The mlep Reference Manual

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The mlep Reference Manual

This is the mlep Reference Manual, version 0.0.1, generated automatically by Declt version 2.4 "Will Decker" on Wed Jun 20 11:15:07 2018 GMT+0.


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1 Introduction

cl-mlep

Quicklisp

cl-mlep is a Common Lisp Machine Learning library for Educational Purposes.

It aims at providing a collection of simple machine learning algorithms with the following claims:

There is an HTML documentation (http://fzalkow.github.io/cl-mlep) including usage examples and an API. Its source is to be found in the branch gh-pages.

To start straight away, you can call (ql:quickload :mlep) or (ql:quickload :mlep-add) (or if you havn't Quicklisp installed load load/load.lisp or load/load-with-add.lisp). The latter one includes the prior one, but needs some dependencies for providing even more machine learning algorithms! You find more information about this in the usage examples, section Why Additional?.

If someone is interested in collaborating, please tell me. I'd be happy about this!


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2 Systems

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


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2.1 mlep

Maintainer

Frank Zalkow <frank_zalkow@web.de>

Author

Frank Zalkow <frank_zalkow@web.de>

License

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

Description

A Common Lisp machine learning library for educational purposes.

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 (file)

Components

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3 Modules

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


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3.1 mlep/macros

Dependency

package.lisp (file)

Parent

mlep (system)

Location

macros/

Component

macros.lisp (file)


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3.2 mlep/datasets

Dependency

macros (module)

Parent

mlep (system)

Location

datasets/

Components

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3.3 mlep/utils

Dependency

datasets (module)

Parent

mlep (system)

Location

utils/

Components

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3.4 mlep/core

Dependency

utils (module)

Parent

mlep (system)

Location

core/

Components

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4 Files

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


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4.1 Lisp


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4.1.1 mlep.asd

Location

/home/quickref/quicklisp/dists/quicklisp/software/cl-mlep-20180430-git/mlep.asd

Systems

mlep (system)

Packages

mlep-asd


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4.1.2 mlep/package.lisp

Parent

mlep (system)

Location

package.lisp

Packages

mlep


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4.1.3 mlep/macros/macros.lisp

Parent

macros (module)

Location

macros/macros.lisp

Internal Definitions

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4.1.4 mlep/datasets/iris.lisp

Parent

datasets (module)

Location

datasets/iris.lisp

Exported Definitions

*iris* (special variable)


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4.1.5 mlep/datasets/heights-weights.lisp

Parent

datasets (module)

Location

datasets/heights-weights.lisp

Exported Definitions

*heights-weights* (special variable)


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4.1.6 mlep/datasets/lenses.lisp

Parent

datasets (module)

Location

datasets/lenses.lisp

Exported Definitions

*lenses* (special variable)


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4.1.7 mlep/datasets/wages.lisp

Parent

datasets (module)

Location

datasets/wages.lisp

Exported Definitions

*wages* (special variable)


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4.1.8 mlep/utils/number-utils.lisp

Parent

utils (module)

Location

utils/number-utils.lisp

Exported Definitions

random-from-to (function)

Internal Definitions

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4.1.9 mlep/utils/list-utils.lisp

Parent

utils (module)

Location

utils/list-utils.lisp

Internal Definitions

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4.1.10 mlep/utils/array-utils.lisp

Parent

utils (module)

Location

utils/array-utils.lisp

Internal Definitions

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4.1.11 mlep/utils/constants.lisp

Parent

utils (module)

Location

utils/constants.lisp

Internal Definitions

+e+ (constant)


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4.1.12 mlep/utils/functions.lisp

Parent

utils (module)

Location

utils/functions.lisp

Internal Definitions

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4.1.13 mlep/utils/plot.lisp

Parent

utils (module)

Location

utils/plot.lisp

Exported Definitions

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4.1.14 mlep/core/generic.lisp

Parent

core (module)

Location

core/generic.lisp

Exported Definitions
Internal Definitions

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4.1.15 mlep/core/k-means.lisp

Parent

core (module)

Location

core/k-means.lisp

Exported Definitions

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4.1.16 mlep/core/k-nearest.lisp

Parent

core (module)

Location

core/k-nearest.lisp

Exported Definitions
Internal Definitions

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4.1.17 mlep/core/markov-chain.lisp

Parent

core (module)

Location

core/markov-chain.lisp

Exported Definitions
Internal Definitions

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4.1.18 mlep/core/naive-bayes.lisp

Parent

core (module)

Location

core/naive-bayes.lisp

Exported Definitions
Internal Definitions

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4.1.19 mlep/core/perceptron.lisp

Parent

core (module)

Location

core/perceptron.lisp

Exported Definitions
Internal Definitions

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4.1.20 mlep/core/likelihood.lisp

Parent

core (module)

Location

core/likelihood.lisp

Exported Definitions
Internal Definitions

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4.1.21 mlep/core/neuronal.lisp

Parent

core (module)

Location

core/neuronal.lisp

Exported Definitions
Internal Definitions

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4.1.22 mlep/core/imputer.lisp

Parent

core (module)

Location

core/imputer.lisp

Exported Definitions
Internal Definitions

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5 Packages

Packages are listed by definition order.


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5.1 mlep-asd

Source

/home/quickref/quicklisp/dists/quicklisp/software/cl-mlep-20180430-git/mlep.asd

Use List

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5.2 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 (file)

Use List

common-lisp

Exported Definitions
Internal Definitions

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6 Definitions

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


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6.1 Exported definitions


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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 (file)

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 (file)

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 (file)

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 (file)


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6.1.2 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 (file)

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 (file)

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 (file)


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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 (file)

Methods
Method: analyze (INSTANCE markov-chain) INPUT &key
Source

markov-chain.lisp (file)

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 (file)

Methods
Method: classify (INSTANCE neuronal-network) &key NEW-DATA-SET VERBOSE
Source

neuronal.lisp (file)

Method: classify (INSTANCE perceptron) &key NEW-DATA-SET
Source

perceptron.lisp (file)

Method: classify (INSTANCE k-means) &key NEW-DATA-SET
Source

k-means.lisp (file)

Generic Function: 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 (file)

Writer

(setf data-set) (generic function)

Methods
Method: data-set (IMPUTER imputer)

The data-set to be analyzed.

Source

imputer.lisp (file)

Method: data-set (NEURONAL-NETWORK neuronal-network)

The data-set to be analyzed.

Source

neuronal.lisp (file)

Method: data-set (MAX-LIKELIHOOD max-likelihood)

The data-set to be analyzed.

Source

likelihood.lisp (file)

Method: data-set (PERCEPTRON perceptron)

The data-set to be analyzed.

Source

perceptron.lisp (file)

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 (file)

Method: data-set (MARKOV-CHAIN markov-chain)

The data-set to be analyzed.

Source

markov-chain.lisp (file)

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 (file)

Method: data-set (K-MEANS k-means)

The data-set to be analyzed.

Source

k-means.lisp (file)

Generic Function: (setf data-set) NEW-VALUE OBJECT
Package

mlep

Reader

data-set (generic function)

Methods
Method: (setf data-set) NEW-VALUE (IMPUTER imputer)

The data-set to be analyzed.

Source

imputer.lisp (file)

Method: (setf data-set) DATA-SET (INSTANCE neuronal-network) after
Source

neuronal.lisp (file)

Method: (setf data-set) NEW-VALUE (NEURONAL-NETWORK neuronal-network)

The data-set to be analyzed.

Source

neuronal.lisp (file)

Method: (setf data-set) NEW-VALUE (MAX-LIKELIHOOD max-likelihood)

The data-set to be analyzed.

Source

likelihood.lisp (file)

Method: (setf data-set) DATA-SET (INSTANCE perceptron) after
Source

perceptron.lisp (file)

Method: (setf data-set) NEW-VALUE (PERCEPTRON perceptron)

The data-set to be analyzed.

Source

perceptron.lisp (file)

Method: (setf data-set) DATA-SET (INSTANCE naive-bayes) after
Source

naive-bayes.lisp (file)

Method: (setf data-set) NEW-VALUE (NAIVE-BAYES naive-bayes)

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

Source

naive-bayes.lisp (file)

Method: (setf data-set) NEW-VALUE (MARKOV-CHAIN markov-chain)

The data-set to be analyzed.

Source

markov-chain.lisp (file)

Method: (setf data-set) NEW-VALUE (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 (file)

Method: (setf data-set) NEW-VALUE (K-MEANS k-means)

The data-set to be analyzed.

Source

k-means.lisp (file)

Generic Function: 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 (file)

Writer

(setf distance) (generic function)

Methods
Method: distance (K-NEAREST-NEIGHBORS k-nearest-neighbors)

A distance measuring function.

Source

k-nearest.lisp (file)

Method: distance (K-MEANS k-means)

A distance measuring function.

Source

k-means.lisp (file)

Generic Function: (setf distance) NEW-VALUE OBJECT
Package

mlep

Reader

distance (generic function)

Methods
Method: (setf distance) NEW-VALUE (K-NEAREST-NEIGHBORS k-nearest-neighbors)

A distance measuring function.

Source

k-nearest.lisp (file)

Method: (setf distance) NEW-VALUE (K-MEANS k-means)

A distance measuring function.

Source

k-means.lisp (file)

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 (file)

Methods
Method: forward (INSTANCE neuronal-network) &key INPUT
Source

neuronal.lisp (file)

Generic Function: 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 (file)

Writer

(setf k) (generic function)

Methods
Method: k (K-NEAREST-NEIGHBORS k-nearest-neighbors)

The number of neighbors to be taken into account.

Source

k-nearest.lisp (file)

Method: k (K-MEANS k-means)

The number of groups/clusters to be determined.

Source

k-means.lisp (file)

Generic Function: (setf k) K INSTANCE
Package

mlep

Reader

k (generic function)

Methods
Method: (setf k) NEW-VALUE (K-NEAREST-NEIGHBORS k-nearest-neighbors)

The number of neighbors to be taken into account.

Source

k-nearest.lisp (file)

Method: (setf k) K (INSTANCE k-means) after
Source

k-means.lisp (file)

Generic Function: 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 (file)

Writer

(setf learning-rate) (generic function)

Methods
Method: learning-rate (NEURONAL-NETWORK neuronal-network)

The learning rate.

Source

neuronal.lisp (file)

Method: learning-rate (PERCEPTRON perceptron)

The learning rate.

Source

perceptron.lisp (file)

Generic Function: (setf learning-rate) NEW-VALUE OBJECT
Package

mlep

Reader

learning-rate (generic function)

Methods
Method: (setf learning-rate) NEW-VALUE (NEURONAL-NETWORK neuronal-network)

The learning rate.

Source

neuronal.lisp (file)

Method: (setf learning-rate) NEW-VALUE (PERCEPTRON perceptron)

The learning rate.

Source

perceptron.lisp (file)

Generic Function: 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 (file)

Writer

(setf means) (generic function)

Methods
Method: means (K-MEANS k-means)

The means of the data points.

Source

k-means.lisp (file)

Generic Function: (setf means) NEW-VALUE OBJECT
Package

mlep

Reader

means (generic function)

Methods
Method: (setf means) NEW-VALUE (K-MEANS k-means)

The means of the data points.

Source

k-means.lisp (file)

Generic Function: 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 (file)

Writer

(setf order) (generic function)

Methods
Method: order (MARKOV-CHAIN markov-chain)

The order of the markov chain.

Source

markov-chain.lisp (file)

Generic Function: (setf order) NEW-VALUE OBJECT
Package

mlep

Reader

order (generic function)

Methods
Method: (setf order) NEW-VALUE (MARKOV-CHAIN markov-chain)

The order of the markov chain.

Source

markov-chain.lisp (file)

Generic Function: 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 (file)

Writer

(setf probabilities) (generic function)

Methods
Method: probabilities (MARKOV-CHAIN markov-chain)

A matrix/tensor with probabilities.

Source

markov-chain.lisp (file)

Generic Function: (setf probabilities) NEW-VALUE OBJECT
Package

mlep

Reader

probabilities (generic function)

Methods
Method: (setf probabilities) NEW-VALUE (MARKOV-CHAIN markov-chain)

A matrix/tensor with probabilities.

Source

markov-chain.lisp (file)

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 (file)

Methods
Method: run (INSTANCE imputer) &key
Source

imputer.lisp (file)

Method: run (INSTANCE neuronal-network) &key EPOCHS
Source

neuronal.lisp (file)

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

likelihood.lisp (file)

Method: run (INSTANCE perceptron) &key THRESHOLD
Source

perceptron.lisp (file)

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

naive-bayes.lisp (file)

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

markov-chain.lisp (file)

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

k-nearest.lisp (file)

Method: run (INSTANCE k-means) &key EPOCHS
Source

k-means.lisp (file)

Generic Function: 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 (file)

Writer

(setf set-labels) (generic function)

Methods
Method: set-labels (NEURONAL-NETWORK neuronal-network)

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

Source

neuronal.lisp (file)

Method: set-labels (PERCEPTRON perceptron)

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

Source

perceptron.lisp (file)

Method: set-labels (NAIVE-BAYES naive-bayes)

The labels for @code{data-set}.

Source

naive-bayes.lisp (file)

Method: set-labels (K-NEAREST-NEIGHBORS k-nearest-neighbors)

The labels for @code{data-set}.

Source

k-nearest.lisp (file)

Generic Function: (setf set-labels) NEW-VALUE OBJECT
Package

mlep

Reader

set-labels (generic function)

Methods
Method: (setf set-labels) SET-LABELS (INSTANCE neuronal-network) after
Source

neuronal.lisp (file)

Method: (setf set-labels) NEW-VALUE (NEURONAL-NETWORK neuronal-network)

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

Source

neuronal.lisp (file)

Method: (setf set-labels) SET-LABELS (INSTANCE perceptron) after
Source

perceptron.lisp (file)

Method: (setf set-labels) NEW-VALUE (PERCEPTRON perceptron)

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

Source

perceptron.lisp (file)

Method: (setf set-labels) SET-LABELS (INSTANCE naive-bayes) after
Source

naive-bayes.lisp (file)

Method: (setf set-labels) NEW-VALUE (NAIVE-BAYES naive-bayes)

The labels for @code{data-set}.

Source

naive-bayes.lisp (file)

Method: (setf set-labels) NEW-VALUE (K-NEAREST-NEIGHBORS k-nearest-neighbors)

The labels for @code{data-set}.

Source

k-nearest.lisp (file)

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 (file)

Methods
Method: synthesize (INSTANCE markov-chain) &key START HOWMANY
Source

markov-chain.lisp (file)

Generic Function: 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 (file)

Writer

(setf test-set) (generic function)

Methods
Method: test-set (NAIVE-BAYES naive-bayes)

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

Source

naive-bayes.lisp (file)

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 (file)

Generic Function: (setf test-set) NEW-VALUE OBJECT
Package

mlep

Reader

test-set (generic function)

Methods
Method: (setf test-set) NEW-VALUE (NAIVE-BAYES naive-bayes)

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

Source

naive-bayes.lisp (file)

Method: (setf test-set) NEW-VALUE (K-NEAREST-NEIGHBORS k-nearest-neighbors)

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

Source

k-nearest.lisp (file)

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 (file)

Methods
Method: transform (INSTANCE imputer) &key NEW-DATA
Source

imputer.lisp (file)

Generic Function: 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 (file)

Methods
Method: unique (MARKOV-CHAIN markov-chain)

Unique values of data-set

Source

markov-chain.lisp (file)


Previous: , Up: Exported definitions   [Contents][Index]

6.1.4 Classes

Class: imputer ()

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

Package

mlep

Source

imputer.lisp (file)

Direct superclasses

standard-object (class)

Direct methods
Direct slots
Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set (generic function)

Writers

(setf data-set) (generic function)

Slot: missing-value

The value that is recognized as a missing value.

Initargs

:missing-value

Readers

missing-value (generic function)

Writers

(setf missing-value) (generic function)

Slot: missing-value-test

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

Initargs

:missing-value-test

Initform

(function eq)

Readers

missing-value-test (generic function)

Writers

(setf missing-value-test) (generic function)

Slot: replacers

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

Readers

replacers (generic function)

Writers

(setf replacers) (generic function)

Class: k-means ()

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

Package

mlep

Source

k-means.lisp (file)

Direct superclasses

standard-object (class)

Direct methods
Direct slots
Slot: k

The number of groups/clusters to be determined.

Initargs

:k

Initform

2

Readers

k (generic function)

Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set (generic function)

Writers

(setf data-set) (generic function)

Slot: distance

A distance measuring function.

Initargs

:distance

Initform

(function mlep::euclidian-distance)

Readers

distance (generic function)

Writers

(setf distance) (generic function)

Slot: means

The means of the data points.

Initargs

:means

Readers

means (generic function)

Writers

(setf means) (generic function)

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 (file)

Direct superclasses

standard-object (class)

Direct methods
Direct slots
Slot: k

The number of neighbors to be taken into account.

Initargs

:k

Initform

2

Readers

k (generic function)

Writers

(setf k) (generic function)

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 (generic function)

Writers

(setf data-set) (generic function)

Slot: set-labels

The labels for @code{data-set}.

Initargs

:set-labels

Readers

set-labels (generic function)

Writers

(setf set-labels) (generic function)

Slot: distance

A distance measuring function.

Initargs

:distance

Initform

(function mlep::euclidian-distance)

Readers

distance (generic function)

Writers

(setf distance) (generic function)

Slot: test-set

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

Initargs

:test-set

Readers

test-set (generic function)

Writers

(setf test-set) (generic function)

Class: markov-chain ()

A Markov-Chain.

Package

mlep

Source

markov-chain.lisp (file)

Direct superclasses

standard-object (class)

Direct methods
Direct slots
Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set (generic function)

Writers

(setf data-set) (generic function)

Slot: order

The order of the markov chain.

Initargs

:order

Initform

1

Readers

order (generic function)

Writers

(setf order) (generic function)

Slot: unique

Unique values of data-set

Initargs

:unique

Readers

unique (generic function)

Slot: probabilities

A matrix/tensor with probabilities.

Initargs

:probabilities

Readers

probabilities (generic function)

Writers

(setf probabilities) (generic function)

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 (file)

Direct superclasses

standard-object (class)

Direct methods
Direct slots
Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set (generic function)

Writers

(setf data-set) (generic function)

Slot: degrees-of-freedom

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

Initargs

:ddof

Initform

0

Readers

degrees-of-freedom (generic function)

Writers

(setf degrees-of-freedom) (generic function)

Class: naive-bayes ()

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

Package

mlep

Source

naive-bayes.lisp (file)

Direct superclasses

standard-object (class)

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 (generic function)

Writers

(setf data-set) (generic function)

Slot: set-labels

The labels for @code{data-set}.

Initargs

:set-labels

Readers

set-labels (generic function)

Writers

(setf set-labels) (generic function)

Slot: test-set

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

Initargs

:test-set

Readers

test-set (generic function)

Writers

(setf test-set) (generic function)

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 (generic function)

Writers

(setf possible-data-values) (generic function)

Slot: all-labels

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

Initargs

:all-labels

Readers

all-labels (generic function)

Writers

(setf all-labels) (generic function)

Slot: label-count

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

Initargs

:label-count

Readers

label-count (generic function)

Writers

(setf label-count) (generic function)

Slot: prior-probabilities

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

Initargs

:prior-probabilities

Readers

prior-probabilities (generic function)

Writers

(setf prior-probabilities) (generic function)

Slot: likelihoods

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

Initargs

mlep::likelihoods

Readers

likelihoods (generic function)

Writers

(setf likelihoods) (generic function)

Class: neuronal-network ()

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

Package

mlep

Source

neuronal.lisp (file)

Direct superclasses

standard-object (class)

Direct methods
Direct slots
Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set (generic function)

Writers

(setf data-set) (generic function)

Slot: set-labels

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

Initargs

:set-labels

Readers

set-labels (generic function)

Writers

(setf set-labels) (generic function)

Slot: net-structure

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

Initargs

:net-structure

Readers

net-structure (generic function)

Writers

(setf net-structure) (generic function)

Slot: weights

The weights from input to output values.

Initargs

:weights

Readers

weights (generic function)

Writers

(setf weights) (generic function)

Slot: output-net

The output of all neurons of the network.

Initargs

:output-net

Readers

output-net (generic function)

Writers

(setf output-net) (generic function)

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 (generic function)

Writers

(setf output-net-before-activation) (generic function)

Slot: weight-init-range

The maximum range for initializing the weights.

Initargs

:weight-init-range

Initform

(quote (-0.1d0 0.1d0))

Readers

weight-init-range (generic function)

Writers

(setf weight-init-range) (generic function)

Slot: activation-function

The activation function, usually a Heaviside step function.

Initargs

:activation-function

Initform

(function tanh)

Readers

activation-function (generic function)

Writers

(setf activation-function) (generic function)

Slot: learning-rate

The learning rate.

Initargs

:learning-rate

Initform

0.2d0

Readers

learning-rate (generic function)

Writers

(setf learning-rate) (generic function)

Class: perceptron ()

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

Package

mlep

Source

perceptron.lisp (file)

Direct superclasses

standard-object (class)

Direct methods
Direct slots
Slot: data-set

The data-set to be analyzed.

Initargs

:data-set

Readers

data-set (generic function)

Writers

(setf data-set) (generic function)

Slot: set-labels

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

Initargs

:set-labels

Readers

set-labels (generic function)

Writers

(setf set-labels) (generic function)

Slot: weights

The weights from input to output values.

Initargs

:weights

Readers

weights (generic function)

Writers

(setf weights) (generic function)

Slot: max-weight-init-value

The maximum value for initializing the weights.

Initargs

:max-weight-init-value

Initform

0.1d0

Readers

max-weight-init-value (generic function)

Writers

(setf max-weight-init-value) (generic function)

Slot: activation-function

The activation function, usually a Heaviside step function.

Initargs

:activation-function

Initform

(function mlep::step-function)

Readers

activation-function (generic function)

Writers

(setf activation-function) (generic function)

Slot: learning-rate

The learning rate.

Initargs

:learning-rate

Initform

0.2d0

Readers

learning-rate (generic function)

Writers

(setf learning-rate) (generic function)


Previous: , Up: Definitions   [Contents][Index]

6.2 Internal definitions


Next: , Previous: , Up: Internal definitions   [Contents][Index]

6.2.1 Constants

Constant: +e+
Package

mlep

Source

constants.lisp (file)


Next: , Previous: , Up: Internal definitions   [Contents][Index]

6.2.2 Special variables

Special Variable: *diff-function-dict*
Package

mlep

Source

functions.lisp (file)


Next: , Previous: , Up: Internal definitions   [Contents][Index]

6.2.3 Macros

Macro: do-lists ((&rest VARS) (&rest LISTS) &optional RESULT) &body BODY
Package

mlep

Source

macros.lisp (file)

Macro: dolist-with-index (INDEX LIST-ITEM LIST &optional RESULT) &body BODY
Package

mlep

Source

macros.lisp (file)

Macro: dotimes-fromto (VAR FROM TO &optional RESULT) &body BODY
Package

mlep

Source

macros.lisp (file)


Next: , Previous: , Up: Internal definitions   [Contents][Index]

6.2.4 Functions

Function: 2d-col-to-vector ARRAY
Package

mlep

Source

array-utils.lisp (file)

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 (file)

Function: altern BOUL PROB
Package

mlep

Source

markov-chain.lisp (file)

Function: col-of-array ARRAY COL

Return column of 2d array as vector

Package

mlep

Source

array-utils.lisp (file)

Writer

(setf col-of-array) (function)

Function: (setf col-of-array) VECTOR ARRAY COL
Package

mlep

Source

array-utils.lisp (file)

Reader

col-of-array (function)

Function: compute-likelihoods SET DATA-LABELS ALL-LABELS LABEL-COUNT POSSIBLE-DATA-VALUES
Package

mlep

Source

naive-bayes.lisp (file)

Function: compute-possible-data-values DATA-SET
Package

mlep

Source

naive-bayes.lisp (file)

Function: compute-prior-probabilities DATA-LABELS LABEL-COUNT
Package

mlep

Source

naive-bayes.lisp (file)

Function: d-sigmoid X
Package

mlep

Source

functions.lisp (file)

Function: d-step-function X
Package

mlep

Source

functions.lisp (file)

Function: d-tanh X
Package

mlep

Source

functions.lisp (file)

Function: determinant MATRIX

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

Package

mlep

Source

array-utils.lisp (file)

Function: determinant-helper MATRIX
Package

mlep

Source

array-utils.lisp (file)

Function: diff FUNC
Package

mlep

Source

functions.lisp (file)

Function: euclidian-distance P1 P2

Gives the euclidian distance between to points of arbitrary dimension.

Package

mlep

Source

number-utils.lisp (file)

Function: get-actual-output ACTIVATION-FUNCTION WEIGHTS INPUT
Package

mlep

Source

perceptron.lisp (file)

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 (file)

Function: get-overall-error ALL-INPUT DESIRED ACTIVATION-FUNCTION WEIGHTS
Package

mlep

Source

perceptron.lisp (file)

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 (file)

Function: get-row-ranges-array SET &key KEY
Package

mlep

Source

array-utils.lisp (file)

Function: get-row-ranges-list SET &key KEY
Package

mlep

Source

list-utils.lisp (file)

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 (file)

Function: k-maxmin-idx VECTOR &key K KEY

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

Package

mlep

Source

k-nearest.lisp (file)

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 (file)

Function: list-to-vector LIST &key ELEMENT-TYPE

Converts a list to a vector.

Package

mlep

Source

array-utils.lisp (file)

Function: map-pointwise FN &rest ARRAYS

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

Package

mlep

Source

array-utils.lisp (file)

Function: matrix-expt MATRIX EXP

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

Package

mlep

Source

array-utils.lisp (file)

Function: matrix-identity DIM &key ELEMENT-TYPE

Creates a new identity matrix of size dim*dim

Package

mlep

Source

array-utils.lisp (file)

Function: max-arg L

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

Package

mlep

Source

list-utils.lisp (file)

Function: mean L

Gets the mean of ‘l’.

Package

mlep

Source

list-utils.lisp (file)

Function: min-arg L

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

Package

mlep

Source

list-utils.lisp (file)

Function: most-frequent-value X &key TEST

Get the most frequent value of a sequence x.

Package

mlep

Source

list-utils.lisp (file)

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 (file)

Function: net-structure-to-array NET-STRUCTURE &optional VALUE
Package

mlep

Source

neuronal.lisp (file)

Function: net-structure-to-weights NET-STRUCTURE &optional WEIGHT-INIT-RANGE
Package

mlep

Source

neuronal.lisp (file)

Function: normalize-tensor TENSOR
Package

mlep

Source

markov-chain.lisp (file)

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 (file)

Function: range FROM TO &optional STEP

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

Package

mlep

Source

number-utils.lisp (file)

Function: row-of-array ARRAY ROW

Return row of 2d array as vector

Package

mlep

Source

array-utils.lisp (file)

Writer

(setf row-of-array) (function)

Function: (setf row-of-array) VECTOR ARRAY ROW
Package

mlep

Source

array-utils.lisp (file)

Reader

row-of-array (function)

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 (file)

Function: shuffle X

Shuffle a list.

Package

mlep

Source

list-utils.lisp (file)

Function: sigmoid X
Package

mlep

Source

functions.lisp (file)

Function: square N

Squares number N.

Package

mlep

Source

number-utils.lisp (file)

Function: step-function X
Package

mlep

Source

functions.lisp (file)

Function: transpose X

Transpose 2-dimensional list or array.

Package

mlep

Source

list-utils.lisp (file)

Function: transpose-array ARRAY

Transpose an 2-dimensional array.

Package

mlep

Source

array-utils.lisp (file)

Function: transpose-list L

Transpose a list of lists.

Package

mlep

Source

list-utils.lisp (file)

Function: vector-to-2d-col VEC
Package

mlep

Source

array-utils.lisp (file)


Previous: , Up: Internal definitions   [Contents][Index]

6.2.5 Generic functions

Generic Function: activation-function OBJECT
Generic Function: (setf activation-function) NEW-VALUE OBJECT
Package

mlep

Methods
Method: activation-function (NEURONAL-NETWORK neuronal-network)
Method: (setf activation-function) NEW-VALUE (NEURONAL-NETWORK neuronal-network)

The activation function, usually a Heaviside step function.

Source

neuronal.lisp (file)

Method: activation-function (PERCEPTRON perceptron)
Method: (setf activation-function) NEW-VALUE (PERCEPTRON perceptron)

The activation function, usually a Heaviside step function.

Source

perceptron.lisp (file)

Generic Function: all-labels OBJECT
Generic Function: (setf all-labels) NEW-VALUE OBJECT
Package

mlep

Methods
Method: all-labels (NAIVE-BAYES naive-bayes)
Method: (setf all-labels) NEW-VALUE (NAIVE-BAYES naive-bayes)

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

Source

naive-bayes.lisp (file)

Generic Function: backprop INSTANCE INPUT WANTED

Adjust weights with backpropagation.

Package

mlep

Source

generic.lisp (file)

Methods
Method: backprop (INSTANCE neuronal-network) INPUT WANTED
Source

neuronal.lisp (file)

Generic Function: degrees-of-freedom OBJECT
Generic Function: (setf degrees-of-freedom) NEW-VALUE OBJECT
Package

mlep

Methods
Method: degrees-of-freedom (MAX-LIKELIHOOD max-likelihood)
Method: (setf degrees-of-freedom) NEW-VALUE (MAX-LIKELIHOOD max-likelihood)

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

Source

likelihood.lisp (file)

Generic Function: initialize INSTANCE &key

Initialize the arguments of INSTANCE.

Package

mlep

Source

generic.lisp (file)

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

neuronal.lisp (file)

Method: initialize (INSTANCE perceptron) &key
Source

perceptron.lisp (file)

Method: initialize (INSTANCE naive-bayes) &key

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

Source

naive-bayes.lisp (file)

Generic Function: label-count OBJECT
Generic Function: (setf label-count) NEW-VALUE OBJECT
Package

mlep

Methods
Method: label-count (NAIVE-BAYES naive-bayes)
Method: (setf label-count) NEW-VALUE (NAIVE-BAYES naive-bayes)

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

Source

naive-bayes.lisp (file)

Generic Function: likelihoods OBJECT
Generic Function: (setf likelihoods) NEW-VALUE OBJECT
Package

mlep

Methods
Method: likelihoods (NAIVE-BAYES naive-bayes)
Method: (setf likelihoods) NEW-VALUE (NAIVE-BAYES naive-bayes)

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

Source

naive-bayes.lisp (file)

Generic Function: max-weight-init-value OBJECT
Generic Function: (setf max-weight-init-value) NEW-VALUE OBJECT
Package

mlep

Methods
Method: max-weight-init-value (PERCEPTRON perceptron)
Method: (setf max-weight-init-value) NEW-VALUE (PERCEPTRON perceptron)

The maximum value for initializing the weights.

Source

perceptron.lisp (file)

Method: (setf max-weight-init-value) MAX-WEIGHT-INIT-VALUE (INSTANCE perceptron) after
Source

perceptron.lisp (file)

Generic Function: missing-value OBJECT
Generic Function: (setf missing-value) NEW-VALUE OBJECT
Package

mlep

Methods
Method: missing-value (IMPUTER imputer)
Method: (setf missing-value) NEW-VALUE (IMPUTER imputer)

The value that is recognized as a missing value.

Source

imputer.lisp (file)

Generic Function: missing-value-test OBJECT
Generic Function: (setf missing-value-test) NEW-VALUE OBJECT
Package

mlep

Methods
Method: missing-value-test (IMPUTER imputer)
Method: (setf missing-value-test) NEW-VALUE (IMPUTER imputer)

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

Source

imputer.lisp (file)

Generic Function: net-structure OBJECT
Generic Function: (setf net-structure) NEW-VALUE OBJECT
Package

mlep

Methods
Method: net-structure (NEURONAL-NETWORK neuronal-network)
Method: (setf net-structure) NEW-VALUE (NEURONAL-NETWORK neuronal-network)

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

Source

neuronal.lisp (file)

Method: (setf net-structure) NET-STRUCTURE (INSTANCE neuronal-network) after
Source

neuronal.lisp (file)

Generic Function: output-net OBJECT
Generic Function: (setf output-net) NEW-VALUE OBJECT
Package

mlep

Methods
Method: output-net (NEURONAL-NETWORK neuronal-network)
Method: (setf output-net) NEW-VALUE (NEURONAL-NETWORK neuronal-network)

The output of all neurons of the network.

Source

neuronal.lisp (file)

Generic Function: output-net-before-activation OBJECT
Generic Function: (setf output-net-before-activation) NEW-VALUE OBJECT
Package

mlep

Methods
Method: output-net-before-activation (NEURONAL-NETWORK neuronal-network)
Method: (setf output-net-before-activation) NEW-VALUE (NEURONAL-NETWORK neuronal-network)

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

Source

neuronal.lisp (file)

Generic Function: possible-data-values OBJECT
Generic Function: (setf possible-data-values) NEW-VALUE OBJECT
Package

mlep

Methods
Method: possible-data-values (NAIVE-BAYES naive-bayes)
Method: (setf possible-data-values) NEW-VALUE (NAIVE-BAYES naive-bayes)

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

Source

naive-bayes.lisp (file)

Generic Function: prior-probabilities OBJECT
Generic Function: (setf prior-probabilities) NEW-VALUE OBJECT
Package

mlep

Methods
Method: prior-probabilities (NAIVE-BAYES naive-bayes)
Method: (setf prior-probabilities) NEW-VALUE (NAIVE-BAYES naive-bayes)

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

Source

naive-bayes.lisp (file)

Generic Function: replacers OBJECT
Generic Function: (setf replacers) NEW-VALUE OBJECT
Package

mlep

Methods
Method: replacers (IMPUTER imputer)
Method: (setf replacers) NEW-VALUE (IMPUTER imputer)

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

Source

imputer.lisp (file)

Generic Function: weight-init-range OBJECT
Generic Function: (setf weight-init-range) NEW-VALUE OBJECT
Package

mlep

Methods
Method: weight-init-range (NEURONAL-NETWORK neuronal-network)
Method: (setf weight-init-range) NEW-VALUE (NEURONAL-NETWORK neuronal-network)

The maximum range for initializing the weights.

Source

neuronal.lisp (file)

Method: (setf weight-init-range) WEIGHT-INIT-RANGE (INSTANCE neuronal-network) after
Source

neuronal.lisp (file)

Generic Function: weights OBJECT
Generic Function: (setf weights) NEW-VALUE OBJECT
Package

mlep

Methods
Method: weights (NEURONAL-NETWORK neuronal-network)
Method: (setf weights) NEW-VALUE (NEURONAL-NETWORK neuronal-network)

The weights from input to output values.

Source

neuronal.lisp (file)

Method: weights (PERCEPTRON perceptron)
Method: (setf weights) NEW-VALUE (PERCEPTRON perceptron)

The weights from input to output values.

Source

perceptron.lisp (file)


Previous: , Up: Top   [Contents][Index]

Appendix A Indexes


Next: , Previous: , Up: Indexes   [Contents][Index]

A.1 Concepts

Jump to:   F   L   M  
Index Entry  Section

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

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

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

Jump to:   F   L   M  

Next: , Previous: , Up: Indexes   [Contents][Index]

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

2
2d-col-to-vector: Internal functions

A
activation-function: Internal generic functions
activation-function: Internal generic functions
activation-function: Internal generic functions
all-labels: Internal generic functions
all-labels: Internal generic functions
all-positions: Internal functions
altern: Internal functions
analyze: Exported generic functions
analyze: Exported generic functions

B
backprop: Internal generic functions
backprop: Internal generic functions

C
classify: Exported generic functions
classify: Exported generic functions
classify: Exported generic functions
classify: Exported generic functions
col-of-array: Internal functions
compute-likelihoods: Internal functions
compute-possible-data-values: Internal functions
compute-prior-probabilities: Internal functions

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

E
euclidian-distance: Internal functions

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

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

H
histogram: Internal functions

I
initialize: Internal generic functions
initialize: Internal generic functions
initialize: Internal generic functions
initialize: Internal generic functions

K
k: Exported generic functions
k: Exported generic functions
k: Exported generic functions
k-maxmin-idx: Internal functions

L
label-count: Internal generic functions
label-count: Internal generic functions
learning-rate: Exported generic functions
learning-rate: Exported generic functions
learning-rate: Exported generic functions
likelihoods: Internal generic functions
likelihoods: Internal generic functions
list-to-2d-array: Internal functions
list-to-vector: Internal functions

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

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

O
order: Exported generic functions
order: Exported generic functions
outer-product: Internal functions
output-net: Internal generic functions
output-net: Internal generic functions
output-net-before-activation: Internal generic functions
output-net-before-activation: Internal generic functions

P
plot-points: Exported functions
plot-values: Exported functions
possible-data-values: Internal generic functions
possible-data-values: Internal generic functions
prior-probabilities: Internal generic functions
prior-probabilities: Internal generic functions
probabilities: Exported generic functions
probabilities: Exported generic functions

R
random-from-to: Exported functions
range: Internal functions
replacers: Internal generic functions
replacers: Internal generic functions
row-of-array: Internal functions
run: Exported generic functions
run: Exported generic functions
run: Exported generic functions
run: Exported generic functions
run: Exported generic functions
run: Exported generic functions
run: Exported generic functions
run: Exported generic functions
run: Exported generic functions

S
scalar-product: Internal functions
set-labels: Exported generic functions
set-labels: Exported generic functions
set-labels: Exported generic functions
set-labels: Exported generic functions
set-labels: Exported generic functions
shuffle: Internal functions
sigmoid: Internal functions
square: Internal functions
step-function: Internal functions
synthesize: Exported generic functions
synthesize: Exported generic functions

T
test-set: Exported generic functions
test-set: Exported generic functions
test-set: Exported generic functions
transform: Exported generic functions
transform: Exported generic functions
transpose: Internal functions
transpose-array: Internal functions
transpose-list: Internal functions

U
unique: Exported generic functions
unique: Exported generic functions

V
vector-to-2d-col: Internal functions

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

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A.3 Variables

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Index Entry  Section

*
*diff-function-dict*: Internal special variables
*heights-weights*: Exported special variables
*iris*: Exported special variables
*lenses*: Exported special variables
*wages*: Exported special variables

+
+e+: Internal constants

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

C
Constant, +e+: Internal constants

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

K
k: Exported classes
k: Exported classes

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

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

N
net-structure: Exported classes

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

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

R
replacers: Exported classes

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

T
test-set: Exported classes
test-set: Exported classes

U
unique: Exported classes

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

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A.4 Data types

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Index Entry  Section

C
Class, imputer: Exported classes
Class, k-means: Exported classes
Class, k-nearest-neighbors: Exported classes
Class, markov-chain: Exported classes
Class, max-likelihood: Exported classes
Class, naive-bayes: Exported classes
Class, neuronal-network: Exported classes
Class, perceptron: Exported classes

I
imputer: Exported classes

K
k-means: Exported classes
k-nearest-neighbors: Exported classes

M
markov-chain: Exported classes
max-likelihood: Exported classes
mlep: The mlep system
mlep: The mlep package
mlep-asd: The mlep-asd package

N
naive-bayes: Exported classes
neuronal-network: Exported classes

P
Package, mlep: The mlep package
Package, mlep-asd: The mlep-asd package
perceptron: Exported classes

S
System, mlep: The mlep system

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