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Description
See here for more.
When attempting to simulate an Nxp random matrix in which the rows are draws from a multivariate normal, a shape issue pops up. The example below should
a) Generate a 10 x 2 array X
b) generate a 2 dimensional array, beta
c) Compute their matrix product to yield a 10 dimensional array y
import pymc3 as pm
N = 10
Sigma = np.eye(2)
with pm.Model() as model:
X = pm.MvNormal('X', mu=np.zeros(2), cov = Sigma, shape = (N,2))
betas = pm.Normal('betas', 0, 1, shape = 2)
y = pm.Deterministic('y', pm.math.dot(X,betas))
prior_pred = pm.sample_prior_predictive(1)Please provide the full traceback.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-18-0c1a293243c3> in <module>
10 y = pm.Deterministic('y', pm.math.dot(X,betas))
11
---> 12 prior_pred = pm.sample_prior_predictive(1)
/opt/anaconda3/lib/python3.7/site-packages/pymc3/sampling.py in sample_prior_predictive(samples, model, vars, var_names, random_seed)
1320 names = get_default_varnames(model.named_vars, include_transformed=False)
1321 # draw_values fails with auto-transformed variables. transform them later!
-> 1322 values = draw_values([model[name] for name in names], size=samples)
1323
1324 data = {k: v for k, v in zip(names, values)}
/opt/anaconda3/lib/python3.7/site-packages/pymc3/distributions/distribution.py in draw_values(params, point, size)
437 point=point,
438 givens=givens.values(),
--> 439 size=size)
440 evaluated[param_idx] = drawn[(param, size)] = value
441 givens[param.name] = (param, value)
/opt/anaconda3/lib/python3.7/site-packages/pymc3/distributions/distribution.py in _draw_value(param, point, givens, size)
615 input_vals = []
616 func = _compile_theano_function(param, input_vars)
--> 617 output = func(*input_vals)
618 return output
619 raise ValueError('Unexpected type in draw_value: %s' % type(param))
/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in __call__(self, *args, **kwargs)
2089 vargs.extend([kwargs[_n] for _n in names])
2090
-> 2091 return self._vectorize_call(func=func, args=vargs)
2092
2093 def _get_ufunc_and_otypes(self, func, args):
/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in _vectorize_call(self, func, args)
2155 """Vectorized call to `func` over positional `args`."""
2156 if self.signature is not None:
-> 2157 res = self._vectorize_call_with_signature(func, args)
2158 elif not args:
2159 res = func()
/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in _vectorize_call_with_signature(self, func, args)
2185
2186 broadcast_shape, dim_sizes = _parse_input_dimensions(
-> 2187 args, input_core_dims)
2188 input_shapes = _calculate_shapes(broadcast_shape, dim_sizes,
2189 input_core_dims)
/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in _parse_input_dimensions(args, input_core_dims)
1854 dim_sizes = {}
1855 for arg, core_dims in zip(args, input_core_dims):
-> 1856 _update_dim_sizes(dim_sizes, arg, core_dims)
1857 ndim = arg.ndim - len(core_dims)
1858 dummy_array = np.lib.stride_tricks.as_strided(0, arg.shape[:ndim])
/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in _update_dim_sizes(dim_sizes, arg, core_dims)
1820 '%d-dimensional argument does not have enough '
1821 'dimensions for all core dimensions %r'
-> 1822 % (arg.ndim, core_dims))
1823
1824 core_shape = arg.shape[-num_core_dims:]
ValueError: 1-dimensional argument does not have enough dimensions for all core dimensions ('i_0_0', 'i_0_1')Please provide any additional information below.
Versions and main components
- PyMC3 Version: 3.7
- Theano Version: 1.0.4
- Python Version: 3.7
- Operating system: OSX 10.14.6
- How did you install PyMC3: conda