merge_asof tolerance is not working in both directions
#### A small, complete example of the issue

```python
from io import StringIO

from pandas import Timedelta, merge_asof, read_csv, to_datetime

file1 = '''DATE_TIME,Label
2015-03-02 08:36:51,0
2015-03-02 08:36:54,1'''

df1 = read_csv(StringIO(file1))
df1.DATE_TIME = to_datetime(df1.DATE_TIME)
print(df1)

file2 = '''DATE_TIME,OtherData
2015-03-02 08:36:50,0
2015-03-02 08:36:51,1
2015-03-02 08:36:52,2
2015-03-02 08:36:53,3
2015-03-02 08:36:54,4
2015-03-02 08:36:55,5
2015-03-02 08:36:56,6
2015-03-02 08:36:57,7
2015-03-02 08:36:58,8
2015-03-02 08:36:59,9
2015-03-02 08:37:00,10'''

df2 = read_csv(StringIO(file2))
df2.DATE_TIME = to_datetime(df2.DATE_TIME)
print(df2)

print(merge_asof(df2, df1, on='DATE_TIME'))
print(merge_asof(df2, df1, on='DATE_TIME', tolerance=Timedelta(seconds=1)))
print(merge_asof(df2, df1, on='DATE_TIME', tolerance=Timedelta(seconds=5)))
```

#### Current Output
```
           DATE_TIME  Label
0 2015-03-02 08:36:51      0
1 2015-03-02 08:36:54      1
             DATE_TIME  OtherData
0  2015-03-02 08:36:50          0
1  2015-03-02 08:36:51          1
2  2015-03-02 08:36:52          2
3  2015-03-02 08:36:53          3
4  2015-03-02 08:36:54          4
5  2015-03-02 08:36:55          5
6  2015-03-02 08:36:56          6
7  2015-03-02 08:36:57          7
8  2015-03-02 08:36:58          8
9  2015-03-02 08:36:59          9
10 2015-03-02 08:37:00         10
             DATE_TIME  OtherData  Label
0  2015-03-02 08:36:50          0    NaN
1  2015-03-02 08:36:51          1    0.0
2  2015-03-02 08:36:52          2    0.0
3  2015-03-02 08:36:53          3    0.0
4  2015-03-02 08:36:54          4    1.0
5  2015-03-02 08:36:55          5    1.0
6  2015-03-02 08:36:56          6    1.0
7  2015-03-02 08:36:57          7    1.0
8  2015-03-02 08:36:58          8    1.0
9  2015-03-02 08:36:59          9    1.0
10 2015-03-02 08:37:00         10    1.0
             DATE_TIME  OtherData  Label
0  2015-03-02 08:36:50          0    NaN
1  2015-03-02 08:36:51          1    0.0
2  2015-03-02 08:36:52          2    0.0
3  2015-03-02 08:36:53          3    NaN
4  2015-03-02 08:36:54          4    1.0
5  2015-03-02 08:36:55          5    1.0
6  2015-03-02 08:36:56          6    NaN
7  2015-03-02 08:36:57          7    NaN
8  2015-03-02 08:36:58          8    NaN
9  2015-03-02 08:36:59          9    NaN
10 2015-03-02 08:37:00         10    NaN
             DATE_TIME  OtherData  Label
0  2015-03-02 08:36:50          0    NaN
1  2015-03-02 08:36:51          1    0.0
2  2015-03-02 08:36:52          2    0.0
3  2015-03-02 08:36:53          3    0.0
4  2015-03-02 08:36:54          4    1.0
5  2015-03-02 08:36:55          5    1.0
6  2015-03-02 08:36:56          6    1.0
7  2015-03-02 08:36:57          7    1.0
8  2015-03-02 08:36:58          8    1.0
9  2015-03-02 08:36:59          9    1.0
10 2015-03-02 08:37:00         10    NaN
```
#### Expected Output
```
           DATE_TIME  Label
0 2015-03-02 08:36:51      0
1 2015-03-02 08:36:54      1
             DATE_TIME  OtherData
0  2015-03-02 08:36:50          0
1  2015-03-02 08:36:51          1
2  2015-03-02 08:36:52          2
3  2015-03-02 08:36:53          3
4  2015-03-02 08:36:54          4
5  2015-03-02 08:36:55          5
6  2015-03-02 08:36:56          6
7  2015-03-02 08:36:57          7
8  2015-03-02 08:36:58          8
9  2015-03-02 08:36:59          9
10 2015-03-02 08:37:00         10
             DATE_TIME  OtherData  Label
0  2015-03-02 08:36:50          0    NaN
1  2015-03-02 08:36:51          1    0.0
2  2015-03-02 08:36:52          2    0.0
3  2015-03-02 08:36:53          3    0.0
4  2015-03-02 08:36:54          4    1.0
5  2015-03-02 08:36:55          5    1.0
6  2015-03-02 08:36:56          6    1.0
7  2015-03-02 08:36:57          7    1.0
8  2015-03-02 08:36:58          8    1.0
9  2015-03-02 08:36:59          9    1.0
10 2015-03-02 08:37:00         10    1.0
             DATE_TIME  OtherData  Label
0  2015-03-02 08:36:50          0    **0.0**
1  2015-03-02 08:36:51          1    0.0
2  2015-03-02 08:36:52          2    0.0
3  2015-03-02 08:36:53          3    NaN
4  2015-03-02 08:36:54          4    1.0
5  2015-03-02 08:36:55          5    1.0
6  2015-03-02 08:36:56          6    NaN
7  2015-03-02 08:36:57          7    NaN
8  2015-03-02 08:36:58          8    NaN
9  2015-03-02 08:36:59          9    NaN
10 2015-03-02 08:37:00         10    NaN
             DATE_TIME  OtherData  Label
0  2015-03-02 08:36:50          0    **0.0**
1  2015-03-02 08:36:51          1    0.0
2  2015-03-02 08:36:52          2    0.0
3  2015-03-02 08:36:53          3    0.0
4  2015-03-02 08:36:54          4    1.0
5  2015-03-02 08:36:55          5    1.0
6  2015-03-02 08:36:56          6    1.0
7  2015-03-02 08:36:57          7    1.0
8  2015-03-02 08:36:58          8    1.0
9  2015-03-02 08:36:59          9    1.0
10 2015-03-02 08:37:00         10    NaN
```
#### Output of ``pd.show_versions()``

<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.19.1
nose: None
pip: 9.0.0
setuptools: 28.0.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.0
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
None
</details>