def __check__(self):
Objective.__check__(self)
# arrays
self.edge = atleast_2d_col(self.edge)
if not isinstance(self.scale,VyPy.data.scaling.ScalingFunction):
self.scale = atleast_2d_col(self.scale)
def __check__(self):
# evaluator class
if not isinstance(self.evaluator, Evaluator):
self.evaluator = Evaluator(function=self.evaluator)
# gradients and hessians
if self.evaluator.gradient is None:
self.gradient = None
if self.evaluator.hessian is None:
self.hessian = None
# arrays
self.edge = atleast_2d_col(self.edge)
if not isinstance(self.scale,VyPy.data.scaling.ScalingFunction):
self.scale = atleast_2d_col(self.scale)
def __init__(self, XB, X, Y, DY=None):
# make sure all are 2D arrays
XB = atleast_2d_row(XB)
X = atleast_2d_row(X)
Y = atleast_2d_col(Y)
# sizes
ntx, nx = X.shape
nty, ny = Y.shape
assert ntx == nty, 'different training data X and targets Y'
assert XB.shape[
0] == nx, 'different training data X and bounds XB dimension'
assert ny == 1, 'training targets Y must be a column vector'
# handle optional gradients
if DY is None:
DY = np.empty([0, nx])
ntdy, ndy = DY.shape
else:
DY = atleast_2d_row(DY)
ntdy, ndy = DY.shape
assert ntx == ntdy, 'different training data X and target gradients DY'
# make sure data is of type float
X = X.astype(float)
Y = Y.astype(float)
DY = DY.astype(float)
XB = XB.astype(float)
# store data
self['X'] = X
self['Y'] = Y
self['DY'] = DY
self['XB'] = XB
def __call__(self, variables):
step = self.step
variables = deepcopy(variables)
if not isinstance(variables, obunch):
variables = obunch(variables)
# arrayify variables
values_init = variables.pack_array('vector')
values_init = atleast_2d_row(values_init)
nx = values_init.shape[1]
# prepare step
if not isinstance(step, (array_type, list, tuple)):
step = [step] * nx
step = atleast_2d_row(step)
if not step.shape[0] == 1:
step = step.T
values_run = np.vstack(
[values_init,
np.tile(values_init, [nx, 1]) + np.diag(step[0])])
variables_run = [
deepcopy(variables).unpack_array(val) for val in values_run
]
# run the function
#for i in variables_run: print i
results = self.function(variables_run)
#for i in results: print i
results_values = np.vstack(
[atleast_2d_row(res.pack_array('vector')) for res in results])
# pack results
gradients_values = (results_values[1:, :] -
results_values[None, 0, :]) / step.T
gradients_values = np.ravel(gradients_values)
variables = variables.unpack_array(values_init[0] * 0.0)
gradients = deepcopy(results[0])
if not isinstance(gradients, obunch):
gradients = obunch(gradients)
for i, g in gradients.items():
if isinstance(g, array_type):
g = atleast_2d_col(g)
gradients[i] = deepcopy(variables)
for j, v in gradients[i].items():
if isinstance(v, array_type):
v = atleast_2d_row(v)
gradients[i][j] = g * v
gradients.unpack_array(gradients_values)
return gradients
def __call__(self,variables):
step = self.step
variables = deepcopy(variables)
if not isinstance(variables,obunch):
variables = obunch(variables)
# arrayify variables
values_init = variables.pack_array('vector')
values_init = atleast_2d_row(values_init)
nx = values_init.shape[1]
# prepare step
if not isinstance(step,(array_type,list,tuple)):
step = [step] * nx
step = atleast_2d_row(step)
if not step.shape[0] == 1:
step = step.T
values_run = np.vstack([ values_init ,
np.tile(values_init,[nx,1]) + np.diag(step[0]) ])
variables_run = [ deepcopy(variables).unpack_array(val) for val in values_run ]
# run the function
#for i in variables_run: print i
results = self.function(variables_run)
#for i in results: print i
results_values = np.vstack([ atleast_2d_row( res.pack_array('vector') ) for res in results ])
# pack results
gradients_values = ( results_values[1:,:] - results_values[None,0,:] ) / step.T
gradients_values = np.ravel( gradients_values )
variables = variables.unpack_array( values_init[0] * 0.0 )
gradients = deepcopy(results[0])
if not isinstance(gradients,obunch):
gradients = obunch(gradients)
for i,g in gradients.items():
if isinstance(g,array_type):
g = atleast_2d_col(g)
gradients[i] = deepcopy(variables)
for j,v in gradients[i].items():
if isinstance(v,array_type):
v = atleast_2d_row(v)
gradients[i][j] = g * v
gradients.unpack_array(gradients_values)
return gradients
def __call__(self,variables):
step = self.step
variables = deepcopy(variables)
if not isinstance(variables,obunch):
variables = obunch(variables)
# arrayify variables
values_init = variables.pack_array('vector')
values_init = atleast_2d_row(values_init)
nx = values_init.shape[1]
# prepare step
if not isinstance(step,(array_type,list,tuple)):
step = [step] * nx
step = atleast_2d_col(step)
if not step.shape[1] == 1:
step = step.T
values_run = np.hstack([ values_init ,
np.tile(values_init,[nx,1]) + np.diag(step) ])
# run the function
results = self.function(values_run)
# pack results
gradients_values = ( results[1:,:] - results[None,0,:] ) / step
gradients_values = np.ravel(gradients_values)
variables.unpack_array( values_init * 0.0 )
gradients = deepcopy(results[0])
for k in gradients.keys():
gradients[k] = deepcopy(variables)
gradients.unpack_array(gradients_values)
return gradients
