use more interesting range for C in logistic l1 l2 example.

Before this change, output is (http://scikit-learn.org/dev/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html#example-linear-model-plot-logistic-l1-l2-sparsity-py):
C=10
Sparsity with L1 penalty: 6.25%
score with L1 penalty: 0.9104
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9093
C=100
Sparsity with L1 penalty: 6.25%
score with L1 penalty: 0.9098
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9098
C=1000
Sparsity with L1 penalty: 4.69%
score with L1 penalty: 0.9098
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9098

With this change, output is:
C=100.00
Sparsity with L1 penalty: 6.25%
score with L1 penalty: 0.9110
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9098
C=1.00
Sparsity with L1 penalty: 9.38%
score with L1 penalty: 0.9104
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.9093
C=0.01
Sparsity with L1 penalty: 85.94%
score with L1 penalty: 0.8625
Sparsity with L2 penalty: 4.69%
score with L2 penalty: 0.8915

And here is the new image:
![l1_l2](https://cloud.githubusercontent.com/assets/1739/3613943/6d27c668-0dbc-11e4-95d1-0c508266e130.png)