The mlep Reference Manual

This is the mlep Reference Manual, version 0.0.1, generated automatically by Declt version 4.0 beta 2 "William Riker" on Mon Feb 26 15:27:55 2024 GMT+0.

Table of Contents


1 Introduction


2 Systems

The main system appears first, followed by any subsystem dependency.


2.1 mlep

A Common Lisp machine learning library for educational purposes.

Maintainer

Frank Zalkow <>

Author

Frank Zalkow <>

License

MIT License <http://opensource.org/licenses/MIT>

Long Description

mlep is a Machine Learning library for Educational Purposes.

It aims at providing a collection of simple machine learning algorithms with the following claims:
* to be implementation independent
* to be fairly easy to use so that even intermediate Common Lisp programmers should be able to use this library instantly without pain
* to provide a tutorial-style documentation so that one should get to know this library easily

Version

0.0.1

Source

mlep.asd.

Child Components

3 Modules

Modules are listed depth-first from the system components tree.


3.1 mlep/macros

Dependency

package.lisp (file).

Source

mlep.asd.

Parent Component

mlep (system).

Child Component

macros.lisp (file).


3.2 mlep/datasets

Dependency

macros (module).

Source

mlep.asd.

Parent Component

mlep (system).

Child Components

3.3 mlep/utils

Dependency

datasets (module).

Source

mlep.asd.

Parent Component

mlep (system).

Child Components

3.4 mlep/core

Dependency

utils (module).

Source

mlep.asd.

Parent Component

mlep (system).

Child Components

4 Files

Files are sorted by type and then listed depth-first from the systems components trees.


4.1 Lisp


4.1.1 mlep/mlep.asd

Source

mlep.asd.

Parent Component

mlep (system).

ASDF Systems

mlep.

Packages

mlep-asd.


4.1.2 mlep/package.lisp

Source

mlep.asd.

Parent Component

mlep (system).

Packages

mlep.


4.1.3 mlep/macros/macros.lisp

Source

mlep.asd.

Parent Component

macros (module).

Internals

4.1.4 mlep/datasets/iris.lisp

Source

mlep.asd.

Parent Component

datasets (module).

Public Interface

*iris* (special variable).


4.1.5 mlep/datasets/heights-weights.lisp

Source

mlep.asd.

Parent Component

datasets (module).

Public Interface

*heights-weights* (special variable).


4.1.6 mlep/datasets/lenses.lisp

Source

mlep.asd.

Parent Component

datasets (module).

Public Interface

*lenses* (special variable).


4.1.7 mlep/datasets/wages.lisp

Source

mlep.asd.

Parent Component

datasets (module).

Public Interface

*wages* (special variable).


4.1.8 mlep/utils/number-utils.lisp

Source

mlep.asd.

Parent Component

utils (module).

Public Interface

random-from-to (function).

Internals

4.1.9 mlep/utils/list-utils.lisp

Source

mlep.asd.

Parent Component

utils (module).

Internals

4.1.10 mlep/utils/array-utils.lisp

Source

mlep.asd.

Parent Component

utils (module).

Internals

4.1.11 mlep/utils/constants.lisp

Source

mlep.asd.

Parent Component

utils (module).

Internals

+e+ (constant).


4.1.12 mlep/utils/functions.lisp

Source

mlep.asd.

Parent Component

utils (module).

Internals

4.1.13 mlep/utils/plot.lisp

Source

mlep.asd.

Parent Component

utils (module).

Public Interface

4.1.14 mlep/core/generic.lisp

Source

mlep.asd.

Parent Component

core (module).

Public Interface
Internals

4.1.15 mlep/core/k-means.lisp

Source

mlep.asd.

Parent Component

core (module).

Public Interface

4.1.16 mlep/core/k-nearest.lisp

Source

mlep.asd.

Parent Component

core (module).

Public Interface
Internals

4.1.17 mlep/core/markov-chain.lisp

Source

mlep.asd.

Parent Component

core (module).

Public Interface
Internals

4.1.18 mlep/core/naive-bayes.lisp

Source

mlep.asd.

Parent Component

core (module).

Public Interface
Internals

4.1.19 mlep/core/perceptron.lisp

Source

mlep.asd.

Parent Component

core (module).

Public Interface
Internals

4.1.20 mlep/core/likelihood.lisp

Source

mlep.asd.

Parent Component

core (module).

Public Interface
Internals

4.1.21 mlep/core/neuronal.lisp

Source

mlep.asd.

Parent Component

core (module).

Public Interface
Internals

4.1.22 mlep/core/imputer.lisp

Source

mlep.asd.

Parent Component

core (module).

Public Interface
Internals

5 Packages

Packages are listed by definition order.


5.1 mlep

@code{mlep} is a Machine Learning library for Educational Purposes.

It aims at providing a collection of simple machine learning algorithms with the following claims:
@itemize{
@item{to use only ANSI Common Lisp (thus to be implementation independent)} @item{to be fairly easy to use so that even intermediate Common Lisp programmers should be able to use this library instantly without pain}
@item{to provide a tutorial-style documentation so that one should get to know this library easily}}

Source

package.lisp.

Use List

common-lisp.

Public Interface
Internals

5.2 mlep-asd

Source

mlep.asd.

Use List
  • asdf/interface.
  • common-lisp.

6 Definitions

Definitions are sorted by export status, category, package, and then by lexicographic order.


6.1 Public Interface


6.1.1 Special variables

Special Variable: *heights-weights*

SOCR Data Dinov 020108 HeightsWeights

Human Height and Weight are mostly hereditable, but lifestyles, diet, health and environmental factors also play a role in determining individual’s physical characteristics. The dataset below contains 25,000 records of human heights and weights. These data were obtained in 1993 by a Growth Survey of 25,000 children from birth to 18 years of age recruited from Maternal and Child Health Centres (MCHC) and schools and were used to develop Hong Kong’s current growth charts for weight, height, weight-for-age, weight-for-height and body mass index (BMI). See also the Major League Baseball Players Height and Weight dataset.

@a[http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_Dinov_020108_HeightsWeights]{http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_Dinov_020108_HeightsWeights}

Attribute Information:

@itemize{@item{1. Height (Inches)}
@item{2. Weight (Pounds)}}

Package

mlep.

Source

heights-weights.lisp.

Special Variable: *iris*

Iris flower data set by Sir Ronald Fisher (1936)

@a[http://archive.ics.uci.edu/ml/datasets/Iris]{http://archive.ics.uci.edu/ml/datasets/Iris}

Attribute Information:

@itemize{
@item{1. sepal length in cm}
@item{2. sepal width in cm}
@item{3. petal length in cm}
@item{4. petal width in cm}
@item{5. class (Iris Setosa, Iris Versicolour, Iris Virginica)}}

Package

mlep.

Source

iris.lisp.

Special Variable: *lenses*

Lenses Data Set by J. Cendrowska (1987)

@a[https://archive.ics.uci.edu/ml/datasets/Lenses]{https://archive.ics.uci.edu/ml/datasets/Lenses}

Attribute Information:

@itemize{
@item{1. age of the patient (1 = young, 2 = pre-presbyopic, 3 = presbyopic)}
@item{2. spectacle prescription (1 = myope, 2 = hypermetrope)}
@item{3. astigmatic (1 = no, 2 = yes)}
@item{4. tear production rate (1 = reduced, 2 = normal)}
@item{5. class @itemize{@item{1 = the patient should be fitted with hard contact lenses,} @item{2 = the patient should be fitted with soft contact lenses,}
@item{3 = the patient should not be fitted with contact lenses.}}}}

Package

mlep.

Source

lenses.lisp.

Special Variable: *wages*

Determinants of Wages from the 1985 Current Population Survey

Therese Stukel

The datafile contains 534 observations on 11 variables
sampled from the Current Population Survey of 1985

@a[http://lib.stat.cmu.edu/datasets/CPS_85_Wages]{http://lib.stat.cmu.edu/datasets/CPS_85_Wages}

Attribute Information:

@itemize{
@item{ 1. EDUCATION: Number of years of education.}
@item{ 2. SOUTH: Indicator variable for Southern Region (1=Person lives in South, 0=Person lives elsewhere).} @item{ 3. SEX: Indicator variable for sex (1=Female, 0=Male).}
@item{ 4. EXPERIENCE: Number of years of work experience.}
@item{ 5. UNION: Indicator variable for union membership (1=Union member, 0=Not union member).}
@item{ 6. WAGE: Wage (dollars per hour).}
@item{ 7. AGE: Age (years).}
@item{ 8. RACE: Race (1=Other, 2=Hispanic, 3=White).}
@item{ 9. OCCUPATION: Occupational category (1=Management, 2=Sales, 3=Clerical, 4=Service, 5=Professional, 6=Other).} @item{ 10. SECTOR: Sector (0=Other, 1=Manufacturing, 2=Construction).}
@item{ 11. MARR: Marital Status (0=Unmarried, 1=Married)}}

Package

mlep.

Source

wages.lisp.


6.1.2 Ordinary functions

Function: plot-points (vals &key height width char)

@arg[vals]{a list of list with x/y-points or a 2d-array – @code{((x1 y1) ... (xn yn))} or @code{#2a((x1 y1) ... (xn yn))}} @arg[height]{the height in characters used for the plot}
@arg[width]{the width in characters used for the plot}
@arg[char]{the character used for printing}
@return{nothing}
Plotting points with x/y-coordinates.

Package

mlep.

Source

plot.lisp.

Function: plot-values (vals &key height char)

@arg[vals]{a sequence of numbers to be plotted} @arg[height]{the height in characters used for the plot} @arg[char]{the character used for printing} @return{nothing}
Plot the values of @code{vals} successively.

Package

mlep.

Source

plot.lisp.

Function: random-from-to (from to &key state)

@arg[from]{the lower bound (inclusive)}
@arg[to]{the upper bound (exclusive)}
@arg[state]{a random state object containing information used by the pseudo-random number generator} @return{a random number}
Gives a random number in certain range.

Package

mlep.

Source

number-utils.lisp.


6.1.3 Generic functions

Generic Function: analyze (instance input &key)

@arg[instance]{an instance of @code{markov-chain}}
@arg[input]{some input data}
@return{the probability of @code{input}}
Check the probability of @code{input} being generated by @code{instance}.

Package

mlep.

Source

generic.lisp.

Methods
Method: analyze ((instance markov-chain) input &key)
Source

markov-chain.lisp.

Generic Function: classify (instance &key new-data-set verbose)

@arg[instance]{an instance of @code{k-means}, @code{perceptron} or @code{neuronal-network}} @arg[new-data-set]{use @code{new-data-set} instead of the internal @code{data-set}} @arg[verbose]{print some more information (only taken into account for @code{neuronal-network})} @return{a list with a classification number according to each sample in the classified data-set} Classifying some data-set.

Package

mlep.

Source

generic.lisp.

Methods
Method: classify ((instance neuronal-network) &key new-data-set verbose)
Source

neuronal.lisp.

Method: classify ((instance perceptron) &key new-data-set)
Source

perceptron.lisp.

Method: classify ((instance k-means) &key new-data-set)
Source

k-means.lisp.

Generic Reader: data-set (instance)

@arg[instance]{an instance of any @code{mlep} learning algorithm} @return{the data-set of @code{instance}}
Get the data-set of @code{instance}.

Package

mlep.

Source

generic.lisp.

Methods
Reader Method: data-set ((imputer imputer))

The data-set to be analyzed.

Source

imputer.lisp.

Target Slot

data-set.

Reader Method: data-set ((neuronal-network neuronal-network))

The data-set to be analyzed.

Source

neuronal.lisp.

Target Slot

data-set.

Reader Method: data-set ((max-likelihood max-likelihood))

The data-set to be analyzed.

Source

likelihood.lisp.

Target Slot

data-set.

Reader Method: data-set ((perceptron perceptron))

The data-set to be analyzed.

Source

perceptron.lisp.

Target Slot

data-set.

Reader Method: data-set ((naive-bayes naive-bayes))

The data-set that is already known. (@code{set-labels} go hand in hand with it.)

Source

naive-bayes.lisp.

Target Slot

data-set.

Reader Method: data-set ((markov-chain markov-chain))

The data-set to be analyzed.

Source

markov-chain.lisp.

Target Slot

data-set.

Reader Method: data-set ((k-nearest-neighbors k-nearest-neighbors))

The data-set that is already known. (@code{set-labels} go hand in hand with it.)

Source

k-nearest.lisp.

Target Slot

data-set.

Reader Method: data-set ((k-means k-means))

The data-set to be analyzed.

Source

k-means.lisp.

Target Slot

data-set.

Generic Function: (setf data-set) (object)
Package

mlep.

Methods
Writer Method: (setf data-set) ((imputer imputer))

The data-set to be analyzed.

Source

imputer.lisp.

Target Slot

data-set.

Writer Method: (setf data-set) :after ((instance neuronal-network))
Source

neuronal.lisp.

Target Slot

data-set.

Method: (setf data-set) ((neuronal-network neuronal-network))

The data-set to be analyzed.

Source

neuronal.lisp.

Writer Method: (setf data-set) ((max-likelihood max-likelihood))

The data-set to be analyzed.

Source

likelihood.lisp.

Target Slot

data-set.

Writer Method: (setf data-set) :after ((instance perceptron))
Source

perceptron.lisp.

Target Slot

data-set.

Method: (setf data-set) ((perceptron perceptron))

The data-set to be analyzed.

Source

perceptron.lisp.

Writer Method: (setf data-set) :after ((instance naive-bayes))
Source

naive-bayes.lisp.

Target Slot

data-set.

Method: (setf data-set) ((naive-bayes naive-bayes))

The data-set that is already known. (@code{set-labels} go hand in hand with it.)

Source

naive-bayes.lisp.

Writer Method: (setf data-set) ((markov-chain markov-chain))

The data-set to be analyzed.

Source

markov-chain.lisp.

Target Slot

data-set.

Writer Method: (setf data-set) ((k-nearest-neighbors k-nearest-neighbors))

The data-set that is already known. (@code{set-labels} go hand in hand with it.)

Source

k-nearest.lisp.

Target Slot

data-set.

Writer Method: (setf data-set) ((k-means k-means))

The data-set to be analyzed.

Source

k-means.lisp.

Target Slot

data-set.

Generic Reader: distance (instance)

@arg[instance]{an instance of @code{k-means} or @code{k-nearest-neighbors}}
@return{the function for calculating the distance for @code{instance}}
Get the function for calculating the distance for @code{instance}, e.g. @code{#’euclidian-distance}.

Package

mlep.

Source

generic.lisp.

Methods
Reader Method: distance ((k-nearest-neighbors k-nearest-neighbors))

A distance measuring function.

Source

k-nearest.lisp.

Target Slot

distance.

Reader Method: distance ((k-means k-means))

A distance measuring function.

Source

k-means.lisp.

Target Slot

distance.

Generic Writer: (setf distance) (object)
Package

mlep.

Methods
Writer Method: (setf distance) ((k-nearest-neighbors k-nearest-neighbors))

A distance measuring function.

Source

k-nearest.lisp.

Target Slot

distance.

Writer Method: (setf distance) ((k-means k-means))

A distance measuring function.

Source

k-means.lisp.

Target Slot

distance.

Generic Function: forward (instance &key input)

@arg[instance]{an instance of @code{neuronal-network}} @arg[input]{the input data to be considered}
@return{the output of the @code{neuronal-network} given the @code{input}} Computes a forward path through the network and gives its output.

Package

mlep.

Source

generic.lisp.

Methods
Method: forward ((instance neuronal-network) &key input)
Source

neuronal.lisp.

Generic Reader: k (instance)

@arg[instance]{an instance of @code{k-means} or @code{k-nearest-neighbors}}
@return{the parameter @code{k}}
@code{k} determines how many means are assumed (for @code{k-means}) resp. how many neighbors are considered (for @code{k-nearest-neighbors}).

Package

mlep.

Source

generic.lisp.

Methods
Reader Method: k ((k-nearest-neighbors k-nearest-neighbors))

The number of neighbors to be taken into account.

Source

k-nearest.lisp.

Target Slot

k.

Reader Method: k ((k-means k-means))

The number of groups/clusters to be determined.

Source

k-means.lisp.

Target Slot

k.

Generic Function: (setf k) (instance)
Package

mlep.

Methods
Writer Method: (setf k) ((k-nearest-neighbors k-nearest-neighbors))

The number of neighbors to be taken into account.

Source

k-nearest.lisp.

Target Slot

k.

Method: (setf k) :after ((instance k-means))
Source

k-means.lisp.

Generic Reader: learning-rate (instance)

@arg[instance]{an instance of @code{neuronal-network} or @code{perceptron}} @return{the learning-rate}
The learning rate controls the size of change during updating the weights.

Package

mlep.

Source

generic.lisp.

Methods
Reader Method: learning-rate ((neuronal-network neuronal-network))

The learning rate.

Source

neuronal.lisp.

Target Slot

learning-rate.

Reader Method: learning-rate ((perceptron perceptron))

The learning rate.

Source

perceptron.lisp.

Target Slot

learning-rate.

Generic Writer: (setf learning-rate) (object)
Package

mlep.

Methods
Writer Method: (setf learning-rate) ((neuronal-network neuronal-network))

The learning rate.

Source

neuronal.lisp.

Target Slot

learning-rate.

Writer Method: (setf learning-rate) ((perceptron perceptron))

The learning rate.

Source

perceptron.lisp.

Target Slot

learning-rate.

Generic Reader: means (instance)

@arg[instance]{an instance of @code{k-means} or @code{principal-component-analysis}} @return{the current means}
Get the current means.

Package

mlep.

Source

generic.lisp.

Methods
Reader Method: means ((k-means k-means))

The means of the data points.

Source

k-means.lisp.

Target Slot

means.

Generic Writer: (setf means) (object)
Package

mlep.

Methods
Writer Method: (setf means) ((k-means k-means))

The means of the data points.

Source

k-means.lisp.

Target Slot

means.

Generic Reader: order (instance)

@arg[instance]{an instance of @code{markov-chain}}
@return{the order of the markov chain}
The order of a markov chain determines how much past events are considered for producing a current event.

Package

mlep.

Source

generic.lisp.

Methods
Reader Method: order ((markov-chain markov-chain))

The order of the markov chain.

Source

markov-chain.lisp.

Target Slot

order.

Generic Writer: (setf order) (object)
Package

mlep.

Methods
Writer Method: (setf order) ((markov-chain markov-chain))

The order of the markov chain.

Source

markov-chain.lisp.

Target Slot

order.

Generic Reader: probabilities (instance)

@arg[instance]{an instance of @code{markov-chain}}
@return{the probabilities of the markov chain}
Get the probability matrix (or tensor) – it’s rank is @code{order+1}.

Package

mlep.

Source

generic.lisp.

Methods
Reader Method: probabilities ((markov-chain markov-chain))

A matrix/tensor with probabilities.

Source

markov-chain.lisp.

Target Slot

probabilities.

Generic Writer: (setf probabilities) (object)
Package

mlep.

Methods
Writer Method: (setf probabilities) ((markov-chain markov-chain))

A matrix/tensor with probabilities.

Source

markov-chain.lisp.

Target Slot

probabilities.

Generic Function: run (instance &key epochs threshold)

@arg[instance]{an instance of any @code{mlep} learning algorithm}
@arg[threshold]{a threshold that is the minimum global error to be achieved – iterative training runs until threshold is reached (supported by @code{perceptron})}
@arg[epochs]{number of how often a iterative algorithm should be performed (supported by @code{k-means} and @code{neuronal-network})}
@return{depends on the learning algorithm:
@itemize{
@item{@code{k-means}: the computed means}
@item{@code{k-nearest-neighbors}: the classes assigned to @code{test-set}}
@item{@code{max-likelihood}: a list with the first item being the mean and the second one being the co-variance matrix of the normal distribution}
@item{@code{markov-chain}: the probability matrix (or tensor)}
@item{@code{naive-bayes}: the classes assigned to @code{test-set}}
@item{@code{neuronal-network}: nothing}
@item{@code{perceptron}: nothing}
@item{@code{principal-component-analysis}: list with three matrices - @code{unitary-matrix1 U} (orthogonal matrix), @code{unitary-matrix2 Vt} (orthogonal matrix) and @code{singular-values} (a diagonal matrix with the diagonal elements being the singular values called @code{D}; @code{UxDxVt} should be a reconstruction of the input matrix)} @item{@code{imputer}: The default replace values for each column.}
}}
A general interface for ’running’ a learning algorithm.

Package

mlep.

Source

generic.lisp.

Methods
Method: run ((instance imputer) &key)
Source

imputer.lisp.

Method: run ((instance neuronal-network) &key epochs)
Source

neuronal.lisp.

Method: run ((instance max-likelihood) &key)
Source

likelihood.lisp.

Method: run ((instance perceptron) &key threshold)
Source

perceptron.lisp.

Method: run ((instance naive-bayes) &key)
Source

naive-bayes.lisp.

Method: run ((instance markov-chain) &key)
Source

markov-chain.lisp.

Method: run ((instance k-nearest-neighbors) &key)
Source

k-nearest.lisp.

Method: run ((instance k-means) &key epochs)
Source

k-means.lisp.

Generic Reader: set-labels (instance)

@arg[instance]{an instance of @code{k-nearest-neighbors}, @code{naive-bayes}, @code{neuronal-network} or @code{perceptron}} @return{the target labels for @code{data-set} of a supervised learning algorithm.}
Get the target labels.

Package

mlep.

Source

generic.lisp.

Methods
Reader Method: set-labels ((neuronal-network neuronal-network))

The output-values for @code{data-set}.

Source

neuronal.lisp.

Target Slot

set-labels.

Reader Method: set-labels ((perceptron perceptron))

The output-values for @code{data-set}.

Source

perceptron.lisp.

Target Slot

set-labels.

Reader Method: set-labels ((naive-bayes naive-bayes))

The labels for @code{data-set}.

Source

naive-bayes.lisp.

Target Slot

set-labels.

Reader Method: set-labels ((k-nearest-neighbors k-nearest-neighbors))

The labels for @code{data-set}.

Source

k-nearest.lisp.

Target Slot

set-labels.

Generic Function: (setf set-labels) (object)
Package

mlep.

Methods
Writer Method: (setf set-labels) :after ((instance neuronal-network))
Source

neuronal.lisp.

Target Slot

set-labels.

Method: (setf set-labels) ((neuronal-network neuronal-network))

The output-values for @code{data-set}.

Source

neuronal.lisp.

Writer Method: (setf set-labels) :after ((instance perceptron))
Source

perceptron.lisp.

Target Slot

set-labels.

Method: (setf set-labels) ((perceptron perceptron))

The output-values for @code{data-set}.

Source

perceptron.lisp.

Writer Method: (setf set-labels) :after ((instance naive-bayes))
Source

naive-bayes.lisp.

Target Slot

set-labels.

Method: (setf set-labels) ((naive-bayes naive-bayes))

The labels for @code{data-set}.

Source

naive-bayes.lisp.

Writer Method: (setf set-labels) ((k-nearest-neighbors k-nearest-neighbors))

The labels for @code{data-set}.

Source

k-nearest.lisp.

Target Slot

set-labels.

Generic Function: synthesize (instance &key start howmany)

@arg[instance]{an instance of @code{markov-chain}}
@arg[start]{1) the symbol @code{random} – a new random sequence as beginning is generated; 2) @code{nil} (default) – a literal subsequence with random starting index is taken as beginning, 3) a list with an user given starting sequence} @arg[howmany]{number of elements to be synthesized}
Synthesize some data.

Package

mlep.

Source

generic.lisp.

Methods
Method: synthesize ((instance markov-chain) &key start howmany)
Source

markov-chain.lisp.

Generic Reader: test-set (instance)

@arg[instance]{an instance of @code{k-nearest-neighbors} or @code{naive-bayes}} @return{the test, i.e. a set that has no target labels and needs to be classified.} Get the test-set.

Package

mlep.

Source

generic.lisp.

Methods
Reader Method: test-set ((naive-bayes naive-bayes))

The data-set that has no labels and needs to be classified.

Source

naive-bayes.lisp.

Target Slot

test-set.

Reader Method: test-set ((k-nearest-neighbors k-nearest-neighbors))

The data-set that has no labels and needs to be classified.

Source

k-nearest.lisp.

Target Slot

test-set.

Generic Writer: (setf test-set) (object)
Package

mlep.

Methods
Writer Method: (setf test-set) ((naive-bayes naive-bayes))

The data-set that has no labels and needs to be classified.

Source

naive-bayes.lisp.

Target Slot

test-set.

Writer Method: (setf test-set) ((k-nearest-neighbors k-nearest-neighbors))

The data-set that has no labels and needs to be classified.

Source

k-nearest.lisp.

Target Slot

test-set.

Generic Function: transform (instance &key new-data)

@arg[instance]{an instance of @code{principal-component-analysis} or @code{imputer}}
@arg[components]{a number that states how many dimensions should be used for a transformation (default is @code{nil} which means that it should use all dimensions of @code{data-set}} @arg[inverse]{do an inverse transformation (@code{t} or @code{nil}, default: @code{nil})}
@arg[new-data]{do the transformation on this data-set (default is @code{nil} which means, that it should use @code{data-set})}
@return{the transformed data-set}
Project some data on its principal components. / Fit missing values.

Package

mlep.

Source

generic.lisp.

Methods
Method: transform ((instance imputer) &key new-data)
Source

imputer.lisp.

Generic Reader: unique (instance)

@arg[instance]{an instance of @code{markov-chain}} @return{all unique elements in @code{data-set}} Get all unique values are considered by the chain.

Package

mlep.

Source

generic.lisp.

Methods
Reader Method: unique ((markov-chain markov-chain))

Unique values of data-set

Source

markov-chain.lisp.

Target Slot

unique.


6.1.4 Standalone methods

Method: initialize-instance :after ((instance neuronal-network) &key)
Source

neuronal.lisp.

Method: initialize-instance :after ((instance perceptron) &key)
Source

perceptron.lisp.

Method: initialize-instance :after ((instance k-means) &key)
Source

k-means.lisp.

Method: initialize-instance :after ((instance naive-bayes) &key)
Source

naive-bayes.lisp.


6.1.5 Classes

Class: imputer

Replace missing value by the mean (for numercial data) or the mode (for categorical data).

Package

mlep.

Source

imputer.lisp.

Direct methods
Direct slots
Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set.

Writers

(setf data-set).

Slot: missing-value

The value that is recognized as a missing value.

Initargs

:missing-value

Readers

missing-value.

Writers

(setf missing-value).

Slot: missing-value-test

The test-function for comparing each item in a data-set with this missing value.

Initform

(function eq)

Initargs

:missing-value-test

Readers

missing-value-test.

Writers

(setf missing-value-test).

Slot: replacers

The values that are used for replacing missing values per column.

Readers

replacers.

Writers

(setf replacers).

Class: k-means

k-means is a simple unsupervised clustering algorithm for a known number of clusters.

Package

mlep.

Source

k-means.lisp.

Direct methods
Direct slots
Slot: k

The number of groups/clusters to be determined.

Initform

2

Initargs

:k

Readers

k.

Writers

This slot is read-only.

Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set.

Writers

(setf data-set).

Slot: distance

A distance measuring function.

Initform

(function mlep::euclidian-distance)

Initargs

:distance

Readers

distance.

Writers

(setf distance).

Slot: means

The means of the data points.

Initargs

:means

Readers

means.

Writers

(setf means).

Class: k-nearest-neighbors

k-nearest-neighbors is a simple supervised clustering algorithm for a known number of clusters.

Package

mlep.

Source

k-nearest.lisp.

Direct methods
Direct slots
Slot: k

The number of neighbors to be taken into account.

Initform

2

Initargs

:k

Readers

k.

Writers

(setf k).

Slot: data-set

The data-set that is already known. (@code{set-labels} go hand in hand with it.)

Initargs

:data-set

Readers

data-set.

Writers

(setf data-set).

Slot: set-labels

The labels for @code{data-set}.

Initargs

:set-labels

Readers

set-labels.

Writers

(setf set-labels).

Slot: distance

A distance measuring function.

Initform

(function mlep::euclidian-distance)

Initargs

:distance

Readers

distance.

Writers

(setf distance).

Slot: test-set

The data-set that has no labels and needs to be classified.

Initargs

:test-set

Readers

test-set.

Writers

(setf test-set).

Class: markov-chain

A Markov-Chain.

Package

mlep.

Source

markov-chain.lisp.

Direct methods
Direct slots
Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set.

Writers

(setf data-set).

Slot: order

The order of the markov chain.

Initform

1

Initargs

:order

Readers

order.

Writers

(setf order).

Slot: unique

Unique values of data-set

Initargs

:unique

Readers

unique.

Writers

This slot is read-only.

Slot: probabilities

A matrix/tensor with probabilities.

Initargs

:probabilities

Readers

probabilities.

Writers

(setf probabilities).

Class: max-likelihood

With max-likelihood one can estimate the parameters of the normal distributed probability density function that fits the data-set.

Package

mlep.

Source

likelihood.lisp.

Direct methods
Direct slots
Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set.

Writers

(setf data-set).

Slot: degrees-of-freedom

Delta Degrees of Freedom. Divisor is length of @code{data-set} minus @code{degrees-of-freedom}.

Initform

0

Initargs

:ddof

Readers

degrees-of-freedom.

Writers

(setf degrees-of-freedom).

Class: naive-bayes

Naive-bayes takes an probabilistic approach for a simple supervised clustering.

Package

mlep.

Source

naive-bayes.lisp.

Direct methods
Direct slots
Slot: data-set

The data-set that is already known. (@code{set-labels} go hand in hand with it.)

Initargs

:data-set

Readers

data-set.

Writers

(setf data-set).

Slot: set-labels

The labels for @code{data-set}.

Initargs

:set-labels

Readers

set-labels.

Writers

(setf set-labels).

Slot: test-set

The data-set that has no labels and needs to be classified.

Initargs

:test-set

Readers

test-set.

Writers

(setf test-set).

Slot: possible-data-values

All possible values occurring in each attribute of @code{data-set}. To be pre-computed.

Initargs

:possible-data-values

Readers

possible-data-values.

Writers

(setf possible-data-values).

Slot: all-labels

All given data-labels. To be pre-computed.

Initargs

:all-labels

Readers

all-labels.

Writers

(setf all-labels).

Slot: label-count

Counting of each item of a label. To be pre-computed.

Initargs

:label-count

Readers

label-count.

Writers

(setf label-count).

Slot: prior-probabilities

The prior-probabilities of each class. To be pre-computed.

Initargs

:prior-probabilities

Readers

prior-probabilities.

Writers

(setf prior-probabilities).

Slot: likelihoods

The likelihoods of a feature attribute given a class. To be pre-computed.

Initargs

mlep::likelihoods

Readers

likelihoods.

Writers

(setf likelihoods).

Class: neuronal-network

A fully-connected Feed-Forward Multi-Layer Perceptron.

Package

mlep.

Source

neuronal.lisp.

Direct methods
Direct slots
Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set.

Writers

(setf data-set).

Slot: set-labels

The output-values for @code{data-set}.

Initargs

:set-labels

Readers

set-labels.

Writers

(setf set-labels).

Slot: net-structure

The net-structure of the net. (Neurons per layer.)

Initargs

:net-structure

Readers

net-structure.

Writers

(setf net-structure).

Slot: weights

The weights from input to output values.

Initargs

:weights

Readers

weights.

Writers

(setf weights).

Slot: output-net

The output of all neurons of the network.

Initargs

:output-net

Readers

output-net.

Writers

(setf output-net).

Slot: output-net-before-activation

The output of all neurons of the network before the activation function was applied.

Initargs

:output-net-before-activation

Readers

output-net-before-activation.

Writers

(setf output-net-before-activation).

Slot: weight-init-range

The maximum range for initializing the weights.

Initform

(quote (-0.1d0 0.1d0))

Initargs

:weight-init-range

Readers

weight-init-range.

Writers

(setf weight-init-range).

Slot: activation-function

The activation function, usually a Heaviside step function.

Initform

(function tanh)

Initargs

:activation-function

Readers

activation-function.

Writers

(setf activation-function).

Slot: learning-rate

The learning rate.

Initform

0.2d0

Initargs

:learning-rate

Readers

learning-rate.

Writers

(setf learning-rate).

Class: perceptron

A perceptron is a very simple neuron model and turns out to be a linear classificator.

Package

mlep.

Source

perceptron.lisp.

Direct methods
Direct slots
Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set.

Writers

(setf data-set).

Slot: set-labels

The output-values for @code{data-set}.

Initargs

:set-labels

Readers

set-labels.

Writers

(setf set-labels).

Slot: weights

The weights from input to output values.

Initargs

:weights

Readers

weights.

Writers

(setf weights).

Slot: max-weight-init-value

The maximum value for initializing the weights.

Initform

0.1d0

Initargs

:max-weight-init-value

Readers

max-weight-init-value.

Writers

(setf max-weight-init-value).

Slot: activation-function

The activation function, usually a Heaviside step function.

Initform

(function mlep::step-function)

Initargs

:activation-function

Readers

activation-function.

Writers

(setf activation-function).

Slot: learning-rate

The learning rate.

Initform

0.2d0

Initargs

:learning-rate

Readers

learning-rate.

Writers

(setf learning-rate).


6.2 Internals


6.2.1 Constants

Constant: +e+
Package

mlep.

Source

constants.lisp.


6.2.2 Special variables

Special Variable: *diff-function-dict*
Package

mlep.

Source

functions.lisp.


6.2.3 Macros

Macro: do-lists (((&rest vars) (&rest lists) &optional result) &body body)
Package

mlep.

Source

macros.lisp.

Macro: dolist-with-index ((index list-item list &optional result) &body body)
Package

mlep.

Source

macros.lisp.

Macro: dotimes-fromto ((var from to &optional result) &body body)
Package

mlep.

Source

macros.lisp.


6.2.4 Ordinary functions

Function: 2d-col-to-vector (array)
Package

mlep.

Source

array-utils.lisp.

Function: all-positions (item list &key test key)

Gets the indices of all elements in LIST that safisfy the TEST.

Package

mlep.

Source

list-utils.lisp.

Function: altern (boul prob)
Package

mlep.

Source

markov-chain.lisp.

Function: col-of-array (array col)

Return column of 2d array as vector

Package

mlep.

Source

array-utils.lisp.

Function: (setf col-of-array) (array col)
Package

mlep.

Source

array-utils.lisp.

Function: compute-likelihoods (set data-labels all-labels label-count possible-data-values)
Package

mlep.

Source

naive-bayes.lisp.

Function: compute-possible-data-values (data-set)
Package

mlep.

Source

naive-bayes.lisp.

Function: compute-prior-probabilities (data-labels label-count)
Package

mlep.

Source

naive-bayes.lisp.

Function: d-sigmoid (x)
Package

mlep.

Source

functions.lisp.

Function: d-step-function (x)
Package

mlep.

Source

functions.lisp.

Function: d-tanh (x)
Package

mlep.

Source

functions.lisp.

Function: determinant (matrix)

Computes the determinant of ‘matrix’ - which must a square matrix of rank 2..

Package

mlep.

Source

array-utils.lisp.

Function: determinant-helper (matrix)
Package

mlep.

Source

array-utils.lisp.

Function: diff (func)
Package

mlep.

Source

functions.lisp.

Function: euclidian-distance (p1 p2)

Gives the euclidian distance between to points of arbitrary dimension.

Package

mlep.

Source

number-utils.lisp.

Function: get-actual-output (activation-function weights input)
Package

mlep.

Source

perceptron.lisp.

Function: get-column-ranges (set &key key)

SET is a two-dimensional list or array. Gets the minmal and maximal value of each column of SET. Column means the corresponding nth values of each sublist.

Package

mlep.

Source

list-utils.lisp.

Function: get-overall-error (all-input desired activation-function weights)
Package

mlep.

Source

perceptron.lisp.

Function: get-row-ranges (set &key key)

SET is a two-dimensional list or array. Gets the minimal and maximal value of each row (i.e. sublist) of SET.

Package

mlep.

Source

list-utils.lisp.

Function: get-row-ranges-array (set &key key)
Package

mlep.

Source

array-utils.lisp.

Function: get-row-ranges-list (set &key key)
Package

mlep.

Source

list-utils.lisp.

Function: histogram (vector)

Count all occurrences in vector. Returns list of all unique items in vector and their frequencies.

Package

mlep.

Source

k-nearest.lisp.

Function: k-maxmin-idx (vector &key k key)

Get the index of k max/min values in vector.

Package

mlep.

Source

k-nearest.lisp.

Function: list-to-2d-array (list &key element-type)

Converts a list of list to a 2-dimensional array.

Package

mlep.

Source

array-utils.lisp.

Function: list-to-vector (list &key element-type)

Converts a list to a vector.

Package

mlep.

Source

array-utils.lisp.

Function: map-pointwise (fn &rest arrays)

Calls ‘function’ to one or more ‘arrays’ pointwise.

Package

mlep.

Source

array-utils.lisp.

Function: matrix-expt (matrix exp)

Takes the first argument (a matrix) and multiplies it by itself exp times

Package

mlep.

Source

array-utils.lisp.

Function: matrix-identity (dim &key element-type)

Creates a new identity matrix of size dim*dim

Package

mlep.

Source

array-utils.lisp.

Function: max-arg (l)

Gets the index of the maximum value of ‘l’.

Package

mlep.

Source

list-utils.lisp.

Function: mean (l)

Gets the mean of ‘l’.

Package

mlep.

Source

list-utils.lisp.

Function: min-arg (l)

Gets the index of the minimum value of ‘l’.

Package

mlep.

Source

list-utils.lisp.

Function: most-frequent-value (x &key test)

Get the most frequent value of a sequence x.

Package

mlep.

Source

list-utils.lisp.

Function: multiply-matrices (matrix-0 matrix-1)

Takes two 2D arrays and returns their product, or an error if they cannot be multiplied

Package

mlep.

Source

array-utils.lisp.

Function: net-structure-to-array (net-structure &optional value)
Package

mlep.

Source

neuronal.lisp.

Function: net-structure-to-weights (net-structure &optional weight-init-range)
Package

mlep.

Source

neuronal.lisp.

Function: normalize-tensor (tensor)
Package

mlep.

Source

markov-chain.lisp.

Function: outer-product (x1 x2)

Computes the outer product (aka tensor product) of two vectors/lists ‘x1’ and ‘x2’.

Package

mlep.

Source

array-utils.lisp.

Function: range (from to &optional step)

Gives a list with all numbers from ‘FROM’ to (exclusive) ‘TO’.

Package

mlep.

Source

number-utils.lisp.

Function: row-of-array (array row)

Return row of 2d array as vector

Package

mlep.

Source

array-utils.lisp.

Function: (setf row-of-array) (array row)
Package

mlep.

Source

array-utils.lisp.

Function: scalar-product (x1 x2)

Computes the scalar product (aka dot product aka inner product) of two vectors/lists.

Package

mlep.

Source

array-utils.lisp.

Function: shuffle (x)

Shuffle a list.

Package

mlep.

Source

list-utils.lisp.

Function: sigmoid (x)
Package

mlep.

Source

functions.lisp.

Function: square (n)

Squares number N.

Package

mlep.

Source

number-utils.lisp.

Function: step-function (x)
Package

mlep.

Source

functions.lisp.

Function: transpose (x)

Transpose 2-dimensional list or array.

Package

mlep.

Source

list-utils.lisp.

Function: transpose-array (array)

Transpose an 2-dimensional array.

Package

mlep.

Source

array-utils.lisp.

Function: transpose-list (l)

Transpose a list of lists.

Package

mlep.

Source

list-utils.lisp.

Function: vector-to-2d-col (vec)
Package

mlep.

Source

array-utils.lisp.


6.2.5 Generic functions

Generic Reader: activation-function (object)
Generic Writer: (setf activation-function) (object)
Package

mlep.

Methods
Reader Method: activation-function ((neuronal-network neuronal-network))
Writer Method: (setf activation-function) ((neuronal-network neuronal-network))

The activation function, usually a Heaviside step function.

Source

neuronal.lisp.

Target Slot

activation-function.

Reader Method: activation-function ((perceptron perceptron))
Writer Method: (setf activation-function) ((perceptron perceptron))

The activation function, usually a Heaviside step function.

Source

perceptron.lisp.

Target Slot

activation-function.

Generic Reader: all-labels (object)
Generic Writer: (setf all-labels) (object)
Package

mlep.

Methods
Reader Method: all-labels ((naive-bayes naive-bayes))
Writer Method: (setf all-labels) ((naive-bayes naive-bayes))

All given data-labels. To be pre-computed.

Source

naive-bayes.lisp.

Target Slot

all-labels.

Generic Function: backprop (instance input wanted)

Adjust weights with backpropagation.

Package

mlep.

Source

generic.lisp.

Methods
Method: backprop ((instance neuronal-network) input wanted)
Source

neuronal.lisp.

Generic Reader: degrees-of-freedom (object)
Generic Writer: (setf degrees-of-freedom) (object)
Package

mlep.

Methods
Reader Method: degrees-of-freedom ((max-likelihood max-likelihood))
Writer Method: (setf degrees-of-freedom) ((max-likelihood max-likelihood))

Delta Degrees of Freedom. Divisor is length of @code{data-set} minus @code{degrees-of-freedom}.

Source

likelihood.lisp.

Target Slot

degrees-of-freedom.

Generic Function: initialize (instance &key)

Initialize the arguments of INSTANCE.

Package

mlep.

Source

generic.lisp.

Methods
Method: initialize ((instance neuronal-network) &key)
Source

neuronal.lisp.

Method: initialize ((instance perceptron) &key)
Source

perceptron.lisp.

Method: initialize ((instance naive-bayes) &key)

Pre-computes: ‘all-labels’, ‘label-count’, ‘possible-data-values’, ‘prior-probabilities’, ‘likelihoods’

Source

naive-bayes.lisp.

Generic Reader: label-count (object)
Generic Writer: (setf label-count) (object)
Package

mlep.

Methods
Reader Method: label-count ((naive-bayes naive-bayes))
Writer Method: (setf label-count) ((naive-bayes naive-bayes))

Counting of each item of a label. To be pre-computed.

Source

naive-bayes.lisp.

Target Slot

label-count.

Generic Reader: likelihoods (object)
Generic Writer: (setf likelihoods) (object)
Package

mlep.

Methods
Reader Method: likelihoods ((naive-bayes naive-bayes))
Writer Method: (setf likelihoods) ((naive-bayes naive-bayes))

The likelihoods of a feature attribute given a class. To be pre-computed.

Source

naive-bayes.lisp.

Target Slot

likelihoods.

Generic Reader: max-weight-init-value (object)
Package

mlep.

Methods
Reader Method: max-weight-init-value ((perceptron perceptron))

The maximum value for initializing the weights.

Source

perceptron.lisp.

Target Slot

max-weight-init-value.

Generic Function: (setf max-weight-init-value) (object)
Package

mlep.

Methods
Writer Method: (setf max-weight-init-value) :after ((instance perceptron))
Source

perceptron.lisp.

Target Slot

max-weight-init-value.

Method: (setf max-weight-init-value) ((perceptron perceptron))

The maximum value for initializing the weights.

Source

perceptron.lisp.

Generic Reader: missing-value (object)
Generic Writer: (setf missing-value) (object)
Package

mlep.

Methods
Reader Method: missing-value ((imputer imputer))
Writer Method: (setf missing-value) ((imputer imputer))

The value that is recognized as a missing value.

Source

imputer.lisp.

Target Slot

missing-value.

Generic Reader: missing-value-test (object)
Generic Writer: (setf missing-value-test) (object)
Package

mlep.

Methods
Reader Method: missing-value-test ((imputer imputer))
Writer Method: (setf missing-value-test) ((imputer imputer))

The test-function for comparing each item in a data-set with this missing value.

Source

imputer.lisp.

Target Slot

missing-value-test.

Generic Reader: net-structure (object)
Package

mlep.

Methods
Reader Method: net-structure ((neuronal-network neuronal-network))

The net-structure of the net. (Neurons per layer.)

Source

neuronal.lisp.

Target Slot

net-structure.

Generic Function: (setf net-structure) (object)
Package

mlep.

Methods
Writer Method: (setf net-structure) :after ((instance neuronal-network))
Source

neuronal.lisp.

Target Slot

net-structure.

Method: (setf net-structure) ((neuronal-network neuronal-network))

The net-structure of the net. (Neurons per layer.)

Source

neuronal.lisp.

Generic Reader: output-net (object)
Generic Writer: (setf output-net) (object)
Package

mlep.

Methods
Reader Method: output-net ((neuronal-network neuronal-network))
Writer Method: (setf output-net) ((neuronal-network neuronal-network))

The output of all neurons of the network.

Source

neuronal.lisp.

Target Slot

output-net.

Generic Reader: output-net-before-activation (object)
Generic Writer: (setf output-net-before-activation) (object)
Package

mlep.

Methods
Reader Method: output-net-before-activation ((neuronal-network neuronal-network))
Writer Method: (setf output-net-before-activation) ((neuronal-network neuronal-network))

The output of all neurons of the network before the activation function was applied.

Source

neuronal.lisp.

Target Slot

output-net-before-activation.

Generic Reader: possible-data-values (object)
Generic Writer: (setf possible-data-values) (object)
Package

mlep.

Methods
Reader Method: possible-data-values ((naive-bayes naive-bayes))
Writer Method: (setf possible-data-values) ((naive-bayes naive-bayes))

All possible values occurring in each attribute of @code{data-set}. To be pre-computed.

Source

naive-bayes.lisp.

Target Slot

possible-data-values.

Generic Reader: prior-probabilities (object)
Generic Writer: (setf prior-probabilities) (object)
Package

mlep.

Methods
Reader Method: prior-probabilities ((naive-bayes naive-bayes))
Writer Method: (setf prior-probabilities) ((naive-bayes naive-bayes))

The prior-probabilities of each class. To be pre-computed.

Source

naive-bayes.lisp.

Target Slot

prior-probabilities.

Generic Reader: replacers (object)
Generic Writer: (setf replacers) (object)
Package

mlep.

Methods
Reader Method: replacers ((imputer imputer))
Writer Method: (setf replacers) ((imputer imputer))

The values that are used for replacing missing values per column.

Source

imputer.lisp.

Target Slot

replacers.

Generic Reader: weight-init-range (object)
Package

mlep.

Methods
Reader Method: weight-init-range ((neuronal-network neuronal-network))

The maximum range for initializing the weights.

Source

neuronal.lisp.

Target Slot

weight-init-range.

Generic Function: (setf weight-init-range) (object)
Package

mlep.

Methods
Writer Method: (setf weight-init-range) :after ((instance neuronal-network))
Source

neuronal.lisp.

Target Slot

weight-init-range.

Method: (setf weight-init-range) ((neuronal-network neuronal-network))

The maximum range for initializing the weights.

Source

neuronal.lisp.

Generic Reader: weights (object)
Generic Writer: (setf weights) (object)
Package

mlep.

Methods
Reader Method: weights ((neuronal-network neuronal-network))
Writer Method: (setf weights) ((neuronal-network neuronal-network))

The weights from input to output values.

Source

neuronal.lisp.

Target Slot

weights.

Reader Method: weights ((perceptron perceptron))
Writer Method: (setf weights) ((perceptron perceptron))

The weights from input to output values.

Source

perceptron.lisp.

Target Slot

weights.


Appendix A Indexes


A.1 Concepts


A.2 Functions

Jump to:   (   2  
A   B   C   D   E   F   G   H   I   K   L   M   N   O   P   R   S   T   U   V   W  
Index Entry  Section

(
(setf activation-function): Private generic functions
(setf activation-function): Private generic functions
(setf activation-function): Private generic functions
(setf all-labels): Private generic functions
(setf all-labels): Private generic functions
(setf col-of-array): Private ordinary functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf data-set): Public generic functions
(setf degrees-of-freedom): Private generic functions
(setf degrees-of-freedom): Private generic functions
(setf distance): Public generic functions
(setf distance): Public generic functions
(setf distance): Public generic functions
(setf k): Public generic functions
(setf k): Public generic functions
(setf k): Public generic functions
(setf label-count): Private generic functions
(setf label-count): Private generic functions
(setf learning-rate): Public generic functions
(setf learning-rate): Public generic functions
(setf learning-rate): Public generic functions
(setf likelihoods): Private generic functions
(setf likelihoods): Private generic functions
(setf max-weight-init-value): Private generic functions
(setf max-weight-init-value): Private generic functions
(setf max-weight-init-value): Private generic functions
(setf means): Public generic functions
(setf means): Public generic functions
(setf missing-value): Private generic functions
(setf missing-value): Private generic functions
(setf missing-value-test): Private generic functions
(setf missing-value-test): Private generic functions
(setf net-structure): Private generic functions
(setf net-structure): Private generic functions
(setf net-structure): Private generic functions
(setf order): Public generic functions
(setf order): Public generic functions
(setf output-net): Private generic functions
(setf output-net): Private generic functions
(setf output-net-before-activation): Private generic functions
(setf output-net-before-activation): Private generic functions
(setf possible-data-values): Private generic functions
(setf possible-data-values): Private generic functions
(setf prior-probabilities): Private generic functions
(setf prior-probabilities): Private generic functions
(setf probabilities): Public generic functions
(setf probabilities): Public generic functions
(setf replacers): Private generic functions
(setf replacers): Private generic functions
(setf row-of-array): Private ordinary functions
(setf set-labels): Public generic functions
(setf set-labels): Public generic functions
(setf set-labels): Public generic functions
(setf set-labels): Public generic functions
(setf set-labels): Public generic functions
(setf set-labels): Public generic functions
(setf set-labels): Public generic functions
(setf set-labels): Public generic functions
(setf test-set): Public generic functions
(setf test-set): Public generic functions
(setf test-set): Public generic functions
(setf weight-init-range): Private generic functions
(setf weight-init-range): Private generic functions
(setf weight-init-range): Private generic functions
(setf weights): Private generic functions
(setf weights): Private generic functions
(setf weights): Private generic functions

2
2d-col-to-vector: Private ordinary functions

A
activation-function: Private generic functions
activation-function: Private generic functions
activation-function: Private generic functions
all-labels: Private generic functions
all-labels: Private generic functions
all-positions: Private ordinary functions
altern: Private ordinary functions
analyze: Public generic functions
analyze: Public generic functions

B
backprop: Private generic functions
backprop: Private generic functions

C
classify: Public generic functions
classify: Public generic functions
classify: Public generic functions
classify: Public generic functions
col-of-array: Private ordinary functions
compute-likelihoods: Private ordinary functions
compute-possible-data-values: Private ordinary functions
compute-prior-probabilities: Private ordinary functions

D
d-sigmoid: Private ordinary functions
d-step-function: Private ordinary functions
d-tanh: Private ordinary functions
data-set: Public generic functions
data-set: Public generic functions
data-set: Public generic functions
data-set: Public generic functions
data-set: Public generic functions
data-set: Public generic functions
data-set: Public generic functions
data-set: Public generic functions
data-set: Public generic functions
degrees-of-freedom: Private generic functions
degrees-of-freedom: Private generic functions
determinant: Private ordinary functions
determinant-helper: Private ordinary functions
diff: Private ordinary functions
distance: Public generic functions
distance: Public generic functions
distance: Public generic functions
do-lists: Private macros
dolist-with-index: Private macros
dotimes-fromto: Private macros

E
euclidian-distance: Private ordinary functions

F
forward: Public generic functions
forward: Public generic functions
Function, (setf col-of-array): Private ordinary functions
Function, (setf row-of-array): Private ordinary functions
Function, 2d-col-to-vector: Private ordinary functions
Function, all-positions: Private ordinary functions
Function, altern: Private ordinary functions
Function, col-of-array: Private ordinary functions
Function, compute-likelihoods: Private ordinary functions
Function, compute-possible-data-values: Private ordinary functions
Function, compute-prior-probabilities: Private ordinary functions
Function, d-sigmoid: Private ordinary functions
Function, d-step-function: Private ordinary functions
Function, d-tanh: Private ordinary functions
Function, determinant: Private ordinary functions
Function, determinant-helper: Private ordinary functions
Function, diff: Private ordinary functions
Function, euclidian-distance: Private ordinary functions
Function, get-actual-output: Private ordinary functions
Function, get-column-ranges: Private ordinary functions
Function, get-overall-error: Private ordinary functions
Function, get-row-ranges: Private ordinary functions
Function, get-row-ranges-array: Private ordinary functions
Function, get-row-ranges-list: Private ordinary functions
Function, histogram: Private ordinary functions
Function, k-maxmin-idx: Private ordinary functions
Function, list-to-2d-array: Private ordinary functions
Function, list-to-vector: Private ordinary functions
Function, map-pointwise: Private ordinary functions
Function, matrix-expt: Private ordinary functions
Function, matrix-identity: Private ordinary functions
Function, max-arg: Private ordinary functions
Function, mean: Private ordinary functions
Function, min-arg: Private ordinary functions
Function, most-frequent-value: Private ordinary functions
Function, multiply-matrices: Private ordinary functions
Function, net-structure-to-array: Private ordinary functions
Function, net-structure-to-weights: Private ordinary functions
Function, normalize-tensor: Private ordinary functions
Function, outer-product: Private ordinary functions
Function, plot-points: Public ordinary functions
Function, plot-values: Public ordinary functions
Function, random-from-to: Public ordinary functions
Function, range: Private ordinary functions
Function, row-of-array: Private ordinary functions
Function, scalar-product: Private ordinary functions
Function, shuffle: Private ordinary functions
Function, sigmoid: Private ordinary functions
Function, square: Private ordinary functions
Function, step-function: Private ordinary functions
Function, transpose: Private ordinary functions
Function, transpose-array: Private ordinary functions
Function, transpose-list: Private ordinary functions
Function, vector-to-2d-col: Private ordinary functions

G
Generic Function, (setf activation-function): Private generic functions
Generic Function, (setf all-labels): Private generic functions
Generic Function, (setf data-set): Public generic functions
Generic Function, (setf degrees-of-freedom): Private generic functions
Generic Function, (setf distance): Public generic functions
Generic Function, (setf k): Public generic functions
Generic Function, (setf label-count): Private generic functions
Generic Function, (setf learning-rate): Public generic functions
Generic Function, (setf likelihoods): Private generic functions
Generic Function, (setf max-weight-init-value): Private generic functions
Generic Function, (setf means): Public generic functions
Generic Function, (setf missing-value): Private generic functions
Generic Function, (setf missing-value-test): Private generic functions
Generic Function, (setf net-structure): Private generic functions
Generic Function, (setf order): Public generic functions
Generic Function, (setf output-net): Private generic functions
Generic Function, (setf output-net-before-activation): Private generic functions
Generic Function, (setf possible-data-values): Private generic functions
Generic Function, (setf prior-probabilities): Private generic functions
Generic Function, (setf probabilities): Public generic functions
Generic Function, (setf replacers): Private generic functions
Generic Function, (setf set-labels): Public generic functions
Generic Function, (setf test-set): Public generic functions
Generic Function, (setf weight-init-range): Private generic functions
Generic Function, (setf weights): Private generic functions
Generic Function, activation-function: Private generic functions
Generic Function, all-labels: Private generic functions
Generic Function, analyze: Public generic functions
Generic Function, backprop: Private generic functions
Generic Function, classify: Public generic functions
Generic Function, data-set: Public generic functions
Generic Function, degrees-of-freedom: Private generic functions
Generic Function, distance: Public generic functions
Generic Function, forward: Public generic functions
Generic Function, initialize: Private generic functions
Generic Function, k: Public generic functions
Generic Function, label-count: Private generic functions
Generic Function, learning-rate: Public generic functions
Generic Function, likelihoods: Private generic functions
Generic Function, max-weight-init-value: Private generic functions
Generic Function, means: Public generic functions
Generic Function, missing-value: Private generic functions
Generic Function, missing-value-test: Private generic functions
Generic Function, net-structure: Private generic functions
Generic Function, order: Public generic functions
Generic Function, output-net: Private generic functions
Generic Function, output-net-before-activation: Private generic functions
Generic Function, possible-data-values: Private generic functions
Generic Function, prior-probabilities: Private generic functions
Generic Function, probabilities: Public generic functions
Generic Function, replacers: Private generic functions
Generic Function, run: Public generic functions
Generic Function, set-labels: Public generic functions
Generic Function, synthesize: Public generic functions
Generic Function, test-set: Public generic functions
Generic Function, transform: Public generic functions
Generic Function, unique: Public generic functions
Generic Function, weight-init-range: Private generic functions
Generic Function, weights: Private generic functions
get-actual-output: Private ordinary functions
get-column-ranges: Private ordinary functions
get-overall-error: Private ordinary functions
get-row-ranges: Private ordinary functions
get-row-ranges-array: Private ordinary functions
get-row-ranges-list: Private ordinary functions

H
histogram: Private ordinary functions

I
initialize: Private generic functions
initialize: Private generic functions
initialize: Private generic functions
initialize: Private generic functions
initialize-instance: Public standalone methods
initialize-instance: Public standalone methods
initialize-instance: Public standalone methods
initialize-instance: Public standalone methods

K
k: Public generic functions
k: Public generic functions
k: Public generic functions
k-maxmin-idx: Private ordinary functions

L
label-count: Private generic functions
label-count: Private generic functions
learning-rate: Public generic functions
learning-rate: Public generic functions
learning-rate: Public generic functions
likelihoods: Private generic functions
likelihoods: Private generic functions
list-to-2d-array: Private ordinary functions
list-to-vector: Private ordinary functions

M
Macro, do-lists: Private macros
Macro, dolist-with-index: Private macros
Macro, dotimes-fromto: Private macros
map-pointwise: Private ordinary functions
matrix-expt: Private ordinary functions
matrix-identity: Private ordinary functions
max-arg: Private ordinary functions
max-weight-init-value: Private generic functions
max-weight-init-value: Private generic functions
mean: Private ordinary functions
means: Public generic functions
means: Public generic functions
Method, (setf activation-function): Private generic functions
Method, (setf activation-function): Private generic functions
Method, (setf all-labels): Private generic functions
Method, (setf data-set): Public generic functions
Method, (setf data-set): Public generic functions
Method, (setf data-set): Public generic functions
Method, (setf data-set): Public generic functions
Method, (setf data-set): Public generic functions
Method, (setf data-set): Public generic functions
Method, (setf data-set): Public generic functions
Method, (setf data-set): Public generic functions
Method, (setf data-set): Public generic functions
Method, (setf data-set): Public generic functions
Method, (setf data-set): Public generic functions
Method, (setf degrees-of-freedom): Private generic functions
Method, (setf distance): Public generic functions
Method, (setf distance): Public generic functions
Method, (setf k): Public generic functions
Method, (setf k): Public generic functions
Method, (setf label-count): Private generic functions
Method, (setf learning-rate): Public generic functions
Method, (setf learning-rate): Public generic functions
Method, (setf likelihoods): Private generic functions
Method, (setf max-weight-init-value): Private generic functions
Method, (setf max-weight-init-value): Private generic functions
Method, (setf means): Public generic functions
Method, (setf missing-value): Private generic functions
Method, (setf missing-value-test): Private generic functions
Method, (setf net-structure): Private generic functions
Method, (setf net-structure): Private generic functions
Method, (setf order): Public generic functions
Method, (setf output-net): Private generic functions
Method, (setf output-net-before-activation): Private generic functions
Method, (setf possible-data-values): Private generic functions
Method, (setf prior-probabilities): Private generic functions
Method, (setf probabilities): Public generic functions
Method, (setf replacers): Private generic functions
Method, (setf set-labels): Public generic functions
Method, (setf set-labels): Public generic functions
Method, (setf set-labels): Public generic functions
Method, (setf set-labels): Public generic functions
Method, (setf set-labels): Public generic functions
Method, (setf set-labels): Public generic functions
Method, (setf set-labels): Public generic functions
Method, (setf test-set): Public generic functions
Method, (setf test-set): Public generic functions
Method, (setf weight-init-range): Private generic functions
Method, (setf weight-init-range): Private generic functions
Method, (setf weights): Private generic functions
Method, (setf weights): Private generic functions
Method, activation-function: Private generic functions
Method, activation-function: Private generic functions
Method, all-labels: Private generic functions
Method, analyze: Public generic functions
Method, backprop: Private generic functions
Method, classify: Public generic functions
Method, classify: Public generic functions
Method, classify: Public generic functions
Method, data-set: Public generic functions
Method, data-set: Public generic functions
Method, data-set: Public generic functions
Method, data-set: Public generic functions
Method, data-set: Public generic functions
Method, data-set: Public generic functions
Method, data-set: Public generic functions
Method, data-set: Public generic functions
Method, degrees-of-freedom: Private generic functions
Method, distance: Public generic functions
Method, distance: Public generic functions
Method, forward: Public generic functions
Method, initialize: Private generic functions
Method, initialize: Private generic functions
Method, initialize: Private generic functions
Method, initialize-instance: Public standalone methods
Method, initialize-instance: Public standalone methods
Method, initialize-instance: Public standalone methods
Method, initialize-instance: Public standalone methods
Method, k: Public generic functions
Method, k: Public generic functions
Method, label-count: Private generic functions
Method, learning-rate: Public generic functions
Method, learning-rate: Public generic functions
Method, likelihoods: Private generic functions
Method, max-weight-init-value: Private generic functions
Method, means: Public generic functions
Method, missing-value: Private generic functions
Method, missing-value-test: Private generic functions
Method, net-structure: Private generic functions
Method, order: Public generic functions
Method, output-net: Private generic functions
Method, output-net-before-activation: Private generic functions
Method, possible-data-values: Private generic functions
Method, prior-probabilities: Private generic functions
Method, probabilities: Public generic functions
Method, replacers: Private generic functions
Method, run: Public generic functions
Method, run: Public generic functions
Method, run: Public generic functions
Method, run: Public generic functions
Method, run: Public generic functions
Method, run: Public generic functions
Method, run: Public generic functions
Method, run: Public generic functions
Method, set-labels: Public generic functions
Method, set-labels: Public generic functions
Method, set-labels: Public generic functions
Method, set-labels: Public generic functions
Method, synthesize: Public generic functions
Method, test-set: Public generic functions
Method, test-set: Public generic functions
Method, transform: Public generic functions
Method, unique: Public generic functions
Method, weight-init-range: Private generic functions
Method, weights: Private generic functions
Method, weights: Private generic functions
min-arg: Private ordinary functions
missing-value: Private generic functions
missing-value: Private generic functions
missing-value-test: Private generic functions
missing-value-test: Private generic functions
most-frequent-value: Private ordinary functions
multiply-matrices: Private ordinary functions

N
net-structure: Private generic functions
net-structure: Private generic functions
net-structure-to-array: Private ordinary functions
net-structure-to-weights: Private ordinary functions
normalize-tensor: Private ordinary functions

O
order: Public generic functions
order: Public generic functions
outer-product: Private ordinary functions
output-net: Private generic functions
output-net: Private generic functions
output-net-before-activation: Private generic functions
output-net-before-activation: Private generic functions

P
plot-points: Public ordinary functions
plot-values: Public ordinary functions
possible-data-values: Private generic functions
possible-data-values: Private generic functions
prior-probabilities: Private generic functions
prior-probabilities: Private generic functions
probabilities: Public generic functions
probabilities: Public generic functions

R
random-from-to: Public ordinary functions
range: Private ordinary functions
replacers: Private generic functions
replacers: Private generic functions
row-of-array: Private ordinary functions
run: Public generic functions
run: Public generic functions
run: Public generic functions
run: Public generic functions
run: Public generic functions
run: Public generic functions
run: Public generic functions
run: Public generic functions
run: Public generic functions

S
scalar-product: Private ordinary functions
set-labels: Public generic functions
set-labels: Public generic functions
set-labels: Public generic functions
set-labels: Public generic functions
set-labels: Public generic functions
shuffle: Private ordinary functions
sigmoid: Private ordinary functions
square: Private ordinary functions
step-function: Private ordinary functions
synthesize: Public generic functions
synthesize: Public generic functions

T
test-set: Public generic functions
test-set: Public generic functions
test-set: Public generic functions
transform: Public generic functions
transform: Public generic functions
transpose: Private ordinary functions
transpose-array: Private ordinary functions
transpose-list: Private ordinary functions

U
unique: Public generic functions
unique: Public generic functions

V
vector-to-2d-col: Private ordinary functions

W
weight-init-range: Private generic functions
weight-init-range: Private generic functions
weights: Private generic functions
weights: Private generic functions
weights: Private generic functions


A.3 Variables

Jump to:   *   +  
A   C   D   K   L   M   N   O   P   R   S   T   U   W  
Index Entry  Section

*
*diff-function-dict*: Private special variables
*heights-weights*: Public special variables
*iris*: Public special variables
*lenses*: Public special variables
*wages*: Public special variables

+
+e+: Private constants

A
activation-function: Public classes
activation-function: Public classes
all-labels: Public classes

C
Constant, +e+: Private constants

D
data-set: Public classes
data-set: Public classes
data-set: Public classes
data-set: Public classes
data-set: Public classes
data-set: Public classes
data-set: Public classes
data-set: Public classes
degrees-of-freedom: Public classes
distance: Public classes
distance: Public classes

K
k: Public classes
k: Public classes

L
label-count: Public classes
learning-rate: Public classes
learning-rate: Public classes
likelihoods: Public classes

M
max-weight-init-value: Public classes
means: Public classes
missing-value: Public classes
missing-value-test: Public classes

N
net-structure: Public classes

O
order: Public classes
output-net: Public classes
output-net-before-activation: Public classes

P
possible-data-values: Public classes
prior-probabilities: Public classes
probabilities: Public classes

R
replacers: Public classes

S
set-labels: Public classes
set-labels: Public classes
set-labels: Public classes
set-labels: Public classes
Slot, activation-function: Public classes
Slot, activation-function: Public classes
Slot, all-labels: Public classes
Slot, data-set: Public classes
Slot, data-set: Public classes
Slot, data-set: Public classes
Slot, data-set: Public classes
Slot, data-set: Public classes
Slot, data-set: Public classes
Slot, data-set: Public classes
Slot, data-set: Public classes
Slot, degrees-of-freedom: Public classes
Slot, distance: Public classes
Slot, distance: Public classes
Slot, k: Public classes
Slot, k: Public classes
Slot, label-count: Public classes
Slot, learning-rate: Public classes
Slot, learning-rate: Public classes
Slot, likelihoods: Public classes
Slot, max-weight-init-value: Public classes
Slot, means: Public classes
Slot, missing-value: Public classes
Slot, missing-value-test: Public classes
Slot, net-structure: Public classes
Slot, order: Public classes
Slot, output-net: Public classes
Slot, output-net-before-activation: Public classes
Slot, possible-data-values: Public classes
Slot, prior-probabilities: Public classes
Slot, probabilities: Public classes
Slot, replacers: Public classes
Slot, set-labels: Public classes
Slot, set-labels: Public classes
Slot, set-labels: Public classes
Slot, set-labels: Public classes
Slot, test-set: Public classes
Slot, test-set: Public classes
Slot, unique: Public classes
Slot, weight-init-range: Public classes
Slot, weights: Public classes
Slot, weights: Public classes
Special Variable, *diff-function-dict*: Private special variables
Special Variable, *heights-weights*: Public special variables
Special Variable, *iris*: Public special variables
Special Variable, *lenses*: Public special variables
Special Variable, *wages*: Public special variables

T
test-set: Public classes
test-set: Public classes

U
unique: Public classes

W
weight-init-range: Public classes
weights: Public classes
weights: Public classes


A.4 Data types

Jump to:   A   C   D   F   G   H   I   K   L   M   N   P   S   U   W  
Index Entry  Section

A
array-utils.lisp: The mlep/utils/array-utils․lisp file

C
Class, imputer: Public classes
Class, k-means: Public classes
Class, k-nearest-neighbors: Public classes
Class, markov-chain: Public classes
Class, max-likelihood: Public classes
Class, naive-bayes: Public classes
Class, neuronal-network: Public classes
Class, perceptron: Public classes
constants.lisp: The mlep/utils/constants․lisp file
core: The mlep/core module

D
datasets: The mlep/datasets module

F
File, array-utils.lisp: The mlep/utils/array-utils․lisp file
File, constants.lisp: The mlep/utils/constants․lisp file
File, functions.lisp: The mlep/utils/functions․lisp file
File, generic.lisp: The mlep/core/generic․lisp file
File, heights-weights.lisp: The mlep/datasets/heights-weights․lisp file
File, imputer.lisp: The mlep/core/imputer․lisp file
File, iris.lisp: The mlep/datasets/iris․lisp file
File, k-means.lisp: The mlep/core/k-means․lisp file
File, k-nearest.lisp: The mlep/core/k-nearest․lisp file
File, lenses.lisp: The mlep/datasets/lenses․lisp file
File, likelihood.lisp: The mlep/core/likelihood․lisp file
File, list-utils.lisp: The mlep/utils/list-utils․lisp file
File, macros.lisp: The mlep/macros/macros․lisp file
File, markov-chain.lisp: The mlep/core/markov-chain․lisp file
File, mlep.asd: The mlep/mlep․asd file
File, naive-bayes.lisp: The mlep/core/naive-bayes․lisp file
File, neuronal.lisp: The mlep/core/neuronal․lisp file
File, number-utils.lisp: The mlep/utils/number-utils․lisp file
File, package.lisp: The mlep/package․lisp file
File, perceptron.lisp: The mlep/core/perceptron․lisp file
File, plot.lisp: The mlep/utils/plot․lisp file
File, wages.lisp: The mlep/datasets/wages․lisp file
functions.lisp: The mlep/utils/functions․lisp file

G
generic.lisp: The mlep/core/generic․lisp file

H
heights-weights.lisp: The mlep/datasets/heights-weights․lisp file

I
imputer: Public classes
imputer.lisp: The mlep/core/imputer․lisp file
iris.lisp: The mlep/datasets/iris․lisp file

K
k-means: Public classes
k-means.lisp: The mlep/core/k-means․lisp file
k-nearest-neighbors: Public classes
k-nearest.lisp: The mlep/core/k-nearest․lisp file

L
lenses.lisp: The mlep/datasets/lenses․lisp file
likelihood.lisp: The mlep/core/likelihood․lisp file
list-utils.lisp: The mlep/utils/list-utils․lisp file

M
macros: The mlep/macros module
macros.lisp: The mlep/macros/macros․lisp file
markov-chain: Public classes
markov-chain.lisp: The mlep/core/markov-chain․lisp file
max-likelihood: Public classes
mlep: The mlep system
mlep: The mlep package
mlep-asd: The mlep-asd package
mlep.asd: The mlep/mlep․asd file
Module, core: The mlep/core module
Module, datasets: The mlep/datasets module
Module, macros: The mlep/macros module
Module, utils: The mlep/utils module

N
naive-bayes: Public classes
naive-bayes.lisp: The mlep/core/naive-bayes․lisp file
neuronal-network: Public classes
neuronal.lisp: The mlep/core/neuronal․lisp file
number-utils.lisp: The mlep/utils/number-utils․lisp file

P
Package, mlep: The mlep package
Package, mlep-asd: The mlep-asd package
package.lisp: The mlep/package․lisp file
perceptron: Public classes
perceptron.lisp: The mlep/core/perceptron․lisp file
plot.lisp: The mlep/utils/plot․lisp file

S
System, mlep: The mlep system

U
utils: The mlep/utils module

W
wages.lisp: The mlep/datasets/wages․lisp file