SVD computation error message #108

Describe the bug
With my input data, I get a SVD error computation about "array must not contain infs or NaNs" when I do fit_transform to reduce dimensionality of input data. Note that problem occurs whether I use mds= "classic" or "nonmetric".

I attached a copy of the input data

Thanks for your help,

Ivan

To Reproduce
Please refer to attached zip file, in there you will find Python script and input data

Expected behavior
Be able to reduce dimensionality of input data

Actual behavior
projected_data= embedding.fit_transform(X= input_data)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\phate\phate.py", line 961, in fit_transform
self.fit(X)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\phate\phate.py", line 857, in fit
self.diff_op
File "C:\Temp\Python\Python3.6.5\lib\site-packages\phate\phate.py", line 281, in diff_op
diff_op = self.graph.landmark_op
File "C:\Temp\Python\Python3.6.5\lib\site-packages\graphtools\graphs.py", line 593, in landmark_op
self.build_landmark_op()
File "C:\Temp\Python\Python3.6.5\lib\site-packages\graphtools\graphs.py", line 663, in build_landmark_op
random_state=self.random_state,
File "C:\Temp\Python\Python3.6.5\lib\site-packages\sklearn\utils\extmath.py", line 340, in randomized_svd
Uhat, s, V = linalg.svd(B, full_matrices=False)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\scipy\linalg\decomp_svd.py", line 106, in svd
a1 = _asarray_validated(a, check_finite=check_finite)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\scipy_lib_util.py", line 272, in _asarray_validated
a = toarray(a)
File "C:\Temp\Python\Python3.6.5\lib\site-packages\numpy\lib\function_base.py", line 486, in asarray_chkfinite
"array must not contain infs or NaNs")
ValueError: array must not contain infs or NaNs

System information:

Output of phate.__version__:

Please run phate.__version__ and paste the results here.

You can do this with `python -c 'import phate; print(phate.__version__)'`
phate-1.0.7

Output of pd.show_versions():

Please run pd.show_versions() and paste the results here.

You can do this with `python -c 'import pandas as pd; pd.show_versions()'`