I am using pymc3 to find a best fit for a 3D surface. This is the code that I am using.
with Model() as model:
# specify glm and pass in data. The resulting linear model, its likelihood and
# and all its parameters are automatically added to our model.
glm.glm('z ~ x**2 + y**2 + x + y + np.sin(x) + np.cos(y)' , flatimage)
start = find_MAP()
step = NUTS(scaling=start) # Instantiate MCMC sampling algorithm
trace = sample(2000, step, progressbar=False) # draw 2000 posterior samples using NUTS sampling
I got an error in this line:
glm.glm('z ~ x**2 + y**2 + x + y + np.sin(x) + np.cos(y)' , flatimage)
The error is :
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
I had tried to fix it by changing sin(x) and cos(y) to np.sin(x) and np.cos(y), but that didn't work, and I don't know what else to do.
I think the problem is related to your definition of flatimage. You need your data labeled for the glm module to work. Something like this:
# synthetic data (just an example)
x = np.random.normal(size=100)
y = np.random.normal(size=100)
z = x**2 + y**2 + x + y + np.sin(x) + np.cos(y)
data = dict(x=x, y=y, z=z) # a pandas dataframe will also work
with pm.Model() as model:
pm.glm.glm('z ~ x**2 + y**2 + x + y + np.sin(x) + np.cos(y)' , data)
start = pm.find_MAP()
step = pm.NUTS(scaling=start)
trace = pm.sample(2000, step, start)
Check this example for other details.