| (*|) [Omlp] | |
| (|*|) [Omlp] | |
| (|->) [Teacher] |
Concatenation of two teachers
|
A | |
| assign [Prop_logic] |
assign a value to a variable in a model
|
B | |
| bfsearch [Uninf_search] |
Breadth first search.
|
| boundeddfsearch [Uninf_search] |
Bounded Depth First Search.
|
| byDepth [Minimax.M] |
Search the game up to a given depth
|
| byNodes [Minimax.M] |
Search by iterativly deepening and returning, when the last search expands
a certain number of nodes
|
| byTime [Minimax.M] |
Search by iteratively deepening and returning after a given
maximum time
|
C | |
| callback_both [Teacher] |
see callback_student
|
| callback_grades [Teacher] |
see callback_student
|
| callback_student [Teacher] |
call some callback that has the student, grades or both as a parameter
|
| combine [Prop_logic] |
combine two assingments into one
|
| conc [Mlp] |
concatenate two MLPs to form a new one.
|
| conc_in_place [Mlp] |
concatenate two MLPs.
|
| copy [Mlp] |
copy a given MLP
|
| copy [Types] | |
| count [Types] |
count the occurence of each attribut in the data
|
| create [Prop_logic] |
create a model from a list of assignments
|
| create [Types] | |
D | |
| dfsearch [Uninf_search] |
Simple Depth First Search.
|
E | |
| empty [Teacher] |
A do nothing teacher
|
| empty [Prop_logic] |
the empty models without any assignments
|
| epsilon [Reinforcement] | |
| evaluate [Mlp] |
evaluate a MLP for the given input
|
| evaluate [Bayes] |
find the most fitting category given some Data
|
| exhaustive_search [Uninf_search] |
Exhaustive search of the search space.
|
F | |
| find_models [Prop_logic] |
Tries to find all modules in which the formula is true.
|
| find_vars [Prop_logic] |
finds all variables that occur in a formula
|
| fixed_shuffle [Teacher] |
Determines the order once, then the same shuffled order is used on subsequent runs
|
G | |
| get_data [Types] | |
| get_depth [Mlp] |
get the number of layers
|
| get_in_size [Mlp] |
get the size of the input space
|
| get_moves [Minimax.Game] |
get a number of possible moves on a given position.
|
| get_out_size [Mlp] |
get the size of the output space
|
I | |
| id [Teacher] |
A permutation that keeps everything in place
|
| ignore_grades [Teacher] |
Ignore all grades, that are generated by the teacher, still the exams are taken
|
| is_endpos [Minimax.Game] |
Tell us if the position is a endposition
|
| is_solvable [Prop_logic] |
Tries to find a model that satisfies the formula.
|
| iterativedfs [Uninf_search] |
Iterated Depth First Search.
|
L | |
| learn [Bayes] |
learns a Bayes klassifier from given examples
|
| lookup [Prop_logic] |
lookup an assignment for a variable in a model.
|
M | |
| make_approximator [Mlp] |
create a net that can be used as a function approximator.
|
| make_classifier [Mlp] |
create a net that can be used as a binary classifier with outputs of 1 or 0.
|
| make_data_exam [Teacher] |
Make an exam that grades how well the Hypothesis is doing on some data.
|
| make_exam [Teacher] |
Perform a number of exams on a student
|
| make_move [Minimax.Game] |
Find out the postion that is reached when doing a certain move
|
| make_net [Mlp] |
create a net of the given size and the given layout of transfer functions
|
| make_strukture_exam [Teacher] |
Make an exam that will grade according to the structure of the Hypothesis.
|
| make_teacher [Teacher] |
Make a teacher from training function and some data
|
P | |
| path_comb [Uninf_search] |
another very simple cobinator.
|
| print_attribute [Types] | |
| print_bool [Prop_logic] |
prints a single bool to stdout
|
| print_grades [Teacher] |
Print a list of exam results, as returned by the teach_graded function
|
| print_knowledge [Bayes] |
print some knowledge object
|
| print_model [Prop_logic] |
prints a complete model to stdout
|
R | |
| really_ignore_grades [Teacher] |
Like ignore_grades, but there is actually no grading done by this teacher.
|
| repeat [Teacher] |
repeat the teacher n times
|
| res_comb [Uninf_search] |
very simple combinator.
|
| reset_grades [Teacher] |
Reset all grades that have been collected so far
|
S | |
| set [Types] | |
| shuffle [Teacher] |
Shuffles the order of the data each time the permutation is used
|
| sigmoid [Omlp] | |
| solve [Prop_logic] |
solve a formula in a given model.
|
| sort [Types] |
split a list according to a function
|
| sort_by_cat [Types] |
split examples according to their categories
|
| squared_error [Mlp] |
simple and often used error function on mlps
|
T | |
| teach [Teacher] |
Apply a given teacher to a Hypothesis, returning a new Hypothesis.
|
| teach_graded [Teacher] |
Teach and grade at the same time, the return value gives all
the grades as well as the new Hypothesis
|
| teach_sync [Teacher] |
Sync teaching to a event.
|
| teach_until_both [Teacher] |
see teach_until_student
|
| teach_until_grades [Teacher] |
see teach_until_student
|
| teach_until_student [Teacher] |
repeat until a given predicate on the student, grades or both will be satisfied or
after max passes of the teacher
|
| teacher_of_fun [Teacher] |
Make a teacher from a function on the Hypothesis space
|
| train [Mlp] |
train a MLP and return a new one.
|
| train_in_place [Mlp] |
train a MLP.
|
W | |
| weight_init [Mlp] |
Initial weights are taken from a range of +- weight_init
|