A | |
| add [Knn.S] |
Adds a point to the tree.
|
| add [Knn.Make] |
Add a point to a tree.
|
C | |
| contents [Knn.S] |
Flattens the tree to a list.
|
| contents [Knn.Make] |
The contents of the tree, as a list.
|
| contents1 [Knn.Make] | |
| create_balanced [Knn.S] |
As above, but tries to balance the tree
|
| create_balanced [Knn.Make] | |
| create_subtrees [Knn.Make] | |
D | |
| dist [Knntest] |
Euclidean distance.
|
| distance [Knntest.Params] | |
| distance [Knn.KnnParams] | |
| distance [Knn.Make] | |
| distances_to_distribution [Classify] |
Given the list of (distance, x, y) tuples found by the
k-nearest-neighbors tree, normalizes it to a
probability distribution.
|
| distant_roots [Knn.Make] |
Finds distant points to use as top-level roots.
|
E | |
| empty [Knn.S] |
The empty tree.
|
| empty [Knn.Make] | |
F | |
| find_k_nearest [Knn.S] |
Finds the k nearest things in the tree.
|
| find_k_nearest [Knn.Make] | |
| find_k_nearest_1 [Knn.Make] |
Find the k nearest neighbors.
|
| float_list_inner_product [Kernel] |
Dot product of a list of floats.
|
| float_list_min [Knn.Make] | |
| float_list_sum [Knn.Make] |
Some list-of-float utilities.
|
| float_sum [Classify] |
The sum of a list of floats
|
K | |
| kernel_to_distance [Kernel] |
Convert a kernal function to a distance.
|
L | |
| lg [Classify] |
log base 2
|
M | |
| max_degree [Knntest.Params] | |
| max_degree [Knn.KnnParams] | |
| max_degree [Knn.Make] | |
N | |
| normalized_kernel_to_distance [Kernel] |
Same as above, but normalizes based on the transformed distance.
|
O | |
| one_letter_cross_entropy [Classify] | |
| order_by_asc [Select] |
These convert a function to an ordering relation.
|
| order_by_desc [Select] | |
P | |
| poly [Kernel] |
Polynomial kernel functions.
|
| print_floats [Knntest] | |
R | |
| random_data [Knntest] |
Makes up random data, as a list of points in d-dimensional space,
each paired with a distinct integer.
|
| random_float_list [Knntest] |
A random list of floats.
|
| rebalance [Knn.S] |
Rebalances the tree.
|
| rebalance [Knn.Make] |
Rebalance the whole tree.
|
| run_test [Knntest] |
Runs the test for a given number of points, and search width.
|
S | |
| sample_from_dist [Classify] | |
| select [Select] |
Given a list, selects the k smallest elements from that list,
using Hoare's selection algorithm.
|
T | |
| take [Select] |
Takes the first few elements from a list.
|