Added new solver code
Initial checkin of SGD like code
Updated to use random subsets.
Temp checkin sgd
Minor fixes
Fixed brokenness after adding s_n
Deleted extra file
Reverted some preliminary changes.
Fixed spacing issue.
Added KAB covariance estimator
Adjust rcond in np.linalg.pinv()
Added svd-ew-kab method for covariance calculation.
Minor updates to benchmarking scripts
Don't allocated N^2 storage in covariance method inverse.  See #106
Add check for only one state with samples before running solver.
Added extra tests for two state checks.
Initial cleanup with fast code deleted.
Make rescaling False by default
Changed the warning messages and corrected behavior when square
uncertainty is neagitve. Previously, if it was within the tolerance
(e.g. -1E-13 with cutoff of 1E-10), a True/False was being passed into
d2DeltaF[True/False] which was  doing:

d2DeltaF[0,:] = 0 or d2DeltaF[1,:] = 0

instead of what I think it was trying to do of

d2DeltaF[d2DeltaF < 0.0] = 0.0

If any of the negative values of d2DeltaF are greater in magnitude than
the cutoff, a warning is issued saying how many entries violate the
cutoff and no entries are set to 0.0. This results in NaN's for
dDeltaF_ij, but such behavior should be left to the user to figure out
why they are getting them.
Merge pull request #1 from Lnaden/subsampling

Set negative square uncertainty within cutoff to zero.