fix(lird.py): Integer classes instead of characters

When experimenting with RandomTreeForests [1] I noticed that they could not
work on our input data.

Also the documentation for DecisionTreeClassifier.fit(X,y) [2] states for the
class parameter `y`

    > The target values (integers that correspond to classes in
    > classification, real numbers in regression).

So I ran the classifier again with integer classes instead of characters and
the success rate of the tree dramatically improved:

Old:
```
Using Dataset Out of the Box Integer Data :
    Training Decision Tree Classifier:
        Training in 0 Seconds
        Prediction in 0 Seconds
        2680 errors out of 4000 validation vectors

Using Dataset Normalizerd Float Data :
    Training Decision Tree Classifier:
        Training in 0 Seconds
        Prediction in 0 Seconds
        2679 errors out of 4000 validation vectors
```

New:
```
Using Dataset Out of the Box Integer Data :
    Training Decision Tree Classifier:
        Training in 0 Seconds
        Prediction in 0 Seconds
        518 errors out of 4000 validation vectors

Using Dataset Normalizerd Float Data :
    Training Decision Tree Classifier:
        Training in 0 Seconds
        Prediction in 0 Seconds
        512 errors out of 4000 validation vectors
```

[1] http://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees
[2] http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier.fit

Closes #1
Merge pull request #1 from vsaw/1-int-classes-instead-of-chars

fix(lird.py): Integer classes instead of characters
feat(lird.py): Add more SVC experiments

This adds the SVC with different kernels to the list of classifiers.

For the SVC, the following kernels are added:

1. rbf
2. poly
3. sigmoid
4. linear

Also the LinearSVC is added and the SVC(kernel='linear') is run in a one-vs-rest
mode to be able to directly compare it to the LinearSVC.

Closes #3
Merge pull request #3 from vsaw/3-extend-svm-experiments

feat(lird.py): Add more SVC experiments