = observation[self.outputs[0]]
if not (self.l <= x <= self.h):
raise ValueError('Invalid NormalTrunc(%f,%f): %s' %
(self.l, self.h, str(x)))
self.N += 1
self.sum_x += x
self.sum_x_sq += x * x
self.data[rowid] = x
def incorporate(self, rowid, observation, inputs=None):
DistributionGpm.incorporate(self, rowid, observation, inputs)
x = observation[self.outputs[0]]
if np.allclose(0, x):
x = 0.001
elif np.allclose(1, x):
x = 0.999
if not 0 < x < 1:
raise ValueError('Invalid Beta: %s' % str(x))
self.N += 1
self.sum_log_x += log(x)
self.sum_minus_log_x += log(1.-x)
self.data[rowid] = x