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import numpy as np
from numpy.lib.stride_tricks import as_strided
import numbers


def make_patches(arr, patch_shape=2, extraction_step=1):
    arr_ndim = arr.ndim

    if isinstance(patch_shape, numbers.Number):
        patch_shape = tuple([patch_shape] * arr_ndim)

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import numpy as np
from sklearn.linear_model import Ridge, SGDRegressor

rng = np.random.RandomState(42)

n_samples, n_features = 2000, 50

X = rng.randn(n_samples, n_features)
w = rng.randn(n_features)

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import numpy as np

# X.dot(X.T) + alpha * eye = U.dot(S ** 2 + alpha).dot(U.T)
# X.T.dot(inv(X.dot(X.T) + alpha * eye)) = V.dot(S / (S **2 + alpha).dot(U.T))


def _linear_kernel_ridge_path_svd(
    U_train, S_train, VT_train, Y_train, alphas, X_test=None):

    UTY = U_train.T.dot(Y_train)

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---------------------------------------------------------------------------
SystemError                               Traceback (most recent call last)
/home/parietal/eickenbe/software/python_pkg/anaconda/lib/python2.7/site-packages
/IPython/utils/py3compat.pyc in execfile(fname, *where)
    202             else:
    203                 filename = fname
--> 204             __builtin__.execfile(filename, *where)
 
/home/parietal/eickenbe/code/ofv/ofb.py in <module>()
    369         dump_theano_r_layer(l)

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# Coordinate descent algorithm for Lasso in pure python
# Goal: evaluate speed up potential when using dynamic screening
# rules every few coordinate sweeps, on all types of design
# See Bonnefoy et al., 2014, https://hal.inria.fr/hal-00880787v4
# Author: Michael Eickenberg, michael.eickenberg@nsup.org
import numpy as np

def screen_dictionary(X, residual, y, alpha,
                      current_support=None,
                      current_radius=None,

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import numpy as np
rng = np.random.RandomState(42)

n_samples, n_features = 100, 50

X = rng.randn(n_samples, n_features)

V1, S1, VT1 = np.linalg.svd(X.T.dot(X), full_matrices=True)
U2, S2, VT2 = np.linalg.svd(X, full_matrices=True)

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# Implements functionality corresponding to numpy.mgrid / numpy.ogrid for symbolic theano variables
# Author: Michael Eickenberg, michael.eickenberg@nsup.org

import theano
import theano.tensor as T

class _nd_grid(object):
    """Implements the mgrid and ogrid functionality for theano tensor
    variables.

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# Maxpooling with arbitrary pooling strides and pooling shapes
# Based on theano.tensor.signal.downsample.max_pool_2d. This
# operation is repeated the minimum necessary times to account for
# all stride steps.

#Author: Michael Eickenberg, michael.eickenberg@nsup.org


import theano
import numpy as np

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import theano
import theano.tensor as T

import numpy as np

# 0 and some arbitrarily small positive and negative numbers
test_vector = np.array(
    [0., 1., -1., .1, -.1, 10., -10., 1e-8, -1e-8,
     1e-10, -1e-10, 1e-16, -1e-16]).astype(np.float32)

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# Author Michael Eickenberg <michael.eickenberg@nsup.org>, Fabian Pedregosa
# Coded in 2012, another era, pure python, no guarantees for 1000% correctness or speed
# requires loess.py


import numpy as np
from loess import lowess

VERBOSE = 100