Updated documentation reference code on tuning a linearGAM model

[LucLapenta]

Updated documentation reference code on tuning a linearGAM model to be accurate compared to what the comments describe.

In the original reference code the numerical outputs at each step did not match the expected results based on the comments: lams = np.random.rand(100, 3) # random points on [0, 1], with shape (100, 3) lams = lams * 8 - 3 # shift values to -3, 3 lams = np.exp(lams) # transforms values to 1e-3, 1e3

The second step lams = lams * 8 - 3 # shift values to -3, 3 results in shifting the random values from -3,5 rather than -3,3.

The third step lams = np.exp(lams) # transforms values to 1e-3, 1e3 is computing e^lams when is seems like it is supposed to be computing 10^lams.

After running the original reference code, the min and max values are the resulting matrix are 0.04992659817158999 and 148.3540116029961 respectively.

The reference code in the quick_start.ipynb notebook was modified to: lams = np.random.rand(100, 3) # random points on [0, 1], with shape (100, 3) lams = lams * 6 - 3 # shift values to -3, 3 lams = 10 ** lams # transforms values to 1e-3, 1e3

Which results in more correct minimum and maximum values in the range of [1e-3, 1e3].

[codecov[bot]]

Codecov Report

Merging #276 into master will increase coverage by 0.09%. The diff coverage is n/a.

@@            Coverage Diff             @@
##           master     #276      +/-   ##
==========================================
+ Coverage   95.12%   95.21%   +0.09%     
==========================================
  Files          22       22              
  Lines        3178     3178              
==========================================
+ Hits         3023     3026       +3     
+ Misses        155      152       -3     

Impacted Files	Coverage Δ
pygam/pygam.py	94.79% <0.00%> (+0.12%)	:arrow_up:
pygam/utils.py	87.73% <0.00%> (+0.30%)	:arrow_up:
pygam/tests/test_GAM_methods.py	100.00% <0.00%> (+0.36%)	:arrow_up:

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[dswah]

@LucLapenta Thanks for the PR! looks good :)