resample interpolate gives unexpected results in 0.18.1
#### A small, complete example of the issue

``` python
a = pd.Series([1.,3.,4.,3.,5.,6.,7.,8.], ['2016-05-25 00:00:35','2016-05-25 00:00:50','2016-05-25 00:01:05','2016-05-25 00:01:35','2016-05-25 00:02:05','2016-05-25 00:03:00','2016-05-25 00:04:00','2016-05-25 00:06:00'])                                   

In [79]: a
Out[79]: 
2016-05-25 00:00:35    1.0
2016-05-25 00:00:50    3.0
2016-05-25 00:01:05    4.0
2016-05-25 00:01:35    3.0
2016-05-25 00:02:05    5.0
2016-05-25 00:03:00    6.0
2016-05-25 00:04:00    7.0
2016-05-25 00:06:00    8.0
dtype: float64

In [80]: a.index = pd.to_datetime(a.index)

In [81]: a.resample('15S', base=5).interpolate()


```
#### Expected Output

I expect that I would get valid values, based on the input at 2:05 and later.  It appears that the data data after 2:05 is ignored.  
#### Output of `pd.show_versions()`

In [146]: pd.show_versions()
## INSTALLED VERSIONS

commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.2
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 1.0b3
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
<details>
# Paste the output here

Out[81]: 
2016-05-25 00:00:35    1.0
2016-05-25 00:00:50    3.0
2016-05-25 00:01:05    4.0
2016-05-25 00:01:20    3.5
2016-05-25 00:01:35    3.0
2016-05-25 00:01:50    4.0
2016-05-25 00:02:05    5.0
2016-05-25 00:02:20    5.0
2016-05-25 00:02:35    5.0
2016-05-25 00:02:50    5.0
2016-05-25 00:03:05    5.0
2016-05-25 00:03:20    5.0
2016-05-25 00:03:35    5.0
2016-05-25 00:03:50    5.0
2016-05-25 00:04:05    5.0
2016-05-25 00:04:20    5.0
2016-05-25 00:04:35    5.0
2016-05-25 00:04:50    5.0
2016-05-25 00:05:05    5.0
2016-05-25 00:05:20    5.0
2016-05-25 00:05:35    5.0
2016-05-25 00:05:50    5.0
Freq: 15S, dtype: float64

I was told that this error does not show up in 0.18.0, but I have not confirmed that.

This comes from my attempts to interpolate some irregular data as shown in this question:  https://stackoverflow.com/questions/39599192/fill-in-time-data-in-pandas

</details>
