# Path: gelato/tests/datasets.py
# def generate_linear_regression(intercept, slope, sd=.2, size=700):
#     x = np.random.uniform(-10, 10, size)
#     y = intercept + x * slope
#     return x, y + np.random.normal(size=size, scale=sd)


from theano import theano
from .datasets import generate_linear_regression
from gelato.layers import *
import pymc3 as pm


def test_stats_layers():
    intercept = .2
    slope = 2
    x_, _ = generate_linear_regression(intercept, slope)
    x = theano.shared(x_[None].T)

# Path: gelato/tests/datasets.py
# def generate_linear_regression(intercept, slope, sd=.2, size=700):
#     x = np.random.uniform(-10, 10, size)
#     y = intercept + x * slope
#     return x, y + np.random.normal(size=size, scale=sd)


# Path: gelato/tests/test_layers.py
from theano import theano
from .datasets import generate_linear_regression
from gelato.layers import *
import pymc3 as pm



def test_stats_layers():
    intercept = .2
    slope = 2
    x_, _ = generate_linear_regression(intercept, slope)
    x = theano.shared(x_[None].T)

    inp = InputLayer((None, 1), input_var=x)