Scale exploration with Gini impurity of policy values

[XInTheDark]

First, when computing policy values in a position, we also calculate the Gini impurity, defined as (1 - sum of squares of policy values). A high Gini impurity indicates that there are many strong candidate moves in a position, and vice versa.

This Gini impurity is then used to adjust the exploration scaling using a logarithmic formula. For higher values of Gini impurity, we decrease the exploration value so that the search focuses more on exploring variations with high q values. Conversely, for positions with low Gini impurity where one move is much better than the others, we increase the exploration value to ensure that other potential lines are not prematurely discarded.

The idea to use Gini impurity was first proposed and tested by @Viren6.

Passed STC: https://montychess.org/tests/view/66aef6280f6f1e65cfa2b1f8
LLR: 2.93 (-2.94,2.94) <0.00,4.00>
Total: 6944 W: 1626 L: 1460 D: 3858
Ptnml(0-2): 54, 787, 1643, 915, 73

Passed LTC: https://montychess.org/tests/view/66af1fd90f6f1e65cfa2b235
LLR: 2.93 (-2.94,2.94) <1.00,5.00>
Total: 8718 W: 1877 L: 1705 D: 5136
Ptnml(0-2): 40, 931, 2255, 1083, 50

Rebased STC: https://montychess.org/tests/view/66b053380f6f1e65cfa2b657
LLR: 2.91 (-2.94,2.94) <0.00,4.00>
Total: 9216 W: 2083 L: 1913 D: 5220
Ptnml(0-2): 82, 998, 2291, 1142, 95

Bench: 1490523

[XInTheDark]

closing, #44 is the new PR