I’m trying to write a simple TD-learning model with theano/pytensor:

```
import numpy as np
import pymc as pm
import pytensor
import pytensor.tensor as T
def sig(x):
"""Just a sigmoid function"""
return 1/(1 + np.exp(x))
def Q_update(opt1, opt2, Q, lr):
# Q is 1x4 vector
# choice is scalar (which element of Q should be updated)
# lr = learning rate
# outcome : reward (1) or no reward (0)
ch_opt1 = T.cast(opt1, 'int64')
ch_opt2 = T.cast(opt2, 'int64')
p1 = pm.Deterministic("p1", sig(Q[ch_opt1]-Q[ch_opt2])) # prob of choosing 1st response option (=better response option)
choice = pm.Binomial("choice", n=1, p = p1) # 1 if opt1 was chosen, 0 if opt2 was chosen
outcome = pm.Binomial("outcome", n=1, p = 0.2+choice*0.6) # prob is 0.8 if opt1 was chosen and 0.2 if opt2 was chosen
chidx = ch_opt1*choice + ch_opt2*(1-choice) # Either ch_opt1 (if opt1 was chosen) or ch_opt2 (otherwise)
Q = T.set_subtensor(Q[chidx], Q[chidx] + lr*(outcome-Q[chidx]))
return Q
with pm.Model() as vbm_model:
lr = pm.Beta('lr', alpha = 1, beta = 1) # learning rate
Q = np.array([0,0,0,0]).astype(pytensor.config.floatX)
# Get opt1_val and opt2_val from experiment, for now generate randomly
opt1_values = np.random.randint(0,4,100).astype('int32')
opt2_values = np.random.randint(0,4,100).astype('int32')
Q = pytensor.shared(Q, "Q")
opt1 = pytensor.shared(opt1_values, "opt1")
opt2 = pytensor.shared(opt2_values, "opt2")
output, updates = pytensor.scan(fn=Q_update, sequences=[opt1, opt2], outputs_info=[Q], non_sequences=[lr])
with vbm_model:
trace = pm.sample(2000, tune=3000, target_accept=0.9, return_inferencedata=False)
```

I keep getting this error:

```
MissingInputError: Input 0 (opt2[t]) of the graph (indices start from 0), used to compute Elemwise{Cast{int64}}(opt2[t]), was not provided and not given a value. Use the PyTensor flag exception_verbosity='high', for more information on this error.
```

I think it has to do with the line `choice = pm.Binomial("choice", n=1, p = p1)`

, because when I replace p1 with a number between 0 & 1, it works. Also, this error seems to occur when values of random variables depend on values that were defined elsewhere in the script (like on p1), see here or here.I still don’t know how to remedy this, tho. I’m using pymc 5.0.1, pytensor 2.8.11, python 3.9.13, numpy 1.21.5.

Any help is greatly appreciated!