def _check_input(self, x, y):
x = self._convert2tensor(x)
y = self._convert2tensor(y)
if x.shape != y.shape:
shape = np.broadcast(x.value, y.value).shape
if x.shape != shape:
x = broadcast_to(x, shape)
if y.shape != shape:
y = broadcast_to(y, shape)
return x, y
def _check_input(self, loc, scale):
loc = self._convert2tensor(loc)
scale = self._convert2tensor(scale)
if loc.shape != scale.shape:
shape = np.broadcast(loc.value, scale.value).shape
if loc.shape != shape:
loc = broadcast_to(loc, shape)
if scale.shape != shape:
scale = broadcast_to(scale, shape)
return loc, scale
def _check_input(self, x, t):
x = self._convert2tensor(x)
t = self._convert2tensor(t)
if x.shape != t.shape:
shape = np.broadcast(x.value, t.value).shape
if x.shape != shape:
x = broadcast_to(x, shape)
if t.shape != shape:
t = broadcast_to(t, shape)
return x, t
def _check_input(self, shape, rate):
shape = self._convert2tensor(shape)
rate = self._convert2tensor(rate)
if shape.shape != rate.shape:
shape_ = np.broadcast(shape.value, rate.value).shape
if shape.shape != shape_:
shape = broadcast_to(shape, shape_)
if rate.shape != shape_:
rate = broadcast_to(rate, shape_)
return shape, rate
def _check_input(self, x, y):
x = self._convert2tensor(x)
y = self._convert2tensor(y)
if x.shape != y.shape:
shape = np.broadcast(x.value, y.value).shape
if x.shape != shape:
x = broadcast_to(x, shape)
if y.shape != shape:
y = broadcast_to(y, shape)
return x, y
def _check_input(self, shape, rate):
shape = self._convert2tensor(shape)
rate = self._convert2tensor(rate)
if shape.shape != rate.shape:
shape_ = np.broadcast(shape.value, rate.value).shape
if shape.shape != shape_:
shape = broadcast_to(shape, shape_)
if rate.shape != shape_:
rate = broadcast_to(rate, shape_)
return shape, rate
def _check_input(self, x, t):
x = self._convert2tensor(x)
t = self._convert2tensor(t)
if x.shape != t.shape:
shape = np.broadcast(x.value, t.value).shape
if x.shape != shape:
x = broadcast_to(x, shape)
if t.shape != shape:
t = broadcast_to(t, shape)
return x, t
def _check_input(self, x, mu, tau):
x = self._convert2tensor(x)
mu = self._convert2tensor(mu)
tau = self._convert2tensor(tau)
if not x.shape == mu.shape == tau.shape:
shape = np.broadcast(x.value, mu.value, tau.value).shape
if x.shape != shape:
x = broadcast_to(x, shape)
if mu.shape != shape:
mu = broadcast_to(mu, shape)
if tau.shape != shape:
tau = broadcast_to(tau, shape)
return x, mu, tau
def _check_input(self, x, mu, tau):
x = self._convert2tensor(x)
mu = self._convert2tensor(mu)
tau = self._convert2tensor(tau)
if not x.shape == mu.shape == tau.shape:
shape = np.broadcast(x.value, mu.value, tau.value).shape
if x.shape != shape:
x = broadcast_to(x, shape)
if mu.shape != shape:
mu = broadcast_to(mu, shape)
if tau.shape != shape:
tau = broadcast_to(tau, shape)
return x, mu, tau
def _check_input(self, coef, mu, std):
coef = self._convert2tensor(coef)
mu = self._convert2tensor(mu)
std = self._convert2tensor(std)
if not coef.shape == mu.shape == std.shape:
shape = np.broadcast(coef.value, mu.value, std.value).shape
if coef.shape != shape:
coef = broadcast_to(coef, shape)
if mu.shape != shape:
mu = broadcast_to(mu, shape)
if std.shape != shape:
std = broadcast_to(std, shape)
self.n_component = coef.shape[self.axis]
return coef, mu, std
def _check_input(self, coef, mu, std):
coef = self._convert2tensor(coef)
mu = self._convert2tensor(mu)
std = self._convert2tensor(std)
if not coef.shape == mu.shape == std.shape:
shape = np.broadcast(coef.value, mu.value, std.value).shape
if coef.shape != shape:
coef = broadcast_to(coef, shape)
if mu.shape != shape:
mu = broadcast_to(mu, shape)
if std.shape != shape:
std = broadcast_to(std, shape)
self.n_component = coef.shape[self.axis]
return coef, mu, std
def test_broadcast(self):
x = nn.Parameter(np.ones((1, 1)))
shape = (5, 2, 3)
y = broadcast_to(x, shape)
self.assertEqual(y.shape, shape)
y.backward(np.ones(shape))
self.assertTrue((x.grad == np.ones((1, 1)) * 30).all())
def _pdf(self, x):
assert x.shape[-1] == self.mu.size
if x.ndim == 1:
squeeze = True
x = broadcast_to(x, (1, self.mu.size))
else:
squeeze = False
assert x.ndim == 2
d = x - self.mu
d = d.transpose()
p = (exp(-0.5 * (solve(self.cov, d) * d).sum(axis=0)) /
(2 * np.pi)**(self.mu.size * 0.5) / sqrt(det(self.cov)))
if squeeze:
p = p.sum()
return p
def _log_pdf(self, x):
assert x.shape[-1] == self.mu.size
if x.ndim == 1:
squeeze = True
x = broadcast_to(x, (1, self.mu.size))
else:
squeeze = False
assert x.ndim == 2
d = x - self.mu
d = d.transpose()
logp = (-0.5 * (solve(self.cov, d) * d).sum(axis=0) -
(self.mu.size * 0.5) * log(2 * np.pi) - 0.5 * logdet(self.cov))
if squeeze:
logp = logp.sum()
return logp
