def compute_partials(self, inputs, partials):
"""
Collect computed partial derivatives and return them.
Checks if the needed derivatives are cached already based on the
inputs vector. Refreshes the cache by re-computing the current point
if necessary.
"""
pt = np.array([inputs[pname].flatten() for pname in self.pnames]).T
if self.metadata['training_data_gradients']:
dy_ddata = np.zeros(self.sh)
for j in range(self.metadata['num_nodes']):
for i, axis in enumerate(self.params):
e_i = np.eye(axis.size)
interp = make_interp_spline(axis,
e_i,
k=self._ki[i],
axis=0)
if i == 0:
val = interp(pt[j, i])
else:
val = np.outer(val, interp(pt[j, i]))
dy_ddata[j] = val.reshape(self.sh[1:])
for out_name in self.interps:
dval = self.interps[out_name].gradient(pt).T
for i, p in enumerate(self.pnames):
partials[out_name, p] = np.diag(dval[i])
if self.metadata['training_data_gradients']:
partials[out_name, "%s_train" % out_name] = dy_ddata
def compute_partials(self, inputs, partials):
"""
Collect computed partial derivatives and return them.
Checks if the needed derivatives are cached already based on the
inputs vector. Refreshes the cache by re-computing the current point
if necessary.
Parameters
----------
inputs : Vector
unscaled, dimensional input variables read via inputs[key]
partials : Jacobian
sub-jac components written to partials[output_name, input_name]
"""
pt = np.array([inputs[pname].flatten() for pname in self.pnames]).T
if self.options['training_data_gradients']:
dy_ddata = np.zeros(self.sh)
for j in range(self.options['vec_size']):
for i, axis in enumerate(self.params):
e_i = np.eye(axis.size)
interp = make_interp_spline(axis,
e_i,
k=self._ki[i],
axis=0)
if i == 0:
val = interp(pt[j, i])
else:
val = np.outer(val, interp(pt[j, i]))
dy_ddata[j] = val.reshape(self.sh[1:])
for out_name in self.interps:
dval = self.interps[out_name].gradient(pt).T
for i, p in enumerate(self.pnames):
partials[out_name, p] = dval[i]
if self.options['training_data_gradients']:
partials[out_name, "%s_train" % out_name] = dy_ddata
def compute_partials(self, inputs, partials):
"""
Collect computed partial derivatives and return them.
Checks if the needed derivatives are cached already based on the
inputs vector. Refreshes the cache by re-computing the current point
if necessary.
Parameters
----------
inputs : Vector
unscaled, dimensional input variables read via inputs[key]
partials : Jacobian
sub-jac components written to partials[output_name, input_name]
"""
pt = np.array([inputs[pname].flatten() for pname in self.pnames]).T
if self.metadata['training_data_gradients']:
dy_ddata = np.zeros(self.sh)
for j in range(self.metadata['num_nodes']):
for i, axis in enumerate(self.params):
e_i = np.eye(axis.size)
interp = make_interp_spline(axis,
e_i,
k=self._ki[i],
axis=0)
if i == 0:
val = interp(pt[j, i])
else:
val = np.outer(val, interp(pt[j, i]))
dy_ddata[j] = val.reshape(self.sh[1:])
for out_name in self.interps:
dval = self.interps[out_name].gradient(pt).T
for i, p in enumerate(self.pnames):
partials[out_name, p] = dval[i]
if self.metadata['training_data_gradients']:
partials[out_name, "%s_train" % out_name] = dy_ddata