from setuptools import setup

setup(
    name='autograd',
    version='1.1.3',
    description='Efficiently computes derivatives of numpy code.',
    author='Dougal Maclaurin and David Duvenaud and Matthew Johnson',
    author_email="maclaurin@physics.harvard.edu, dduvenaud@seas.harvard.edu, mattjj@csail.mit.edu",
    packages=['autograd', 'autograd.numpy', 'autograd.scipy', 'autograd.scipy.stats'],
    install_requires=['numpy>=1.9', 'future == 0.15.2'],
    setup_requires=['numpy>=1.9'],
    keywords=['Automatic differentiation', 'backpropagation', 'gradients',
              'machine learning', 'optimization', 'neural networks',
              'Python', 'Numpy', 'Scipy'],
    url='https://github.com/HIPS/autograd',
    license='MIT',
    classifiers=['Development Status :: 4 - Beta',
                 'License :: OSI Approved :: MIT License',
                 'Programming Language :: Python :: 2.7',
                 'Programming Language :: Python :: 3.4'],
)

@@ -443,12 +443,10 @@
 ture
- == 
+%3E=
 0.15

make package 'future' requirement a minimum version (not exact version)