I have followed the thread
How to fix some dimensions of a kernel lengthscale in gpflow?, and created a kernel as below
class MyKernel(gpflow.kernels.Matern52):
#property
def lengthscales(self) -> tf.Tensor:
return tf.stack([self.lengthscale_0, self.lengthscale_1, self.lengthscale_2])
#lengthscales.setter
def lengthscales(self, value):
self.lengthscale_0 = value[0] # fixed
self.lengthscale_1 = gpflow.Parameter(value[1], transform=tfp.bijectors.Exp())
self.lengthscale_2 = gpflow.Parameter(value[2], transform=tfp.bijectors.Exp())
But when I run the model, I have got below error, any suggestions on how to solve this are appreciated.
` /Users/anaconda3/lib/python3.9/site-packages/gpflux/layers/gp_layer.py:284 call *
outputs = super().call(inputs, *args, **kwargs)
/Users/anaconda3/lib/python3.9/site-packages/tensorflow_probability/python/layers/distribution_layer.py:236 call **
distribution, value = super(DistributionLambda, self).call(
/Users/anaconda3/lib/python3.9/site-packages/tensorflow/python/keras/layers/core.py:919 call
result = self.function(inputs, **kwargs)
/Users/anaconda3/lib/python3.9/site-packages/tensorflow_probability/python/layers/distribution_layer.py:176 _fn
d = make_distribution_fn(*fargs, **fkwargs)
/Users/anaconda3/lib/python3.9/site-packages/gpflux/layers/gp_layer.py:320 _make_distribution_fn
mean, cov = self.predict(
/Users/anaconda3/lib/python3.9/site-packages/gpflux/layers/gp_layer.py:257 predict
mean_cond, cov = conditional(
/Users/anaconda3/lib/python3.9/site-packages/multipledispatch/dispatcher.py:278 __call__
return func(*args, **kwargs)
/Users/anaconda3/lib/python3.9/site-packages/gpflow/conditionals/conditionals.py:80 _conditional
return posterior.fused_predict_f(Xnew, full_cov=full_cov, full_output_cov=full_output_cov)
/Users/anaconda3/lib/python3.9/site-packages/gpflow/posteriors.py:145 fused_predict_f
mean, cov = self._conditional_fused(
/Users/anaconda3/lib/python3.9/site-packages/gpflow/posteriors.py:603 _conditional_fused
Kmm = covariances.Kuu(self.X_data, self.kernel, jitter=default_jitter()) # [M, M]
/Users/anaconda3/lib/python3.9/site-packages/multipledispatch/dispatcher.py:278 __call__
return func(*args, **kwargs)
/Users/anaconda3/lib/python3.9/site-packages/gpflow/covariances/kuus.py:25 Kuu_kernel_inducingpoints
Kzz = kernel(inducing_variable.Z)
/Users/anaconda3/lib/python3.9/site-packages/gpflow/kernels/base.py:173 __call__
return self.K(X, X2)
/Users/anaconda3/lib/python3.9/site-packages/gpflow/kernels/stationaries.py:88 K
r2 = self.scaled_squared_euclid_dist(X, X2)
/Users/anaconda3/lib/python3.9/site-packages/gpflow/kernels/stationaries.py:102 scaled_squared_euclid_dist
return square_distance(self.scale(X), self.scale(X2))
/Users/anaconda3/lib/python3.9/site-packages/gpflow/kernels/stationaries.py:64 scale
X_scaled = X / self.lengthscales if X is not None else X
/Users/dgpClassify2021-05-29.py:48 lengthscales
return tf.stack([self.lengthscale_0, self.lengthscale_1, self.lengthscale_2])
/Users/anaconda3/lib/python3.9/site-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/Users/anaconda3/lib/python3.9/site-packages/tensorflow/python/ops/array_ops.py:1412 stack
return ops.convert_to_tensor(values, name=name)
/Users/anaconda3/lib/python3.9/site-packages/tensorflow/python/profiler/trace.py:163 wrapped
return func(*args, **kwargs)
/Users/anaconda3/lib/python3.9/site-packages/tensorflow/python/framework/ops.py:1566 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
/Users/anaconda3/lib/python3.9/site-packages/tensorflow/python/ops/array_ops.py:1536 _autopacking_conversion_function
return _autopacking_helper(v, dtype, name or "packed")
/Users/anaconda3/lib/python3.9/site-packages/tensorflow/python/ops/array_ops.py:1471 _autopacking_helper
constant_op.constant(elem, dtype=dtype, name=str(i)))
/Users/anaconda3/lib/python3.9/site-packages/tensorflow/python/framework/constant_op.py:264 constant
return _constant_impl(value, dtype, shape, name, verify_shape=False,
/Users/anaconda3/lib/python3.9/site-packages/tensorflow/python/framework/constant_op.py:281 _constant_impl
tensor_util.make_tensor_proto(
/Users/anaconda3/lib/python3.9/site-packages/tensorflow/python/framework/tensor_util.py:435 make_tensor_proto
values = np.asarray(values)
/Users/anaconda3/lib/python3.9/site-packages/numpy/core/_asarray.py:83 asarray
return array(a, dtype, copy=False, order=order)
/Users/anaconda3/lib/python3.9/site-packages/tensorflow_probability/python/util/deferred_tensor.py:377 __array__
raise NotImplementedError(
NotImplementedError: Cannot convert a symbolic (graph mode) `DeferredTensor` to a numpy array.
`
Not sure about the above 'NotImplementedError'.
Related
I know that the issue 'ValueError: setting an array element with a sequence.' is normally because the function being optimized is a vector and not a scalar, anyhow my ARMA model below still gets this issue. Here is the code
def loghood(parm,endog,exog,p,q):
arparams,maparams,exogparams,bias = parm
armapredicts=np.zeros(endog.shape[0])
bias=0
res=abs(endog - np.mean(endog))
if p==0:
for i in range(1,endog.shape[0]-p):
armapredicts[i] = np.array([[res[i-f+q]] for f in range(0,q)]).dot(maparams.T) + exog.iloc[i,:].dot(exogparams.T) + bias
if q==0:
for i in range(1,endog.shape[0]-q):
armapredicts[i] = np.array([ [endog[i-f+p]] for f in range(0,p)]).T.dot(arparams) + exog.iloc[i,:].T.dot(exogparams) + bias
else:
for i in range(1,endog.shape[0]-2):
armapredicts[i] = np.array([ [endog[i-f+p]] for f in range(0,p)]).T.dot(arparams.reshape(-1,1)) + np.array([[res[i-f+q]] for f in range(0,q)]).T.dot(maparams.reshape(-1,1)) + exog.iloc[i,:].T.dot(exogparams.reshape(-1,1)) + bias
print(np.array([ [endog[i-f+p]] for f in range(0,p)]).T.shape )
print(maparams.reshape(-1,1).shape)
liklihood=1/((2*np.pi*armapredicts)**(1/2))*np.exp(-res**2/(2*armapredicts**2))
print(liklihood.shape)
log_hood=np.sum(np.log(liklihood.values))
print(log_hood)
return log_hood
x0=[np.ones(2),np.ones(2),np.ones(returnsant.shape[1]-1),1]
x0=np.array(x0,dtype=object).flatten()
res = spop.minimize(loghood,x0 ,args=(returnsant['Hedge Fund'],returnsant.drop('Hedge Fund',axis= 1),2,2), method='Nelder-mead')
print(res)
I know that likelihood is a 1-d vector and the log_hood is certainly scalar after np.sum, so how is this error occurring?? Thank you for your time.
EDIT:forgot to include the full error message
TypeError Traceback (most recent call last)
TypeError: only size-1 arrays can be converted to Python scalars
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
/var/folders/7f/gmpqnwqx0lb4nrsz2vqhvgn40000gp/T/ipykernel_30671/32993526.py in <module>
1 x0=[np.ones(2),np.ones(2),np.ones(returnsant.shape[1]-1),1]
2 #x0=np.array(x0,dtype=object).flatten()
----> 3 res = spop.minimize(loghood,x0 ,args=(returnsant['Hedge Fund'],returnsant.drop('Hedge Fund',axis= 1),2,2), method='Nelder-mead')
4 print(res)
~/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
609
610 if meth == 'nelder-mead':
--> 611 return _minimize_neldermead(fun, x0, args, callback, bounds=bounds,
612 **options)
613 elif meth == 'powell':
~/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/optimize.py in _minimize_neldermead(func, x0, args, callback, maxiter, maxfev, disp, return_all, initial_simplex, xatol, fatol, adaptive, bounds, **unknown_options)
687 zdelt = 0.00025
688
--> 689 x0 = asfarray(x0).flatten()
690
691 if bounds is not None:
~/opt/anaconda3/lib/python3.9/site-packages/numpy/core/overrides.py in asfarray(*args, **kwargs)
~/opt/anaconda3/lib/python3.9/site-packages/numpy/lib/type_check.py in asfarray(a, dtype)
112 if not _nx.issubdtype(dtype, _nx.inexact):
113 dtype = _nx.float_
--> 114 return asarray(a, dtype=dtype)
115
116
ValueError: setting an array element with a sequence.
FINAL EDIT:
I resolved the issue by simply creating a big array x0 of all my parameters
x0=np.zeros(5+returnsant.shape[1]-1)
res = spop.minimize(loghood,x0,args=(returnsant['Hedge Fund'],returnsant.drop('Hedge Fund',axis= 1),0,2), method='Nelder-mead')
print(res)
now I declare my subparameters in loghood
def loghood(parms,endog,exog,p,q):
arparams,maparams,exogparams,bias = parms[0:p],parms[p:p+q],parms[p+q:p+q+exog.shape[1]],parms[p+q+exog.shape[1]:-1]
armapredicts=np.zeros(endog.shape[0])
bias=0
res=abs(endog - np.mean(endog))
if p==0:
for i in range(1,endog.shape[0]-q):
armapredicts[i] = np.array([[res[i-f+q]] for f in range(0,q)]).T.dot(maparams) + exog.iloc[i,:].T.dot(exogparams) + bias
if q==0:
for i in range(1,endog.shape[0]-p):
armapredicts[i] = np.array([ [endog[i-f+p]] for f in range(0,p)]).T.dot(arparams) + exog.iloc[i,:].T.dot(exogparams) + bias
else:
for i in range(1,endog.shape[0]-2):
armapredicts[i] = np.array([ [endog[i-f+p]] for f in range(0,p)]).T.dot(arparams.reshape(-1,1)) + np.array([[res[i-f+q]] for f in range(0,q)]).T.dot(maparams.reshape(-1,1)) + exog.iloc[i,:].T.dot(exogparams.reshape(-1,1)) + bias
liklihood=1/((2*np.pi*armapredicts)**(1/2))*np.exp(-res**2/(2*armapredicts**2))
log_hood=np.sum(-np.log(liklihood.squeeze()))
return log_hood
I'm working on a custom dataset of images and using a Neural Net to classify them.
The data set is about 6000 images of 58 classes. But on training I keep getting a "target is out of bounds" error.
I've double checked the number of classes and image size but still get the same error.
#hyperprams
learning_rate = 5e-4
#3 for RGB values
in_channel = 3
#classes from data set
num_classes = 58
# arbitray choice
batch_size = 32
#total number of epochs used to train the model
epochs = 3
traffic_dataset = TrafficSigns(csv_file='annotations.csv',
root_directory='/Users/*****/Desktop/images/',
transform = transforms.ToTensor())
train_size = int(0.8 * len(traffic_dataset))
test_size = len(traffic_dataset) - train_size
train, test = torch.utils.data.random_split(traffic_dataset,
[train_size, test_size])
train_loader = torch.utils.data.DataLoader(train,
batch_size= batch_size,
shuffle= True,
num_workers= 4)
test_loader = torch.utils.data.DataLoader(test,
batch_size = batch_size,
shuffle= True,
num_workers= 4)
#Create a fully connected nn
class Net(nn.Module):
#use the constructor w/ arguments size of data and number of classes
def __init__(self,
input_size,
num_classes):
super(Net, self).__init__()
self.fc1 = nn.Linear(input_size, 60)
self.fc2 = nn.Linear(60, num_classes)
#define your forward step function with relu as the non-linear function of the weights
#x will be the datapassed to the model
def forward(self, x):
x=f.relu(self.fc1(x))
x = self.fc2(x)
return x
#sanity check
test = Net(2028, num_classes)
x = torch.randn(24, 2028)
print(test(x).shape)
#instantiate the class object of NN
net = Net(2028, num_classes)
criterion = nn.CrossEntropyLoss()
nn_optimizer = optim.Adam(net.parameters(),
lr = learning_rate)
#train on multiple epochs using the criterion and gradient decent algorthim estabilished above
for epoch in range(1):
for i, (data, target) in enumerate(tqdm.tqdm(train_loader)):
data = data.reshape(data.shape[0], -1)
#forward
outputs = net(data)
loss = criterion(outputs, target)
#backward propigation
nn_optimizer.zero_grad()
loss.backward()
#gradiant decent choosen
nn_optimizer.step()
Im also using a custom dataset class to import the images and labels.
My first thought was that the class is not iterating over the CSV and images correctly but I can't seem to find where they might be not matching up.
class TrafficSigns(Dataset):
#constructure will need csv file of labels images and the transform function defined above
def __init__(self,
csv_file,
root_directory,
transform = None):
self.labels = pd.read_csv(csv_file)
self.root_directory = root_directory
self.transform = transform
#returns the length
def __len__(self):
return len(self.labels)
#get data index by indes
def __getitem__(self, i):
image_path = os.path.join(self.root_directory, self.labels.iloc[i,0])
image = io.imread(image_path)
y_label = torch.tensor(int(self.labels.iloc[i, 1]))
#if statement needed since transform can be set to None
if self.transform:
image = self.transform(image)
return (image, y_label)
Any help would be awesome, thank you.
Here is the full stacktrace error that's getting thrown.
IndexError Traceback (most recent call last)
/var/folders/t_/rcfcs8g56jn7trwnsvmdyh_r0000gn/T/ipykernel_34551/1839343274.py in <module>
11 #forward
12 outputs = net(data)
---> 13 loss = criterion(outputs, target)
14 #backward propigation
15 nn_optimizer.zero_grad()
~/Library/Python/3.8/lib/python/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
~/Library/Python/3.8/lib/python/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
1148
1149 def forward(self, input: Tensor, target: Tensor) -> Tensor:
-> 1150 return F.cross_entropy(input, target, weight=self.weight,
1151 ignore_index=self.ignore_index, reduction=self.reduction,
1152 label_smoothing=self.label_smoothing)
~/Library/Python/3.8/lib/python/site-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)
2844 if size_average is not None or reduce is not None:
2845 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2846 return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
2847
2848
IndexError: Target 125 is out of bounds.
I came across same issue where I used sequential model (LSTM) for next sequence prediction. I check data loader where labels contained -1 because of which cross entropy loss throwing exception. here is my sequence chunks where model found -1 sequence as label in data loader:
Solved please check your null rows and remove those or set accordingly.
Hello I am learning MDAnalysis through python-3.7. Would you please check my code and advise how to resolve the following error:
Traceback (most recent call last):
File "/home/pulokdeb/projects/def-sohrabz/pulokdeb/beluga_python/Closest_atom_Oxy_group.py", line 242, in <module>
eigen_value = iio.eigen_vals()
File "/home/pulokdeb/ENV/lib/python3.7/site-packages/MDAnalysis/core/topologyattrs.py", line 1347, in eigen_vals
com = atomgroup.center_of_mass(pbc=pbc)
NameError: name 'pbc' is not defined
The code (partial) is below:
def radius_of_gyration(group, pbc=False, **kwargs):
"""Radius of gyration.
Parameters
----------
pbc : bool, optional
If ``True``, move all atoms within the primary unit cell before
calculation. [``False``]
.. versionchanged:: 0.8 Added *pbc* keyword
"""
atomgroup = group.atoms
masses = atomgroup.masses
com = atomgroup.center_of_mass(pbc=pbc)
if pbc:
recenteredpos = atomgroup.pack_into_box(inplace=False) - com
else:
recenteredpos = atomgroup.positions - com
rog_sq = np.sum(masses * np.sum(recenteredpos**2,
axis=1)) / atomgroup.total_mass()
return np.sqrt(rog_sq)
transplants[GroupBase].append(
('radius_of_gyration', radius_of_gyration))
I changed a few lines (def_eif_vals) in topologyattrs.py file and got my results. Hope it works for my future simulations.
def shape_parameter(group, pbc=False, **kwargs):
"""Shape parameter.
See [Dima2004a]_ for background information.
Parameters
----------
pbc : bool, optional
If ``True``, move all atoms within the primary unit cell before
calculation. [``False``]
References
----------
.. [Dima2004a] Dima, R. I., & Thirumalai, D. (2004). Asymmetry
in the shapes of folded and denatured states of
proteins. *J Phys Chem B*, 108(21),
6564-6570. doi:`10.1021/jp037128y
<https://doi.org/10.1021/jp037128y>`_
.. versionadded:: 0.7.7
.. versionchanged:: 0.8 Added *pbc* keyword
"""
atomgroup = group.atoms
masses = atomgroup.masses
com = atomgroup.center_of_mass(pbc=pbc)
if pbc:
recenteredpos = atomgroup.pack_into_box(inplace=False) - com
else:
recenteredpos = atomgroup.positions - com
tensor = np.zeros((3, 3))
for x in range(recenteredpos.shape[0]):
tensor += masses[x] * np.outer(recenteredpos[x, :],
recenteredpos[x, :])
tensor /= atomgroup.total_mass()
eig_vals = np.linalg.eigvalsh(tensor)
shape = 27.0 * np.prod(eig_vals - np.mean(eig_vals)
) / np.power(np.sum(eig_vals), 3)
return shape
transplants[GroupBase].append(
('shape_parameter', shape_parameter))
def eigen_vals(group, pbc=False, **kwargs):
""" Changed by Pulok Deb
"""
atomgroup = group.atoms
masses = atomgroup.masses
com = atomgroup.center_of_mass(pbc=pbc)
if pbc:
recenteredpos = atomgroup.pack_into_box(inplace=False) - com
else:
recenteredpos = atomgroup.positions - com
tensor = np.zeros((3, 3))
for x in range(recenteredpos.shape[0]):
tensor += masses[x] * np.outer(recenteredpos[x, :],
recenteredpos[x, :])
tensor /= atomgroup.total_mass()
eig_vals = np.linalg.eigvalsh(tensor)
return eig_vals
transplants[GroupBase].append(
('eigen_vals', eigen_vals))
#warn_if_not_unique
#check_pbc_and_unwrap
I found a code online for Naive bayes classification for a small research I am doing. The code I am using is showing some errors and cannot find the solution for them. I would greatly appreciate your help.
The code is below:
# Example of Naive Bayes implemented from Scratch in Python
import csv
import random
import math
def loadCsv(filename):
lines = csv.reader(open(filename, "rt"))
dataset = list(lines)
for i in range(len(dataset)):
dataset[i] = [float(x) for x in dataset[i]]
return dataset
def splitDataset(dataset, splitRatio):
trainSize = int(len(dataset) * splitRatio)
trainSet = []
copy = list(dataset)
while len(trainSet) < trainSize:
index = random.randrange(len(copy))
trainSet.append(copy.pop(index))
return [trainSet, copy]
def separateByClass(dataset):
separated = {}
for i in range(len(dataset)):
vector = dataset[i]
if (vector[-1] not in separated):
separated[vector[-1]] = []
separated[vector[-1]].append(vector)
return separated
def mean(numbers):
return sum(numbers) / float(len(numbers))
def stdev(numbers):
avg = mean(numbers)
variance = sum([pow(x - avg, 2) for x in numbers]) / float(len(numbers) - 1)
return math.sqrt(variance)
def summarize(dataset):
summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]
del summaries[-1]
return summaries
def summarizeByClass(dataset):
separated = separateByClass(dataset)
summaries = {}
for classValue, instances in separated.items():
summaries[classValue] = summarize(instances)
return summaries
def calculateProbability(x, mean, stdev):
exponent = math.exp(-(math.pow(x - mean, 2) / (2 * math.pow(stdev, 2))))
return (1 / (math.sqrt(2 * math.pi) * stdev)) * exponent
def calculateClassProbabilities(summaries, inputVector):
probabilities = {}
for classValue, classSummaries in summaries.items():
probabilities[classValue] = 1
for i in range(len(classSummaries)):
mean, stdev = classSummaries[i]
x = inputVector[i]
probabilities[classValue] *= calculateProbability(x, mean, stdev)
return probabilities
def predict(summaries, inputVector):
probabilities = calculateClassProbabilities(summaries, inputVector)
bestLabel, bestProb = None, -1
for classValue, probability in probabilities.items():
if bestLabel is None or probability > bestProb:
bestProb = probability
bestLabel = classValue
return bestLabel
def getPredictions(summaries, testSet):
predictions = []
for i in range(len(testSet)):
result = predict(summaries, testSet[i])
predictions.append(result)
return predictions
def getAccuracy(testSet, predictions):
correct = 0
for i in range(len(testSet)):
if testSet[i][-1] == predictions[i]:
correct += 1
return (correct / float(len(testSet))) * 100.0
def main():
filename = 'E:\iris.data.csv'
splitRatio = 0.67
dataset = loadCsv(filename)
trainingSet, testSet = splitDataset(dataset, splitRatio)
print(('Split {0} rows into train={1} and test={2} rows').format(len(dataset), len(trainingSet), len(testSet)))
# prepare model
summaries = summarizeByClass(trainingSet)
# test model
predictions = getPredictions(summaries, testSet)
accuracy = getAccuracy(testSet, predictions)
print(('Accuracy: {0}%').format(accuracy))
main()
The traceback for the same is below:
File "<ipython-input-18-4397d9969e66>", line 1, in <module>
runfile('C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py', wdir='C:/Users/Lenovo/Desktop/EE Codes')
File "C:\Users\Lenovo\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile
execfile(filename, namespace)
File "C:\Users\Lenovo\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 76, in <module>
main()
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 69, in main
neighbors = getNeighbors(trainingSet, testSet[x], k)
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 31, in getNeighbors
dist = euclideanDistance(testInstance, trainingSet[x], length)
File "C:/Users/Lenovo/Desktop/EE Codes/Knn with prima.py", line 24, in euclideanDistance
distance += pow((instance1[x] - instance2[x]), 2)
TypeError: unsupported operand type(s) for -: 'str' and 'str'
I would request you all to please provide a solution to how to solve this error for the respective code. If you require the dataset then please do ask. I can provide you the link for that too.
Thanks in advance
I'm trying to solve a car problem.
first, I have an original code of car problem:
# Ampl Car Example
#
# Shows how to convert a minimize final time optimal control problem
# to a format pyomo.dae can handle by removing the time scaling from
# the ContinuousSet.
#
# min tf
# dxdt = 0
# dvdt = a-R*v^2
# x(0)=0; x(tf)=L
# v(0)=0; v(tf)=0
# -3<=a<=1
from pyomo.environ import *
from pyomo.dae import *
m = ConcreteModel()
m.R = Param(initialize=0.001) # Friction factor
m.L = Param(initialize=100.0) # Final position
m.tau = ContinuousSet(initialize=[0.0, 0.80, 1.0]) # Unscaled time
m.time = Var(m.tau) # Scaled time
m.tf = Var()
m.x = Var(m.tau,bounds=(0,None))
m.v = Var(m.tau,bounds=(0,None))
m.a = Var(m.tau, bounds=(-3.0,1.0),initialize=0)
m.dtime = DerivativeVar(m.time)
m.dx = DerivativeVar(m.x)
m.dv = DerivativeVar(m.v)
m.obj = Objective(expr=m.tf)
def _ode1(m,i):
if i == 0 :
return Constraint.Skip
return m.dx[i] == m.tf * m.v[i]
m.ode1 = Constraint(m.tau, rule=_ode1)
def _ode2(m,i):
if i == 0 :
return Constraint.Skip
return m.dv[i] == m.tf*(m.a[i] - m.R*m.v[i]**2)
m.ode2 = Constraint(m.tau, rule=_ode2)
def _ode3(m,i):
if i == 0:
return Constraint.Skip
return m.dtime[i] == m.tf
m.ode3 = Constraint(m.tau, rule=_ode3)
def _init(m):
yield m.x[0] == 0
yield m.x[1] == m.L
yield m.v[0] == 0
yield m.v[1] == 0
yield m.time[0] == 0
m.initcon = ConstraintList(rule=_init)
discretizer = TransformationFactory('dae.collocation')
discretizer.apply_to(m,ncp=1, scheme='LAGRANGE-RADAU')
solver = SolverFactory('ipopt')
solver.solve(m, tee=True)
print("final time = %6.2f" %(value(m.tf)))
Now, I want to use class to express a car,then I could instantiate two cars.
So I write like this:
from pyomo.environ import *
from pyomo.dae import *
m = ConcreteModel()
class Car():
def __init__(self,friction):
self.friction = friction
self.R = Param(initialize = self.friction) # Friction factor
self.tau = ContinuousSet(bounds=(0, 1)) # Unscaled time
self.time = Var(self.tau) # Scaled time
self.tf = Var()
self.x = Var(self.tau, bounds=(0, None), initialize=0)
self.v = Var(self.tau, bounds=(0, None))
self.a = Var(self.tau, bounds=(-3.0, 1.0), initialize=0)
self.dtime = DerivativeVar(self.time)
self.dx = DerivativeVar(self.x)
self.dv = DerivativeVar(self.v)
def _ode1(m, i):
if i == 0:
return Constraint.Skip
return self.dx[i] == m.tf * self.v[i]
self.ode1 = Constraint(self.tau, rule=_ode1)
def _ode2(m, i):
if i == 0:
return Constraint.Skip
return self.dv[i] == m.tf * (self.a[i] - self.R * self.v[i] ** 2)
self.ode2 = Constraint(self.tau, rule=_ode2)
def _ode3(m, i):
if i == 0:
return Constraint.Skip
return self.dtime[i] == m.tf
self.ode3 = Constraint(self.tau, rule=_ode3)
m.car1 = Car(0.001)
m.obj = Objective(expr=m.car1.tf)
def _init(m):
yield m.car1.x[0] == 0
yield m.car1.x[1] == 100
yield m.car1.v[0] == 0
yield m.car1.v[1] == 0
yield m.car1.time[0] == 0
m.car1.initcon = ConstraintList(rule=_init)
discretizer = TransformationFactory('dae.finite_difference')
discretizer.apply_to(m, nfe=10, scheme='BACKWARD')
solver = SolverFactory('ipopt')
solver.solve(m, tee=True)
print("final time = %6.2f" % (value(m.car1.tf)))
However, I get this:
Traceback (most recent call last):
File "D:/pyo/pyomoceshi/ceshi3/car/classcar3.py", line 79, in <module>
solver.solve(m, tee=True)
File "D:\python\m\lib\site-packages\pyomo\opt\base\solvers.py", line 582, in solve
self._presolve(*args, **kwds)
File "D:\python\m\lib\site-packages\pyomo\opt\solver\shellcmd.py", line 196, in _presolve
OptSolver._presolve(self, *args, **kwds)
File "D:\python\m\lib\site-packages\pyomo\opt\base\solvers.py", line 661, in _presolve
**kwds)
File "D:\python\m\lib\site-packages\pyomo\opt\base\solvers.py", line 729, in _convert_problem
**kwds)
File "D:\python\m\lib\site-packages\pyomo\opt\base\convert.py", line 110, in convert_problem
problem_files, symbol_map = converter.apply(*tmp, **tmpkw)
File "D:\python\m\lib\site-packages\pyomo\solvers\plugins\converter\model.py", line 164, in apply
io_options=io_options)
File "D:\python\m\lib\site-packages\pyomo\core\base\block.py", line 1646, in write
io_options)
File "D:\python\m\lib\site-packages\pyomo\repn\plugins\ampl\ampl_.py", line 357, in __call__
include_all_variable_bounds=include_all_variable_bounds)
File "D:\python\m\lib\site-packages\pyomo\repn\plugins\ampl\ampl_.py", line 783, in _print_model_NL
list(self_varID_map[id(var)] for var in ampl_repn._linear_vars),
File "D:\python\m\lib\site-packages\pyomo\repn\plugins\ampl\ampl_.py", line 783, in <genexpr>
list(self_varID_map[id(var)] for var in ampl_repn._linear_vars),
KeyError: 68767416L
I want to know how to solve it or use other ways.
Below is a working version of your script. I changed things so that instead of a Car class there is a Car function that returns a Pyomo Block representing the car. By having a Car class you were essentially trying to create a subclass of Block and running into several subtle challenges that go along with that. You can see the blog post here for more information. The second change I made was in your declaration of the initial conditions, I changed the name of the ConstraintList from m.car1.initcon to m.car1_initcon. The difference is whether you want the ConstraintList to live on the car1 Block or the model. In your code, the 'dot' in the name meant you were trying to put it on the car1 Block but the constraints yielded in the rule were relative to the model. I changed the name to resolve this inconsistency.
from pyomo.environ import *
from pyomo.dae import *
m = ConcreteModel()
def Car(model, friction):
def construct_car_block(b):
b.R = Param(initialize = friction) # Friction factor
b.tau = ContinuousSet(bounds=(0, 1)) # Unscaled time
b.time = Var(b.tau) # Scaled time
b.tf = Var()
b.x = Var(b.tau, bounds=(0, None), initialize=0)
b.v = Var(b.tau, bounds=(0, None))
b.a = Var(b.tau, bounds=(-3.0, 1.0), initialize=0)
b.dtime = DerivativeVar(b.time)
b.dx = DerivativeVar(b.x)
b.dv = DerivativeVar(b.v)
def _ode1(b, i):
if i == 0:
return Constraint.Skip
return b.dx[i] == b.tf * b.v[i]
b.ode1 = Constraint(b.tau, rule=_ode1)
def _ode2(b, i):
if i == 0:
return Constraint.Skip
return b.dv[i] == b.tf * (b.a[i] - b.R * b.v[i] ** 2)
b.ode2 = Constraint(b.tau, rule=_ode2)
def _ode3(m, i):
if i == 0:
return Constraint.Skip
return b.dtime[i] == b.tf
b.ode3 = Constraint(b.tau, rule=_ode3)
return Block(rule=construct_car_block)
m.car1 = Car(m, friction=0.001)
m.obj = Objective(expr=m.car1.tf)
def _init(m):
yield m.car1.x[0] == 0
yield m.car1.x[1] == 100
yield m.car1.v[0] == 0
yield m.car1.v[1] == 0
yield m.car1.time[0] == 0
m.car1_initcon = ConstraintList(rule=_init)
discretizer = TransformationFactory('dae.finite_difference')
discretizer.apply_to(m, nfe=15, scheme='BACKWARD')
solver = SolverFactory('ipopt')
solver.solve(m, tee=True)
print("final time = %6.2f" % (value(m.car1.tf)))