matrix
packagematrix
methodstokens
packagedataset
packageann
packageann.loss
packageann.optimizer
packageann.graph
packageann.autoencoders
packagetrainable
packagerandom
packageautodiff
packageautodiff.ann
packagematlab
packagestats
packagestats.MI
packagecomplex
packageutil
packagegzio
packageImage
packageImageIO
packageAffineTransform2D
packageclass
packageclustering
packageknn
packageAPRILANN (A Pattern Recognizer In Lua with Artificial Neural Networks) is more than an ANNs toolkit. It is pattern recognizer project.
Simple Lua scripts could be implemented to run ANNs experiments. Some examples are below.
Take note that APRILANN offers an inline help with two basic commands:
april_help(...)
april_dir(...)
april_list(...)
The april_help(object)
function takes an object (Lua table, function, userdata, ...) as a parameter and shows the corresponding help via standard output.
The april_dir(object)
function takes an object as a parameter and shows the corresponding help via standard output. It is the same as april_help
but a lot less verbose.
The april_list(table)
function takes a table and shows its content using pairs
function. It has nothing to do with inline help, but is useful in a lot of circumstances when developing scripts.
Play a little with it, so execute april_help(ann.components)
and after april_help(ann.components.base)
and see what happens ;)
If you want to access instance methods documentation, you have two ways:
..
(that is, __concat
metamethod) with a class table plus method name string:> april_help(ann.components.base .. "forward")
method Computes forward step with the given token
description: Computes forward step with the given token
parameters:
1 An input token (usually a matrix)
2 A boolean indicating if the forward is
during_training or not. This information is used by
ann.components.actf objects to apply dropout
during training, and to halve the activation during
validation (or test). It is [optional], by default
is false.
outputs:
1 An output token (usually a matrix)
april_help(obj)
:> c = ann.components.base()
> april_help(c.forward)
method Computes forward step with the given token
description: Computes forward step with the given token
parameters:
1 An input token (usually a matrix)
2 A boolean indicating if the forward is
during_training or not. This information is used by
ann.components.actf objects to apply dropout
during training, and to halve the activation during
validation (or test). It is [optional], by default
is false.
outputs:
1 An output token (usually a matrix)
APRILANN incorporates an adaptation of https://github.com/rrthomas/luarlcompleter for Lua 5.2, and for the APRILANN object oriented implementation. It allows to autocomplete pathnames, global names, table fields, and object methods, by using the <tab>
key.
> matrix.<tab><tab>
_NAME dict fromString ...
_VERSION fromFilename fromTabFilename ...
__sliding_window__ fromHEX join ...
as fromMMap loadImage ...
... ... ...
> matrix.fromTab<tab>
> matrix.fromTabFilename<tab><tab>
fromTabFilename
> matrix.fromTabFilename
Almost any object can be serialized to a disk file, string or a stream using the function util.serialize()
. Similarly, it can be deserialized using util.deserialize()
function.
The code described here is at the repo path EXAMPLES/xor.lua
. First, we need to create an ANN component object which will be trained:
thenet = ann.mlp.all_all.generate("2 inputs 2 logistic 1 logistic")
The object thenet
is a Multilayer Perceptron (MLP) with 2 inputs, a hidden layer with 2 neurons with logistic activation function, and 1 output neuron with logistic activation function. Some activation functions are available: logistic, tanh, linear, softmax, log_logistic, sin, softsign, softplus, .... (see april_help(ann.components.actf)
).
Now, in order to do easy and fast development of scripts, a trainer helper wrapper can be used:
bunch_size=4
trainer = trainable.supervised_trainer(thenet, ann.loss.mse(1), bunch_size)
The trainer needs the ANN component, the loss function, and the bunch_size. Bunch size is the same as minibatch size, it is used to train several patterns at the same time, increasing the speed of the experiment. Values between 32 and 64 are tipically used, but in this example onlt 4 is possible, so the XOR problem is composed by 4 patterns.
The next step is to build the component and randomize its weights:
trainer:build()
trainer:randomize_weights{
random = random(1234),
inf = 0.1,
sup = 0.1 }
The weights will be initialized uniformly in the range [inf, sup], using the given random
object with 1234 as random seed. It is also possible to indicate if you want to initialize weights.
The components has several learning parameters which needs to be configured:
trainer:set_option("learning_rate", 1.0)
trainer:set_option("momentum", 0.5)
trainer:set_layerwise_option("w.*", "weight_decay", 1e05)
Data to train the ANN is defined using matrix
and dataset
objects. It is possible to build XOR problem on a matrix
and use it as training datasets
:
m_xor = matrix.fromString[[
4 3
ascii
0 0 0
0 1 1
1 0 1
1 1 0
]]
ds_input = dataset.matrix(m_xor, {patternSize={1,2}})
ds_output = dataset.matrix(m_xor, {offset={0,2}, patternSize={1,1}})
The variable m_xor
is a matrix object, loaded from the given string. ds_input
is a dataset.matrix
object, which traverses the matrix by rows, computing a sliding window of patternSize={1,2}
. The desired output of the ANN is another dataset.matrix
, but in this case computing the sliding window with size (1,1) and skipping the first two columns offset={0,2}
.
Finally, we need to train the ANN:
for i=1,10000 do
local error = trainer:train_dataset{ input_dataset = ds_input,
output_dataset = ds_output }
print(i, error)
end
This code trains the ANN for 10,000 epochs, feeding the ANN with input_dataset
and using as desired output the given output_dataset
. Patterns are grouped at minibatches of size 4 (bunch_size), and each training epoch is the training with the full dataset.
This simple example gives you some insight about how to use APRILANN toolkit, but it is not useful in a bit more complicated problems. Next section will explain DIGITS problem, which trains an ANN to classify handwritten digits.
The task aborded at this section is classification of handwritten digits. The code is at EXAMPLES/digits.lua
, and could be executed following this command: aprilann digits.lua
. This task uses as data a large PNG image with handwritten digits ordered by columns and rows. Each columns corresponds to each digit class (from 0 to 9), and each row contains 10 examples (one for each class). There are 1000 patterns (100 for each clasS). So, first the image is loaded using this code, and converted to a matrix where 0 represents white color and 1 represents black color:
digits_image = ImageIO.read(string.get_path(arg[0]).."digits.png")
m1 = digits_image:to_grayscale():invert_colors():matrix()
This code uses ImageIO.read
function to load the PNG image (you need to compile libpng package), and uses string.get_path
function in order to find where the file is located. The image is converted to grayscale, colors are inverted to be 0=white and 1=black, and finally the corresponding matrix of this image is generated.
Second, the training input and output dataset are generated following this code:
 TRAINING 
train_input = dataset.matrix(m1,
{
patternSize = {16,16},
offset = {0,0},
numSteps = {80,10},
stepSize = {16,16},
orderStep = {1,0}
})
 a simple matrix for the desired output
m2 = matrix(10,{1,0,0,0,0,0,0,0,0,0})
 a circular dataset which advances with step 1
train_output = dataset.matrix(m2,
{
patternSize = {10},
offset = {0},
numSteps = {800},
stepSize = {1},
circular = {true}
})
This is a more complicated example of how to create datasets from matrices. The variable train_input
is a dataset.matrix
generated by a slidingwindow of size 16x16 (the size of one digit), which moves in steps of 16x16 (first 16 in columns, and when arrive to the end it moves 16 in rows and returns to column 0). The number of patterns (numSteps
) is 80 by rows and 10 by columns. The output dataset needs an special matrix which contains only one 1 and 9 zeroes, so the 1 on each pattern will correspond to its class. The dataset.matrix
in this case slides backwards (stepSize={1}
), so the 1 moves forward, and is circular (window positions out of the matrix take the values of the opposite matrix positions). It has 800 patterns (80x10).
For validation datasets the script is coded similarly:
 VALIDATION 
val_input = dataset.matrix(m1,
{
patternSize = {16,16},
offset = {1280,0},
numSteps = {20,10},
stepSize = {16,16},
orderStep = {1,0}
})
val_output = dataset.matrix(m2,
{
patternSize = {10},
offset = {0},
numSteps = {200},
stepSize = {1},
circular = {true}
})
However, in this case the val_input
dataset needs the option parameter offset
to not be 0, because validation patterns are the 200 last patterns (it begins at image row position 1280). The first 800 digits are used for training.
The MLP is generated following same steps as for XOR, but in this case the topology description string uses tanh for activation of hidden layer, and log_softmax for activation of output layer. In this case the use_fanin
and use_fanout
flags are set to true, and the error function is multi_class_cross_entropy
, which is a version of crossentropy error function, but mathematically simplified for log_softmax as output activation functions (if you try other output you must use mse
). The twoclass version of crossentropy (ann.loss.cross_entropy
) is simplified to be used with log_logistic outputs:
bunch_size = 64
thenet = ann.mlp.all_all.generate("256 inputs 128 tanh 10 log_softmax")
trainer = trainable.supervised_trainer(thenet,
ann.loss.multi_class_cross_entropy(),
bunch_size)
trainer:build()
trainer:randomize_weights{
random = random(52324),
use_fanin = true,
use_fanout = true,
inf = 1,
sup = 1,
}
trainer:set_option("learning_rate", 0.01)
trainer:set_option("momentum", 0.01)
trainer:set_layerwise_option("w.*", "weight_decay", 1e05)
For training, it is needed to declare a table which contains the pair input/output datasets and some specific parameters (i.e. shuffle random object to train each epoch with a different permutation of patterns):
training_data = {
input_dataset = train_input,
output_dataset = train_output,
shuffle = random(25234),
}
validation_data = {
input_dataset = val_input,
output_dataset = val_output,
}
The final snippet code train the MLP using holdoutvalidation, following a stopping criterion which depends on the relative value between current_epoch/best_validation_epoch: when this proportion is greater than 2 the training is stopped (that is, MLP training will stop at 200 epochs if the last best validation epoch is at epoch 100; MLP training will stop at 400 epochs if the last best validation epoch is at epoch 200). Stopping criterion is selected using function helper trainable.stopping_criteria.make_max_epochs_wo_imp_relative
, and the MLP is trained using the class trainable.train_holdout_validation
. This last class receives a table which fields are selfexplanatory, and follows a holdoutvalidation algorithm in its execute
method, and after each epoch get_state_string
method is used for output facilities.
print("# Epoch Training Validation BestEpoch BestValidation")
stopping_criterion =
trainable.stopping_criteria.make_max_epochs_wo_imp_relative(2)
train_func = trainable.train_holdout_validation{
min_epochs = 4,
max_epochs = 1000,
stopping_criterion = stopping_criterion,
}
clock = util.stopwatch()
clock:go()
epoch_function = function()
local tr_loss = trainer:train_dataset(training_data)
local va_loss = trainer:validate_dataset(validation_data)
return trainer,tr_loss,va_loss
end
while train_func:execute(epoch_function) do
print(train_func:get_state_string())
end
clock:stop()
cpu,wall = clock:read()
num_epochs = result.last_epoch
printf("# Wall total time: %.3f per epoch: %.3f\n", wall, wall/num_epochs)
printf("# CPU total time: %.3f per epoch: %.3f\n", cpu, cpu/num_epochs)
printf("# Validation error: %f", result.best_val_error)
This introduction explains you the basic steps to write and execute scripts for pattern recognition using ANNs and the toolkit APRILANN. Please, feel free to use this scripts as initial template for yours ;)
APRILANN has a lot of interesting features. The following list show the most important features, which are detailed in the following sections of this documentation:
Multidimensional matrix
library. It allows to perform efficient mathematical operations in Lua.
Abstract token definition. A token represents anything, and is used in several parts of the toolkit for information interchange: matrix
instances can be wrapperd into a tokens.matrix
instance, and they are interchangable in ANN components.
Dataset abstraction. It has the ability to build powerful sliding windows over matrices. At the same time, it is possible to filter datasets producing new datasets onthefly. Two abstraction exists: dataset
, and dataset.token
.
Artificial neural networks. Different packages are implemented to perform efficient training of ANNs. Three main concepts: ANN component, loss function and optimization algorithm.
Trainable package. This package knows all the ANNs stuff, and is a good start point to work with ANNs. Implements a lot of useful code for intrspection, training and testing.
Random package. The generation of pseudorandom numbers is in this package.
Automatic differentiation. For more advanced machine learning, an experimental library for automatic differentiation has been added. It allows to specify totally more general models than ANNs abstraction, but with an important loss in efficiency. However, it is useful to do cool things for research with a little implementation effort, before implement them in ANNs.
Matlab package. It allows to load (not save) matrices and data in MAT format. It stills in experimental phase, but the most important things are available.
Statistics package. Look here for some statistics standard techniques. PCA, running mean and variance computation, pearson correlation, ...
Complex numbers. In experimental phase, APRILANN allows to work with complex numbers, and complex matrices.
Util package. It contains a lot of utilities for Lua script development.
GZIO package. This is the binding of libZ for load/save of compressed files.
Image and ImageIO packages. The class Image allows to work with color or gray images. The package ImageIO implements useful functions for generic read/write of images, depending in their extension.
matrix
packageThis documentation is structured as follows:
dim()
, get()
, rewrap()
, select()
, ...fill()
, zeros()
, linspace()
, ...toString()
, toFilename()
, ...size()
, stride()
, offset()
, data()
, ...axpy()
, gemv()
, gemm()
, ...svd()
, inv()
, ...tan()
, tanh()
, ...min()
, sum()
, eq()
, ...index()
, indexed_fill()
, order()
, ...repmat()
, triu()
, ...real_fftwh()
, iterate()
, convolution()
.Package matrix
could be loaded via the standalone binary, or in Lua with require("aprilann.matrix")
.
A matrix is a multidimensional data container, by default float data. It is similar to the concept of tensor, as defined in libraries like Torch. This notion of tensor is not to be confused with tensors in physics and engineering, known as tensor fields.
The data would be stored following row_major
order by default, but different methods can change the stride of the dimensions. Additionally a matrix
can be transposed or not, being the transposition a symbolical state, sharing the same data reference as the nontransposed matrix
.
From Lua, a matrix is declared using one of the available constructors:
>  row major constructor
> m1 = matrix(3,3)  this is a uninitialitzed 3x3 matrix of floats
>  It is also possible to receive a table with data (in rowmajor order)
> m2 = matrix(3,3, {1, 2, 3, 4, 5, 6, 7, 8, 9})
> print(m2)
1 2 3
4 5 6
7 8 9
# Matrix of size [3,3] in row_major [0x23d5b20 data= 0x23d5e90]
Observe that print
function shows the same for m2
and m3
, but internally the data is in different order. The pretty print of a matrix shows the data and a commented line with the size of the matrix and two memory pointers values, the first is the pointer to the C++ object related with the given matrix, and the second is a pointer to the C++ data object where values are stored (memory block, explained below).
The matrix and its data is separated to allow the declaration of submatrices:
> m4 = m2:slice({2,1},{1,3})
> print(m4)
4 5 6
# Matrix of size [1,3] in row_major [0x2218a90 data= 0x23d5e90]
In this case, the matrix m4
is a slice which begins at position {2,1}
of matrix m2
, and has sizes {1,3}
in each dimension. Note that the matrix pointer is different, but the data pointer is the same as for m2
(any change to m4
will be reflected at m2
.)
Besides, it is possible to do a submatrix cloning the data (deep copy) if you add to slice
method a new boolean argument with true
value:
> m5 = m2:slice({2,1},{1,3}, true)
> print(m5)
4 5 6
# Matrix of size [1,3] in row_major [0x220c6f0 data= 0x2250d60]
A shortcut for slicing is to use the operator m[...]
, which has a meaning similar to Matlab or Octave. It is overloaded and it is parsed into a slice
or select
method call, so, it is slower than using directly the slice
or select
methods, but it is easier to understand and use. This operator receives as argument a number or a table.
m[n]
In case of given a number, the operator is equivalent to a m:select(1,n)
in case the matrix has more than one dimension, and returns a matrix
object with one less dimension. In case of unidimensional matrix, this call is equivalent to m:get(n)
and returns a Lua number value.
m[{s1,s2,...}]
In case of given a table, the should be the same length of the matrix number of dimensions. In every dimension position, three possible values could be given:
m = m2[{2,3}]
m = m2[{2, {1,3}}]
m = m2[{2, '1:3'}]
The following examples are equivalent:
> = m2:slice({2,1},{1,3})
4 5 6
# Matrix of size [1,3] in row_major [0xf44470 data= 0xdeae90]
> = m2[{2, {1,3}]}
4 5 6
# Matrix of size [1,3] in row_major [0xf3c8d0 data= 0xdeae90]
> = m2[{2, '1:3'}]
4 5 6
# Matrix of size [1,3] in row_major [0xe32dc0 data= 0xdeae90]
It is possible to use the string shortcut to indicate a whole dimension, or only start/end positions:
> = m2[{':', '2:3'}]
2 3
5 6
8 9
# Matrix of size [3,2] in row_major [0xef1260 data= 0xdeae90]
> = m2[{'2:', '2:3'}]
5 6
8 9
# Matrix of size [2,2] in row_major [0xe08f50 data= 0xdeae90]
> = m2[{':2', '2:3'}]
2 3
5 6
# Matrix of size [2,2] in row_major [0xf04290 data= 0xdeae90]
A different shortcut exists for assignation operator, using newindex
metamethod. The matrix will be indexed using []
operator or [{}]
operator:
>  assigns a 0 to all the row values at columns 2:3
> m2[{ ':', '2:3' }] = 0
>  assigns another matrix all the row values at columns 2:3
> m2[{ ':', '2:3' }] = matrix(m2:dim(1),2):linspace()
>  assigns 5 to all the values at position 2 in dimension 1
> m2[2] = 5
>  assigns 10 to all values less than 3
> m2[m2:lt(3)] = 10
Every matrix
has an underlying block of memory which is reinterpreted with the matrix
shape (dimension sizes, and offset). It is possible to build a memory block in Lua by using any of the mathcore.block
table constructors. Currently, mathcore.block.float
, mathcore.block.double
and mathcore.block.int32
are available.
> b = mathcore.block.float{1,2,3,4,5}  initialized to a table
> print(b)
1 2 3 4 ... 5
# Float block size 5 [data= 0x1ab98e0]
> b = mathcore.block.float(20)  not initialized
Every memory block has three basic methods:
n = b:size()
returns the number of elements in the memory block.v = b:raw_get(p)
returns the value at the given p
position. Note that the position is 0indexed as in C, because this is a lowlevel C++ method.b:raw_set(p, v)
sets v
as the value at the given p
position. Note that the position is 0indexed as in C.Like in matrix
objects, it is possible to use operator[]
:
v = b[p]
is equivalent to v = b:raw_get(p)
.b[p] = v
is equivalent to b:raw_set(p, v)
.Remember that memory blocks are indexed from 0, like in C, because they are a wrapper around a C pointer.
NOTICE: Almost all matrix methods returns the caller matrix (when it is possible), allowing to chain transformation sequences.
NOTICE: In Lua the arrays start at 1 instead of 0, so, in the matrix
methods the dimensions start at 1.
There are different valid matrix
constructors.
m = matrix(d1, d2, ..., dn)
a list of dimension sizes.m = matrix(d1, d2, ..., dn, table)
a list of dimension sizes besides an array table with all the matrix
elements in rowmajor order.m = matrix(d1, d2, ..., dn, block)
a list of dimension sizes plus a float memory block (mathcore.block.float
).m = matrix{ e1, e2, ..., en }
creates a onedimensional matrix
with the given data elements.m = matrix(block)
creates a onedimensional matrix
with the given float memory block as data.m = matrix.MMapped(...)
It is possible to force the declaration of matrix
memory as a mmapped anonymous file. This function receives exactly same arguments as the default matrix
constructor.
>  the following matrix will be allocated as mmapped memory
>  shared when forking the process
> m = matrix.MMapped(2,2):linear()
> print(m)
0 1
2 3
# Matrix of size [2,2] stride [2,1] ref [0x14c1f50 data= 0x14c2020]
Another way is to serialize a matrix in MMap format (see serialization section).
matrix
methods[table  number] = m:dim([number])
It returns the size of matrix dimensions. Without arguments, it returns a Lua table with the sizes. If an argument is given, it returns the size of the given dimension.
> a = matrix(4,3,2)
> = a:dim()
table: 0x23a4780
> = table.concat(a:dim(), " ")
4 3 2
> = a:dim(1), a:dim(2), a:dim(3)
4 3 2
number = m:get(p1, p2, ...)
This method returns the value of a given matrix position.
> a = matrix(3,4,{1,2,3,4, 5,6,7,8, 10,11,12,13})
> = a:get(1,1)
1
> = a:get(2,3)
7
matrix = m:set(p1, p2, ..., value)
This method sets the value of a matrix position, and returns the caller matrix, allowing a sequence of sets.
> a = matrix(3,4,{1,2,3,4, 5,6,7,8, 10,11,12,13})
> a:set(2,3, 10000)
> a:set(2,4, 500):set(4,1, 200)
> = a
1 2 3 4
5 6 10000 500
200 11 12 13
# Matrix of size [3,4] in row_major [0x27093d0 data= 0x2709960]
matrix = m:clone()
It allows to clone matrices (deep copy). If the caller matrix
was transposed, the resulting clone will contain the data in a transposed shape, but with its stride in row major order.
> a = matrix(2,3,{1,2,3, 4,5,6})  row major matrix
> b = a:clone()  clone (or deep copy) of a
> c = a:transpose()  clone of a with different order
> c = c:clone()  clone of a in row major order
matrix = m:copy_from_table(table)
This method copies the data in the given table into the caller matrix
, traversing the matrix
in row_major
order, as in matrix
constructor. The table must fit in matrix
size. The caller matrix
is returned.
> a = matrix(2,3)
> a:copy_from_table({1,2,3, 4,5,6})
matrix = m:map(m1, m2, ..., function)
Maps the matrix values by a given list of matrices and a Lua map function. The Lua function will be called for every possible matrix position. The Lua function receives the caller matrix value at the given position, the value of the second matrix, the value of the third matrix, and so on. The Lua function returns nil
, or only one value which will be assigned to the caller matrix inplace. All the matrices must have the same dimension sizes. The number of given matrices could be >= 0.
> m = matrix(2,2):linear()
> m2 = matrix(2,2):linear(10)
> m3 = matrix(2,2):linear(100)
> = m
0 1
2 3
# Matrix of size [2,2] in row_major [0x1f12050 data= 0x1f0f6a0]
> = m2
10 11
12 13
# Matrix of size [2,2] in row_major [0x1f11cc0 data= 0x1f12110]
> = m3
100 101
102 103
# Matrix of size [2,2] in row_major [0x1f12740 data= 0x1f11e00]
> m:map(m2,m3,function(x,y,z) return x+y+z end)
> = m
110 113
116 119
# Matrix of size [2,2] in row_major [0x1f12050 data= 0x1f0f6a0]
matrix = m:rewrap(size1, size2, ...)
This method only works if the data is contiguous in memory. The caller matrix is reinterpreted as if it was of another number of dimensions and sizes. A different matrix instance is returned, but the data pointer is shared.
> a = matrix(2,3,{1,2,3, 4,5,6})
> = a
1 2 3
4 5 6
# Matrix of size [2,3] in row_major [0x2700850 data= 0x2700900]
> b = a:rewrap(3,2)
> = b
1 2
3 4
5 6
# Matrix of size [3,2] in row_major [0x2701360 data= 0x2700900]
matrix = m:select(dimension, index [, matrix])
This methods returns a matrix with one less dimension, resulting of select at the caller matrix the indicated dimension at the given index. The resulting matrix references the internal data of original matrix. If given, the third argument must be a matrix
which will be used to store the result of the select
call, and must fit the expected dimensions. In this last case, the computation effort is dismissed to constant.
> m = matrix(4,3):zeros()
> = m
0 0 0
0 0 0
0 0 0
0 0 0
# Matrix of size [4,3] [0x23dcab0 data= 0x23727e0]
> = m:select(2,2):fill(9)
9 9 9 9
# Matrix of size [4] [0x23dd330 data= 0x23727e0]
> = m:select(1,3):fill(4)
4 4 4
# Matrix of size [3] [0x23dd790 data= 0x23727e0]
> = m
0 9 0
0 9 0
4 4 4
0 9 0
# Matrix of size [4,3] [0x23dcab0 data= 0x23727e0]
NOTE that the third argument matrix must be created by a previous call to select
over the same dimension (but not the same index). As example, the following design pattern gives the same variable as third argument result and as leftside of the expression, allocating the memory in the first loop iteration, and reusing it in the following:
> m = matrix(4,5):linear()
> for i=1,m:dim(2) do
result = m:select(2,i,result)
end
matrix = m:transpose()
This method returns a matrix
which is a transposition of the caller object. Note that both, the caller and the transposition, reference the same data.
> m = matrix(3,4):linear()
> = m
0 1 2 3
4 5 6 7
8 9 10 11
# Matrix of size [3,4] in row_major [0x2777140 data= 0x27799b0]
> = m:transpose()
0 4 8
1 5 9
2 6 10
3 7 11
# Matrix of size [4,3] in row_major [0x274e620 data= 0x27799b0]
matrix = m:slice(position, size [, clone])
This methods produces a submatrix of the caller matrix. By default, the returned submatrix shares the data pointer with the caller, but it is also possible to do a deep copy submatrix. The syntax is:
m:slice(pos_table, size_table, clone=false)
being pos_table
a Lua table with the position of first element (starting at 1, not 0), and size_table
a Lua table with the size of each dimension. The last argument, clone
, is an optional boolean (by default false
) indicating if the resulting matrix will be a clone or not.
> a = matrix(3,4,{1,2,3,4, 5,6,7,8, 10,11,12,13})  row major matrix
> = a
1 2 3 4
5 6 7 8
10 11 12 13
# Matrix of size [3,4] in row_major [0x2706530 data= 0x2706b00]
> b = a:slice({2,1},{2,2})  slice at position (2,1) with size 2x2
> = b
5 6
10 11
# Matrix of size [2,2] in row_major [0x2707cd0 data= 0x2706b00]
>  same slice as before but making a clone (deep copy)
> b = a:slice({2,1},{2,2}, true)
> = b
5 6
10 11
# Matrix of size [2,2] in row_major [0x2708a20 data= 0x2708ad0]
matrix = m[number]
A shortcut for select or get methods. The operator is equivalent to a m:select(1,n)
in case the matrix has more than one dimension, and returns a matrix
object with one less dimension. In case of unidimensional matrix, this call is equivalent to m:get(n)
and returns a Lua number value.
This operator can be used in left and/or right hand of an assignment. In the right hand:
m[key] = number
is equivalent to m[key]:fill( number )
in case the righthand is a number.
m[key] = matrix
is equivalent to m[key]:copy( matrix )
in case the righthand is a matrix
.
matrix = m[{s1, s2, ...}]
A shortcut for slicing, using the __index
metamethod. It is similar to Matlab or Octave slice operators. The call is parsed and converted into a slice
method call, so, it is slower than using directly the slice
method, but it is easier to understand. This operator receives a variable number of arguments, as many as dimensions has the caller matrix
object. In every dimension position, three possible values could be given:
A number, indicating an exact dimension position: m = m2[{2,3}]
A table, with start and end positions in the dimension: m = m2[{2, {1,3}}]
A string, with start and end positions in the dimension: m = m2[{2, '1:3'}]
The following examples are equivalent:
> = m2:slice({2,1},{1,3})
4 5 6
# Matrix of size [1,3] in row_major [0xf44470 data= 0xdeae90]
> = m2[{2, {1,3}}]
4 5 6
# Matrix of size [1,3] in row_major [0xf3c8d0 data= 0xdeae90]
> = m2[{2, '1:3'}]
4 5 6
# Matrix of size [1,3] in row_major [0xe32dc0 data= 0xdeae90]
It is possible to use the string shortcut to indicate a whole dimension, or only start/end positions:
> = m2[{':', '2:3'}]
2 3
5 6
8 9
# Matrix of size [3,2] in row_major [0xef1260 data= 0xdeae90]
> = m2[{'2:', '2:3'}]
5 6
8 9
# Matrix of size [2,2] in row_major [0xe08f50 data= 0xdeae90]
> = m2[{':2', '2:3'}]
2 3
5 6
# Matrix of size [2,2] in row_major [0xf04290 data= 0xdeae90]
m[key] = value
Different cases depending in the type of key
and the type of value
.
m:select(1,key):fill(value)
m:select(1,key):copy(value)
m[key]:fill(value)
m[key]:copy(value)
m:masked_fill(key, value)
m:masked_copy(key, value)
>  assigns a 0 to all the row values at columns 2:3
> m2[{ ':', '2:3' }] = 0
>  assigns another matrix all the row values at columns 2:3
> m2[{ ':', '2:3' }] = matrix(m2:dim(1),2):linspace()
matrix = matrix.join(dimension, m1, m2, ...)
This function joins the given matrices by the given dimension. All the dimensions of the matrices must be the same, except the given dimension, which could differ. It is possible to add a new axis at left and join all matrices using the new added axis given a dimension=0
parameter. Similarly, it is possible to add a new axis at right for joining all matrices using this new axis given a dimension=num_dims+1
, being num_dim the number of dimensions of the given matrices.
Warning, this method duplicates the memory needed, because all the matrices are copied to the destination matrix.
> m1 = matrix(10,2):linear()
> m2 = matrix(10,3):linear()
> outm = matrix.join(2, m1, m2)
> = outm
0 1 0 1 2
2 3 3 4 5
4 5 6 7 8
6 7 9 10 11
8 9 12 13 14
10 11 15 16 17
12 13 18 19 20
14 15 21 22 23
16 17 24 25 26
18 19 27 28 29
# Matrix of size [10,5] in row_major [0x1f9c100 data= 0x1f9c1c0]
>
> m1 = matrix(10,3):linear()
>  m2 remains the same
>  add new axis at left
> = matrix.join(0, m1, m2)
# pos [1,1,1]
0 1 2
3 4 5
6 7 8
9 10 11
12 13 14
...
27 28 29
# pos [2,1,1]
0 1 2
3 4 5
6 7 8
9 10 11
12 13 14
...
27 28 29
# Matrix of size [2,10,3] stride [30,3,1] ref [0x1940f90 data= 0x177c280]
>
>  add new axis at right
> = matrix.join(3, m1, m2)
# pos [1,1,1]
0 0
1 1
2 2
# pos [2,1,1]
3 3
4 4
5 5
...
# pos [10,1,1]
27 27
28 28
29 29
# Matrix of size [10,3,2] stride [6,2,1] ref [0x1b42ef0 data= 0x1b42fe0]
matrix = m:clamp(lower, upper)
This method clamps the matrix components to a given range [min,max], modifying the matrix inplace. The caller matrix instance is returned.
> a = matrix(3,3,{1,2,3,4,5,6,7,8,9})
> = a
1 2 3
4 5 6
7 8 9
# Matrix of size [3,3] in row_major [0xe56a30 data= 0xe56f40]
> a:clamp(3,6)
> = a
3 3 3
4 5 6
6 6 6
# Matrix of size [3,3] in row_major [0xe56a30 data= 0xe56f40]
matrix = m:adjust_range(min, max)
This method modifies inplace the matrix components, interpolating the values to be in the given range [min,max]. The caller matrix is returned.
> a = matrix(3,3,{1,2,3,4,5,6,7,8,9})
> a:adjust_range(3,6)
> = a
3 3.375 3.75
4.125 4.5 4.875
5.25 5.625 6
# Matrix of size [3,3] in row_major [0x25cca30 data= 0x25ccf40]
> = a:adjust_range(0,1)
0 0.125 0.25
0.375 0.5 0.625
0.75 0.875 1
# Matrix of size [3,3] in row_major [0x25cca30 data= 0x25ccf40]
> = a:adjust_range(1,9)
1 2 3
4 5 6
7 8 9
# Matrix of size [3,3] in row_major [0x25cca30 data= 0x25ccf40]
boolean = m:is_contiguous()
Indicates if the matrix internal data is contiguous at memory (in row major order).
matrix = m:contiguous()
Returns a contiguous version of the caller matrix. If the matrix is contiguous, returns itself. Otherwise, returns a copy of the caller. Note that, if the matrix is a slice or a transposition of another matrix, therefore it could be noncontiguous.
matrix = m:fill(number)
This is an inplace method which sets all components to a given value.
> a = matrix(2,3):fill(4)  a 2x3 matrix filled with 4
> = a
4 4 4
4 4 4
# Matrix of size [2,3] in row_major [0x26ff9b0 data= 0x26ffa20]
matrix = m:zeros()
This is equivalent to m:fill(0)
matrix = m:ones()
This is equivalent to m:fill(1)
matrix = m:linear(start=0, step=1)
Initializes the matrix starting at the given index and using the given step. The index and the step is optional.
> m = matrix(3,2,2):linear(1,2)
> = m
# pos [1,1,1]
1 3
5 7
# pos [2,1,1]
9 11
13 15
# pos [3,1,1]
17 19
21 23
# Matrix of size [3,2,2] in row_major [0x149de00 data= 0x149daa0]
> m = matrix(2,2):linear()
> = m
0 1
2 3
# Matrix of size [2,2] in row_major [0x149f110 data= 0x149f1e0]
matrix = m:linspace(a=1, b=m:size())
Initializes the matrix
with a linear space distribution. It receives two optional arguments, why default a=1
and b=m:size()
. It returns the caller matrix
.
> m = matrix(5):linspace(1,20)
> = m
1 5.75 10.5 15.25 20
# Matrix of size [5] in row_major [0x291f200 data= 0x291ecd0]
matrix = m:logspace(a=1, b=m:size(), base=10)
Initializes the matrix
with a logarithmic distribution between a
and b
with the given logarithm base
. It receives three optional arguments, why default a=1
, b=m:size()
and base=10
. It returns the caller matrix
.
> m = matrix(5):logspace(0.001,0.1)
> = m
0.001 0.00316228 0.01 0.0316228 0.1
# Matrix of size [5] in row_major [0x291fed0 data= 0x291fd50]
matrix = m:uniform(lower, upper [, random] )
This method initializes the matrix with random integers taken uniformly from the given range of values:
> m = matrix(10):uniform(0,10,random(1234))
> = m
3 6 5 4 8 9 1 7 9 10
# Matrix of size [10] in row_major [0x2716b10 data= 0x2716490]
The random object is optional, but to ensure reproducibility it is recommended.
matrix = m:uniformf(lower=0, upper=1 [, random] )
This method initializes the matrix with random floats taken uniformly from the given range of values:
> m = matrix(2,2):uniformf(10, 10, random(1234))
> = m
6.16961 0.0467267
2.44218 6.35677
# Matrix of size [2,2] in row_major [0x1000e90 data= 0xe47410]
The random object is optional, but to ensure reproducibility it is recommended.
matrix = m:diag(number)
This method sets the matrix diagonal components to a given value, modifying inplace the caller matrix. For any number of dimensions, the diagonal are whose components which positions are equals at all dimensions.
> a = matrix(3,3,3):ones():diag(5)
> = a
# pos [1,1,1]
5 1 1
1 1 1
1 1 1
# pos [2,1,1]
1 1 1
1 5 1
1 1 1
# pos [3,1,1]
1 1 1
1 1 1
1 1 5
# Matrix of size [3,3,3] in row_major [0x1718f10 data= 0x1718d50]
string = m:toString( mode='ascii' )
This method returns a Lua string which represents the caller matrix. It receives an optional argument indicating if the matrix data will be stored in ascii
or binary
format (by default ascii
).
> a = matrix(3,5):ones()
> = a
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
# Matrix of size [3,5] in row_major [0xd80a10 data= 0xd815d0]
> = a:toString()
3 5
ascii
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1
> = a:toString("ascii")
3 5
ascii
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1
> = a:toString("binary")
3 5
binary
8Ffe98Ffe98Ffe98Ffe98Ffe98Ffe98Ffe98Ffe98Ffe98Ffe98Ffe98Ffe98Ffe98Ffe98Ffe9
matrix = matrix.fromString(filename)
This method loads a matrix from a Lua string generated by method matrix.toString
.
> a = matrix.fromString[[3 5
>> ascii
>> 1 1 1 1 1 1 1 1 1
>> 1 1 1 1 1 1
>> ]]
> = a
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
# Matrix of size [3,5] in row_major [0xd983b0 data= 0xdfe5c0]
m:toFilename(filename, mode='ascii')
This method stores a matrix in a given filename. It also receives an optional argument with ascii
or binary
(by default ascii
). It allows to compress the output file using GZIP, if the filename has '.gz' extension.
> a = matrix(3,3)
> a:toFilename("a.mat", "binary")
> a:toFilename("a.mat.gz", "binary")
matrix = matrix.fromFilename(filename)
This method loads a matrix from a given filename, expecting the format used by matrix.toFilename
method. It allows to load compressed files using GZIP, if the filename has '.gz' extension.
> a = matrix.fromFilename("a.mat")
> a = matrix.fromFilename("a.mat.gz")
m:toTabFilename(filename)
This method stores a matrix in a given filename, but without header, and formatting the data to be formatted by lines and spaces, one matrix row per line. It is limited to bidimensional matrices. It allows to compress the output file using GZIP, if the filename has '.gz' extension.
> a = matrix(3,3)
> a:toTabFilename("a.mat")
> a:toTabFilename("a.mat.gz")
matrix = matrix.fromTabFilename(filename)
This method loads a matrix from a given filename, formatted as done by matrix.toTabFilename
. The size of the matrix
is computed in a first loop over all the data, so this method needs two passes to load the matrix. It allows to load compressed files using GZIP, if the filename has '.gz' extension.
> a = matrix.fromTabFilename("a.mat")
> a = matrix.fromTabFilename("a.mat.gz")
m:toMMap(filename)
Stores the matrix
in a file in a binary machinedependent format, so it could be loaded using mmap function (matrix.fromMMap
). The endianism must be the same between machines where matrix is stored/loaded.
matrix = matrix.fromMMap(filename [,write [,shared]])
Loads the matrix
from a file in a binary machinedependent format, by using the mmap function (matrix.toMMap
). The endianism must be the same between machines where matrix is stored/loaded. Two additional boolean arguments are allowed. The second boolean argument indicates if writing is available, by default it is true
. Be careful, if writing is set to false
, any attempt of writing will throw a segmentation fault. The third boolean argument indicates if the data is shared between different processes, by default it is true
. If both arguments are true
, any writing will be available to any process which shares this map. Besides, writings will be synchronized in the hard disk (but not instantly). If writing is true
, but shared is false
, then the memory is mapped as copyonwrite. For more info, see the manual page of mmap function (PROTECT_WRITE, MAP_SHARED and MAP_PRIVATE).
table = m:toTable()
This method returns a plain Lua table (onedimensional table) which contains the matrix data in row_major
order, as expected by matrix
constructors.
> a = matrix(3,2,{1,2,3,4,5,6})
> = a
1 2
3 4
5 6
# Matrix of size [3,2] in row_major [0x9ddce0 data= 0x9ddd30]
> t = a:toTable()
> = table.concat(t, " ")
1 2 3 4 5 6
These methods allows raw accessing of matrix components.
number = m:size()
This method returns the number of elements in the matrix.
> a = matrix(3,4,{1,2,3,4, 5,6,7,8, 10,11,12,13})
> = a:size()
12
table = m:stride()
This method is similar to m:dim
, but returning the stride of the dimension (the offset between elements at each dimension)
> a = matrix(4,3,2)
> = a:stride()
table: 0x23a5fe0
> = table.concat(a:stride(), " ")
6 2 1
> = a:stride(1), a:stride(2), a:stride(3)
6 2 1
> a = a:transpose()
> = a:stride(1), a:stride(2), a:stride(3)
1 4 12
number = m:offset()
It returns the offset from data first position. Only submatrices has an offset!=0.
> a = matrix(2,3)
> = a:offset()
0
> b = a:slice({2,1},{1,1})
> = b:offset()
3
block = m:data()
Returns a float memory block (mathcore.block.float
) with the underlying data pointer.
number = m:raw_get(pos)
It receives a raw position at the underlying data pointer, and returns its value. It is useful to combine stride
and offset
methods in order to compute the raw position.
> a = matrix(3,2, {1,2,3,4,5,6})
> = a
1 2
3 4
5 6
# Matrix of size [3,2] in row_major [0x144fce0 data= 0x144fd90]
> = a:raw_get(a:offset() + a:stride(1)*1 + a:stride(2)*0), a:get(2,1)
3 3
NOTE! that the strides are multiplied by matrix position minus 1.
m:raw_set(pos, value)
It receives a raw position at the underlying data pointer and a number. The given position is set to given number value. It is useful to combine stride
and offset
methods in order to compute the raw position.
> a = matrix(3,2, {1,2,3,4,5,6})
> = a
1 2
3 4
5 6
# Matrix of size [3,2] in row_major [0x144fce0 data= 0x144fd90]
>  equivalent to a:set(2,1, 10)
> a:raw_set(a:offset() + a:stride(1)*1 + a:stride(2)*0, 10)
> = a
1 2
10 4
5 6
# Matrix of size [3,2] in row_major [0x144fce0 data= 0x144fd90]
NOTE! that the strides are multiplied by matrix position minus 1.
For fast and easy matrix traversal, a C++ sliding window object is binded to Lua. It works similarly to dataset.matrix
, but circularity and outofmatrix default values are not supported. The object is constructed using the method sliding_window
of matrix
, and could be iterated using its method iterate()
:
> m = matrix(4,2,3):uniformf(10,10,random(1234))  randomly initialized matrix
> for submat in m:sliding_window():iterate() do print(submat) end
# pos [1,1,1]
6.16961 0.0467267 2.44218
6.35677 1.24545 2.24224
# Matrix of size [1,2,3] in row_major [0x253f160 data= 0x253dec0]
# pos [1,1,1]
5.70717 5.4272 5.59952
7.2134 4.54815 6.98726
# Matrix of size [1,2,3] in row_major [0x253fa40 data= 0x253dec0]
# pos [1,1,1]
4.47071 6.02962 6.03744
6.30326 9.16279 6.82369
# Matrix of size [1,2,3] in row_major [0x2540230 data= 0x253dec0]
# pos [1,1,1]
7.51865 7.67724 2.84365
9.74185 0.0199025 0.263331
# Matrix of size [1,2,3] in row_major [0x25409c0 data= 0x253dec0]
It is possible to modify the default behavior giving this parameters to sliding_window
method:
offset
: a Lua table with offset applied to the window in each coordinate (starting at 0).size
: a Lua table with the window size for each coordinate.step
: a Lua table with the step size at each coordinate (each value must be >= 1).numSteps
: a Lua table with the number of steps in each coordinate (each value must be >= 1).orderStep
: a Lua table with the traversal order of coordinates (starting at 1).> m = matrix(4,2,3):uniformf(10,10,random(1234))
> for w in m:sliding_window{ step={2,1,1}, size={1,1,2} }:iterate() do print(w) end
# pos [1,1,1]
6.16961 0.0467267
# Matrix of size [1,1,2] in row_major [0x9fdb90 data= 0x9cf2d0]
# pos [1,1,1]
4.47071 6.02962
# Matrix of size [1,1,2] in row_major [0x9fe0f0 data= 0x9cf2d0]
Manual iteration of the sliding_window is also possible using the following methods:
matrix = sw:get_matrix([matrix])
: returns the matrix generated by the window at its current position. It is possible to pass an optional argument, a destination matrix
, so the computation effort is dismissed to constant. NOTE that this matrix must be created by a previous call to get_matrix
over the same sliding_window
.
sw:next()
: moves the window to the next position.
sw:is_end()
: returns true if the window has finished the matrix traversal.
> m = matrix(4,2,3):uniformf(10,10,random(1234))
> wo = m:sliding_window{ step={2,1,1}, size={1,1,2} }
> while not wo:is_end() do m=wo:get_matrix(m) print(m) wo:next() end
This operations uses standard Lua math operators for friendly user interaction, but they work with BLAS API for best performance. However, all this operations return a new instantiated matrix, for best performance it is recommended to use directly the BLAS interface.
The operators binary +, , *, /, and unary operators , ^, are implemented as algebraic operations. The + and  operators only work when the matrices has the same sizes:
> a= matrix(3,3,3,{1,2,3,4,5,6,7,8,9,
10,11,12,13,14,15,16,17,18,
19,20,21,22,23,24,25,26,27})
> = a+a
# pos [1,1,1]
2 4 6
8 10 12
14 16 18
# pos [2,1,1]
20 22 24
26 28 30
32 34 36
# pos [3,1,1]
38 40 42
44 46 48
50 52 54
# Matrix of size [3,3,3] in row_major [0x1196d90 data= 0x1196e40]
> = aa
# pos [1,1,1]
0 0 0
0 0 0
0 0 0
# pos [2,1,1]
0 0 0
0 0 0
0 0 0
# pos [3,1,1]
0 0 0
0 0 0
0 0 0
# Matrix of size [3,3,3] in row_major [0x1198d80 data= 0x1198a50]
The operator * only works with vectors or bidimensional matrices. If needed, you can rewrap
the matrix data before the operation. Depending on the dimension of the two matrices, the multiplication could be:
> a, b = matrix(4,{1,2,3,4}), matrix(4,1,{5,6,7,8})
> = a*b
70
# Matrix of size [1] in row_major [0xfa9230 data= 0xfc2300]
> a = matrix(4,{1,2,3,4})
> b = matrix(4,1,{5,6,7,8})
> = b*a
5 10 15 20
6 12 18 24
7 14 21 28
8 16 24 32
# Matrix of size [4,4] in row_major [0x1001940 data= 0x1176a80]
> a = matrix(2,2,{1,2,3,4})
> b = matrix(2,{5,6})
> = a*b
17 39
# Matrix of size [2] in row_major [0x105baa0 data= 0xfe80f0]
> b = matrix(1,2,{5,6})
> = a*b
17
39
# Matrix of size [2,1] in row_major [0x107e3c0 data= 0x107fb30]
> b = matrix(2,1,{5,6})
> = a*b
17
39
# Matrix of size [2,1] in row_major [0x10c4700 data= 0x10c6890]
> a=matrix(3,2,{1,2,3,4,5,6})
> b=matrix(2,4,{1,2,3,4,5,6,7,8})
> = a*b
11 14 17 20
23 30 37 44
35 46 57 68
# Matrix of size [3,4] in row_major [0x1114270 data= 0x11165d0]
A multiplication by a scalar is also possible, if you multiply one matrix by one number.
> a=matrix(3,2,{1,2,3,4,5,6})
> = a*5
5 10
15 20
25 30
# Matrix of size [3,2] in row_major [0x10f2160 data= 0x10d14e0]
The componentwise operator / is allowed for division between matrix and a scalar, or between a scalar and a matrix.
The operator ^ is also allowed only with scalars.
The unary operator  is equivalent to multiply by 1.
matrix = m:scalar_add(number)
Adds to all the components, inplace, the given scalar number. Returns the caller matrix object.
matrix = m:div(scalar)
Produces the computation between the componentwise inversion of the matrix
and the given scalar. This operation is done inplace.
> m = matrix(2,2,{1,2,3,4})
> m:div(1)
> = m
1 0.5
0.3333 0.25
# Matrix of size [3,2] in row_major [0x1cf2160 data= 0x10d15e0]
The most efficient way to do operations if using the BLAS interface directly. All the methods are prepared to adjust the BLAS operations to the given matrices, so you don't need to be worried about strides and sizes.
All of this methods are inplace, so they modify the caller object, and returns it to allow operation sequences.
matrix = m:axpy(alpha, X)
The AXPY operation computes addition of vectors:
Y = alpha * X + Y
The method receives two positional parameters: the alpha scalar and the matrix X. The X and Y matrix sizes must be equals, and the number of dimensions is not a problem. This method interprets all the data as a sequence, calling several times to AXPY BLAS function if necessary:
> a = matrix(4,{1,2,3,4})
> b = matrix(4,{5,6,7,8})
> a:axpy(2.0, b)
> = a
11 14 17 20
# Matrix of size [4] in row_major [0x107e3c0 data= 0x1110970]
> a = matrix(2,2,2,{1,2,3,4,5,6,7,8})
> b = matrix(2,2,2,{9,10,11,12,13,14,15,16})
> a:axpy(1.0, b)
> = a
# pos [1,1,1]
10 12
14 16
# pos [2,1,1]
18 20
22 24
# Matrix of size [2,2,2] in row_major [0xfb1f40 data= 0x1056f00]
matrix = m:gemv{ beta, alpha, Y, X, trans_A}
The GEMV operation computes matrixvector multiplication:
Y = beta * Y + alpha * op(A) * X
being Y the caller matrix (a vector), A another matrix, and X a vector (unidimensional matrix, or bidimensional with one row (or one column)), and beta and alpha are scalars. The op(A) is transposition operation.
The method receives a table with:
A
field, the other matrix.
X
field, the vector.
alpha
field, the scalar
beta
field, the other scalar.
trans_A
field, a boolean which indicates if the A matrix will be transposed or not. It is optional, by default is false
.
> a = matrix(3,2,{1,2, 3,4, 5,6})
> b = matrix(2,{7,8})
> c = matrix(3)
> c:gemv{ A=a, X=b, alpha=2, beta=0 }
> = c
46 106 166
# Matrix of size [3] in row_major [0xfbeff0 data= 0xfaf640]
matrix = m:gemm{ beta, alpha, A, B, ... }
The GEMM operation computes matrixmatrix multiplication:
Y = beta * Y + alpha * op(A) * op(B)
being Y the caller matrix (a vector), A another matrix, and B a matrix, and beta and alpha are scalars. The op(A) and op(B) are transposition operations.
The method receives a table with:
A
field, the other matrix.
B
field, the vector.
alpha
field, the scalar
beta
field, the other scalar.
trans_A
field, a boolean which indicates if the A matrix will be transposed or not. It is optional, by default is false
.
trans_B
field, a boolean which indicates if the B matrix will be transposed or not. It is optional, by default is false
.
> a = matrix(3,2,{1,2, 3,4, 5,6})
> b = matrix(4,2,{7,8, 9,10, 11,12, 13,14})
> c = matrix(3,4):ones()
> c:gemm{ A=a, B=b, alpha=1, beta=1, trans_B=true}
> = c
24 30 36 42
54 68 82 96
84 106 128 150
# Matrix of size [3,4] in row_major [0x1452a20 data= 0x144cbf0]
matrix = m:ger{ X, Y, alpha }
The GER operation computes outter product of vectors:
Z = Z + alpha * X * Y'
being Z the caller matrix (a squared matrix), X and Y two vectors, and beta and alpha are scalars. The Y vector is transposed.
> a = matrix(3,{1,2,3})
> b = matrix(3,{4,5,6})
> c = matrix(3,3):zeros()
> c:ger{ X=a, Y=b, alpha=2 }
> = c
8 10 12
16 20 24
24 30 36
# Matrix of size [3,3] in row_major [0x1f06b20 data= 0x1f18080]
number = m:dot(matrix)
The DOT operation computes the dotproduct of two vectors, the caller matrix and a given matrix. It returns a number.
> a = matrix(3,{1,2,3})
> b = matrix(3,{4,5,6})
> = a:dot(b)
32
# Matrix of size [1] in row_major [0x1f4ffe0 data= 0x2076e20]
matrix = m:scal(number)
The SCAL operation computes the multiplication of a matrix by a scalar.
> a = matrix(3,{1,2,3})
> a:scal(4)
> = a
4 8 12
# Matrix of size [3] in row_major [0x1f3b230 data= 0x201e9a0]
matrix = m:copy(matrix)
The COPY operation copies the content of a given matrix in the caller matrix object.
> a = matrix(3,3,{1,2,3,4,5,6,7,8,9})
> b = matrix(3,3):fill(5)
> a:copy(b)
> = a
5 5 5
5 5 5
5 5 5
# Matrix of size [3,3] in row_major [0x1f7e870 data= 0x1f49ef0]
> a = matrix(3,3,{1,2,3,4,5,6,7,8,9})
> b = matrix(2,2,{1,2,3,4})
> c = a:slice({2,1},{2,2})
> c:copy(b)
> = a
1 2 3
1 2 6
3 4 9
# Matrix of size [3,3] in row_major [0x1fb64e0 data= 0x1fbd600]
U,S,VT = m:svd()
This method computes the Singular Values Decomposition of the caller matrix
. It returns three matrices:
U
a matrix with the left singular vectors.S
a sparse diagonal matrix with singular values.VT
a matrix with the transposed right singular vectors.matrix = m:inv()
Computes the inverse of the caller matrix. Check that your matrix is not singular, otherwise the returned matrix won't be correct.
matrix = m:pinv()
Computes the pseudoinverse of the caller matrix, using the SVD method.
This operations are applied inplace and over all the components of the caller matrix. If it is possible, the caller matrix is returned.
matrix = m:tan()
Computes the TAN function of all the components.
matrix = m:tanh()
Computes inplace the TANH function of all the components.
matrix = m:atan()
Computes inplace the ATAN function of all the components.
matrix = m:atanh()
Computes inplace the ATANH function of all the components.
matrix = m:sin()
Computes inplace the SIN function of all the components.
matrix = m:sinh()
Computes inplace the SINH function of all the components.
matrix = m:asin()
Computes inplace the ASIN function of all the components.
matrix = m:asinh()
Computes inplace the ASINH function of all the components.
matrix = m:cos()
Computes inplace the COS function of all the components.
matrix = m:cosh()
Computes inplace the COSH function of all the components.
matrix = m:acos()
Computes inplace the ACOS function of all the components.
matrix = m:acosh()
Computes inplace the ACOSH function of all the components.
matrix = m:abs()
Computes inplace the ABS function of all the components.
matrix = m:complement()
Computes inplace the complement function of all the components: X = 1  X
matrix = m:log()
Computes inplace the LOG function of all the components.
matrix = m:log1p()
Computes inplace the LOG1P function of all the components.
matrix = m:plogp()
Computes inplace the p*log(p) operation over all components. It is useful to compute entropy related measures.
matrix = m:exp()
Computes inplace the EXP function of all the components.
matrix = m:pow(number)
Computes inplace the POWER of all the components by a given scalar.
matrix = m:sqrt()
Computes the SQRT function of all the components.
matrix = m:cmul(matrix)
Computes inplace a componentwise multiplication between the caller and a given matrix.
This operations are applied taking into account all the data at the matrix.
min,argmin = m:min()
Returns the minimum and its position in the matrix.
> a = matrix(3,4,{1,2,3,4,5,6,7,12,9,10,11,8})
> = a:min()
1 1
matrix,matrixInt32 = m:min(dim [, matrix[, matrixInt32]] )
Applies the min operator over the elements of the given dimension, and returns a matrix with the same number of dimensions, but with the size of dimension dim
equals 1. The second matrix argument is optional, and if given, the returned matrix will be this second argument.
> a = matrix(3,4,{1,2,3,4,
>> 5,6,7,12,
>> 9,10,11,8})
> = a:min(1)
1 2 3 4
# Matrix of size [1,4] in row_major [0x1f06bb0 data= 0x1f06cb0]
> = a:min(2)
1
5
8
# Matrix of size [3,1] in row_major [0x1f07560 data= 0x1f06d90]
max,argmax = m:max()
Returns the maximum and its position in the matrix.
> a = matrix(3,4,{1,2,3,4,5,6,7,12,9,10,11,8})
> = a:max()
12 8
matrix,matrixInt32 = m:max(dim [, matrix[, matrixInt32] ] )
Applies the max operator over the elements of the given dimension, and returns a matrix with the same number of dimensions, but with the size of dimension dim
equals 1. The second matrix argument is optional, and if given, the returned matrix will be this second argument.
> a = matrix(3,4,{1,2,3,4,
>> 5,6,7,12,
>> 9,10,11,8})
> = a:max(1)
9 10 11 12
# Matrix of size [1,4] in row_major [0x1f05500 data= 0x1f05600]
> = a:max(2)
4
12
11
matrixBool = m:eq(number or matrix)
This method computes inplace a comparison between all the components with the given value, and converts the component in true
or false
if it is less than the given value or not. If the value is another matrix
, both matrices will be compared componentbycomponent.
matrixBool = m:lt(number or matrix)
This method computes inplace a comparison between all the components with the given value, and converts the component in true
or false
if it is less than the given value or not. If the value is another matrix
, both matrices will be compared componentbycomponent.
matrixBool = m:gt(number or matrix)
This method computes inplace a comparison between all the components with the given value, and converts the component in true
or false
if it is greater than the given value or not. If the value is another matrix
, both matrices will be compared componentbycomponent.
number = m:sum( )
Computes the sum of all the components of the caller matrix, and returns its value.
matrix = m:sum( number [, matrix] )
Receives a number indicating the dimension where the sum must be run, and returns a matrix with each possible sum of the given dimension. The second matrix argument is optional, and if given, the returned matrix will be this argument.
> m = matrix(2,2,{1,2,3,4})
> = m:sum(1)
4 6
# Matrix of size [1,2] in row_major [0x19e0620 data= 0x19d2480]
> = m:sum(2)
3
7
# Matrix of size [2,1] in row_major [0x19e0a40 data= 0x19d3b90]
number = m:norm2()
The NORM2 operation computes the euclidean norm of the caller matrix. It returns a number.
> a = matrix(2,2,2,{1,2,3,4,5,6,7,8})
> = a:norm2()
14.282856941223
This operations allow to extract or sort matrices by indexing any of its dimensions.
matrix = m:index(dim, idx)
This method returns a new deep cloned matrix
but taking only the indexes of dimension dim
indicated at idx
object. The idx
object can be three different data types:
matrixInt32
with all the indices you want to take.table
with the indices you want to keep. This table will be converted into a matrixInt32
instance.matrixBool
were true
value in the indices you want to keep.This method can be combined with eq
, lt
, gt
methods to select a bunch of rows or columns depending in a simple condition. The following example uses index
and lt
to select all the rows where its first column is less than 0.
> m = matrix(10,2):uniform(10,10,random(1234))
> print(m)
5 9
4 2
10 5
7 1
1 2
...
5 8
# Matrix of size [10,2] stride [2,1] ref [0x204cce0 data= 0x1e3c310]
> m_neg_idx = m:select(2,1):lt(0)
> print(m_neg_idx)
F T F F F F F F F T
# MatrixBool of size [10] stride [1] ref [0x1f64820 data= 0x1e886a0]
> m_neg = m:index(1, m_neg_idx)
> print(m_neg)
4 2
5 8
# Matrix of size [2,2] stride [2,1] ref [0x1d72450 data= 0x1f499a0]
m = m:indexed_fill(dim, idx, number)
This method fills with number
all the values at the given dimension dim
whose indices are in idx
object. The idx
object can be three different data types:
matrixInt32
with all the indices you want to take.table
with the indices you want to keep. This table will be converted into a matrixInt32
instance.matrixBool
were true
value in the indices you want to keep.The following example fills with 0 the values at column 1 which are negative.
> m = matrix(10,2):uniform(10,10,random(1234))
> print(m)
5 9
4 2
10 5
7 1
1 2
...
5 8
# Matrix of size [10,2] stride [2,1] ref [0x204cce0 data= 0x1e3c310]
> m_neg = m:index(1, m:select(2,1):lt(0))
> m_neg_idx = m:select(2,1):lt(0)
> print(m_neg_idx)
F T F F F F F F F T
# MatrixBool of size [10] stride [1] ref [0x1f64820 data= 0x1e886a0]
> col1 = m(':',1)
> col1:indexed_fill(1, m_neg_idx, 0)
> print(m)
5 9
0 2
10 5
7 1
1 2
...
0 8
# Matrix of size [10,2] stride [2,1] ref [0x237cba0 data= 0x237cc70]
m = m:indexed_copy(dim, idx, matrix)
This method copies the values of matrix
at the indices of dimension dim
indicated by idx
object. The idx
object can be three different data types:
matrixInt32
with all the indices you want to take.table
with the indices you want to keep. This table will be converted into a matrixInt32
instance.matrixBool
were true
value in the indices you want to keep.m:masked_fill(mask, value)
m:masked_copy(mask, value)
matrixInt32 = m:order()
Returns a permutation of the caller matrix
which sorts its data. The permutation is given as a matrixInt32
with the indices of the caller matrix
. The caller matrix
shold be a onedimensional matrix (rank 1 tensor).
> m = matrix(10,2):uniform(10,10,random(1234))
> print(m)
5 9
4 2
10 5
7 1
1 2
...
5 8
# Matrix of size [10,2] stride [2,1] ref [0x10bf090 data= 0x10bf160]
> ord = m:select(2,1):order()
> print(ord)
10 2 5 1 ... 3
# MatrixInt32 of size [10] stride [1] ref [0xe4eba0 data= 0xeb3230]
> sorted = m:index(1, ord)
> print(sorted)
5 8
4 2
1 2
5 9
5 8
...
10 5
# Matrix of size [10,2] stride [2,1] ref [0x1c270c0 data= 0x1ef0080]
matrixInt32 = m:order_rank()
Returns the rank of the matrix
elements in its sorted permutation. The caller matrix
should be a onedimensional matrix (rank 1 tensor).
> m = matrix(10,2):uniform(10,10,random(1234))
> print(m)
5 9
4 2
10 5
7 1
1 2
...
5 8
# Matrix of size [10,2] stride [2,1] ref [0x10bf090 data= 0x10bf160]
> rnk = m:select(2,1):order_rank()
> print(rnk)
4 2 10 9 ... 1
# MatrixInt32 of size [10] stride [1] ref [0x2cf0100 data= 0x2cf0020]
More matrix operations are located at matrix.op
, most of them are equivalent to methods in matrix
objects, but implemented to be allocate new memory instead of being inplace, and receiving the caller matrix
as first argument. New operations have been defined in this table to reduce the overhead of matrix
instance table. Only the new operations are documented here.
The table matrix.ext
contains new function extensions which work over matrices. This functions are here because the extensions are pretty new, still in testing, and to not polute too much the matrix
class with new methods.
matrix = matrix.ext.real_fftwh(m, wsize=m:size(), wadvance=wsize, dest=nil)
This functions computes real FFT with Hamming window to the given matrix m
. The FFT is computed for all the possible positions of a sliding window of size wisize
. The sliding window advances wadvance
samples every iteration. By default, the function configures wsize
and wadvance
just to compute FFT over the whole given matrix m
. The matrix m
should have only one dimension (rank 1). The result is new allocated matrix with size NxF
where N=(m:size()  wsize)/wadvance + 1
and F
is the size of the FFT output. In case the argument dest
is given, it should be a matrix
instance of size NxF
, and it would be the result matrix
, avoiding the allocation of a new matrix
.
> a = matrix(200):uniformf(0,1,random(1234))
> f = matrix.ext.real_fftwh(a, 20, 10)
> print(f)
5.78204 17.9694 2.60205 1.93296 ... 0.210018
4.80782 11.8858 0.97495 1.46241 ... 2.67692
4.15355 9.48432 1.71497 0.480176 ... 0.498562
5.12262 16.0089 3.65927 0.276271 ... 1.00497
5.57981 16.7664 2.00985 0.0188873 ... 0.336096
...
4.96313 15.267 3.44396 0.307427 ... 0.539195
# Matrix of size [19,16] stride [16,1] ref [0x1efbe50 data= 0x1f46330]
matrix.sparse
s = matrix.sparse(matrix)
s = matrix.sparse(d1, d2, values, indices, first_index)
s = matrix.sparse.csc(matrix)
s = matrix.sparse.csr(matrix)
s = matrix.sparse.diag(obj, [format="csr"])
number
matrix
table
dok = matrix.sparse.builders.dok()
dok = dok:set(row, col, value)
sparse = dok:build([num_rows, [num_cols, [format="csr"]]])
Currently is possible to use complex, double, int32 and char matrices, supporting load and save, matrix structural methods, and some of them also support mathematical operations:
matrixBool
: a matrix of boolean values. Basic functionality.matrixComplex
: fully working matrix type, with almost all the methods described above.matrixDouble
: partial working matrix type, only allow structural methods (explained at MatFormat section).matrixInt32
: partial working matrix type, only allow structural methods (explained at MatFormat section).matrixChar
: partial working matrix type, only allow structural methods (explained at MatFormat section).In all cases, you could use april_help
to ask which methods are available. Complex, MatrixChar type implements a method to_string_table
.
All matrices implement method convert_to(type)
which receives a type string with one of these values: "float", "double", "complex", "int32", "bool", "char"
; allowing to convert a matrix of one type into a different type with a casting and precision loss in some cases.
> m = matrix(5,5):uniformf(10,10,random(12354))
> print(m)
1.03926 6.82017 7.80579 2.02109 9.40496
1.54086 1.43963 7.11541 1.20382 2.91477
5.89334 9.04666 0.688719 9.25703 3.0825
5.4027 3.25782 0.7066 5.90035 8.35659
0.986174 8.1467 7.01133 9.03494 5.69565
# Matrix of size [5,5] stride [5,1] ref [0x16d0410 data= 0x16d04e0]
> print(m:convert_to("int32"))
1 6 7 2 9
1 1 7 1 2
5 9 0 9 3
5 3 0 5 8
0 8 7 9 5
# MatrixInt32 of size [5,5] stride [5,1] ref [0x187d2b0 data= 0x187d380]
The constructor of a matrixComplex
receives a table with complex numbers (see utils section). A complex number uses float single precission resolution for real and imaginary part:
>  using strings which are converted to complex numbers (slow performance)
> m = matrixComplex(2,2, { "1+1i", "2+2i", "3+2i", "4+1i" })
> = m
1+1i 2+2i
3+2i 4+1i
# MatrixComplex of size [2,2] in row_major [0x24d52c0 data= 0x24d4a00]
>
>  using directly complex numbers
> m = matrixComplex(2,2, { complex(1,1), complex(2,2), complex(3,2), complex(4,1) })
> = m
1+1i 2+2i
3+2i 4+1i
# MatrixComplex of size [2,2] in row_major [0x24d6550 data= 0x24d6650]
Besides the standard matrix
methods, the matrixComplex
implements the following:
caller = m:conj()
computes the conjugate inplace, modifying the caller matrix, and returning the caller matrix instance.matrix = m:real()
returns the real part of the caller matrixComplex
.matrix = m:img()
returns the imaginary part of the caller matrixComplex
.matrix = m:abs()
returns the modulus of the polar form of matrixComplex
.matrix = m:angle()
returns the angle of the polar form of matrixComplex
.matrix = m:to_float()
converts the caller matrix in a matrix
object which has one additional dimension. This additional dimension has always size 2, and keeps the real and imaginary parts of the caller matrixComplex
. The additional dimension will be the last. Note that the returned matrix
and the matrixComplex
caller references the same memory pointer.matrixDouble
is the type of matrices for double data. This kind of matrices don't accept mathematical operations, but yes structural operations as select
, slice
, etc.
> m = matrixDouble(2,3,{1,2,3,4,5,6})
> = m
1 2 3
4 5 6
# MatrixDouble of size [2,3] [0x2512c70 data= 0x251ad70]
matrixInt32
is the type of matrices for integer data. This kind of matrices don't accept mathematical operations, but yes structural operations as select
, slice
, etc.
> m = matrixInt32(2,3,{1,2,3,4,5,6})
> = m
1 2 3
4 5 6
# MatrixInt32 of size [2,3] [0x2512c70 data= 0x251ad70]
matrixChar
is the type of matrices for char data. This kind of matrices don't accept mathematical operations, but yes structural operations as select
, slice
, etc.
Exists an special method, to_string_table()
, which converts the matrix in a table of strings, concatenating the chars in row_major
order.
> m = matrixChar(2,2, { "h","ola" })
> = m
[1,1] = h
[1,2] = o
[2,1] = l
[2,2] = a
# MatrixChar of size [2,2] [0x12c3310 data= 0x12c3410]
> = unpack(m:to_string_table())
ho la
The table matrix.dict
contains several functions which allow to execute matrix
operations over tables of matrices.
another = matrix.dict.clone(tbl)
Returns a deep copy of the table.
another = matrix.dict.clone_only_dims(tbl)
Returns a deep copy of the table, but without copying the matrix data content, only cloning the matrix dimension sizes.
The following list of operations are implemented to be executed over all the contained matrices:
number = matrix.dict.size()
tbl = matrix.dict.fill(tbl,number)
tbl = matrix.dict.ones(tbl)
tbl = matrix.dict.zeros(tbl)
tbl = matrix.dict.axpy(tbl, number, tbl2)
number = matrix.dict.dot(tbl, tbl2)
tbl = matrix.dict.copy(tbl, tbl2)
tbl = matrix.dict.scalar_add(tbl, number)
tbl = matrix.dict.complement(tbl)
tbl = matrix.dict.pow(tbl, number)
tbl = matrix.dict.scal(tbl, number)
tbl = matrix.dict.inv(tbl)
tbl = matrix.dict.sqrt(tbl)
tbl = matrix.dict.exp(tbl)
tbl = matrix.dict.plogp(tbl)
tbl = matrix.dict.log1p(tbl)
tbl = matrix.dict.cos(tbl)
tbl = matrix.dict.cosh(tbl)
tbl = matrix.dict.acos(tbl)
tbl = matrix.dict.acosh(tbl)
tbl = matrix.dict.tan(tbl)
tbl = matrix.dict.tanh(tbl)
tbl = matrix.dict.atan(tbl)
tbl = matrix.dict.atanh(tbl)
tbl = matrix.dict.sin(tbl)
tbl = matrix.dict.sinh(tbl)
tbl = matrix.dict.asin(tbl)
tbl = matrix.dict.asinh(tbl)
tokens
packagePackage tokens
could be loaded via the standalone binary, or in Lua with require("aprilann.tokens")
.
A Token
is an abstract C++ class which has different specializations for different tasks and purposes. This class and its specializations are binded to Lua, allowing Lua scripts to be a glue language between C++ algorithms which are developed over the Token
abstraction.
In this way, the current implementation of Artificial Neural Networks (ANNs) receives as input a token and produce a as output a token. Normally, for ANNs, this token contains a matrix
, but it is also possible to wrap matrix.sparse
objects and token vectors. Every ANN component checks the token type, and specialized computations could be done depending in the token type.
From Lua side, matrix
instances can be given and taken as tokens.
tokens.base
This class defines the basic interface shared for all token types. It is an abstract class which couldn't be instantiated in Lua. If you try, you will see the following message:
> tokens.base()
tokens.base:tokens.base: Abstract class!!!
stack traceback:
[C]: in function 'base'
stdin:1: in main chunk
[C]: in ?
Usually, C++ method calls return an instance to the class tokens.base
, but in some cases it is possible to transform the generic reference to any of the child classes, or in other cases, it is possible to retrieve the contained data.
The transformation between tokens and matrix instances is performed automatically by the glue code between C++ and Lua.
The following methods are in the generic interface of tokens.base
class.
token = t:clone()
The method clone is implemented in all the tokens, and returns a deep copy of the caller.
tokens.vector.bunch
The token bunch vector is an array of tokens. Some ANN components allow to receive a vector of tokens which could contain sparse vectors.
> t = tokens.vector.bunch()
> t = tokens.vector.bunch(10)
> t = tokens.vector.bunch{ t1, t2, ... }
t = t:clear()
number = t:size()
t = t:set(position, token)
> = t:set(1, matrix(2,3):linear())
t = t:push_back(token)
> = t:push_back(matrix(1,2):linear())
token = t:at(position)
> = t:at(1)
> = t:at(2)
lua iterator = t:iterate()
> for i,v in t:iterate() do print(i) print(v) end
dataset
packagePackage dataset
could be loaded via the standalone binary, or in Lua with require("aprilann.dataset")
.
The dataset
table is a namespace and a Lua abstract class which adds an abstraction layer of set of patterns to the multidimensional matrices. It is also possible to do patterns preprocessing and, union and join operations of different datasets, an identity matrix dataset, and so on.
Every dataset implements following methods:
number = ds:numPatterns()
, it returns the number of patterns in the given ds
dataset.number = ds:patternSize()
, it returns the size of one pattern.table = ds:getPattern(i)
, it receives a number between 1 and numPatterns(), and returns a table with the ith pattern.ds:putPattern(i,t)
, it receives a number between 1 and numPatterns(), and a table with patternSize() numbers, and overwrites the ith pattern with the given table.iterator = ds:patterns()
, an iterator function to use in Lua for
statements: for i,t in ds:patterns() do ... end
.table = ds:mean()
, it returns the mean per each pattern component.table,table = ds:mean_deviation()
, it returns the mean and standard deviation per each pattern component.number,number = ds:min_max()
, it returns the minimum and maximum value of the dataset.ds:normalize_mean_deviation()
, it receives two tables of patternSize length, the first with means, and the second with standard deviations, and the method normalizes the data substracting mean and dividing by standard deviation.matrix = ds:toMatrix()
, it returns a new allocated bidimensional matrix
object which contains all dataset patterns (numPatterns rows and patternSize columns).This is the most important kind of dataset, allowing to create patterns moving a multidimensional window through a matrix
object. This dataset
takes the matrix
by reference, so any change in the matrix
will be reflected in the patterns produced by the dataset
:
xor_in = matrix(4,2, {0,0,
0,1,
1,0,
1,1})
xor_out = matrix(4, {0, 1, 1, 0})
 by default, dataset.matrix traverses the matrix by rows
ds_xor_in = dataset.matrix(xor_in)
ds_xor_out = dataset.matrix(xor_out)
For a given matrix with dimensions n1,n2,...,nk, by default the dataset contains n1 number of patterns with size n2 x ... x nk. For a bidimensional matrix it is a rowmajor order traversal. For a vector, it is the traversal of all its elements:
> a = matrix(2, 2, {1,2,3,4})
> b = dataset.matrix(a)
> for i,j in b:patterns() do print(table.concat(j,",")) end
1,2
3,4
> a = matrix(2,2,2,{1,2,3,4,5,6,7,8})
> b = dataset.matrix(a)
> for i,j in b:patterns() do print(table.concat(j,",")) end
1,2,3,4
5,6,7,8
> a = matrix(4,{1,2,3,4})
> b = dataset.matrix(a)
> for i,j in b:patterns() do print(table.concat(j,",")) end
1
2
3
4
Until this point, none benefit of dataset
over matrix
is presented. We are going to show that for the same given matrix
, we could generate several different dataset
modifying some parameters which has been taken by default until now.
When we instantiate a dataset.matrix
, the first argument is a Kdimensional matrix
with size n1 X n2 x ... x nK
. The second argument could be a Lua table with the following fields:
patternSize
, a table array with K positive integers. It indicates the size of each pattern taken from the underlying matrix
. By default it is patternSize={ 1, n2, n3, ..., nK }
.
offset
, a table array with K signed integers. It indicates the offset of the first pattern. A negative value is useful to compute a pattern which traverses the matrix
limits. The first initial position is 0
. Its default value is offset={ 0, 0, ..., 0 }
.
numSteps
, a table with K estrict positive integers (> 0). It indicates the number of steps used for each dimension to generate all the possible patterns. Its default value is numSteps={ n1, 1, ..., 1 }
. The total numPatterns()
method returns the product of all numSteps
components.
stepSize
, a table with K signed integers. It indicates the number of coodinates which are slided for each dimension with every pattern. Its default value is stepSize={ 1, ..., 1 }
. Obviusly, in every i
dimension where numSteps[i]=1
, the stepSize[i]
is not important. Depending on the values of stepSize
and patternSize
, the matrix
will be traversed with overlapping between patterns or not.
orderStep
, a table with a permutation of the K dimensions, indicating the order for matrix
traversal. By default, the matrix
is traversed in row_major
order, so its value is orderStep={ K1, K2, ..., 2, 1, 0 }
. Varying the order of this numbers, it is possible to produce a different order traversal, as for example a col_major
order.
defaultValue
is a number (not necesarily an integer), used to fill the pattern positions which are out of the matrix
limits. By default its value is defaultValue=0
.
circular
is a table with K booleans (true or false) which indicate for every matrix
dimension if it is circular or not. By default it is false in all dimensions circular={ false, false, ..., false }
. When a dimension is not circular, the pattern positions out of the matrix
limits are filled with defaultValue
. When a dimension is circular, the pattern positions out of the matrix
are reinterpreted starting at the first position of this dimension in the matrix. For example, a bidimensional matrix
whith one circular dimension seems cilindrical. If the two dimensions are circular, it seems thyroidal (like a donut).
Look a short example of this parameters. We want to generate a dataset with binary XOR patterns using only one matrix:
> m_xor = matrix.fromString[[
4 3
ascii
0 0 0
0 1 1
1 0 1
1 1 0
]]
> ds_input = dataset.matrix(m_xor,{patternSize={1,2}})
> ds_output = dataset.matrix(m_xor,{offset={0,2},patternSize={1,1}})
> for i=1,ds_input:numPatterns() do
>> printf("%d > Input: %s Output: %s\n",i,
>> table.concat(ds_input:getPattern(i),","),table.concat(ds_output:getPattern(i),","))
>> end
1 > Input: 0,0 Output: 0
2 > Input: 0,1 Output: 1
3 > Input: 1,0 Output: 1
4 > Input: 1,1 Output: 0
We could implement the following function:
function dataset_pair(m,sizein,sizeout)
local d_in = dataset.matrix(m,{patternSize = {1,sizein}})
local d_out = dataset.matrix(m,{offset={0,sizein},patternSize = {1,sizeout}})
return d_in,d_out
end
 which could be used as this
ds_input,ds_output = dataset_pair(m_xor,2,1)
This dataset
represents the traversing of an identity matrix
. It receives as first argument the number of patterns (which is at the same time the patternSize), a second optional argument which is the value of zero (by default is 0.0
), and a third optional argument with the value of one (default is 1.0
).
> ds_eye = dataset.identity(5)
> print(ds_eye:toMatrix())
1 0 0 0 0
0 1 0 0 0
0 0 1 0 0
0 0 0 1 0
0 0 0 0 1
# Matrix of size [5,5] in row_major [0x1418bd0 data= 0x1418cd0]
The dataset.identity
is equivalent to following code, but is more efficient:
> ds_eye = dataset.matrix(matrix(5,5):zeros():diag(1))
> print(ds_eye:toMatrix())
1 0 0 0 0
0 1 0 0 0
0 0 1 0 0
0 0 0 1 0
0 0 0 0 1
# Matrix of size [5,5] in row_major [0x129f930 data= 0x12fb470]
The dataset.indexed
allows to map indexes with patterns. It is useful to specify the output of a classification task, in which case the underlying dataset
will be the association of ANN output for each of the classes. Another possibility is to use dataset.indexed
to select a random set of patterns from the underlying dataset
. NOTE that dataset.indexed
uses float numbers to represent the indices, so the maximum integer number which could be indexed is 16777216
. If you need more resolution, use dataset.index_filter
(which is less general than this).
The constructor receives 2 arguments, the first is the base dataset
. The second is a table array with as many dataset
objects as patternSize()
of the base dataset
, acting every one of this as a dictionary. The patternSize()
of the resulting dataset.indexed
object is equals to the sum of the patternSize()
of all the dictionaries.
Following code is an example for a classification task ANN output:
> dict = dataset.identity(10)
>  a random matrix with integers [1,10]
> m_base = matrix(100):uniform(1,10,random(1234))
> ds_base = dataset.matrix(m_base)
> indexed_ds = dataset.indexed( ds_base, { dict })
The following is code for a random subset of patterns from a given dataset
:
 a matrix with 100 patterns with real numbers in [1,1]
> m_dict = matrix(100, 10):uniformf(1,1,random(1234))
> dict = dataset.matrix(m_dict)
>  a random matrix with 10 integers in range [1,100], a selection of patterns
> m_base = matrix(10):uniform(1,100,random(1234))
> ds_base = dataset.matrix(m_base)
> indexed_ds = dataset.indexed( ds_base, { dict })
The dataset.index_filter
is like dataset.indexed
but only for the case of indexing a random subset of patterns from a given base dataset
, which receives as first argument. As second argument, a vector of unsigned integers (util.vector_uint
) is expected.
>  a dataset with 100 patterns of size 5, randomized at range [0,1]
> base_ds = dataset.matrix(matrix(100,5):uniformf())
> uint_vector = util.vector_uint()
> rnd = random(1234)
>  a subset of 10 patterns from indices at range [1,100]
> for i=1,10 do uint_vector:push_back( rnd:randInt(1,100) ) end
> print(uint_vector)
48 84 39 54 77 25 16 50
24 27
# vector_uint of size 10
> index_filter_ds = dataset.index_filter(base_ds, uint_vector)
> print(index_filter_ds:toMatrix())
0.528819 0.915766 0.220549 0.828223 0.28173
0.73919 0.424762 0.354582 0.368474 0.0355779
0.512678 0.494687 0.731773 0.672073 0.411915
0.575729 0.169612 0.346667 0.925921 0.332662
0.298257 0.460495 0.179573 0.32725 0.610076
0.219746 0.15807 0.581498 0.531874 0.200707
0.00641197 0.86275 0.407079 0.279832 0.602674
0.456097 0.463612 0.521626 0.951389 0.659111
0.4136 0.734821 0.212726 0.314356 0.50499
0.662668 0.584882 0.457253 0.325801 0.217475
# Matrix of size [10,5] in row_major [0x12a2710 data= 0x13eaa10]
The dataset.join
object joins the outputs from several dataset
objects which has the same numPatterns
. The patternSize
of the resulting dataset
is equals to the sum of every patternSize
of its components. It requieres as argument a table with the datasets
which you want to join.
 ds1, ds2 and ds3 are three datasets with the same numPatterns
> join_ds = dataset.join{ ds1, ds2, ds3 }
This dataset
allows to convert several dataset
objects with the same patternSize
as they were one unique dataset
which its numPatterns
is equals to the sum of all the numPatterns
of every given dataset. It receives only one argument, a table with the dataset
which will be unionized.
>  ds1, ds2 and ds3 are datasets with the same patternSize
> union_ds = dataset.union{ ds1, ds2, ds3 }
The dataset.slice
is useful to extract a contiguous subset of patterns from a given dataset
(for more general subsets use dataset.indexed
or dataset.index_filter
). It requieres 3 arguments. The first is the base dataset
. The second and third arguments are the initial and final indices of the patterns which form the subset (first valid index is 1, and last valid index is numPatterns() of base dataset
).
>  slice with 100 patterns, from 101 to 200
> slice_ds = dataset.slice(base_ds, 101, 200)
The dataset.deriv
receives a dataset and outputs the original data, the first derivative, or the second derivative, depending on the parameters received. It receives a table with a maximum of four fields:
dataset
: the base dataset, which contains data for derivative computation.deriv0
: an optinal boolean, by default is true
, which indicates if the output of the dataset will contain the original pattern, without derivative.deriv1
: an optinal boolean, by default is true
, which indicates if the output of the dataset will contain the first derivative.deriv2
: an optinal boolean, by default is true
, which indicates if the output of the dataset will contain the second derivative.>  ds is the base dataset
> only_first_deriv_ds = dataset.deriv{ dataset=ds, deriv0=false, deriv1=true, deriv2=false }
The contextualizer
is a dataset
which adds context from the adjacent patterns (left and right). If any of the adjacent patterns is out of the base dataset
size, it fills it with the first or the last pattern. The constructor receives four arguments:
dataset
.boolean
optionally argument indicating if the left and right contexts needs to be swapped. By default is false
, and in almost all cases it is what you need ;)> ds = dataset.contextualizer(dataset.identity(2,0,1),1,1)
>
> print(ds:toMatrix())
1 0 1 0 0 1
1 0 0 1 0 1
# Matrix of size [2,6] in row_major [0x18357b0 data= 0x18358b0]
This dataset
allows to select a subset of the components of patterns produced by another dataset
. So, the resulting dataset
will have the same number of patterns, but different pattern size. The subset is an interval of the base dataset
. It receives three positional arguments:
dataset
.> ds = dataset.split(dataset.identity(5,0,1), 2, 4)
> print(ds:toMatrix())
0 0 0
1 0 0
0 1 0
0 0 1
0 0 0
# Matrix of size [5,3] in row_major [0xcb0f80 data= 0xcb1080]
This dataset
applies onthefly a subtraction and division normalization, as for example a zeromean onestandarddeviation normalization. So, for a dataset
with N
patternSize, given a vector of sub values s1, s2, ..., sN
, and a vector of div values d1, d2, ..., dN
, a ds:getPattern(i)
of the resulting dataset
will produce a pattern with (v1s1)/d1, (v2s2)/d2, ..., (vNsN)/dN
, being vj
the j
component of pattern i
.
> eye_ds = dataset.identity(5,0,1)
> sub,div = {1,2,1,2,1},{0.1,0.1,0.1,0.1,0.1}
> ds = dataset.sub_and_div_normalization(eye_ds,sub,div)
> print(ds:toMatrix())
0 20 10 20 10
10 10 10 20 10
10 20 20 20 10
10 20 10 10 10
10 20 10 20 20
# Matrix of size [5,5] in row_major [0xf47d70 data= 0xcfa060]
token = ds:getPattern(number)
token = ds:getPatternBunch(table)
ds = dataset.token.sparse_matrix(sparse matrix in CSR)
> m = matrix.sparse.diag{1,2,3,4,5,6}
> ds = dataset.token.sparse_matrix(m)
> print(ds:getPattern(1))
1 0 0 0 0 0
# SparseMatrix of size [1,6] in csr [0x2aea350 data= 0x2aea420 0x2aea4a0 0x2aea4e0], 1 nonzeros
> print(ds:getPatternBunch{3,5})
0 0 3 0 0 0
0 0 0 0 5 0
# SparseMatrix of size [2,6] in csr [0x2aeab70 data= 0x2aea4a0 0x2aea420 0x2aea7b0], 2 nonzeros
ds = dataset.token.union(table)
ds = dataset.token.vector(psize)
ds:push_back(token)
ds = dataset.token.filter(dataset, obj)
It is possible to develop Lua dataset
classes which has to complain interface of dataset.token
class. The unique restriction is that your Lua dataset
couldn't be used as input to other C++ dataset
objects. However, the Lua dataset
can use C++ objects or Lua objects without making any distinction.
The following is a piece of a pure Lua dataset.token
which replicates the behavior of dataset.join
, but using tokens. matrix
type is needed for instances which you want to join.
ds_join,ds_join_methods = class("ds_join")
function ds_join:constructor(t)
assert(type(t)=="table" and #t>0,
"Needs an array of dataset.token instances as argument")
local psize = 0  we sum here the pattern size of all the given datasets
local nump = 0  we store here the number of patterns, which must be
 equals in all the given datasets
local data = {}  this table will store the given datasets
for _,v in ipairs(t) do
psize = psize + v:patternSize()
local aux_nump = v:numPatterns()
assert(nump==0 or nump==aux_nump)
nump = aux_nump
table.insert(data, v)
end
self.data=data
self.num_patterns=nump
self.pattern_size=psize
end
function ds_join_methods:numPatterns() return self.num_patterns end
function ds_join_methods:patternSize() return self.pattern_size end
function ds_join_methods:getPattern(idx)
 use the given matrix or construct a new one
local m = matrix(1,self:patternSize())
local col_pos = 1
for _,ds in ipairs(self.data) do
local psize = ds:patternSize()
local dest_m = m:slice({1,col_pos}, {1,psize})
dest_m:copy(ds:getPattern(idx))
col_pos = col_pos + psize
end
return m
end
function ds_join_methods:getPatternBunch(idxs)
 use the given matrix or construct a new one
local m = matrix(#idxs,self:patternSize())
assert(m:dim(1)==#idxs and m:dim(2)==self:patternSize())
local col_pos = 1
for _,ds in ipairs(self.data) do
local psize = ds:patternSize()
local dest_m = m:slice({1,col_pos}, {#idxs,psize})
dest_m:copy(ds:getPatternBunch(idxs))
col_pos = col_pos + psize
end
return m
end
ann
packageSeveral packages contain neural networks stuff: require("aprilann.ann")
, require("aprilann.ann.loss")
, require("aprilann.ann.optimizer")
, require("aprilann.trainable")
.
This page describe the utilities to build and train ANNs. Four main sections are written: a desciprion of ANN concepts in APRILANN, the easy building procedure for MLPs, the training helpers, and finally the full description of the aprilann.ann
package.
Inspired by other toolkits (as Torch 7 or pyBrain), ANNs are described as a composition of blocks call ANN components, so one component is a neural network itself. A list of all available components appears executing:
april_help(ann.components)
Nevertheless, the composition procedure will be explained later. An ANN component is identified by a name string (which will be automatically generated if not given). The name must be unique. Some components contains weights in their core, which are estimated by gradient descent algorithm (backpropagation). Connection weights objects are identified by a weights name parameter, which could be reused. If two components have the same weights name, then they share the same connections object.
All components have an input and output size, which defines the number of weights (if needed) and the fanin/fanout of the component. Components need to be build (build method) once they are constructed. Build procedure allocates memory for connections and checks input/output sizes of components.
More accurate description is available at april_help
, but don't be affraid, the next section presents an abstraction for train MLPs which automatically does a lot of this work:
april_help(ann.components.base)
april_help(ann.components.base.build)
The simpliest kind of ANN is a Multilayer Perceptron (MLP) where each layer is fully connected with the next layer (feedforward, allall connections).
The method generate
returns an special component object, which cannot be modified. Actually, it is a Lua table formed by an ann.components.stack
instance and other information useful to load and save the MLPs, and it implements wrapper Lua functions to ANN component methods.
 creates an ANN component for a MLP with the given description
thenet = ann.mlp.all_all.generate("256 inputs 128 tanh 10 log_softmax")
 creates an instance of a trainer object for previous ANN component,
 using the multiclass crossentropy loss function (for 10 output units),
 and using a bunch_size of 32. Loss function and bunch_size are optional.
trainer = trainable.supervised_trainer(thenet,
ann.loss.multi_class_cross_entropy(10),
32,
 this last parameter is optional, by default is
 SGD => Stochastig Gradient Descent
ann.optimizer.sgd())
 builds the component contained into trainer object
trainer:build()
 initializes the weights randomly, using fanin and fanout
trainer:randomize_weights{
random = random(1234),
inf = 0.1,
sup = 0.1,
use_fanin = true,
use_fanout = true,
}
As said before, each component has a unique name, and if needed a weights name. The next code iterates over all components:
> for name,c in trainer:iterate_components() do print(name,c) end
actf1 instance 0x7fc3e94850a0 of ann.components.base
actf2 instance 0x7fc3e9485550 of ann.components.base
b1 instance 0x7fc3e9484f80 of ann.components.base
b2 instance 0x7fc3e9485410 of ann.components.base
c1 instance 0x7fc3e9484a10 of ann.components.base
layer1 instance 0x7fc3e9484e80 of ann.components.base
layer2 instance 0x7fc3e9485310 of ann.components.base
w1 instance 0x7fc3e9484ee0 of ann.components.base
w2 instance 0x7fc3e9485370 of ann.components.base
The MLP is composed by 9 components, two activation functions (actf1 and actf2), two bias components (b1 and b2), one stack component which works as a container (c1), two hyperplane components containing one bias and one dot_product each one (layer1 and layer2), and finally two dot_product components (w1 and w2) which contains weight matrixes.
It is also possible to iterate over all weigths names:
> for name,connections in trainer:iterate_weights() do print(name,type(connections)) end
b1 matrix
b2 matrix
w1 matrix
w2 matrix
So, our MLP contains two bias vectors (b1 and b2, corresponding with b1 and b2 components), and two weights matrixes (w1 and w2, corresponding with w1 and w2 components). All MLPs generated automatically assign this names to its components and weights.
One time the component is build by using a trainer instance, the trainer exposes two interesting methods trainer:component(COMPONENT_NAME_STRING)
which returns the component given its name, and trainer:weights(WEIGTHS_NAME_STRING)
which returns the connection weigths object given its weigths_name attribute.
More info about trainable.supervised_trainer
doing:
april_help(trainable.supervised_trainer)
The loss function is used to train the ANNs via gradient descent algorithm. Trainer objects needs an instance of a loss function to perform training, being a very useful abstraction of standard training procedures.
Detailed information about loss functions is in:
april_help(ann.loss)
The loss function could be set at trainer constructor, or using the method set_loss_function:
trainer:set_loss_function(ann.loss.mse())
Three main error functions are implemented: mean square error (MSE), two class crossentropy, and multiclass crossentropy. Note that crossentropy like functions are specialized for log_logistic or log_softmax output activation functions. Almost all the constructors accepts a SIZE=0
parameter, which means that the layer has a dynamic size.:
ann.loss.mse(SIZE)
returns an instance of the Mean Squared Error error function for SIZE neurons. It is a quadratic loss function.
ann.loss.mae(SIZE)
returns an instance of the Mean Absolute Error function, for SIZE neurons. It is not a quadratic loss function.
ann.loss.cross_entropy(SIZE)
returns an instance of the twoclass crossentropy. It only works with log_logistic
output activation function. It is based on KullbackLeibler divergence.
ann.loss.multi_class_cross_entropy(SIZE)
returns an instance of the multiclass crossentropy. The parameter must be SIZE>2
, so for twoclass problems only one output unit with crossentropy is needed. It only works with log_logistic
or log_softmax
output activation function (its better to use log_softmax
). It is based on KullbackLeibler divergence.
The optimizer is an object which implements the learning algorithm. Every class in ann.optimizer
is an optimizer. Several learning hyperparameters are available, depending in the selected optimizer. This learning hyperparameters are known as options, and could be set globally (to all the connection weight layers of the ANN), or layerwise (to a concrete connection weights object, identified by its name). Optimizers implement the following API:
other = optimizer:clone()
: returns a deep copy of the caller object.
value = optimizer:get_option(name)
: return the global value of a given learning option name.
optimizer:set_option(name, value)
: sets the global value of a given learning option name.
optimizer:set_layerwise_option(layer_name, option_name, value)
: sets a layerwise option.
value = optimizer:get_layerwise_option(layer_name, option_name)
: returns the layerwise option of the given.
value = optimizer:get_option_of(layer_name, option_name)
: returns the option which is applicable to the given layer_name
. If a layerwise option was previously defined, the method returns its value. Otherwise, the value of the global option will be returned.
Different optimizer
objects are implemented. They train the neural network following different algorithms which rely in the computation of gradients done by ANN components. Them incorporate regularization and momentum hyperparameters. They options are algorithm dependentendt. In case of Stochastic Gradient Descent, the options are:
learning_rate
: the learning rate controls the portion of the gradient used to update the weights. This value is smoothed depending in the bunch_size
and in the number K
of times that a weight connections object is shared between different components. The smoothing value: learning_rate/sqrt(bunch_size+K)
momentum
: is a inertial hyperparameter which applies a portion of the weight update in the previous iteration.
weight_decay
: a L2 regularization term.
L1_norm
: a L1 regularization term.
max_norm_penalty
: a constrain penalty based on the twonorm of the weights.
The algorithm uses the following learning rule:
w = (1  weight_decay)*w' + momentum*(w'  w'') + lr'*grad(L)/grad(w')
where w
, w'
and w''
are the weight values at next, current, and previous iterations; lr'
is the learning_rate
smoothed by the sqrt
, and grad(L)/grad(w')
is the loss function gradient at the given weight.
The hyperparemters of optimizer
objects can be modified by the trainer
object:
trainer:set_option(name,value)
: sets a global learning option value.
value=trainer:get_option(name)
: gets a global learning option value.
trainer:set_layerwise_option(layer_name_match,option_name,value)
: sets a layerwise learning option value of all the connection weight objects whose name matches the given layer_name_match
Lua pattern string.
value=trainer:get_option_of(layer_name,option_name)
: gets the option value applicable to the given layer.
trainer:build()
trainer:set_option("learning_rate", number)
trainer:set_option("momentum", number)
 regularization is recommended to not be applied at bias connections
trainer:set_layerwise_option("w.*", "weight_decay", number)
trainer:set_layerwise_option("w.*", "max_norm_penalty", number)
 for dropout (see dropout http://www.cs.toronto.edu/~nitish/msc_thesis.pdf)
 dropout is a very especial option, it modifies training, but also modifies
 validation (or test) phase. Also it must be applied carefully to not apply
 dropout at the output of your model. Dropout is applied as another component
 which acts as a stochastic filter.
See the documentation for trainable
package.
See the documentation for trainable
package.
ANNs are implemented as a composition of components which implements define the three main operations of an ANN: forward step (compute outputs), backprop step (neuron gradient computation), and gradient computation step (weight gradients). All components are child classes of ann.components.base
. See april_help(ann.components.base)
for online documentation.
Two main remarks before continue following sections. The components has two special properties:
The components are integrated in Lua via the abstract class token
, which has two specializations for ANNs:
tokens.matrix
is a token which contains a matrix
instance.
tokens.sparse_matrix
is a token which contains a matrix.sparse
instance.
In any case, ANN components wrap the given matrix
objects into a token
, and unwrap matrix
objects when returning a token
. So, in practice, you can ignore the token/matrix association.
NOTE that ANN components work with dense matrix or with csr
sparse matrices.
All components has defined the following basic properties, which are tokens: input, output, error_input, and error_output. Four are the basic methods to train the components:
component,table,table = build()
: this method reserves memory for weights and prepares the component to work with.
reset(iteration)
: it releases all the tokens internally allocated (or given by Lua), and receives the current iteration number. This iteration is not related with the training loop or epoch, it is related to optimizer
objects which implement line search or similar (Conjugate Gradient or RProp).
token=forward(token[, boolean])
: it receives an input token and returns the output token. For simplicity, it is possible to give a matrix
instead of a token
, and the method will wrap automatically the given matrix
. In any case, the returned value is a token
.
token=backprop(token)
: it receives an error input token (gradient), and returns the output error token (gradient). For simplicity, it is possible to give a matrix
instead of a token
, and the method will wrap automatically the given matrix
. In any case, the returned value is a token
.
gradients=compute_gradients( [gradients] )
: compute the weight gradients, by using the data stored at the components (input/output tokens, input/output error tokens), given and produced during forward
and backprop
methods. Additionally, it receives a table of matrix
with previously computed gradients, which will be used to store the data avoiding the allocation of new memory. The method returns a table of matrix
with the gradients computed for each connection weights object.
Combining this methods with loss functions a component could be trained following this basic example. A linear component is trained to follow OR function, for input=[0,1] and target output=[1]. By default the weights are not initialized, so they contains memory trash.
> o = ann.optimizer.gsd()  the optimizer
> l = ann.loss.mse(1)  MSE loss function
>  an hyperplane component (explained later)
> c = ann.components.hyperplane{ input=2, output=1 }
> c:build()  allocates memory for weights, and checks components integrity
> l:reset()  set to zero all the things
> c:reset()  set to zero all the things
> o:execute(function()
 the true indicates training
output_token=c:forward(matrix(1,2,{0,1}), true)
 gradient with desired output 1
output_error=c:backprop(l:gradient(output_token,
matrix(1,1,{1})))
grad = c:compute_gradients(grad)
return l:compute_loss(output_token,
matrix(1,1,{1}),
grad
end, c:copy_weights())
> output_token=c:forward(matrix(1,2,{0,1}))
> print(output_token)  the output is closer to 1
0.2
# Matrix of size [1,1] [0xb01ce0 data= 0xad97d0]
Note that all matrices must had at least two dimensions. All computations are done in bunch mode (using minibatches) and the first dimension size is the number of patterns contained by the bunch. The rest of dimensions must complain the input constrains of the component. A lot of components work with linear inputs, so the input matrix will be bidimensional, but some components work with multidimensional matrices. It is possible to use matrices of only one dimension and they will be reinterpreted as two dimensional matrices with only one row, but better if you work always with twodimensional matrices.
Before doing anything, components could be composed together to build larger components. This procedure needs to call build
method at the end, to check the input/output sizes and reserve memory for weights and biases.
The c:build()
call executes recursively the build method of all the components composition. This method returns two tables:
> caller_component, weights_dict, components_table = c:build()
The caller_component
is the component c
in this case.
The weights_dict
is a table of matrices, which indexes name (weight name) strings with weight matrices.
The components_table
is a Lua table indexed by each name (component name) and contains a reference to the component instance, which is useful to initialize hyperparameter and other stuff in a componentwise manner.
number = c:get_input_size()
: returns the size of the input for the caller component. In case of unknown input size, a zero will be returned.
number = c:get_output_size()
: returns the size of the output for the caller component. In case of unknown output size, a zero will be returned.
table = c:precompute_output_size( [table] )
: allows to compute the output size shape, given an input shape. It is useful to be combined with convolutional ANNs, in order to ask for the output shape size of the convolution. The given table
must complains the expected input shape of the component (normally is one dimension, but with CNNs it could be multidimensional). The returned table
will contain as many dimensions as the produced by the caller component (idem as for input).
token = c:forward( token [, boolean] )
receives a token and an optional boolean (by default false). The boolean indicates if this forward is during training or not, because some components has an special behavior during training. It returns a token with the output computation of the caller component. For simplicity, it is possible to give a matrix
instead of a token
, and the method will wrap automatically the given matrix
. In any case, the returned value is a token
.
token = c:backprop( token )
receives a token with the input error (gradient of each output neuron), and returns another token with the output error (gradient of each input neuron). For simplicity, it is possible to give a matrix
instead of a token
, and the method will wrap automatically the given matrix
. In any case, the returned value is a token
.
gradients = c:compute_gradients( gradients )
returns the weight gradients computed using the tokens given at forward
and backprop
methods.
c:reset()
releases the retained tokens in forward
and backprop
steps.
During forward
and backprop
steps the components compute outputs and error outputs (gradients), and retain the input and error input (gradients) tokens. Before call reset
method, you could ask the component for its retained tokens:
token = c:get_input()
returns the token given as input at forward
method.
token = c:get_output()
returns the token computed as output by forward
method.
token = c:get_error_input()
retruns the token given as error input at backprop
method.
token = c:get_error_output()
returns the token computed as error output by backprop
method.
Components which require weights has internally a matrix
instance. This object is allocated calling the build method of the components (or using the build method of a trainer), and is identified by the weigths_name property, so components with the same weigths_name share the same connections object.
This matrices are defined with OUTPUTxINPUT size (output rows, input columns), so:
Bias vectors: has INPUT=1 and OUTPUT=number of neurons, and they are a column vector.
Weight matrices: contain OUTPUTSxINPUTS weights.
The weights matrices has this format:
w(i1,o1) w(i2,o1) w(i3,o1) ...
w(i1,o2) w(i2,o2) w(i3,o2) ...
... ... ...
where w(a,b)
is the weight which connects input a
with output b
. Be sure that your matrices has this format.
The ANN models are modular components which can be sorted in several ways to produce different topologies.
ann.components.base{ size=0, [name=STRING] }
The class ann.components.base
is the base of all ANN components. It is possible to instance an object of this class, and it performs identity function. The constructor receives optionally the name
of the component. The constructor receives two optional arguments, the size=0
, by default it allows any input size, and the name
of the component.
> c1 = ann.components.base{ name="base1" }
> c2 = ann.components.base()
> input = matrix(10,10):uniformf(0,1,random(237))
> output = c2:forward(input)
> = output:equals(input)
true
ann.components.bias{ size=NUMBER, [name=STRING], [weights=STRING] }
The class ann.components.bias
implements an additive bias of a given size. The bias is added iteratively to all the patterns in the bunch (minibatch). The constructor receives two fields:
name
of the component, an optional field.
weights
name of the component, an optional field.
size
the size of the bias vector.
This components contains a vector of SIZEx1
, which is added transposed to all the input patterns (first dimension of the bunch).
> b1 = ann.components.bias{ name='b1', weights='b1', size=5 }
> _,weights = b1:build()
> weights('b1'):linspace()
> = weights('b1')
1
2
3
4
5
# Matrix of size [5,1] [0x162eb00 data= 0x16b0260]
> input = matrix(4,5):linspace()
> = input
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
# Matrix of size [4,5] [0x185a3d0 data= 0x17e18d0]
> output = b1:forward(input)
> = output
2 4 6 8 10
7 9 11 13 15
12 14 16 18 20
17 19 21 23 25
# Matrix of size [4,5] [0x185b370 data= 0x1718450]
>  the bias component executes the following operation
> for i=1,input:dim(1) do input(i,':'):axpy(1.0, weights('b1'):transpose()) end
> = input
2 4 6 8 10
7 9 11 13 15
12 14 16 18 20
17 19 21 23 25
# Matrix of size [4,5] [0x185a3d0 data= 0x17e18d0]
ann.components.dot_product{ ... }
The class ann.components.dot_product
implements the dot product between a weights vector of every neuron and the given input vector, which is a vectormatrix product. If the input is a matrix with a bunch of patterns, the component executes a matrixmatrix product. The component contains a weights matrix
with size OxI
, where O
is the number of neurons (output size), and I
is the number of inputs (input size). The constructor receives:
name
is a string with the component name, optional.
weights
is a string with the weights name, optional.
input
is a number with the input size.
output
is the number of neurons.
transpose=false
is a boolean indicating if the weights matrix
is transposed. It is optional, by default it is transpose=false
.
> c = ann.components.dot_product{ weights='w1', input=4, output=5 }
> _,weights = c:build()
> weights('w1'):linspace()
> = weights('w1')
1 2 3 4
5 6 7 8
9 10 11 12
13 14 15 16
17 18 19 20
# Matrix of size [5,4] [0x186e620 data= 0x182b050]
> input = matrix(3,4):linspace()
> = input
1 2 3 4
5 6 7 8
9 10 11 12
# Matrix of size [3,4] [0x168f420 data= 0x1835190]
> output = c:forward(input)
> = output
30 70 110 150 190
70 174 278 382 486
110 278 446 614 782
# Matrix of size [3,5] [0x185ee70 data= 0x18655c0]
>  the performed operation is
> = input * weights('w1'):transpose()
30 70 110 150 190
70 174 278 382 486
110 278 446 614 782
# Matrix of size [3,5] [0x1869f50 data= 0x1645e60]
In case of very sparse inputs, it is possible to replace the input matrix
by a tokens.sparse_matrix
, allowing to improve the efficiency of the operation. Transformation of matrices into tokens and tokens into matrix is automatically performed.
>  a matrix with two rows:
>  first row: active components are the 3 with 1, and the 2 with 0.5
>  second row: active components are the 1 with 0.3
> dense_input = matrix(2,4):zeros():set(1,3,1):set(1,2,0.5):set(2,1,0.3)
> sparse_input = matrix.sparse( dense_input )
> = sparse_input
0 0.5 1 0
0.3 0 0 0
# SparseMatrix of size [2,4] in csr [0x17deaa0 data= 0x17864b0 0x17c9540 0x167cdb0], 3 nonzeros
> output = c:forward(input)
> = output
4 10 16 22 28
0.3 1.5 2.7 3.9 5.1
# Matrix of size [2,5] [0x18612d0 data= 0x17fb8a0]
>  which is equivalent to the following
> output = c:forward(dense_input)
> = output
4 10 16 22 28
0.3 1.5 2.7 3.9 5.1
# Matrix of size [2,5] [0x185ee70 data= 0x1636e60]
ann.components.hyperplane{ ... }
The class ann.components.hyperplane
is a wrapper around a bias
and a dot_product
components, implementing an hyperplane separator. The constructor receives:
name
an optional string with the component name.
dot_product
an optional string with the dot_product
component name.
bias
an optional string with the bias
component name.
dot_product_weights
an optional string with the dot_product
component weights name.
bias_weights
an optional string with the bias
component weights name.
input
a number with the input size.
output
a number with the input size.
transpose=false
a boolean indicating if the dot_product
weights will be transposed in the operation.
> c = ann.components.hyperplane{ dot_product_weights='w1', bias_weights='b1',
input=128, output=256 }
> _,weights = c:build()
> for name,w in pairs(weights) do print(name) print(w) end
w1
Large matrix, not printed to display
# Matrix of size [256,128] [0x185ee70 data= 0x16ae840]
b1
Large matrix, not printed to display
# Matrix of size [256,1] [0x1869120 data= 0x165d540]
ann.components.actf.logistic()
ann.components.actf.log_logistic()
ann.components.actf.softmax()
ann.components.actf.log_softmax()
ann.components.actf.tanh()
ann.components.actf.hardtanh()
ann.components.actf.relu()
ann.components.actf.softplus()
ann.components.actf.sin()
ann.components.stack()
> ann.components.reset_id_counters()  reset ID name generator
> mlp = ann.components.stack()
> mlp:push( ann.components.hyperplane{ input=100, output=200 } )
> mlp:push( ann.components.actf.logistic() )
> mlp:push( ann.components.hyperplane{ input=200, output=40 } )
> mlp:push( ann.components.actf.log_softmax() )
> _,weights = mlp:build()
> for name,w in pairs(weights) do print(name) print(w) end
w0
Large matrix, not printed to display
# Matrix of size [200,100] [0x1863df0 data= 0x1668030]
w2
Large matrix, not printed to display
# Matrix of size [40,200] [0x186bfd0 data= 0x17c71b0]
b1
Large matrix, not printed to display
# Matrix of size [200,1] [0x186aee0 data= 0x18159f0]
b3
Large matrix, not printed to display
# Matrix of size [40,1] [0x186d6d0 data= 0x175c910]
ann.components.join()
ann.components.dropout()
> c = ann.components.dropout{ random=random(3284), prob=0.5, value=0.0 }
ann.components.select()
ann.components.slice()
ann.components.gaussian_noise{ random, prob, var, mean }
ann.components.salt_and_pepper{ random, prob, zero, one }
This components are used to build Convolutional Neural Networks. If you use dataset.matrix
, your patterns will be flattened at converted into a one dimensional matrix. This forces to add a rewrap
components at the beginning of your ANN. Follows an example of a FULL CNN for MNIST task (28x28 pixels, images of digits):
 tables for the CNN configuration
ishape = {1, 28, 28}  for input matrix rewrapping
conv1 = {1, 5, 5} nconv1=20
maxp1 = {1, 2, 2}
conv2 = {nconv1, 5, 5,} nconv2=50
maxp2 = {1, 2, 2}
hidden = 500
thenet = ann.components.stack():
push( ann.components.rewrap{ size=ishape } ):
push( ann.components.convolution{ kernel=conv1, n=nconv1 } ):
push( ann.components.convolution_bias{ n=nconv1, ndims=#conv1 } ):
push( ann.components.actf.tanh() ):
push( ann.components.max_pooling{ kernel=maxp1,} ):
push( ann.components.convolution{ kernel=conv2, n=nconv2 } ):
push( ann.components.convolution_bias{ n=nconv2, ndims=#conv2 } ):
push( ann.components.actf.tanh() ):
push( ann.components.max_pooling{ kernel=maxp2 } ):
push( ann.components.flatten() )
 using the method precompute_output_size, it is possible to know
 the size of the convolution after the flatten operation
local conv_size = thenet:precompute_output_size()[1]
thenet:
push( ann.components.hyperplane{ input=conv_size, output=hidden } ):
push( ann.components.actf.tanh() ):
push( ann.components.hyperplane{ input=hidden, output= 10 } ):
push( ann.components.actf.log_softmax() )
ann.components.convolution{ kernel, step, n, name, weights, ... }
A convolutional component could be created as:
> c = ann.components.convolution{ kernel={3, 5, 5}, step={1, 1, 1}, n=10,
name="convW1", weights="W1",
input_planes_dim=1 }
This component executes a convolution using the given kernel
sizes, moving the convolution window following step
table, and using n
different kernels. This module has a dynamic input/output size, the convolution is performed over all the input following the indicated parameters.
input_planes_dim
is a number (optional, by default is 1) which indicates the dimension K at input matrix where are located the input planes.
kernel
is a table which describes the size of each kernel. The K element of this table is always the number of PLANES at the input matrix. Therefore, a kernel over a 1dim signal will be like kernel={1, 5}
being K=1. For a 2D image will be kernel={1, 5, 5}
, for a 2D image with RGB color will be kernel={3, 5, 5}
if K=1, otherwise it could be kernel={5, 3, 5}
if K=2 or kernel={5, 5, 3}
if K=3. For a RGB video sequence the kernel will be kernel={3, 5, 5, 5}
for K=1, and so on.
step
is a table which indicates how to move the kernel. The number of steps at each dimension will be (input_dim[i]  kernel[i])/step[i] + 1
. The K element of this table is forced to be 1
, so that is the number of planes at input matrix. The step
is optional, by default has all its elements assigned to 1
.
n
is the number of kernels to be applied. It is the number of output planes produced by this component (number of neurons).
name
and weights
are the strings with for search components and connection objects.
The output produced by this component will be of:
output_size[1]=n
output_size[i+1]=(input_size[i]  kernel[i])/step[i] + 1, FOR i=1,...,input_planes_dim
1
output_size[i]=(input_size[i]  kernel[i])/step[i] + 1, FOR i=input_planes_dim
+1,...,#kernel
By default, input_planes_dim=1
, so the output size will be simplified as:
output_size[1]=n
output_size[i]=(input_size[i]  kernel[i])/step[i] + 1, FOR i=2,...,#kernel
ann.components.convolution_bias{ n, ndims, name, weights }
> c = ann.components.convolution_bias{ n=10, ndims=3,
name="convB1", weights="B1" }
n
is the number of planes at the input (the first dimension size of the input matrix).
ndims
is the number of dimensions expected at the input matrix.
name
and weights
as usual
ann.components.max_pooling{ kernel, name }
> c = ann.components.max_pooling{ kernel={1, 2, 2}, name="pool2" }
kernel
is a table with the sizes of the kernel applied to the input matrix. Depending on this the behavior of the maxpooling could be to do a downsampling of an input matrix (as in the example), or to convert the input in a fixed size feature vector (kernel = {1, 0, 0}
). The 0
value at one component means to fit this dimension with the same dimension of input matrix. So, the last example {1, 0, 0}
will be a maxpooling computed over all positions for each input plane, producing as output a feature vector of INPUT PLANES size.
name
as usual.
ann.components.flatten{ [name] }
This components converts an input matrix formed by N patterns of any dimensionality to an output bidimensional matrix with N rows and M columns, where M is the product of all input matrix dimensions (except the first one which is the number of patterns).
> c = ann.components.flatten{ name="flatten" }
ann.components.copy
ann.loss
packageRelated with module require("aprilann.ann.loss")
.
This package defines the loss functions included in APRILANN. All loss functions share the same interface. Normally, they are implemented in C++ and binded to Lua.
The interface of loss functions is the following:
loss,loss_matrix = loss:compute_loss(input,target)
: this method computes the loss between two tokens
, the input
and the target
. Normally they are bidimensional matrix
instances with size NxM, where N is the number of patterns in the bunch (minibatch), and M is the number of outputs in the ANN component. The method returns two values, the loss
, which is a number with the mean loss in the given bunch of patterns. The loss_matrix
, which is a onedimensional matrix
of size N containing the loss for every pattern. In some cases, as for example in FMeasurebased loss functions, this loss matrix
is of size 1, because the loss function is computed over the bunch of patterns, and is not separable.
gradient = loss:gradient(input,target)
: this method computes the gradient of the loss between the two input
and the target
. It returns a bidimensional matrix with size NxM. Each component of this matrix
is the partial derivative ANN outputs respect to the loss function.
loss,loss_matrix = loss:accum_loss(loss,loss_matrix)
: this method receives the output of compute_loss
method, and accumulates the given loss in its internal state. It is useful to compute the loss of a large number of patterns.
loss_matrix = loss:accum_loss(loss_matrix)
: this method is a specialization of the previous one, but receiving only the loss_matrix
.
mean,variance = loss:get_accum_loss()
: this method returns two numbers, the mean
and the variance
of the accumulated loss in the internal state of the loss function object.
Tt is possible to develop new loss functions by implementing Lua classes derived from ann.loss
class, following this example:
> myloss,myloss_methods = class("myloss",ann.loss)
> function myloss:constructor()
 Your code to initialize self reference
end
> function myloss_methods:compute_loss(input,target)
 YOUR CODE
return loss,loss_matrix
end
> function myloss_methods:gradient(input,target)
 YOUR CODE
return gradient_token
end
> function myloss_methods:accum_loss(loss,loss_matrix)
local loss_matrix = loss_matrix or loss
 YOUR CODE
return loss or loss_matrix, loss_matrix
end
> function myloss_methods:get_accum_loss()
 YOUR CODE
return loss_mean,loss_variance
end
> function myloss_methods:reset()
 YOUR CODE
end
> function myloss_methods:clone()
 YOUR CODE
return cloned_obj
end
This loss function is defined at the object ann.loss.mse
:
> loss = ann.loss.mse()
The constructor could receive an optional parameter with the expected number of outputs at the ANN component. If given, it will be used as sanity check forcing to be equal to the given input/target sizes. If not given, the size check will be ignored.
This loss function computes the mean squared error between the given input/target patterns. It computes the following expression:
Where N
is the number of patterns, h_i^j
is the position (i,j)
in the input matrix (pattern i
, component j
), and t_i^j
is the same position at the target matrix.
This loss function is defined at the object ann.loss.mae
:
> loss = ann.loss.mae()
The constructor could receive an optional parameter with the expected number of outputs at the ANN component. If given, it will be used as sanity check forcing to be equal to the given input/target sizes. If not given, the size check will be ignored.
This loss function computes the mean absolute error between the given input/target patterns. It computes the following expression:
Where N
is the number of patterns, M
is the number of outputs, h_i^j
is the position (i,j)
in the input matrix (pattern i
, component j
), and t_i^j
is the same position at the target matrix.
This loss function is defined at the object ann.loss.cross_entropy
:
> loss = ann.loss.cross_entropy()
The constructor could receive an optional parameter with the expected number of outputs at the ANN component. If given, it will be used as sanity check forcing to be equal to the given input/target sizes. If not given, the size check will be ignored.
This object is implemented to work only with log_logistic
activation function. This loss function computes the cross entropy between the given input/target patterns, interpreting the ANN component output as a binomial distribution. It computes the following expression:
Where N
is the number of patterns, h_i^j
is the position (i,j)
in the input matrix (pattern i
, component j
, in natural scale), and t_i^j
is the same position at the target matrix.
This loss function is defined at the object ann.loss.multi_class_cross_entropy
:
> loss = ann.loss.multi_class_cross_entropy()
The constructor could receive an optional parameter with the expected number of outputs at the ANN component. If given, it will be used as sanity check forcing to be equal to the given input/target sizes. If not given, the size check will be ignored.
This object is implemented to work only with log_softmax
activation function. This loss function computes the cross entropy between the given input/target patterns, interpreting the ANN component output as a multinomial distribution. It computes the following expression:
Where N
is the number of patterns, h_i^j
is the position (i,j)
in the input matrix (pattern i
, component j
, in natural scale), and t_i^j
is the same position at the target matrix.
This loss function is defined at the object ann.loss.batch_fmeasure_macro_avg
:
> loss = ann.loss.batch_fmeasure_macro_avg{ beta=0.5 }
The constructor could receive an optional table parameter with the following fields:
size=0
: expected number of outputs at the ANN component. If given, it will be used as sanity check forcing to be equal to the given input/target sizes. If not given, the size check will be ignored.
beta=1
: the b
parameter in the FMeasure expression below. By default it is set to 1.
complement=false
: a boolean indicating if the input/target values must be computed complemented (1  value), swapping positive and negative classes.
This object is implemented to work with logistic
or softmax
activation function. This loss function computes the FMeasure between the given input/target patterns, interpreting the ANN component output as a multinomial distribution. It computes the following expression:
Where M
is the number of outputs, b
is the beta parameter of the Fmesaure, h_j · t_j
is the dot product between column vectors with input/target values of class j
, and sum(o_j)
and sum(t_j)
is the sum of all the elements in the column vectors.
This loss function is defined at the object ann.loss.batch_fmeasure_micro_avg
:
> loss = ann.loss.batch_fmeasure_micro_avg{ beta=0.5 }
The constructor could receive an optional table parameter with the following fields:
size=0
: expected number of outputs at the ANN component. If given, it will be used as sanity check forcing to be equal to the given input/target sizes. If not given, the size check will be ignored.
beta=1
: the b
parameter in the FMeasure expression below. By default it is set to 1.
complement=false
: a boolean indicating if the input/target values must be computed complemented (1  value), swapping positive and negative classes.
This object is implemented to work with logistic
activation function. This loss function computes the FMeasure between the given input/target patterns, interpreting the ANN component output as a binomial distribution. If it is used with softmax
(multinomial distribution), then this function computes accuracy. It follows this expression:
Where b
is the beta parameter of the Fmesaure, dot(h,t)
is the dot product between the input/target matrices reinterpreted as two column vectors, and sum(o)
and sum(t)
is the sum of all the elements in the matrices.
This loss function is defined at the object ann.loss.zero_one
:
> loss = ann.loss.zero_one([nil, [, 0.5 ] ])
The constructor could receive an optional first parameter with the expected number of outputs at the ANN component. If given, it will be used as sanity check forcing to be equal to the given input/target sizes. If not given, the size check will be ignored.
It could receive an optional second parameter which by default is 0.5
. This second parameter is the threshold which defines when the output is taken as 1. NOTE that if you are using log_logistic
outputs, this threshold must be set to math.log(0.5)
. This parameter is only useful when the model has one output, that is, for twoclass problems.
This object is not derivable, so the compute_gradient
method is forbidden. The loss function could be use to compute validation error, but not for training. It computes the accuracy of the model classifying to the class with maximum probability.
ann.optimizer
packageRelated with the module require("aprilann.ann.optimizer")
.
The optimizer is an object which implements the learning algorithm. Every class in ann.optimizer
is an optimizer. Several learning hyperparameters are available, depending in the selected optimizer. This learning hyperparameters are known as options, and could be set globally (to all the connection weight layers of the ANN), or layerwise (to a concrete connection weights object, identified by its name). Optimizers implement the following API.
other = opt:clone()
Returns a deep copy of the caller object.
value = opt:get_option(option)
Return the global value of a given learning option
name.
opt:set_option(option, value)
Sets the global value of a given learning option
name.
opt:set_layerwise_option(layer, option, value)
Sets a layerwise option
for the given layer
name.
value = opt:get_layerwise_option(layer, option)
Returns the layerwise option
of the given layer
name.
value = opt:get_option_of(layer, option)
Returns the option which is applicable to the given layer
name. If a layerwise option was previously defined, the method returns its value. Otherwise, the value of the global option will be returned.
loss,gradients,... = opt:execute(eval,cnn)
This is the core function of optimizer objects. It receives two parameters, and return the same values as returned by eval
function. The parameters are:
eval
is a Lua function which returns at least the first two parameters of the list below. The function receives two inputs:
A table of candidate weigths to compute the loss.
The iteration number of the algorithm.
This function could return more values, all of them will be returned by the call to execute:
1. The loss of a bunch of data (a minibatch).
2. A gradients matrix dictionary (as returned by ANN components method
`compute_gradients()`). It must be a table of names=>`matrix`.
cnn
a dictionary of matrices with the connection weight objects, as the dictionary returned by ANN components method copy_weights()
. It must be a table of names=>matrix
. The execute
method can modify the matrices contained at cnn
, by following the given gradients, can use a bunch of weight candidates and call eval
function to test the loss.The trainable.supervised_trainer
class is the default coupling between the optimizer, the ANN component and the loss function. The optimizer could be set at the constructor:
> trainer = trainable.supervised_trainer(ann_component, ann.loss.mse(),
bunch_size, ann.optimizer.sgd())
The hyperparemters of optimizer
objects can be modified by the trainer
object:
trainer:set_option(option,value)
: sets a global learning option value.
value=trainer:get_option(option)
: gets a global learning option value.
trainer:set_layerwise_option(layer_match,option,value)
: sets a layerwise learning option value of all the connection weight objects whose name matches the given layer_match
Lua pattern string.
value=trainer:get_option_of(layer,option)
: gets the option value applicable to the given layer.
trainer = trainable.supervised_trainer(ann_component, ann.loss.mse(),
bunch_size, ann.optimizer.sgd())
trainer:build()
trainer:set_option("learning_rate", number)
trainer:set_option("momentum", number)
 regularization is recommended to not be applied at bias connections
trainer:set_layerwise_option("w.*", "weight_decay", number)
trainer:set_layerwise_option("w.*", "max_norm_penalty", number)
trainer:set_layerwise_option("w.*", "L1_norm", number)
Doing this, when you call trainer:train_dataset(...)
or trainer:validate_dataset(...)
, the object implements a default eval
function for the optimizer.
First, you need to construct the ANN component, the loss function and the optimizer instance, and the configuration of the optimizer.
local thenet = ann.mlp.all_all.generate("256 inputs 128 tanh 10 log_softmax")
local thenet,cnns = thenet:build()
local loss = ann.loss.multi_class_cross_entropy()
local opt = ann.optimizer.sgd()
opt:set_option("learning_rate", 0.01)
The initialization of the weights is required:
local rnd = random(1234)
for _,w in pairs(cnns) do w:uniformf(0.1,0.1,rnd) end
The training is performed over random data, generated onthefly:
local M = matrix.col_major  ANNs need col_major matrices
local weight_grads = {}  upvalue for eval function
for i=1,1000 do
local input = M(1,256):uniformf(0,1,rnd)
local target = M(1,10):zeros():set(1, rnd:randInt(1,10), 1.0)
opt:execute(function(weights,it)
if cnns ~= weights then thenet:build(weights) cnns=weights end
thenet:reset(it)
local out = thenet:forward(input)
local tr_loss,tr_matrix = loss:compute_loss(out,target)
thenet:backprop(loss:gradient(out,target))
weight_grads:zeros()
weight_grads = thenet:compute_gradients(weight_grads)
return tr_loss,weight_grads
end,
cnns)
end
All this code could be modified in many ways, to customize or implement your own methods.
NOTE the full code of this example is available at: EXAMPLES/optimizerfromscratch.lua
The ann.optimizer.sgd
class trains the neural network following the Stochastic Grandient Descent algorithm. It incorporates regularization and momentum hyperparameters.
> opt = ann.optimizer.sgd()
> loss,gradients = opt:execute(function(weights,it)
 HERE YOUR CODE
return loss,gradients
end,
 The connections
cnns)
Its options are (default values are indicated with =
symbol):
learning_rate
: the learning rate controls the portion of the gradient used to update the weights.
decay=1e05
: controls the decay of learning rate.
momentum=0
: is a inertial hyperparameter which applies a portion of the weight update in the previous iteration.
weight_decay=0
: a L2 regularization term.
L1_norm=0
: a L1 regularization term, a naive implementation with ZERO truncation to avoid ZERO cross.
max_norm_penalty=0
: a constrain penalty based on the twonorm of the weights.
The algorithm uses the following learning rule:
w = w  lr'*( grad(L)/grad(w') + weight_decay*w' + L1_norm*sign(w') ) + momentum*(w'  w'')
where w
, w'
and w''
are the weight values at next, current, and previous iterations; lr'
is the learning_rate
, and grad(L)/grad(w')
is the gradient of the loss function at the given weight. The L1 regularization is performed following truncate gradient algorithm. After this learning rule, the constraint max_norm_penalty
is applied, forcing the 2norm of the input weights of every neuron to be less than the given parameter.
> opt = ann.optimizer.sgd()  an instance
The ann.optimizer.asgd
class trains the neural network following the Averaged Stochastic Grandient Descent algorithm, taken from Leon Bottou, Stochastic Gradient Descent Tricks, Microsoft Research, 2012. It incorporates regularization and learning rate decay hyperparameters.
> opt = ann.optimizer.asgd()
> loss,gradients = opt:execute(function(weights,it)
 HERE YOUR CODE
return loss,gradients
end,
 The connections
cnns)
Its options are (default values are indicated with =
symbol):
learning_rate
: the learning rate controls the portion of the gradient used to update the weights.
lr_decay=0.75
: controls the learning rate decay. The effective learning rate is computed as lr / (1 + lr*t)^lr_decay
, being t
the number of presentations, which has not to be confused with number of epochs. Number of presentations would be higher of number of epochs.
t0=0
: when to start averaging. Before t0
, the algorithm will be SGD. As before, t0
is indicated in number of presentations, i.e. one epoch will be t0 = ceil( number_of_patterns / bunch_size )
. It is recommended to set this option to at least one epoch.
weight_decay=0
: a L2 regularization term.
> opt = ann.optimizer.asgd()  an instance
The ann.optimizer.rprop
class trains the neural network following the RProp algorithm. It incorporates an option to set the number of iterations done with every minibatch of patterns. This optimizer has a step size hyperparameter for each weight parameter in the model. All options are global, layerwise options doesn't make sense here.
> opt = ann.optimizer.rprop()
> loss,gradients = opt:execute(function(weights,it)
 HERE YOUR CODE
return loss,gradients
end,
 The connections
cnns)
Its options are (default values are indicated with =
symbol):
initial_step=0.1
: the initial step size of every weight.
eta_plus=1.2
: value which controls how much proportion is the step increased when the gradient sign is the same between two iterations.
eta_minus=0.5
: value which controls how much proportion is the step decreased when the gradient sign changes between two iterations.
max_step=50
: the maximum value for the weight step hyperparameter.
min_step=1e05
: the minimum value for the weight step hyperparameter.
niter=1
: the number of iterations done with one minibatch of patterns.
The algorithm modifies the step(i)
parameter of a weight at iteration i
:
 step(i1) * eta_minus, iff sign(grad(w')/grad(L)) <> sign(grad(w'')/grad(L))
step(i) =  step(i1) * eta_plus, iff sign(grad(w')/grad(L)) == sign(grad(w'')/grad(L))
It updates the weight following this equation:
w = w'  sign(grad(w')/grad(L)) * step(i)
In the above equations, w
, w'
and w''
are the weight values at next, current, and previous iterations and sign(grad(.)/grad(L))
is the sign of the gradient of the loss function at the given weight. Note that the step
is saturated with the values max_step
and min_step
.
> opt = ann.optimizer.rprop()  an instance
The ann.optimizer.cg
class trains the neural network following the CG algorithm. It is a second order Hessian Free optimizer. Its convergence is usually faster than SGD algorithm.
> opt = ann.optimizer.cg()
> loss,gradients = opt:execute(function(weights,it)
 HERE YOUR CODE
return loss,gradients
end,
 The connections
cnns)
Its options are (default values are indicated with =
symbol):
rho=0.01
: Constant for WolfPowell conditions (global option).sig=0.5
: Constant for WolfPowell conditions (global option).int=0.1
: Reevaluation limit (global option).ext=3
: Maximum number of extrapolations (global option).max_iter
: Maximum number of iterations (global option).max_eval=1.25*max_iter
: Maximum number of evaluations (global option).ratio=100
: Maximum slope ratio (global option).weight_decay
: Weights L2 regularization (global and layerwise option).L1_norm
: Weight L1 regularization (global and layerwise option).max_norm_penalty
: Weight max norm upper bound (global and layerwise option).This implementation is rewrite of Torch 7 optim package, which is a rewrite of minimize.m
written by Carl E. Rasmussen.
> opt = ann.optimizer.cg()  an instance
The ann.optimizer.quickprop
class trains the neural network following the Quickprop algorithm. It is a second order optimizer which uses a quadratic approximation to speedup the learning convergence. It is usually faster than SGD, but can suffer of chaotic oscillations.
> opt = ann.optimizer.quickprop()
> loss,gradients = opt:execute(function(weights,it)
 HERE YOUR CODE
return loss,gradients
end,
 The connections
cnns)
Its options are (default values are indicated with =
symbol):
learning_rate
: It is mandatory to be given.mu=1.75
: Maximum growth factor.epsilon=1e04
: Bootstrap factor.max_step=1000
: Maximum step value.weight_decay
: Weights L2 regularization.L1_norm
: Weight L1 regularization.max_norm_penalty
: Weight max norm upper bound.> opt = ann.optimizer.quickprop()  an instance
The ann.optimizer.adadelta
class trains the neural network following the AdaDelta algorithm. It is a method which dynamically adapts the learning rate just using first order information. It as simple as SGD, and appears to be more robust.
> opt = ann.optimizer.adadelta()
> loss,gradients = opt:execute(function(weights,it)
 HERE YOUR CODE
return loss,gradients
end,
 The connections
cnns)
Its options are (default values are indicated with =
symbol):
decay=0.95
: Decay of the accumulated gradient and updates.epsilon=1e06
: To avoid numerical issues.weight_decay=0.0
: Weights L2 regularization.max_norm_penalty=0.0
: Weight max norm upper bound.> opt = ann.optimizer.adadelta()  an instance
ann.graph
packageThis package contains an implementation of ANNs by using a graph description. The graph allow to declare delayed connections, which can be used to develop recurrent neural networks as LSTMs or Elman.
Graph ANNs are declared as instances of the class ann.graph
. The ANN graph is a model where nodes are ANN componentes (any object of ann.components
and objects defined in ann.graph
and ann.graph.blocks
). So, nodes have an input and an output tokens, in the same way as ANN componentes receive a token and produce as output another token. Nodes can receive any number of connections as input and its output can be connected to other multiple nodes. When multiple input connections are received, they are put together into a tokens.vector.bunch
instance. The graph implements properly the propagation of gradients between the nodes, and uses the methods forward
, backprop
and compute_gradients
of the components in every node.
The graph object is considered itself as an ANN component, allowing to declare graphs where nodes are other graphs. Every graph has two special nodes, 'input' and 'output', which are used to connect the visible parts of the ANN.
In ANN graphs loops are allowed, but they cannot be made of normal connections, and the concept of delayed connections is introduced (indeed, normal connections are whose with delay=0
). An ANN graph with delayed connections is equivalent to the concept of Recurrent Neural Network (RNN in the following).
The training of RNNs is done following the BackPropagation Trough Time (BPTT) algorithm. RNNs have an special behavior in forward
method, the graph takes note of the state (input, output, gradient deltas, ...) for every node, allowing to take as input the activation in any past instant. The backprop
method returns a tokens.null
instance, that is, its output is none, this method just annotates the given input error deltas for a future use. Calling the method compute_gradients
the error deltas given at backprop
are propagated through all the space and time, and the weight gradients are computed.
g = ann.graph( [ name ] )
The constructor an optional name
argument.
g:connect( source, dest1, ..., [ delay=0 ] )
This method connects a path of nodes in the graph. It receives as arguments:
source
: The first node in the path (source
). It can be an ANN component or the string 'input'
.dest1
: The second node in the path (dest1
). It can be an ANN component or the string 'output'
....
: A variadic list of arguments with zero or more nodes which form the path. Every node in this list can be an ANN component or output
.delay=0
: The last argument is optional and by default it is zero. This argument is needed to declare delayed connections. All the connections in the path would be declared with the delay given in this argument. Note that normally only the connection between two nodes need to be delayed, and for this purpose the method g:delayed(source,destination)
has been declared.For correction, a graph is valid only if the input
node is a source and output
node is a sink, and every node is reachable from the input
.
The following example shows how to declare a Jordan network.
> g = ann.graph()
> bind = ann.graph.bind()
> out_actf = ann.components.logistic()
> g:connect('input', bind,
ann.components.hyperplane{ input=2, output=4 },
ann.components.actf.logistic(),
ann.components.hyperplane{ input=4, output=1 },
out_actf, 'output')
> g:delayed(out_actf, bind)  recurrent connection
> g:build()
g:delayed( source, destination )
This is equivalent to g:connect(source, destination, 1)
.
g:show_nodes()
This method is used for debug purposes, and show all the nodes in the given graph. If the graph contains other graphs as nodes, their nodes would be shown recursively. The output indicates the level of recursion, the name of the component in the corresponding node, and the type of the object, as in the following example (extracted from a LSTM test):
# (level) name type
(0) a2 ann.components.actf.log_logistic
(0) parity::output string
(0) LSTM ann.graph +
(1) LSTM::f::layer ann.components.hyperplane
(1) LSTM::input string
(1) LSTM::o::actf ann.components.actf.logistic
(1) LSTM::i::peephole ann.graph.bind
(1) LSTM::o::gate ann.graph.cmul
(1) LSTM::o::layer ann.components.hyperplane
(1) LSTM::f::gate ann.graph.cmul
(1) LSTM::actf ann.components.actf.softsign
(1) LSTM::memory ann.graph.add
(1) LSTM::o::peephole ann.graph.bind
(1) LSTM::i::actf ann.components.actf.logistic
(1) LSTM::cell_input ann.components.hyperplane
(1) LSTM::output string
(1) LSTM::i::gate ann.graph.cmul
(1) LSTM::f::actf ann.components.actf.logistic
(1) LSTM::i::layer ann.components.hyperplane
(1) LSTM::f::peephole ann.graph.bind
(0) l2 ann.components.hyperplane
(0) parity::input string
g:dot_graph(filename)
This method is for debug. It writes to the given filename
a DOT graph which can be transformed in PDF using graphviz.
g,weights,components = g:build( [ table ] )
See ANN package doc.
output = g:forward( input, [ during_training=false ] )
See ANN package doc.
output = g:backprop( input )
See ANN package doc.
Note that this method changes its default behavior when the graph is a RNN. In this case, the output of this method is a tokens.null
instance, so it can be ignored.
table = g:compute_gradients( [table] )
See ANN package doc.
table = g:bptt_backprop()
Forces the BPTT algorithm execution, and returns a table (Lua array) with the delta gradients at the ANN input for every time instant.
table = g:get_bptt_state()
Returns a table with the state of the whole ANN for every time instant.
table = g:get_bptt_state(time)
Returns a table with the state of the whole ANN for the given time instant.
g:reset( [ n ] )
See ANN package doc.
Additionally with the standard behavior, this method reinitializes the BPTT tables, and must be called before starting a new sequence.
boolean = g:get_is_recurrent()
Indicates if the caller graph is recurrent or not.
g:set_bptt_truncation( backstep )
Changes the BPTT algorithm behavior, truncating the gradient computation every backstep
number of iterations. This value can be math.huge
to indicate an infinite limit. Besides this value, another usual one is 1
, which transforms allow to use the algorithm as a kind of online learning algorithm.
ann.graph.bind{ [ name=string ], [ input=number ], [ output=number ], [ size=number ] }
ann.graph.add{ [ name=string ], [ input=number ], [ output=number ] }
ann.graph.cmul{ [ name=string ], [ input=number ], [ output=number ] }
ann.graph.index(n, { [ name=string ], [ input=number ], [ output=number ] })
ann.graph.blocks.elman{ [ name=string ], [ input=number ],
[ output=number ], [ actf=string ] })
ann.graph.blocks.lstm{ [ name=string ], [ input=number ],
[ output=number ], [ actf=string ],
[ peepholes=true ], [ input_gate=true ],
[ forget_gate=true], [ output_gate=true ] })
ann.autoencoders
packagePackage autoencoders
could be loaded via the standalone binary, or in Lua with require("aprilann.autoencoders)
.
Stacked Denoising AutoEncoders (SDAE) are a kind of deep neural network which is pretrained following greedy layerwise algorithm but introducing at noise input of each layerwise autoencoder. Some function facilities are implemented to help with the training of SDAE.
Greedy layerwise pretraining consists in train each pair of layers, from input to output, in a greedy way (see Paper SDAE, 2010, Vincent Pascal et al.). Pretraining receives as input a table with parameters of training algorithm. For example, a table like this:
layers = {
{ size= 256, actf="logistic"},  INPUT
{ size= 256, actf="logistic"},  FIRST HIDDEN LAYER
{ size= 128, actf="logistic"},  SECOND HIDDEN LAYER
{ size= 32, actf="logistic"},  THIRD HIDDEN LAYER
}
perturbation_random = random(824283)
params_pretrain = {
input_dataset = train_input,  a dataset which is the input of the autoencoders
replacement = nil,  a number (or nil) indicating replacement
on_the_fly = false,  a boolean (or nil) for onthefly
shuffle_random = random(1234),  for shuffle durint backpropagation
weights_random = random(7890),  for weights random initialization
layers = layers,  layers description
supervised_layer = { size = 10, actf = "log_softmax" },  it is possible to pretrain supervised layer
output_datasets = { train_output },  the output dataset
bunch_size = bunch_size,  the size of the minibatch
optimizer = function() return ann.optimizer.sgd() end,  optimizer function
training_options = {  this table contains learning options and dataset noise filters
 global options
global = {
 pure ANN learning hyperparameters
ann_options = { learning_rate = 0.01,
momentum = 0.02,
weight_decay = 1e05 },
 noise filters (a pipeline of filters applied to input in order). Each one must be a dataset
noise_pipeline = { function(ds) return dataset.perturbation{  gaussian noise
dataset = ds,
mean = 0,  gaussian mean
variance = 0.01,  gaussian variance
random = perturbation_random } end,
function(ds) return dataset.salt_noise{  salt noise (or mask noise)
dataset = ds,
vd = 0.10,  percentage of values masked
zero = 0.0,  mask value
random = perturbation_random } end },
min_epochs = 4,
max_epochs = 200,
pretraining_percentage_stopping_criterion = 0.01,
},
 it is possible to overwrite global values with layerwise dependent values (also noise_pipeline)
layerwise = { { min_epochs=50 },  first autoencoder pretraining
{ min_epochs=20 },  second autoencoder pretraining
{ ann_options = { learning_rate = 0.04,
momentum = 0.02,
weight_decay = 4e05 },
min_epochs=20 },  third autoencoder pretraining
{ min_epochs=10 }, },  supervised pretraining
}
}
Fields supervised_layer
and output_datasets
are optional. If they are given, the last layer will be pretrained in a supervised manner. Anyway, rest of layers are pretrained in a unsupervised manner.
If field input_dataset
is supplied, then distribution
field is forbidden and, in case of pretrain supervised layer, output_datasets
table must contain only one element.
If field distribution
is supplied, then input_dataset
is forbidden and, in case of pretrain supervised layer, output_datasets
table has the same number of items than distribution
table. In this last case, each item output_datasets[i]
is the corresponding supervised output dataset for each item of distribution[i].input_dataset
.
Ths table is used passed as argument to the algorithm:
sdae_table,deep_net = ann.autoencoders.greedy_layerwise_pretraining(params_pretrain)
This function returns one or two tables:
sdae_table = { bias={ ... }, weights={ ... } }
: which contains bias and weights of each unsupervised pretrained layer.
deep_net
: An ANN component. It could be used to finetuning training. If you don't pretrain supervised layer, this component needs that you manually push the supervised layer.
codifier_net = ann.autoencoders.build_codifier_from_sdae_table(sdae_table,
bunch_size,
layers)
The codifier is the SDAE without the supervised layer at output. Needs the same layers
definition as greedy pretrainig function. Returns an ANN object which could receive a pattern as input and produces its encoding.
The supervised deep ANN could be finetuned using crossvalidation training algorithm. If you pretrained supervised layer, object deep_net
is directly the whole ANN. Otherwise, you will need to add a new layer to the codifier_net
, as in this example:
 if you want, you could clone the deep_net to keep it as it is
local codifier_net = deep_net:clone()
codifier_net:build{ weights = deep_net:copy_weights() }
 We add an output layer with 10 neurons and softmax activation function
local last_layer = ann.components.hyperplane{
dot_product_weights="lastw",
bias_weights="lastb",
output=10
}
deep_net:push( last_layer )
deep_net:push( ann.components.actf.log_softmax()
trainer = trainable.supervised_trainer(deep_net, loss_function or nil, bunch_size or nil)
 The output size needs to be overwitten, so it needs to be given at build method
trainer:build{ output = 10 }
weights_random = random(SEED)
 Now, EXITS TWO WAYS to randomize the weights of last_layer
 FIRST using the trainer
trainer:randomize_weights{
name_match="^last[bw]$",  the name_match is to only randomize connections which name matches
inf=0.1,
sup=0.1,
random=weights_random
}
 SECOND using the component
 (BE CAREFUL AND USE ONLY ONE OF THIS WAYS)
for _,cnn in pairs(last_layer:copy_weights()) do
cnn:randomize_weights{
inf=0.1,
sup=0.1,
random=weights_random
end
With a trained SDAE (without supervised layer), it is possible to compute encodings of input patterns using this function:
trainer = trainable.supervised_trainer(codifier_net)
encoded_dataset = trainer:use_dataset(input_dataset)
trainable
packageRelated with the module require("aprilann.trainable")
This package implements the class trainable.supervised_trainer
, which is a powerful tool when you want to train ANNs following standard algorithms. Additionally it implements generic functions and iterators to implement training loops (dataset iterator, and training function).
If you want to do some specific tricks, it is possible to modify in your script the methods described here, or to reimplement the functionality that you need.
The class trainable.supervised_trainer
is the most important piece in this package. This class knows the standard API of ANN components, loss functions, optimizers, datasets, and matrix objects, so, this class use them in the correct way to train ANNs following standard algorithms.
The construction of a trainer needs at least an ANN component object, but optionally, it is possible to indicate loss function, bunch size (minibatch) and optimizer object:
>  a linear component (w1 and b1 are the weight names)
> c = ann.components.hyperplane{ input=10, output=10,
name = "hyperplane",
dot_product_name = "cw1",
bias_name = "cb1",
bias_weights = "b1",
dot_product_weights = "w1" }
>  a trainer
> trainer = trainable.supervised_trainer(c)
The arguments of the constructor are positional, described here:
ANN component: an instance of ann.components.base()
or any subclass of it. The trainer uses a reference to the given object. It is a mandatory argument.
Loss function: an instance of a subclass of ann.loss
. The trainer uses a reference to the given object. If not given, it will be mandatory in train_*
and validate_*
methods. By default is nil
.
Bunchsize (minibatch): a number with the batch size used to compute gradients. Values between 32 and 1024 are usual, depending on the optimizer and in the task. If not given, it will be mandatory in train_*
, validate_*
, or use_*
methods. By default is nil
.
Optimizer: an instance of a subclass of ann.optimizer
. The trainer uses a reference to the given object. If not given, the default optimizer is ann.optimizer.sgd()
.
Smooth flag: a boolean value indicating if the gradients must be smoothed. In case of true
, gradients will be multiplied by 1/sqrt(bunch_size+K)
, being K
the number of times the corresponding weights matrix is shared across the ANN. By default this parameter is true
.
>  without bunchsize, without loss function,
>  using default optimizer, smooth is true
> trainer = trainable.supervised_trainer(c)
>  without bunchsize, using default optimizer, smooth is true
> trainer = trainable.supervised_trainer(c, ann.loss.mse())
>  using default optimizer, smooth is true
> trainer = trainable.supervised_trainer(c, ann.loss.mse(), 64)
>  smooth is true
> trainer = trainable.supervised_trainer(c, ann.loss.mse(), 64,
ann.optimizer.rprop())
>  all arguments given
> trainer = trainable.supervised_trainer(c, ann.loss.mse(), 64,
ann.optimizer.rprop(), false)
trainer:set_loss_function(loss)
loss = trainer:get_loss_function()
trainer:set_optimizer(optimizer)
optimizer = trainer:get_optimizer()
Once the trainer
is instantiated, it is mandatory to build
the ANN by calling the named method.
> trainer:build()
The build
method could be called without arguments, or giving a table with the following optional fields:
input
: a number with the input size of the model. If not given, it will be set to the input of the given ANN component. If given, this size will be used as sanity check, so, it must be equal to the input size of the ANN component.
output
: a number with the output size of the model. Idem as previous field, but with ANN component output.
weights
: a dictionary (table) with strings as keys and instances of matrix
as values. If given, the connection objects used by the components will be taken from this dictionary, so, the sizes must be equals. It could be used to initialize certain components with a given initial parameters.
> trainer:build{
input = 10, output = 10,
weights = {
 ann.connections allocates memory for weight matrices
w1 = ann.connections{ input=10, output=10 },
b1 = ann.connections{ input=1, output=10 },
}
}
>  giving a table, initialized using its constructor
> trainer:build{
input = 10, output = 10,
weights = {
w1 = ann.connections{ input=10, output=10 },
b1 = ann.connections{ input=1, output=10 },
}
}
Once the build
method is called, it is recommended to not modify the structure of the ANN component, if you need so, after your modifications the build
method must be called again. The connection weights must be modified after calling build
, initializing them in some way. Another possibility is to deserialize a previously serialized trainer
.
It is possible to save a trainer
object, always in built
state, using util.serialize()
and util.deserialize()
functions.
> util.serialize(trainer, "mytrainerbinary.net")
> trainer2 = util.deserialize("mytrainerbinary.net")
trainer:randomize_weights{ ... }
The connection weights could be initialized randomly by using the method trainer:randomize_weights
. This method receives a table with the following fields:
inf
: a number with the inferior range bound. It is a mandatory field.
sup
: a number with the superior range bound. It is a mandatory field.
random
: a random
object. It is a mandatory field.
use_fanin
: a boolean value indicating if apply a factor fanin of each layer for its initialization. It is an optional field. By default is false
.
use_fanout
: a boolean value indicating if apply a factor fanout of each layer for its initialization. It is an optional field. By default is false
name_match
: a string with a Lua pattern used to filter which connection objects will be initialized. It is an optional field, by default is .*
.
The weights will be initilized in the range [c*inf, c*sup]
where c
is a factor which depends on use_fanin
and use_fanout
arguments:
If none given, then c=1
for all weight layers.
If given only use_fanin=true
, then c
is computed depending in the fanin of each layer, being c = 1/sqrt(fanin)
.
If given only use_fanout=true
, then c
is computed depending in the fanout of each layer, being c = 1/sqrt(fanout)
.
If both given, use_fanin=true
and use_fanout=true
, then c
is computed depending in the fanin and fanout of each layer, being c = 1/sqrt(fanin + fanout)
.
>  initilize only bias weights
> trainer:randomize_weights{
inf = 0.1,
sup = 0.1,
random = random(213924),
name_match = "b.*",
}
>  initilize only nonbias weights
> trainer:randomize_weights{
inf = 0.1,
sup = 0.1,
random = random(213924),
name_match = "w.*",
use_fanin = true,
use_fanout = true,
}
>  initilize all the weights
> trainer:randomize_weights{
inf = 0.1,
sup = 0.1,
random = random(213924),
}
Once the trainer
is built, it is possible to do some introspection in order to modify or execute methods of connection weights.
number = trainer:count_weights( [pattern=.*] )
This method returns the number of connection weights in the current trainer
. Optionally the method receives a Lua pattern filtering the counting process to only the weights whom name matches the pattern.
> = trainer:count_weights()
2
number = trainer:weights(name)
This method returns the matrix
object with the given name
.
> w1 = trainer:weights("w1")
> = type(w1)
matrix
> b1 = trainer:weights("b1")
> = type(b1)
matrix
table = trainer:get_components_of(weights_name)
This method returns a table with ann.components
objects which share the given weights_name
connection weights. It returns a table because a connection weights object could be shared by more than one ANN components.
> iterator(ipairs( trainer:get_components_of("w1") )):apply(print)
1 instance 0xfcf5b0 of ann.components.base
> iterator(ipairs( trainer:get_components_of("b1") )):apply(print)
1 instance 0xff3c90 of ann.components.base
table = trainer:get_weights_table()
This method returns a dictionary (table) with all the connection weights. This dictionary has the same structure as the weights
field of trainer:build(...)
method.
> weights_table = trainer:get_weights_table()
> print(weights_table)
table: 0x1310a00
... = trainer:iterate_weights( [pattern=.*] )
This method returns a Lua iterator function which iterates over all the connection weights which name matches the given pattern
.
> for cnn_name,cnn in trainer:iterate_weights() do print(cnn_name) end
b1
w1
> iterator( trainer:iterate_weights("w.*") ):select(1):apply(print)
w1
number = trainer:norm2([pattern=.*])
This method computes the 2norm of the connection weight objects whom name matches the given pattern.
> = trainer:norm2()
0.24416591227055
> = trainer:norm2("b.")
0
> = trainer:norm2("w.")
0.24416591227055
number = trainer:size()
This methods returns the number of parameters (weights) in the current ANN component.
> = trainer:size()
110
By using these methods it is possible to manipulate byhand the connection weights, as in the following example, which initializes to zero the bias connections:
> for _,cnn in trainer:iterate_weights("b.*") do cnn:zeros() end
REMEMBER that connection weight objects are matrix
instances.
The following code shows at screen all the weight matrices:
> iterator(trainer:iterate_weights()):apply(print)
0
0
0
0
0
0
0
0
0
0
# Matrix of size [10,1] [0x10aca60 data= 0x10acb20]
0.0575503 0.0516265 0.0306808 0.035404 0.0118243 ...
0.0281929 0.0877731 0.0842627 0.0379949 0.091877 ...
0.0332023 0.0576623 0.0335078 0.0251189 0.0578111 ...
0.0335119 0.0162495 0.00910386 0.0949801 0.00303258 ...
0.0361652 0.0389352 0.0628194 0.0622919 0.0206459 ...
0.0583717 0.0910834 0.0889903 0.0142328 0.0750175 ...
0.0895628 0.0412171 0.0308301 0.0680314 0.0948681 ...
0.00439932 0.0975324 0.00736945 0.013484 0.079681 ...
0.0859327 0.0332012 0.0374489 0.0555631 0.0308727 ...
0.0375495 0.0474079 0.0450424 0.0822513 0.00803252 ...
# Matrix of size [10,10] [0x10fe150 data= 0x10fe210]
Once the trainer
is built, it is also possible to do introspection for getting or modify ANN components.
number = trainer:count_components( [pattern=.*] )
This method returns the number of ANN components in the current trainer
. Optionally the method receives a Lua pattern filtering the counting process to only the components whom name matches the pattern.
> = trainer:count_components()
3
object = trainer:get_component()
This method returns the root ANN component, which was given to the constructor.
> = trainer:get_component()
instance 0x1175430 of ann.components.hyperplane
number = trainer:component(name)
This method returns an ANN component object given its name
.
> = trainer:component("hyperplane")
instance 0x1175430 of ann.components.base
object = trainer:get_weights_of(component_name)
This method returns the matrix
object which belongs to the given component_name
.
> = trainer:get_weights_of("hyperplane")
nil
> = type( trainer:get_weights_of("cw1") )
matrix
> = type( trainer:get_weights_of("cb1") )
matrix
... = trainer:iterate_components( [pattern=.*] )
This method returns a Lua iterator function which iterates over all the ANN components which name matches the given pattern
.
> for name,c in trainer:iterate_components() do print(name,c) end
hyperplane instance 0x1175430 of ann.components.base
cw1 instance 0xfcf5b0 of ann.components.base
cb1 instance 0xff3c90 of ann.components.base
> iterator( trainer:iterate_components("cw.*") ):apply(print)
cw1 instance 0xfcf5b0 of ann.components.base
The following methods are shortcuts to modify hyperparameters of the optimizer
object.
desc = trainer:has_option(option)
Returns the description of the given option
if it exists at the optimizer
. Otherwise it returns nil
.
trainer:set_option(option, value)
Sets the given option
name to the given value
. Throws an error in case the option doesn't exists at the optimizer
.
value = trainer:get_option(option)
Returns the value of a given option
name, or throws an error if the option doesn't exits at the optimizer
.
trainer:set_layerwise_option(pattern, option, value)
This method needs that the trainer
was in built state. It traverses all the connection weight objects which name matches the given pattern
string, and sets its layerwise option
name to the given value
.
value = trainer:get_option_of(name, option)
This method returns the option
value which applies to the given connection weight object name
.
The following methods are prepared to work with a bunch of patterns (minibatch). They do one batch step of the algorithms, and could be rewritten to do specific things.
mu, matrix = trainer:train_step(input, target, loss, optimizer)
This method executes one training step, using the given data:
input
is a token
with a bunch (minibatch) of data, usually it is a matrix
instance, where rows are patterns and columns features.
target
is a token
with a bunch (minibatch) of data. Usually it is a matrix
instance.
loss
is a ann.loss
function object. It is optional, if not given it uses the loss function object instantiated at the trainer
object.
optimizer
is an ann.optimizer
object. It is optional, if not given it uses the optimizer object instantiated at the trainer
object.
The method returns two values:
mu
is the mean of the loss function at the given batch of patterns.
matrix
is a onedimensional matrix with the loss of every pattern.
> mean,loss_matrix = trainer:train_step(input, target)
mu, matrix = trainer:validate_step(input, target, loss)
This method executes one validate step, using the given data:
input
is a token
with a bunch (minibatch) of data. It can be a matrix
instance.
target
is a token
with a bunch (minibatch) of data. It can be a matrix
instance.
loss
is a ann.loss
function object. It is optional, if not given it uses the loss function object instantiated at the trainer
object.
The method returns two values:
mu
is the mean of the loss function at the given batch of patterns.
matrix
is a onedimensional matrix with the loss of every pattern.
The validate step evaluates the performance of the ANN component using the given loss function, but it doesn't train the parameters.
> mean,loss_matrix = trainer:validate_step(input, target)
g, mu, mat = trainer:compute_gradients_step(i, t, l [, g ])
This method compute the gradients of the given data, but doesn't train the parameters. The given gradients could be used to perform a manual training or gradient checking.
The arguments are:
input
is a token
with a bunch (minibatch) of data. It is usually a matrix
instance.
target
is a token
with a bunch (minibatch) of data. It is usually a matrix
instance.
loss
is a ann.loss
function object. It is optional, if not given it uses the loss function object instantiated at the trainer
object.
gradients
is a dictionary with the gradient matrices of every connection weights object. It is optional, if not given, the matrices will be allocated, if given, the allocation could be avoided.
The method returns three values:
gradients
the gradients dictionary.
mu
is the mean of the loss function at the given batch of patterns.
matrix
is a onedimensional matrix with the loss of every pattern.
> gradients,mean,loss_mat = trainer:compute_gradients_step(input, target)
boolean = trainer:grad_check_step(i, t [, boolean [, loss ] ] )
This method compute the gradients of the given data, and executes a gradient checking algorithm using numerical differentiation. The arguments are:
input
is a token
with a bunch (minibatch) of data. It is usually a matrix
instance.
target
is a token
with a bunch (minibatch) of data. It is usually a matrix
instance.
boolean
, if true
, it indicates high verbosity. It is optional, by default is false
.
loss
is a ann.loss
function object. It is optional, if not given it uses the loss function object instantiated at the trainer
object.
The method returns a boolean indicating if the gradient checking algorithm success or fails.
> trainer:grad_check_step(input, target) or error("Gradients checking fails")
This methods perform a traversal over a dataset with a large number of patterns, training or evaluating the model. The dataset is divided into batches of bunch_size
size.
loss_mu,loss_var = trainer:train_dataset{ ... }
This method is used to train by using a given dataset. Different training schedules are possible, depending on the parameters given to the method. In any case, this methods return two values:
The mean of the loss function over all the patterns.
The variance of the loss function over all the patterns.
The following training schedules are available:
input_dataset
a dataset
with the data for input of ANN components.output_dataset
a dataset
with the data for target outputs of ANN components (supervision).bunch_size
the minibatch size, optional parameter. If not given, the bunch size instantiated at the trainer
will be used.loss
a loss function object, optional parameter. If not given, the loss instantiated at the trainer
will be used.optimizer
an optimizer object, optional parameter. If not given, the optimizer instantiated at the trainer
will be used.input_dataset
a dataset
with the data for input of ANN components.output_dataset
a dataset
with the data for target outputs of ANN components (supervision).random
a random
object instance, used to shuffle the patterns.bunch_size
the minibatch size, optional parameter. If not given, the bunch size instantiated at the trainer
will be used.loss
a loss function object, optional parameter. If not given, the loss instantiated at the trainer
will be used.optimizer
an optimizer object, optional parameter. If not given, the optimizer instantiated at the trainer
will be used.input_dataset
a dataset
with the data for input of ANN components.output_dataset
a dataset
with the data for target outputs of ANN components (supervision).random
a random
object instance, used to shuffle the patterns.replacement
a given number with the size of the replacement.bunch_size
the minibatch size, optional parameter. If not given, the bunch size instantiated at the trainer
will be used.loss
a loss function object, optional parameter. If not given, the loss instantiated at the trainer
will be used.optimizer
an optimizer object, optional parameter. If not given, the optimizer instantiated at the trainer
will be used.distribution
is an array of tables, where each table contains:
input_dataset
a dataset
with the data for input of ANN components.output_dataset
a dataset
with the data for target outputs of ANN components (supervision).prob
a number with the probability of taken a pattern from this data source.random
a random
object instance, used to shuffle the patterns.replacement
a given number with the size of the replacement.bunch_size
the minibatch size, optional parameter. If not given, the bunch size instantiated at the trainer
will be used.loss
a loss function object, optional parameter. If not given, the loss instantiated at the trainer
will be used.optimizer
an optimizer object, optional parameter. If not given, the optimizer instantiated at the trainer
will be used.loss_mu,loss_var = trainer:validate_dataset{ ... }
This method is used to validate the model by using a given dataset. Different validation schedules are possible, depending on the parameters given to the method. In any case, this method returns two values:
The mean of the loss function over all the patterns.
The variance of the loss function over all the patterns.
The following validation schedules are available:
input_dataset
a dataset
with the data for input of ANN components.output_dataset
a dataset
with the data for target outputs of ANN components (supervision).bunch_size
the minibatch size, optional parameter. If not given, the bunch size instantiated at the trainer
will be used.loss
a loss function object, optional parameter. If not given, the loss instantiated at the trainer
will be used.input_dataset
a dataset
with the data for input of ANN components.output_dataset
a dataset
with the data for target outputs of ANN components (supervision).random
a random
object instance, used to shuffle the patterns.replacement
a given number with the size of the replacement.bunch_size
the minibatch size, optional parameter. If not given, the bunch size instantiated at the trainer
will be used.loss
a loss function object, optional parameter. If not given, the loss instantiated at the trainer
will be used.output_ds = trainer:use_dataset{ ... }
This method receives a table with a one or two datasets and computes the output of the ANN component for every pattern. Note that this method has a large use of memory, because it needs a dataset where to store the ANN output for every pattern. Please, be careful when using it.
It receives a table with fields:
input_dataset
a dataset
with the input data for the ANN component.
output_dataset
a dataset
with enough space to store the output of the ANN component for every pattern in the input_dataset
. If not given, the output_dataset
will be allocated automatically with the required size.
This method returns the output_dataset
with the produced data.
boolean = trainer:grad_check_dataset{ ... }
This two classes are useful to build a training loop with a default stopping criterion.
trainable.train_wo_validation
class
This class implements the training function without validation, using a stopping criterion based on percentage of improvement in training or in number of epochs. The following methods are defined.
train_func = trainable.train_wo_validation{ ... }
The constructor, which receives a table with the following fields:
min_epochs=1
: the minimum number of epochs of the training. It is optional.
max_epochs
: the maximum number of epochs of the training, if min_epochs==max_epochs
then stopping criteria will be number of epochs.
percentage_stopping_criterion=0.01
: a number in range [0,1]
indicating the threshold for the percentage of improvement in training loss between two consecutive epochs. If the train loss improvement is less than this number, the training will stops. It is an optional field.
first_epoch=1
: indicates the number of the first epoch. It is optional.
>  instance using percentage_stopping_criterion=0.01 (default value)
> train_func = trainable.train_wo_validation{ max_epochs = 100 }
boolean = train_func:execute(epoch_function)
This method executes one epoch step. It is the most important method. It receives an epoch function, which is a closure with the responsibility of perform training with one epoch, and it must returns two values: the trained model and the training loss. The method returns true
or false
depending in if the stopping criterion is satisfied or not.
> while train_func:execute(function()
local tr = trainer:train_dataset(datosentrenar)
return trainer,tr
end) do
print(train_func:get_state_string())
end
train_func:set_param(name,value)
This method modifies the value of a parameter previously given at the constructor (or used with its default value).
value = train_func:get_param(name)
This method returns the value of a parameter previously given at the constructor (or used with its default value).
epoch,tr_loss,tr_improvement,last=train_func:get_state()
This method returns the internal state of the object. last
is the trained model returned by the last call to epoch function.
state = train_func:get_state_table()
This method returns a table with the following fields:
state.current_epoch
: the current epoch.
state.train_error
: the train loss at last epoch.
state.train_improvement
: the train loss relative improvement.
state.last
: the trained model returned by the last call to epoch function.
string = train_func:get_state_string()
This method returns a string for printing purposes, with the following format:
string.format("%5d %.6f %.6f",
state.current_epoch,
state.train_error,
state.train_improvement)
Finally, here is a code example showing how to use this class:
> trainer = trainable.supervised_trainer(thenet, ann.loss.mse(), 64)
> train_func = trainable.train_wo_validation{ max_epochs = 100 }
> while train_func:execute(function()
local tr = trainer:train_dataset(training_data)
return trainer,tr
end) do
print(train_func:get_state_string())
util.serialize({ train_func, training_data.shuffle }, "training.lua")
end
The following is an example of loading previously saved object:
> train_func,shuffle = util.deserialize("training.lua")
> trainer = train_func:get_state_table().last
> thenet = trainer:get_component()
> training_data.shuffle = shuffle
> while train_func:execute(function()
local tr = trainer:train_dataset(training_data)
return trainer,tr
end) do
print(train_func:get_state_string())
util.serialize({ train_func, training_data.shuffle }, "training.lua")
end
trainable.train_holdout_validation
class
This class implements the training function with a holdout validation set, using a stopping criterion based on validation or error in number of epochs. This object follows the Pocket Algorithm, so, it keeps the model which has the best validation loss during the training. A tolerance in the relative error could be used to decided a minimum improvement to take the model as the best. The following methods are defined.
train_func = trainable.train_holdout_validation{ ... }
The constructor, which receives a table with the following fields:
min_epochs=1
: the minimum number of epochs of the training. It is optional.
max_epochs
: the maximum number of epochs of the training, if min_epochs==max_epochs
then stopping criteria will be number of epochs.
epochs_wo_validation=0
: the number of epochs where validation loss is ignored, so the best model is the last given. It is optional.
stopping_criterion=function() return false end
: a stopping criterion function. It will be used to determine when the training must be stopped. The given function is called given it a table with the output of get_state_table()
method. It is optional. Basic criterion functions are defined in trainable
table, and described below this section.
first_epoch=1
: indicates the number of the first epoch. It is optional.
tolerance=0
: the tolerance>=0
is the minimum relative difference to take the current validation loss as the best. It is optional.
> criterion = trainable.stopping_criteria.make_max_epochs_wo_imp_relative(2)
> train_func = trainable.train_holdout_validation{
stopping_criterion = criterion,
max_epochs = max_epochs
}
boolean = train_func:execute(epoch_function)
This method executes one epoch step. It is the most important method. It receives an epoch function, which is a closure with the responsibility of perform training with one epoch, and it must returns three values: the trained model, the training loss and the validation loss. The method returns true
or false
depending in if the stopping criterion is satisfied or not.
> while train_func:execute(function()
local tr = trainer:train_dataset(training_data)
local va = trainer:validate_dataset(validation_data)
return trainer,tr,va
end) do
print(train_func:get_state_string())
local state = train_func:get_state_table()
if state.best_epoch == state.current_epoch then
util.serialize({ train_func, training_data.shuffle }, "training.lua")
end
end
train_func:set_param(name,value)
This method modifies the value of a parameter previously given at the constructor (or used with its default value).
value = train_func:get_param(name)
This method returns the value of a parameter previously given at the constructor (or used with its default value).
epoch,tr_loss,va_loss,...=train_func:get_state()
This method returns the internal state of the object: epoch, training loss, validation loss, best epoch, best validation loss, best model clone, last given model.
state = train_func:get_state_table()
This method returns a table with the following fields:
state.current_epoch
: the current epoch.
state.train_error
: the train loss at last epoch.
state.validation_error
: the validation loss at last epoch.
state.best_epoch
: the epoch where the best validation loss where found.
state.best_val_error
: the validation loss at the best epoch.
state.best
: the trained model which achieves the best validation error.
state.last
: the trained model returned by the last call to epoch function.
string = train_func:get_state_string()
This method returns a string for printing purposes, with the following format:
string.format("%5d %.6f %.6f %5d %.6f",
state.current_epoch,
state.train_error,
state.validation_error,
state.best_epoch,
state.best_val_error)
Finally, here is a code example showing how to use this class:
> trainer = trainable.supervised_trainer(thenet, ann.loss.mse(), 64)
> criterion = trainable.stopping_criteria.make_max_epochs_wo_imp_relative(2)
> train_func = trainable.train_holdout_validation{
stopping_criterion = criterion,
max_epochs = max_epochs
}
> while train_func:execute(function()
local tr = trainer:train_dataset(training_data)
local va = trainer:validate_dataset(validation_data)
return trainer,tr,va
end) do
print(train_func:get_state_string())
local state = train_func:get_state_table()
if state.best_epoch == state.current_epoch then
util.serialize({ train_func, training_data.shuffle }, "training.lua")
end
end
For holdoutvalidation scheme, exists two predefined stopping criteria, which are function builders (they return the function used as criterion):
trainable.stopping_criteria.make_max_epochs_wo_imp_absolute
: which receives a constant indicating the maximum number of epochs without improve validation. A tipical value is between 10 and 20, depending in the task.
trainable.stopping_criteria.make_max_epochs_wo_imp_relative
: which receives a constant indicating the maximum value for current_epoch/best_epoch. A tipical value for this is 2.
This two criteria could be used as this:
train_func = trainable.train_holdout_validation{
...
stopping_criterion = trainable.stopping_criteria.make_max_epochs_wo_imp_relative(2),
...
}
Also you can create your own stopping criterion, which is a function which receives a table:
train_func = trainable.train_holdout_validation{
...
stopping_criterion = function(t)
 t contains this fields:
 * current_epoch
 * best_epoch
 * best_val_error
 * train_error
 * validation_error
return true IF STOPPING CRITERIA(t) IS TRUE
end,
...
}
The class trainable.supervised_trainer
uses some generic dataset iterator functions, available for the user if needed. Two functions are available: trainable.dataset_pair_iterator
and trainable.dataset_multiple_iterator
. The first is a wrapper around the second one. This iterators could perform different traverse methods, depending in the given parameters.
Lua iterator = trainable.dataset_pair_iterator{ ... }
This iterator performs a synchronized traversal of two given datasets (normally it is a pair input/output). The function returns a Lua iterator which returns three values every time it is called: input
pattern (a token
, usually a matrix
instance), output
pattern (a token
, usually a matrix
instance), and a Lua table with the indexes of the patterns taken in the bunch.
The available traversal modes are:
input_dataset
a dataset
with the data for input of ANN components.output_dataset
a dataset
with the data for target outputs of ANN components (supervision).bunch_size
the minibatch size.input_dataset
a dataset
with the data for input of ANN components.output_dataset
a dataset
with the data for target outputs of ANN components (supervision).shuffle
a random
object instance, used to shuffle the patterns.bunch_size
the minibatch size.input_dataset
a dataset
with the data for input of ANN components.output_dataset
a dataset
with the data for target outputs of ANN components (supervision).shuffle
a random
object instance, used to shuffle the patterns.replacement
a given number with the size of the replacement.bunch_size
the minibatch size.distribution
is an array of tables, where each table contains:
input_dataset
a dataset
with the data for input of ANN components.output_dataset
a dataset
with the data for target outputs of ANN components (supervision).prob
a number with the probability of taken a pattern from this data source.shuffle
a random
object instance, used to shuffle the patterns.replacement
a given number with the size of the replacement.bunch_size
the minibatch size.> ds_params = { input_dataset = my_input_ds, output_dataset = my_output_ds }
> for input,output,idxs in trainable.dataset_pair_iterator(ds_params) do
 you can give the input/output to an ANN and loss function
print(input,output,idxs)
end
Lua iterator = trainable.dataset_multiple_iterator{ ... }
This iterator performs a synchronized traversal of any number given datasets (normally it is a pair input/output). The function returns a Lua iterator which returns as many values as the number of given datasets plus one: one pattern for each dataset, plus a Lua table with the indexes of the patterns taken in the bunch.
The available traversal modes are:
datasets
: a Lua table with the list of dataset for traversal.bunch_size
the minibatch size.datasets
: a Lua table with the list of dataset for traversal.shuffle
a random
object instance, used to shuffle the patterns.bunch_size
the minibatch size.datasets
: a Lua table with the list of dataset for traversal.shuffle
a random
object instance, used to shuffle the patterns.replacement
a given number with the size of the replacement.bunch_size
the minibatch size.distribution
is an array of tables, where each table contains:
datasets
: a Lua table with the list of dataset for traversal.prob
a number with the probability of taken a pattern from this data source.shuffle
a random
object instance, used to shuffle the patterns.replacement
a given number with the size of the replacement.bunch_size
the minibatch size.> ds_params = { datasets = { my_ds1, my_ds2, my_ds3 } }
> for token1,token2,token3,idxs in trainable.dataset_multiple_iterator(ds_params) do
 you can give the token1,token2,token3 to an ANN or a loss function
print(token1,token2,token3,idxs)
end
random
packagePackage random
could be loaded via the standalone binary, or in Lua with require("aprilann.random)
.
The random
class is useful to generate pseudorandom numbers, and is widely used by ANN components and other classes of APRILANN. It is based on Mersenne Twister, basically it is a binding of the original C++ code of Mersenne Twister.
random
contains the following methods:
obj = random( [seed] )
A constructor of the object. The parameter is optional, if not given, it is taken from the current time of the machine. If given, it could be:
a seed number
for the initialization of the random generator;
a table
with seeds for the initialization of the random generator.
number = obj:rand( [number] )
Returns a double random number in the interval [0,n]
, being n
the given parameter. If not given any parameter, by default n=1
.
number = obj:randExc( [number] )
Returns a double random number in the interval [0,n)
, being n
the given parameter. If not given any parameter, by default n=1
.
number = obj:randDblExc( [number] )
Returns a double random number in the interval (0,n)
, being n
the given parameter. If not given any parameter, by default n=1
.
number = obj:randInt( [x, [ y ] ] )
Returns an integer random number in the interval [x,y]
. If only one argument is given, then the interval will be [0,x]
. If zero argument are given, the interval will be [0,2^321]
.
table = obj:shuffle(N)
Returns a table with size N
, which is a permutation of the indices of an Nsized array.
table = obj:shuffle(table)
Returns a random permutation of the given table array.
number = obj:choose(size)
$eturns a random element for an array of the given size
. It is equivalent to obj:randInt(1,size)
.
number = obj:randNorm(mean,variance)
Returns a random number sampled from a Gaussian with the given mean
and variance
parameters.
obj:seed(number)
Modifies the seed, see the constructor.
obj:seed(table)
Modifies the seed, see the constructor.
another = obj:clone()
Returns a deep copy of the caller object.
autodiff
packageRelated with package require("aprilann.autodiff")
.
The autodiff
package is an indevelopment package, which adds automatic differentiation to APRILANN toolkit. It is inspired into Theano, but, in this case, using Lua and APRILANN matrix library instead of tensors in Python.
This packages works over three main data types: constants, scalars, matrices. For example, the addition of two scalars will need the following code:
> AD = autodiff  for simplicity
> a,b = AD.scalar('a b')
> c = a + b
> = c
(+ a b)
Every expression is stored as a graph of dependencies between operations and its arguments. Every symbol (like a
, b
or c
) is a special Lua table. Symbols has a name, in the case of ground symbols (like a
or b
), its name is the symbol itself. In the case of operations (like c
), the name is what you see when you do print(c)
.
> = a.name,b.name,c.name
a b (+ a b)
A symbol only exists once, so, if you declare another symbol with the same name, or the same operation, it will be a reference to the first symbol declaration. They are exactly the same variable. In this way, the computation is performed in a symbolic graph, and the use of memoization allows to ensures that every operation is only computed once, even if it is repeated several times.
However, depending in the declaration order, the same operation could be represented in different ways. It is possible to optimize the graph applying basic arithmetic properties.
> d = c + b + a
> = d
(+ (+ (+ a b) b) a)
> e = AD.optimize(d)
> = e
(+ (* 2 a) (* 2 b))
It is possible to operate with Lua numbers, which are automatically coerced into autodiff.constant
symbol:
> f = 4*b*a/b + 2
> = f
(+ (* (* (* 4 b) a) (^ b 1)) 2)
> = AD.optimize(f)
(+ (* 4 a) 2)
Math operators are overloaded using Lua metamethods, so it is possible to do use math operators in a standard way: addition, subtraction, multiplication, division, power, unary minus. Nevertheless, this operations are defined as functions in autodiff
package.
> op = AD.op  for simplicity
> c = AD.op.div( AD.op.add(a,b), 4)
> = c
(* (+ a b) 0.25)
Constants are automatically folded, performing constant operations. Subtraction is transformed in addition with multiplication by 1. Division is transformed into multiplication with power by 1. This reduces a lot the simplification effort.
> c = a/b  a
(+ (* (^ b 1) a) (* 1 a))
But this transformations makes difficult to follow the printed string. It is possible to produce a graph in dot format.
> AD.dot_graph(c, "mygraph.dot")
The most interesting thing is the automatic differentiation procedure, which receives a list of variables w.r.t differentiate. The function returns a variable number of arguments, one graph for each differentiated variable.
> c = a * b
> da,db = AD.diff(c, {a,b})
> = da
b
> = db
a
Any graph could be evaluated with a given instantiation of its internal ground variables.
> c = a * b
> = c:eval{ a=5, b=2 }
10
> = da:eval{ a=3, b=2 }
2
> = db:eval{ a=3, b=2 }
3
Additionally, it is possible to compile several graphs together, sharing the computation of equal operations. The compilation procedure automatically applies the optimization function to the given graphs. It receives the list of graphs (symbols) which you want to compile together, and a list of input variables (in order). Optionally it is possible to give a dictionary Lua table with values which will be shared between the compiled function and the caller Lua virtual machine.
>  AD.func does the compilation
> my_func1 = AD.func({ c, da, db }, { a,b })
> = my_func1(2,5)
10 5 2
> shared = { b=4 }
> my_func2 = AD.func({ c, da, db }, { a }, shared)
> = my_func2(2)  2 * 4
8 4 2
> shared.b = 10
> = my_func2(2)  2 * 10
20 10 2
The most important concept is the symbol, which could be a ground variable (with a declared type) or a computational graph. Every symbol has a name which identifies it univocally. Every symbol has overloaded basic math operations (addition, subtraction, multiplication, division, unary minus, power), allowing to use standard math symbols to produce complex operations. Computational graph symbols represent operations. Every symbol implements a generic interface. Depending in the type, symbols are constructed in different ways:
> AD = autodiff  for simplicity
> a,b = AD.scalar('a b')  scalar constructor
> c = a + 4*b  4 is casted to AD.constant type
> m = AD.matrix('m')  matrix constructor
> d = m * c
The type of computational graph symbols (operations) depends in the arguments of the operation. Inference rules are declared in order to decide when an operation is over matrices, scalar, constants, or any of the possible symbol types.
value = s:eval( table [, cache] )
Evaluates the caller symbol by using the given table of ground variables. All the ground variables need to be defined (except constants).
> = d:eval{ m=matrix(3,3):linear(), a=10, b=2 }
0 18 36
54 72 90
108 126 144
# Matrix of size [3,3] in row_major [0x2b6cd60 data= 0x293e4a0]
An optional table could be given as second argument, which will be used as cache table (for memoization), allowing to share computations between different symbols evaluation. In this case, it is not necessary to give all the ground variables, as far as all the needed partial operations where computed and moemoized in the given cache table.
> cache = {}
> = c:eval({ a=10, b=2 }, cache)  computes c=a + 4*b
18
> = d:eval({ m=matrix(3,3):linear() }, cache)  computes d = m * 18
0 18 36
54 72 90
108 126 144
# Matrix of size [3,3] in row_major [0x2b6cd60 data= 0x293e4a0]
function = autodiff.func(symbols, args, shared [, optimize=true])
This function compiles together the given set of symbols, producing as result a Lua function which receives as arguments the ground variables indicated in the args
table (ordered), and shares the given table of shared
ground variables. Additionally, an optional fourth argument could be given, indicating with a boolean
if optimization needs to be performed or not. If not given, this fourth argument will be considered true
.
The symbols list (first argument) could be a table of symbols or a symbol. The returned Lua function will receive as many arguments as the size of args
table array, and will produce as many values as size of symbols
list.
> f = AD.func(d, {a,b}, {m = matrix(3,3):linear()})
> = f(10,2)
0 18 36
54 72 90
108 126 144
# Matrix of size [3,3] in row_major [0x1cddcd0 data= 0x1c86b20]
ds1,ds2,... = autodiff.diff(symbol, { s1, s2, ... } [, seed] )
This function differentiates the given symbol w.r.t. all the given array of ground variables (second argument). It returns multiple outputs, as many as the size of the ground variables array.
> dm,da = autodiff.diff(d, { m, a })
> = dm
(.* (+ (* 4 b) a) (fill (.* m (+ (* 4 b) a)) 1))
> = da
(.* m (fill (.* m (+ (* 4 b) a)) 1))
> = d
(.* m (+ (* 4 b) a))
Looking to the produced computations, they have one thing in common: both uses a fill operation which receives as input the original d
computational graph and the number 1
. The fill operations produces a symbol with the same shape and type of the given first argument, but replacing all its components with the given second argument, 1
in this case. This is the seed needed to implement reverse accumulation algorithm for automatic differentiation. This seed forces to compute the output produced by the original computation. It is possible to avoid this computation using the optional third argument of the diff
function, given a seed symbol which will be treated as a new ground variable. When compiling or evaluation the differentiated symbols, you will need to give the value of the seed. The shape and type of the seed must be exactly the same as the nondifferentiated symbol (in this case d
), and normally filled with 1s, but it could be a seed produced as derivative by other function.
> seed = AD.matrix('seed')
> dm,da = AD.diff(d, { m, a })
> = dm
(.* (+ (* 4 b) a) seed)
> = da
(.* m seed)
table = s:diff(seed, table)
s:compile(compiler)
Lua iterator = s:arg_ipairs()
This method returns a Lua iterator which traverses using ipairs
all the arguments received by the given symbol. If it is a ground variable, this method returns an empty iterator.
symbol = s:replace(new_symbol)
This method is used in optimization procedures. It allows to replace the caller symbol by the given symbol. It returns the caller symbol.
symbol = s:clear_var_name()
This method removes the variable name associated with compilation. It returns the caller symbol.
symbol = s:set_dims(d1, d2, ...)
This method indicates the shape of the caller symbol. It is useful with matrix
type, allowing to check if matrices fit the declared operations. It returns the caller symbol.
> a,b = AD.matrix('a b')
> a:set_dims(2,4)
> b:set_dims(4,3)
> c = a * b
> = table.unpack( c.dims )
2 3
> a:set_dims(2,2)
> c = a * b
[string "luaopen_aprilann_autodiff"]:5: Incorrect matrix dims for multiplication: 2x2 * 4x3
stack traceback:
[C]: in function 'assert'
[string "luaopen_aprilann_autodiff"]:5: in function <[string "luaopen_aprilann_autodiff"]:5>
(...tail calls...)
stdin:1: in main chunk
[C]: in ?
symbol = s:set_broadcast(b1, b2, ...)
The term broadcasting describes how matrices with different shapes are treated during operations. Some mathematical operators allow to work with matrices with not fitted shapes, replicating one of the matrices over broadcasted dimensions. This concept is similar to numpy broadcast. As example, broadcasting is used in ANNs when you add bias vector, allowing to compute operations with several samples at the same time (minibatch or bunchmode):
> x,w,b = AD.matrix('x w b')
> x:set_dims(20, 4)  20 features, 4 samples
> w:set_dims(10, 20)  10 outputs, 20 inputs
> b:set_dims(10, 1)  10 bias components
> b:set_broadcast(false, true)
> output = w*x + b  bias is broadcasted to fit the addition operation
> = output:eval{ x=matrix(20,4):uniformf(),
w=matrix(10,20):uniformf(),
b=matrix(10,1):uniformf() }
4.92572 6.4316 5.24994 5.73787
5.13588 5.06926 4.93599 5.89694
5.82963 6.1323 6.2065 7.06305
4.51586 5.76797 5.27805 6.3306
4.17794 5.31377 4.05488 4.4576
4.20612 5.04744 4.77355 5.41917
5.74113 6.56931 5.7724 6.52165
6.00033 6.34304 5.77687 7.40072
7.1957 8.11042 7.12002 8.2262
5.53413 6.22189 5.72926 6.46156
# Matrix of size [10,4] in row_major [0x29c1750 data= 0x2979e00]
If you don't indicate true
in the corresponding broadcasting dimension, an error will occur. By default, all the dimensions has a false
broadcasting property.
> b:set_broadcast(false, false)
> output = w*x + b  bias is broadcasted to fit the addition operation
> = output:eval{ x=matrix(20,4):uniformf(),
w=matrix(10,20):uniformf(),
b=matrix(10,1):uniformf() }
[string "luaopen_aprilann_autodiff"]:5: Not broadcasted dimensions must be equal, found broadcasted_dims[2]=4 and dims[2]=1
stack traceback:
[C]: in function 'assert'
[string "luaopen_aprilann_autodiff"]:5: in function 'eval_func'
[string "luaopen_aprilann_autodiff"]:1: in function <[string "luaopen_aprilann_autodiff"]:1>
(...tail calls...)
[C]: in ?
autodiff.ann
packageRelated with package require("aprilann.autodiff.ann")
.
matlab
packagePackage matlab
could be loaded via the standalone binary, or in Lua with require("aprilann.matlab)
.
The MATfile format belongs to Matlab software. We follow this documentation to implement this loader. Saving is not available.
Currently, only Cell Arrays, Structures, and Numeric Matrices could be loaded, all of them only in binary format. Compression is allowed. All the data must follow the guidelines described at the documentation.
We use three test files (test1.mat, test2.mat, and test3.mat) produced by the following Matlab commands:
> x = [ 1.34187 1.77726 1.73478 ...
> 0.932328 0.59467 0.332692 ...
> ...
> ];
> save("test1.mat", "x")
> A = [ 1 2 3; 4 5 6 ];
> B = [ 7 8 9; 10 11 12 ];
> C = { A, B };
> save("test2.mat", "C")
> X.w = 1
> X.y = 2
> X.z = 3
> save("test3.mat", "X")
The MATfile could be loaded using the function matlab.read
. This function shows at the screen commented lines which indicates the kind of data loaded and the name of the variables. All the Matlab variables will be allocated at a Lua table, indexed by the name of the variable.
> a_table = matlab.read("test1.mat")
> print(a_table.x)
1.34187 1.77726 1.73478 ...
0.932328 0.59467 0.332692 ...
0.254006 2.86238 0.877438 ...
... ... ... ...
It is also possible to print fields of the table using the print
or tostring
functions. The following example shows the print
function for a Cell Array.
> a_table = matlab.read("test2.mat")
> print(a_table)
C : matlab.cell_array dims [1,2]
> print(a_table.C:get(1,1))
1 2 3
4 5 6
# MatrixDouble of size [2,3] stride [3,1] ref [0x22bebe0 data= 0x2206400]
> print(a_table.C:get(1,2))
7 8 9
10 11 12
# MatrixDouble of size [2,3] stride [3,1] ref [0x239f120 data= 0x2379ed0]
When a MATfile with MATmatrix variables is loaded, every MATmatrix is converted to APRILANN matrix objects. Five matrices are available, depending on the MATmatrix datatype: matrix
for float, matrixDouble
for double, matrixInt32
for int32, matrixComplex
for float complex numbers, and matrixChar
for char.
If any of the variables is a Cell Array, it becomes a Lua object (a table with methamethods) which has the following methods:
table = c:dim()
returns a table with the size of each dimension of the array.
number = c:dim(number)
returns the size of the given dimension (starging in 1)
element = c:get(p1,p2,...,pn)
returns the element at the position (p1,p2,...,pn), where element could be a matrix
, matrixChar
, matrixInt32
, cell_array
, or structure
, depending on the class of data.
> a_table = matlab.read("test2.mat")
> print(a_table.C:get(1,1))
1 2 3
4 5 6
# Matrix of size [2,3] in row_major [0x27a0340 data= 0x26cf9c0]
> print(a_table.C:get(1,2))
7 8 9
10 11 12
# Matrix of size [2,3] in row_major [0x283c6a0 data= 0x26e4a40]
The following methods are for lowlevel access, which could be useful to do a loop over all the elements:
number = c:size()
returns the number of elements at the array.
element = c:raw_get(number)
returns the element at row_major
sorted position number
, being number between 0
and c:size()1
. The number is the position of the element if all elements where sorted as a continuous array.
table = c:compute_coords(number)
returns the coordinate position of a given raw position number. As previous method, the number must be between 0
and c:size()1
.
> a_table = matlab.read("test2.mat")
> print(a_table)
C : matlab.cell_array dims [1,2]
> C = a_table.C
> for i=0,C:size()1 do e=C:raw_get(i)print("COORDS",unpack(C:compute_coords(i)))print(e)end
COORDS 1 1
1 2 3
4 5 6
# Matrix of size [2,3] in row_major [0x1904b20 data= 0x18c90f0]
COORDS 1 2
7 8 9
10 11 12
# Matrix of size [2,3] in row_major [0x19054e0 data= 0x18caed0]
The Structures are transformed in Lua tables (as dictionaries), indexed by the name of the fields, and as values the corresponding elements. As before, the elements could be any kind of matrix
, cell_array
, or structure
.
> a_table = matlab.read("test3.mat")
> print(a_table)
X : table
> print(a_table.X)
y : matrixDouble
z : matrixDouble
w : matrixDouble
> print(a_table.X.y)
2
# Matrix of size [1,1] in row_major [0xd999c0 data= 0xd99690]
> print(a_table.X.w)
1
# Matrix of size [1,1] in row_major [0xd99b60 data= 0xd99c20]
> print(a_table.X.z)
3
# Matrix of size [1,1] in row_major [0xd99d30 data= 0xd99df0]
stats
packagePackage stats
could be loaded via the standalone binary, or in Lua with require("aprilann.stats)
.
This package contains utilities for statistical purposes.
result = stats.amean(m[, dim])
result = stats.gmean(m[, dim])
result = stats.hmean(m[, dim])
result = stats.var(m[, dim])
result = stats.std(m[, dim])
result,lags = stats.acf(m[, { lag_max, lag_step, cor }])
result = stats.cov(x[, y[, { centered, true_mean }]])
result = stats.cor(x[, y[, { centered }]])
r1,r2,... = stats.percentile(m, p1, p2, ...)
result,center = stats.center(m[, center])
result,center,scale = stats.standardize(m[,{ center, scale }])
tbl = stats.summary(m)
stats.running.mean_var`
This class is useful to compute mean and variance over a large number of elements in an efficient way, following (this method)[http://www.johndcook.com/standard_deviation.html] to avoid instability. This class has the following methods:
obj=stats.running.mean_var()
it is a constructor which builds an instance.
obj = obj:add(number)
a method for adds a number to the set. It returns the caller object.
obj = obj:add(iterator)
a method which adds the sequence of numbers returned by the given iterator function. It returns the caller object.
obj = obj:add(table)
a method which adds all the elements of the given table (as array) to the set. The elements could be numbers or functions. It returns the caller object.
mean,variance = obj:compute()
computes and returns the accumulated mean and variance from all the calls to add
method.
number = obj:size()
returns the number of elements added.
obj = obj:clear()
reinitializes the object.
> obj = stats.running.mean_var()
> obj:add(4)
> obj:add(10)
> print(obj:compute())
7 18
> obj:add({2,8,6,24})
> print(obj:compute())
9 62
> obj:add( pairs({ a=2, b=10 }) )
> print(obj:compute())
8.25 50.785714285714
> print(obj:size())
8
result = stats.comb(n,k)
result = stats.log_comb(n,k)
table = stats.boot{ ... }
This function receives a data sample, performs a random resampling of the data and gives it to a statistic function. The procedure is repeated several times (bootstrapping technique), and the function returns a matrix with the statistics computed for all the repetitions.
The function receives a table with this fields:
size=number or table
the sample population size, or a table with several sample population sizes.
R=number
the number of repetitions of the procedure.
k=number
the number of results returned by statistic
function. By default it is 1.
statistic=function
a function which receives a matrixInt32
with a list of indices for resampling the data. The function can compute a number of k statistics (with k>=1), being returned as multiple results.
verbose=false
an optional boolean indicating if you want or not a verbose output. By default it is false
.
seed=1234
an optional number indicating the initial seed for random numbers, by default it is 1234
.
random
an optional random number generator. Fields seed
and random
are forbidden together, only one can be indicated.
ncores=1
an optional number indicating the number of CPU cores to use for the computation. It allows to speedup large bootstraps. By default it is 1
.
The following example takes 1000
random values and performs bootstrap resampling over them, computing the mean of the resampled population:
 the random population
> errors = stats.dist.normal():sample(random(567),1000)
>  executes the bootstrap resampling function
> resampled_data = stats.boot{
size = errors:size(), R = 1000, seed = 1234, k=2,
statistic = function(sample)
local s = errors:index(1, sample)
local var,mean = stats.var(s)
 this function returns two results, the mean and the variance (k=2)
return mean,var
end,
}
>  the 95% confidence interval is computed, being the range [a,b]
> a,b = stats.boot.ci(resampled_data, 0.95, 1)
> print(a,b)
0.073265952430665 0.051443199906498
a,b = stats.boot.ci(data [, confidence=0.95 [, pos=1 ]])
This function receives a matrix with data computed by stats.boot
and returns the confidence interval for the given confidence
value and the given statistic function position. It returns two numbers, the left limit, and the right limit, being the interval [a,b]
.
 the 95% confidence interval [a,b] of sample mean
local a,b = stats.boot.ci(resampled_data, 0.95, 1)
 the 95% confidence interval [a,b] of sample variance
local a,b = stats.boot.ci(resampled_data, 0.95, 2)
matrix = stats.pca.center_by_pattern(matrix)
This function modifies inplace the given matrix
, centering all the patterns to have zeromean. The patterns must be ordered by rows, and the features by columns. The function returns the same matrix
as given.
>  four patterns, 6 features
> m = matrix(4,6):uniformf(1,2)
> = m
1.10795 1.55917 1.35379 1.22971 1.38055 1.81201
1.1907 1.58711 1.38786 1.55067 1.01365 1.6242
1.94076 1.33665 1.28377 1.72529 1.26619 1.60847
1.64965 1.65704 1.55421 1.80517 1.68546 1.92166
# Matrix of size [4,6] in row_major [0x2575340 data= 0x249c490]
> stats.pca.center_by_pattern(m)
> = m
0.299245 0.151974 0.0534089 0.177485 0.0266466 0.404811
0.201669 0.194743 0.00450444 0.15831 0.378713 0.231833
0.413907 0.190203 0.243086 0.198439 0.260668 0.081612
0.0625433 0.0551615 0.15799 0.0929705 0.0267389 0.209462
# Matrix of size [4,6] in row_major [0x2575340 data= 0x249c490]
> = m:sum(2):scal(1/m:dim(2))  each pattern is centered
3.97364e08
1.58946e07
1.19209e07
1.39078e07
# Matrix of size [4,1] in row_major [0x2519460 data= 0x2491bd0]
U,S,VT = stats.pca(matrix)
This function implements standard PCA algorithm, based on covariance matrix computation, and Singular Values Decomposition (SVD). The function receives a matrix
, where features are columns, and data samples are by rows, that is, for M
patterns and N
features, the matrix
will be of MxN
size. The function needs that input matrix
was normalized, normally by centering the mean of each pattern (for example, using stats.pca.center_by_pattern(...)
function, or other normalization depending in the task, see UFLDL Tutorial for more information about data normalization).
The function return three matrix
objects, where:
U
is an orthogonal matrix which contains the eigenvectors of the covariance matrix, with one eigenvector per column, sorted in decreasing order.
S
is a vector (diagonal matrix) which corresponds with the singular values of the covariance matrix, sorted in decreasing order.
VT
is equivalent to U
, and can be ignored.
>  10000 patterns, 100 features
> m = matrix(10000,100):uniformf(1, 2, random(1234))
> m = stats.pca.center_by_pattern(m)
> U,S,VT = stats.pca(m)
i,s_i,s_mass = stats.pca.threshold(S [,mass=0.99] )
This function computes the cutting threshold for a given S
vector with the singular values sorted in decreasing order. It receives the matrix, and an optional number with the probability mass which you want to retain, by default it is mass=0.99
. The function computes three values:
i
is the index where you need to cut.
s_i
is the singular value at the index i
, that is S:get(i)
.
s_mass
is the mass accumulated until this index.
> = stats.pca.threshold(S, 0.5)
45 0.084392011165619 0.49575713463855
> = stats.pca.threshold(S, 0.7)
65 0.079482264816761 0.69391569135546
> = stats.pca.threshold(S)
97 0.06935129314661 0.98337004707581
matrix = stats.pca.whitening(X, U, S [, epsilon=0.0] )
This function implments PCA whitening, given a data matrix
X
(patterns by rows, features by columns), the U
and S
matrix
objects returned by stats.pca(X)
, and an optional regularization value epsilon
which by default is epsilon=0.0
. The function returns a new allocated matrix
, result of applying the whitening process.
>  loading digits from TEST/digitos/digits.png
> img = ImageIO.read("TEST/digitos/digits.png")
> m = img:to_grayscale():matrix()
> ds = dataset.matrix(m, { patternSize={16,16}, stepSize={16,16},
numSteps={100,10} })
> data_matrix = ds:toMatrix():clone()
> data_matrix = stats.pca.center_by_pattern(data_matrix)
>  PCA computation
> U,S,VT = stats.pca(data_matrix)
>  PCA whitening
> data_whitened = stats.pca.whitening(data_matrix, U, S, 0.02)
> = data_whitened
Large matrix, not printed to display
# Matrix of size [1000,256] [0x1be71b0 data= 0x1b23540]
If you want, it is possible to do dimension reduction by given U
and S
matrix slices, instead of the whole matrices.
>  PCA whitening and dimensionality reduction (256 => 100)
> out = stats.pca.whitening(data_matrix, U(':','1:100'), S('1:100'), 0.02)
> = out
Large matrix, not printed to display
# Matrix of size [1000,100] [0xe5c070 data= 0xe5c140]
See also documentation of ann.components.pca_whitening
(when available).
X = stats.zca.whitening(X,U,S,epsilon)
This function receives the same arguments as stats.pca.whitening
, but instead of do a PCA whitening, it computes a ZCA whitening. In this case, the output is the given matrix
X
, so, the computation is done inplace.
>  ZCA whitening
> data_whitened = stats.zca.whitening(data_matrix:clone(), U, S, 0.02)
>  write to disk
> aux = data_whitened:clone("row_major")
> for sw in aux:sliding_window():iterate() do sw:adjust_range(0,1) end
> ImageIO.write(Image(aux), "digitswhitened.png")
T,P,R = stats.pca.gs_pca{ ... }
WARNING this implementation is in experimental stage.
Implementation of PCAGS algorithm, an iterative efficient algorithm for PCA computation. This code is translated from GSL CBLAS implementation of the paper Parallel GPU Implementation of Iterative PCA Algorithms, M. Andrecut. The function receives a table with the following fields:
X=matrix
: a MxN matrix
, M number of patterns, N pattern size.
K=number
: the number of components that you want to compute, K <= N.
max_iter=number
: the maximum number of iterations computing every component. It is an optional parameter, by default it is max_iter=10000
.
epsilon=number
the convergence criterion. It is an optional parameter, by default it is epsilon=1e07
.
The function returns three matrices:
The T
scores matrix, with size MxK.
The P
loads matrix, with size NxK.
The R
residuals matrix, with size MxN.
stats.confusion_matrix
stats.MI
packagePackage stats.MI
could be loaded via the standalone binary, or in Lua with require("aprilann.stats.MI)
.
The table stats.MI
contains functions useful to compute the Mutual Information between matrices of data. It has the following functions:
number = stats.MI.entropy(matrix, histogram, levels=256)
this function receives three optional arguments. The first two are related to the set of data from computing the entropy. One of them must be given, the other must be nil
. The third argument is by default 256, and is only useful if the matrix
is given, and indicates the number of levels for the histogram computation.
matrix
argument is given, a histogram is computed to estimate the probability distribution of the data, using the given number of levels
, 256 by default.histogram
argument is given, the function takes this histogram as the source for the probability distribution estimation.MI,NMI = stats.MI.mutual_information(matrix1, matrix2, levels=256)
this function computes the amount of information mutually shared by the given two matrices of data, using levels
for the histogram computation. The two matrices will be reinterpreted as a linear sequence of data, so the must have exactly the same size, and is recommended both matrices to being a vector of data, so multidimensional feature vectors are not allowed. The function returns the Mutual Information, and the Normalized Mutual Information.> m1 = matrix(1,10):linear(1)
> m2 = matrix(1,10)
> m2:slice({1,1},{1,5}):linear(2)
> m2:slice({1,6},{1,5}):linear(2)
> print(m1)
1 2 3 4 5 6 7 8 9 10
# Matrix of size [1,10] in row_major [0x260dae0 data= 0x260dbc0]
> print(m2)
2 3 4 5 6 2 3 4 5 6
# Matrix of size [1,10] in row_major [0x260e280 data= 0x260de70]
> print(stats.MI.mutual_information(m1,m2))
2.321927794305 1.6989699041453
complex
packagePackage complex
could be loaded via the standalone binary, or in Lua with require("aprilann.complex)
.
The complex
is a new kind of data added binded from C++, which could be used with matrixComplex
and has available math operations in Lua and using CBLAS interface.
IMPORTANT as the complex
datatype is a C++ object, it is available via a reference pointer, be careful because the assignation is done by reference, not by content.
Exists two possible constructors:
> c = complex(2,1)  2 is real part, 1 is imaginary part
> print(c)
21i
>
>  the string is parsed in C++, worst performance than previous constructor
> c = complex("2+4i")
> print(c)
2+4i
The opreators '==', '', '/', '+', '' are defined to work with complex
objects. If the other operand is a number, it is converted to a complex with only real part. If the other operand is a string*, it will be converted to a complex number using the constructor from string.
Besides previous operations, the following math methods are available:
self = c:conj()
conjugates the given object. It is done inplace, so the object will be modified. Returns the caller object (self).
real,imaginary = c:plane()
returns the real and imaginary part.
number = c:real()
returns the real part of the number.
number = c:img()
returns the real part of the number.
abs,angle = c:polar()
returns the abs and angle of its polar form.
number = c:abs()
returns the 2norm of the caller complex number.
number = c:angle()
returns the angle of its polar form.
other_complex = c:exp()
returns the exponential (e^z) of the caller complex number.
number = c:sqrt()
returns the squareroot of the caller complex number.
> c1 = complex(1,2)
> c2 = complex(4,5)
> print(c1+c2)
33i
> print(c1*c2)
613i
> print(c1c2)
5+7i
> print(c1:exp(), c2:exp())
1.46869+2.47173i 0.0119719+0.0175633i
> print(c1:abs(), c2:abs())
2.2360680103302 6.403124332428
other = c:clone()
produces a new complex
instance which has the same content as the caller.util
packagePackage util
could be loaded via the standalone binary, or in Lua with require("aprilann.util)
. This package is the most important and dangerous. It extends standard Lua tables with new functionalities, and adds several utilities at GLOBALs table.
List of utilities added to Lua for scripting purposes.
Package util
could be loaded via the standalone binary, or in Lua with require("aprilann.util)
.
str = util.serialize(obj)
util.serialize(obj, filename)
util.serialize(obj, stream)
obj = util.deserialize(str)
obj = util.deserialize(filename)
obj = util.deserialize(stream)
str = util.to_lua_string(obj, format)
Lua ha been extended by the addition of new functions which works on the top of Lua iterators. Basic concepts as map, reduce, and filter has been implemented.
func = bind(function, ...)
Allow to freeze any of the positional arguments of any function. The arguments of bind
can be nil
, and the returned function would merge its arguments with the list given to bind
filling nil
gaps adequately.
> f = bind(math.add, 2)
> = f(4)
6
> f = bind(math.div, nil, 3)
> = f(6)
2
whatever = reduce(function, initial_value, iterator)
The reduce
function applies a function
operator by pairs of values, the first argument is the accumulated value of the reduction until current iteration, and the second argument is value at current iteration. If the iterator returns two or more elements every time it is called, the second will be taken.
> value = reduce(math.min, math.huge, ipairs({4, 2, 1, 10}))
> print(value)
1
> value = reduce(function(acc,v) return acc*2+v end, 0, string.gmatch("01101", "." ))
> print(value)
13
apply(func, iterator)
Applies a function to all the elements produced by the iterator. The function is called passing all the elements returned by one iterator call.
> t = { "a", "c", 3, 2 }
> apply(function(i,v1,v2) print(i,v1,v2) end, multiple_ipairs(t,t))
1 a a
2 c c
3 3 3
4 2 2
table = map(func, iterator)
Returns a table which is the result of apply the given function over all the items of the given iterator function.
> tmapped = map(bind(math.mul, 2), ipairs({1, 2, 3, 4}))
> print(table.concat(tmapped, " "))
2 4 6 8
table = map2(func, iterator)
The same as the previous, but given the function the pair key,value.
> tmapped = map2(function(k,v) return k+v*2 end, ipairs({1, 2, 3, 4}))
> print(table.concat(tmapped, " "))
3 6 9 12
table = mapn(func, iterator)
The same as the previous, but given the function all the elements returned by the iterator at each iteration.
> tmapped = mapn(function(idx, ...) return table.pack(...) end,
>> multiple_ipairs({1, 2, 3, 4},{5, 6, 7, 8}))
> for i,v in ipairs(tmapped) do print(i, table.concat(v, " ")) end
1 1 5
2 2 6
3 3 7
4 4 8
table = filter(func, iterator)
Returns a table which contains only the elements produced by the iterator which were evaluated with true by the given func
function. The function receives only one value.
> t = filter(function(v) return v%2 == 0 end, ipairs{1,2,3,4,5,6,7})
> print(table.concat(t, " "))
2 4 6
another_iterator = iterable_map(func, iterator)
Returns an iterator which every time is called maps the given function func
using the given iterator
. It allows multiple returned values from the given iterator
(map
and map2
only allow pairs key,value).
Additionally, using coroutine.yield(...)
, the mapping function could return more than one set of values at each iteration, allowing the implementation of ConcatMap iterators.
>  standard map using iterable_map
> t = { Lemon = "sour", Cake = "nice", }
> for ingredient, modifier, taste in iterable_map(function(a, b)
> return a:lower(),"slightly",b:upper()
> end, pairs(t)) do
> print(ingredient .." is ".. modifier .. " " .. taste)
> end
lemon is slightly SOUR
cake is slightly NICE
>
>  ConcatMap iterator using iterable_map
> t = { Lemon = "sour", Cake = "nice", }
> for ingredient, modifier, taste in iterable_map(function(a, b)
>> coroutine.yield(a:lower(),"very",b:upper())
>> return a, "slightly", b
>> end, pairs(t)) do
>> print(ingredient .." is ".. modifier .. " " .. taste)
>> end
cake is very NICE
Cake is slightly nice
lemon is very SOUR
Lemon is slightly sour
The following example uses this function to extract all the words contained in a file:
> for str in iterable_map(function(line)
>> for _,str in ipairs(string.tokenize(line)) do
>> coroutine.yield(str)
>> end
>> end, io.lines("AUTHORS.txt")) do
>> print(str)
>> end
In
this
project
has
been
worked:

Salvador
España
Boquera

Jorge
Gorbe
Moya

Adrián
Palacios
Corella

Joan
Pastor
Pellicer

Francisco
Zamora
Martínez
This function is taken from http://www.corsix.org/content/mappingandluaiterators.
another_iterator = iterable_filter(func, iterator)
Returns an iterator which every time is called filters using the given function func
the elements produced by the given iterator
. It allows multiple returned values from the given iterator
.
> for v in iterable_filter(function(key,value) return value%2==0 end,
>> ipairs{1,2,3,4,5,6,7}) do
>> print(v)
>> end
2
4
6
The iterator
class is developed to provide an easy and natural interface with previous and newer functions. The most important advantage is that iterator
class relies always in Lua iterators, so, it is lazy in the way that the code is not executed until the iterator is traversed. iterator
class is a wrapper of Lua iterators.
The following methods returns an iterator object or a Lua iterator:
obj = iterator(Lua iterator)
: the constructor receives an iterator, as for example the output of ipairs
function, and returns an instance of iterator
class.> it = iterator(ipairs{ 1, 2, 3})
Lua iterator = obj:get()
: returns the current state of the underlying Lua iterator.
Lua iterator = obj()
: the same as previous method.
> it = iterator(ipairs{ 1, 2, 3})
> for k,v in it() do print(k,v) end
1 1
2 2
3 3
iterator = obj:map(func)
: this method is a wrapper of iterable_map
function, and returns an instance of iterator
class.> it = iterator(ipairs{ 1, 2, 3}):map(function(k,v) return v*2 end)
> for v in it() do print(v) end
2
4
6
iterator = obj:filter(func)
: this method is a wrapper of iterable_filter
function, and returns an instance of iterator
class.> it = iterator(range(1,50)):filter(function(n) return (n%10)==0 end)
> for v in it() do print(v) end
10
20
30
40
50
iterator = obj:field(...)
: this method receives a list of keys. It expects the underlying iterator to produce a list of tables. It returns an iterator which filters all the tables in the list taken the values at given keys, and returns a flatten list of values. There is an example below the following method.
iterator = obj:select(...)
: this method receives a list of numbers. It returns an iterator which selects only the output variables produced by the iterator at the given position numbers.
> layers = { { size=10 }, { size=100 } }
> iterator(ipairs(layers)):select(2):field("size"):apply(print)
10
100
iterator = obj:enumerate()
: enumerates the returned values, adding at first position a number.> iterator(pairs{ a=4, b=3 }):enumerate():apply(print)
1 a 4
2 b 3
iterator = obj:iterate(func)
: this method is an specialization of map
method for applying Lua iterator functions to each element of obj
. The given func
is expected to return an iterator over the given element. It is useful to do things like word counting:> out = iterator(io.lines("AUTHORS.txt")):
>> iterate(function(line) return string.gmatch(line, "[^\r\n\t ]+") end):
>> reduce(function(acc,w) acc[w] = (acc[w] or 0) + 1 return acc end,{})
> iterator(pairs(out)):apply(print)
has 1
Pastor 1
In 1
worked: 1
Palacios 1
 5
España 1
Boquera 1
Joan 1
Francisco 1
Adrián 1
Martínez 1
been 1
Pellicer 1
Jorge 1
Zamora 1
Corella 1
this 1
Moya 1
Gorbe 1
Salvador 1
project 1
iterator = obj:call(funcname, ...)
: this method is a map over all the values by calling the method funcname
(a string) using the given arguments. Because it is a method, the first argument of funcname
will be each iterator value.> for k in iterator(ipairs({ "h", "w" })):select(2):call("toupper"):get() do
print(k)
end
H
W
The following methods are finalizers, so, they return a value, not an iterator:
whatever = obj:reduce(func, initial_value)
: this method is a wrapper of reduce
function.> = iterator(range(1,50)):reduce(function(acc,a) return acc+a end, 0)
1275
> = iterator(range(1,50)):reduce(math.add, 0)
1275
obj:apply(func)
: this method is a wrapper of apply
function.> iterator(range(1,50)):filter(function(n) return (n%10)==0 end):apply(print)
10
20
30
40
50
string = obj:concat(sep1,sep2)
: concats all the elements using sep1
and sep2
strings. sep1
is used to concat the elements of one iterator call. sep2
is used to concat the elements between different iterations. By default, empty string will be used when sep1
and sep2
are nil
. If only sep1
is given, therefore sep2=sep1
.> = iterator(range(1,50)):filter(function(n) return (n%10)==0 end):concat(" ")
10 20 30 40 50
table = obj:table()
: returns a table with all the iterator values, using as key the first produced value, and the rest as value. If only one value is produced, the table will be indexed as an array.> t = { "one", "two", "three" }
> p = iterator(ipairs(t)):map(function(k,v) return v,k end):table()
> iterator(pairs(p)):apply(print)
one 1
two 2
three 3
Using objects of this class, it is possible to produce code like this:
 This example computes the dot product of two array tables. math.mul and math.sum are
 auxiliary functions implemented in APRILANN for the fast development of reductions.
> v = iterator(multiple_ipairs({1,2,3},{4,5,6})):select(2,3):
>> map(math.mul):
>> reduce(math.add, 0)
> print(v)
32
>
>  The following code is equivalent without using iterator class
> v = reduce(function(a,b) return a+b end, 0,
>> iterable_map(function(k,a,b) return a*b end,
>> multiple_ipairs({1,2,3},{4,5,6})))
> print(v)
32
april_list(table)
This function is this piece of code: for i,v in pairs(table) do print(i,v) end
april_help(obj)
Shows the documentation of the object given as argument. If the object is a class, you can access to instance methods by using ..
operator:
>  using .. operator to access instance method get_state_table
> april_help(trainable.train_holdout_validation.."get_state_table")
method Returns the state table of the training
description: Returns the state table of the training
outputs:
best Best trained model
best_epoch Best epoch
best_val_error Best epoch validation loss
current_epoch Current epoch
last Last trained model
train_error Train loss
validation_error Validation loss
>  showing help of the given class
> april_help(trainable.train_holdout_validation)
ID: trainable.train_holdout_validation
class Training class using holdout validation
description: This training class defines a train_func which follows a training
schedule based on validation error or in number of epochs. Method
execute receives a function which trains one epoch and returns the
trainer object, the training loss and the validation loss. This
method returns true in case the training continues, or false if the
stop criterion is true.
...
april_dir(string)
This is a the same has april_help
, but less verbose.
luatype(whatever)
The original type
function is replaced by APRILANN with a new function which returns the object id if it is a class instance. If you need to know the exact type given by Lua, this function is what you need.
boolean = check_version(major,minor,commit)
Checks if the version of the software is major.minor with the given commit, returning true if success, and returning false and showing a message in stderr otherwise.
april_print_script_header(arg, file=stdout)
This function writes at the given file (or stdout if not given) the given arg table (normally the arg received by the script), besides information about the HOST where the script is executed and the current DATETIME:
> april_print_script_header({ [0]="hello" })
# HOST: django
# DATE: dv jul 5 14:16:53 CEST 2013
# CMD: hello
iterator,s,v = multiple_ipairs(...)
Returns an iterator which traverses a several number of tables. If they don't have the same size, the remaining elements will be nil
, ensuring that in all the iterations the number of returned elements is equals to the maximum size of given tables.
> for i,a,b,c in multiple_ipairs({1,2,3,4},{1,2},{3,4,5}) do print(i,a,b,c) end
1 1 1 3
2 2 2 4
3 3 nil 5
4 4 nil nil
... = multiple_unpack( [table1, [table2, [...]]] )
Allow to unpack multiple tables together, one at a time, and in a sequential fashion.
> print( multiple_unpack( {1,2,3}, {4,5}, {6,7,8,9} ) )
1 2 3 4 5 6 7 8 9
table = glob(...)
Returns a list of filenames which match all the wildcard arguments received by the function.
>  prints the name of all the files which have .lua or .h extensions
> for i,filename in ipairs(glob("*.lua", "*.h")) do print(filename) end
results = parallel_foreach(num_processes, iterator or array or number, func)
Executes a function over the given iterator (instance of class iterator
), array table or the given number of repetitions, but forking the calling process in num_processes
, improving the performance of the operation. NOTE that the parallelization is performed forking the caller process, so all child processes could access to the memory variables assigned and allocated before the fork, but they don't share the memory, it will be copied on write.
> t = map(function(v)return v end, 10)
> parallel_foreach(2, t, function(value) print(value*100) end)
200
400
600
800
1000
100
300
500
700
900
Additionally, if the function returns any value, this function would serialize the output of each process to a temporal file, and at the end, deserialize the content to the original process. This is useful when the overhead of serializationdeserialization procedure is less than the computing power needed by the processes.
> ret = parallel_foreach(2, 10, function(value) return value*100 end)
> print(table.concat(ret, "\n"))
100
200
300
400
500
600
700
800
900
1000
You can use iterators to control which data receives the called function:
> ret = parallel_foreach(2, iterator(ipairs{4,3,2,1}),
function(key,value) return key+value*100 end)
> print(table.concat(ret, "\n"))
401
302
203
104
>
> ret = parallel_foreach(2, iterator(ipairs{4,3,2,1}),
function(key,value) return key,key+value*100 end)
> print(table.tostring(ret))
{{1,401,["n"]=2},{2,302,["n"]=2},{3,203,["n"]=2},{4,104,["n"]=2}}
clrscr()
Clears the screen.
printf(...)
Equivalent to C printf
function.
fprintf(file,...)
Idem, but for the C fprintf
function.
range(inf,sup, step=1 )
This function returns an iterator which starts at inf
, ends at sup
, and performs steps of the given step
size.
> for i in range(10,20,2) do print(i) end
10
12
14
16
18
20
major,minor,commit = util.version()
Returns the version numbers.
util.omp_set_num_threads(number)
Modifies the number of threads for OMP.
> util.omp_set_num_threads(8)
number = util.omp_get_num_threads()
Returns the number of threads used by OMP.
> print(util.omp_get_num_threads())
8
number = math.add( a ,b )
Returns the result of a+b
.
> = math.add(2,3)
5
number = math.sub( a, b )
Returns the result of ab
.
> = math.sub(2,3)
1
number = math.mul( a, b )
Returns the result of a*b
.
> = math.mul(2,3)
6
number = math.div( a, b )
Returns the result of a/b
.
> = math.div(2,3)
0.66666666666667
number = math.eq( a, b )
Returns the result of a==b
.
number = math.lt( a, b )
Returns the result of a<b
.
number = math.le( a, b )
Returns the result of a<=b
.
number = math.gt( a, b )
Returns the result of a>b
.
number = math.ge( a, b )
Returns the result of a>=b
.
number = math.land( a, b )
Returns the result of a and b
.
number = math.lor( a, b )
Returns the result of a or b
.
number = math.lnot( a )
Returns the result of not a
.
number = math.round(number)
Returns the rounding integer number for the given real number.
> = math.round(4/3)
1
number = math.clamp(value,lower,upper)
Clamp the given value to be between [lower,upper], if it is out of the range, it is forced to be at the limit.
> print(math.clamp(15,3,6), math.clamp(0,3,6), math.clamp(4,3,6))
6 3 4
%
Inspired in penlight library, a Pythonlike operator %
has been defined. It allows to produce formatted strings, and implements maplike substitutions:
> = "$obj1 = %.4f\n$obj2 = %.4f" % {20.4, 12.36, obj1="cat", obj2="dog"}
cat = 20.4000
dog = 12.3600
string = str:truncate(columns, prefix)
string = path:basename()
Returns the basename (the last filename) of a given path.
> print(("/a/path/to/my/file.txt"):basename())
file.txt
Returns the path, removing the basename.
> print(("/a/path/to/my/file.txt"):dirname())
/a/path/to/my/
string,string = path:remove_extension()
Removes the extension of the filename in the given path, and returns the path without the extension and the extension string.
> print(("/a/path/to/my/file.txt"):remove_extension())
/a/path/to/my/file txt
string = path:get_extension()
Returns only the extension of the given path string.
> print(("/a/path/to/my/file.txt"):get_extension())
txt
string = path_with_filename:get_path(sep)
Synonim of dirname()
.
string = str:lines_of()
Returns an iterator function which traverses the given string splited by newline character.
> for line in ("one\ntwo"):lines_of() do print(line) end
one
two
iterator = str:chars_of()
Returns an iterator function which traverses the given string splited by chars.
> for i,ch in ("one two"):chars_of() do print(i,ch) end
1 o
2 n
3 e
4
5 t
6 w
7 o
table = str:tokenize(sep=' \t\n\r')
Returns a table with the string tokenized using the given sep
set of characters.
> for i,token in ipairs((" one\ntwo\tthree four"):tokenize("\t\n ")) do print(i,token) end
1 one
2 two
3 three
4 four
> for i,token in ipairs(string.tokenize(" one\ntwo\tthree four", "\n ")) do print(i,token) end
1 one
2 two three
3 four
table = str:tokenize_width(width=1)
The string.join
function is equivalent to Lua table.concat
function.
table = table.insert(table,value)
The original table.insert
function was replaced with a new one which returns the table given as first argument. It is combinable with reduce
function.
table.luainsert(table,value)
The original Lua table.insert
function.
table.clear(table)
Removes all the elements of a table, but it doesn't forces Lua to deallocate the memory. This function is useful if you want to reuse a table variable several times inside a loop, it is better to clear the table than to allocate a new one table.
> t = {}
> for i=1,1000 do table.clear(t) STUFF USING t end
table.unpack_on(table, dest_table)
This function puts the fields of the given table at the table dest_table
. It is useful to put table fields on the global scope of Lua.
> print(a, b, c)
nil nil nil
> t = { a=1, b=2, c=3 }
> table.unpack_on(t, _G)
> print(a, b, c)
1 2 3
table = table.invert(table)
Returns the table resulting from the inversion of key,value pairs of the given table argument.
> t = { "a", "b", "c" }
> t_inv = table.invert(t)
> for i,v in pairs(t_inv) do print(i,v) end
a 1
c 3
b 2
table = table.slice(t, ini, fin)
Returns from the given table the slice of elements starting at ini
and finishing at fin
.
> t = { 1, 2, 3, 4, 5, 6, 7, 8, 9 }
> print(unpack(table.slice(t, 2, 4)))
2 3 4
key = table.search_key_from_value(table,value)
This function searchs a value at the given table and returns its key. If the value is repeated (obviously using different keys), any of the possible keys will be returned, but it is not possible to determine which one.
> print(table.search_key_from_value({ a=15, b=12 }, 12))
b
whatever = table.reduce(table,function,initial_value)
Equivalent to reduce(function, initial_value, pairs(table))
.
table = table.imap(table,function)
Equivalent to map(function, ipairs(table))
.
table = table.map(table,function)
Equivalent to map(function, pairs(table))
.
table = table.imap2(table,function)
Equivalent to map2(function, ipairs(table))
.
table = function table.map2(table,function)
Equivalent to map2(function, pairs(table))
.
table = table.ifilter(table,function)
This functions traverses the given table as an array (using ipairs function), and returns a new table which contains only the elements where the given function returns true. The function is called passing the pair key,value as two arguments.
table = table.filter(table,function)
Idem as the previous one but for general tables (using pairs functions).
table = table.join(t1,t2)
Returns a table which is the concatenation of the two given tables.
> t = table.join({1,2,3}, {10,11,12})
> print(table.concat(t, " "))
1 2 3 10 11 12
table = table.deep_copy(table)
Returns a table which is a deep copy of the Lua datavalues contained at the given table, and a shallow copy (copied by reference) of its C++ references.
table = table.linearize(table)
Converts an unsorted dictionary in an array, throwing away the keys. The order of the array is not determined.
string = table.tostring(table)
This function converts the given table to a string which contains the table values, and which could be loaded as a Lua chunk. It only works with tables which doesn't contain C++ references.
> t = { 1, 2, a={ ["foo"] = "bar" } }
> print(table.tostring(t))
{
[1]=1,[2]=2,["a"]=
{
["foo"]="bar"
}
}
number,index = table.max(table)
This function returns the maximum value and the index of the key which contains it. The table is traversed using pairs function.
number,index = table.min(table)
This function returns the minimum value and the index of the key which contains it. The table is traversed using pairs function.
index = table.argmax(table)
This function is equivalent to table.max
returning only the index.
index = table.argmin(table)
This function is equivalent to table.min
returning only the index.
iterator = io.uncommented_lines( [filename] )
Returns a function iterator which traverses the given filename (if not given, it uses io.stdin
), removing the lines which begins with #
symbol.
> for line io.uncommented_lines() do STUFF end
util.stopwatch
util.vector_uint
util.vector_float
gzio
packagePackage gzio
could be loaded via the standalone binary, or in Lua with require("aprilann.gzio)
.
gzio
class, GZip filesNOTE that io.open
is overwritten by APRILANN to automatically open gzipped files by using gzio
class, if the filename has .gz
extension.
The gzio
class is compatible with standard Lua file
. See Lua documentation for more details
obj = gzio.open(path,mode="r")
constructs the object and opens the given path using the given mode.
obj = io.open(path,mode="r")
opens the given path using the given mode, and returns a gzio
object if the file has .gz
extension, otherwise it returns a Lua file
.
obj:close()
closes the file.
obj:flush()
flushes the file.
position = obj:seek(whence="cur",offset=0)
moves the cursor from the given base position whence
plus the given offset
. The whence
could be "cur" or "set", the "end" value is forbidden in ZLib. It returns the position of the cursor at the file.
value,... = obj:read(format="*l", ...)
reads a sequence of values from the file, following the given format strings.
obj:write(value, ...)
write the given sequence of values to the file. A valid value is a string
or a number
.
iterator = obj:lines(...)
returns an iterator which read by lines following the given values, by default "*l". The file is not closed at end.
iterator = io.lines(path, ..)
returns an iterator which traverse the given path by lines, following the given values, by default "*l". Read Lua documentation for details. This function uses gzio
object if the file has .gz
extension, otherwise it uses the standard io.lines
.
tar
class, TAR filesImage
packageThis package is available with require("aprilann.Image")
.
Two globals are declared in this package: Image
and ImageRGB
. Both classes represent its internal data using floats, in the case of Image
with one float, in the case of ImageRGB
with 3 floats (Red, Green, Blue). So, usually 8 bits per color images will be transformed to be the color 0=0.0
and the color 255=1.0
.
Images cannot be serialized as a Lua object into a .lua
filename using util.serialize()
and util.deserialize()
functions. Instead of that, it is possible to write images into png
, tiff
or jpeg
files by using the package ImageIO
. This package allows to write Image
or ImageRGB
images.
A tool module has been deployed to facilitate visualization of images and matrices. It depends on lgi binding library for Lua 5.2, and uses the GTK binding. In order to do the require "tools.lgi.gtk"
you need to have the APRILANN ROOT directory in your package.path
Lua variable, or in your LUA_PATH
environment variable. By default, the current directory is included, so if you execute the require
being your working directory the APRILANN GIT root directory, it will work. In any case, NEVER add the april_tools directory to the LUA_PATH, because the lgi module will collide with the tools.lgi module.
> gtk = require "tools.lgi.gtk"
> gtk.show("sheared.png")
If it doesn't work, try add the APRILANN ROOT directory to the package.path
variable.
> package.path = "YOUR_APRIL_ANN_PATH/?.lua;" .. package.path
> gtk = require "tools.lgi.gtk"
> gtk.show("sheared.png")
gtk.show(img1, img2, ...)
This function allows to visualize images using GTK library. It receives a variable number of arguments, and every argument will be displayed in a different window. Every argument could be:
A filename, opening the contained image.
A matrix
, which needs to be bidimensional. It will be converted to an Image
and then showed.
An Image
or ImageRGB
.
Note that the Lua command shell will be blocked after executing GTK main loop. Any of the created windows has an exit button to allow closing the GTK main loop.
Image
classThe Image
class allows to work with grayscale images. Every image contains an underlying matrix
instance (floats).
img = Image(matrix)
The constructor receives a matrix
where the image is contained. This matrix must be simple, that is, contiguous in memory, and in row_major
. Given a matrix with data, it is possible to load an Image
with:
> m = matrix(100,100)
>  make a gradient
> for sw in m:sliding_window():iterate() do sw:linear():adjust_range(0,1) end
>  constructor for Image
> img = Image(m)
> = img
# Image 100x100+0+0 [0x2490810 data= 0x25843e0]
>  you can see the image writing it with ImageIO or using the gtk module
> ImageIO.write(img, "gradient.png")
Image = img:crop(string)
This method allows to select a portion of an Image
, and returns a new instance which references the given portion. The crop is given by using a string (like in convert command), with the following format: <width>x<height>{+}<x>{+}<y>
.
> img2 = img:crop("20x20+10+10")
> = img2
# Image 20x20+10+10 [0x2490810 data= 0x25843e0]
Image = img:crop(width, height, offsetx, offsety)
Similar to previous one, but given the crop using four arguments.
> img2 = img:crop(20, 20, 10, 10)
> = img2
# Image 20x20+10+10 [0x2490810 data= 0x25843e0]
matrix = img:matrix()
This method returns the underlying matrix
. Be careful with this method, the memory is shared between the Image
object and the returned matrix
object.
> m2 = img:matrix()
> = m2
...
# Matrix of size [100,100] in row_major [0x2490810 data= 0x25843e0]
> = m  the matrix references the original
...
# Matrix of size [100,100] in row_major [0x2490810 data= 0x25843e0]
width,height,offsetx,offsety = img:geometry()
This method returns the geometry information of the image.
> = img2:geometry()
20 20 10 10
number = img:getpixel(x,y)
This method returns the given position (x,y) pixel value.
img:putpixel(x,y,number)
This method assigns the given value at the given (x,y) pixel position.
Image = img:clone()
This method returns a deepcopy of the caller object.
matrix = img:projection_h()
This method returns the horizontal projection of the caller object.
matrix = img:projection_v()
This method returns the vertical projection of the caller object.
Image = img:shear_h(angle, [, units="rad" [, WHITE=0.0 ] ] )
This method returns an Image
which is a shear transformation with the given angle
. Optionally, a second argument could be given indicating with the string rad
, deg
or grad
the angle unit, by default it is rad
. The thrird argument is also optional, and it indicates which pixel value is taken as white color, by default is 0.0
.
> img_sh = img:shear_h(0.1)
> ImageIO.write(img_sh, "sheared.png")
img:shear_h_inplace(angle, [, units="rad" [, WHITE=0.0 ] ] )
This method applies the same transformation as the img:shear_h(...)
, but instead of returning a new Image
, the transformation is performed inplace.
w,h,x,y = img:min_bounding_box(threshold)
This method returns the bounding box of the caller object, using threshold
for deciding when a value is considered as background or not.
img:copy(Image, dest_x, dest_y)
This method copies the given Image
in the given (dest_x,dest_y) position of the caller object.
Image = img:substract(Image)
This method applies image substraction.
img:threshold(low, high)
This method transforms inplace the caller object with the given range for black/white thresholding.
Image = img:rotate90cw(param)
This method returns a new Image
which is a rotation of 90º in clockwise direction (if param=1
) or in counterclockwise (if param=1
) of the caller object.
Image = img:invert_colors()
This method returns an image with the colors inverted.
Image = img:remove_blank_columns()
Image = img:add_rows(top, bottom, value)
Image = img:convolution5x5(table [, default_value ])
Image = img:resize(x,y)
Image,x,y = img:affine_transform(AffineTransform2D, default_value)
ImageRGB = img:to_RGB()
matrix = img:comb_lineal_forward(x, y, w, h, w2, h2, LinearCombConFloat)
ImageRGB
classThis class is an instantiation of the same C++ template used for Image
class. Both classes has similar methods.
ImageRGB = ImageRGB(matrix)
ImageRGB
class could be loaded from a matrix
with 3 dimensions. The last dimension of the matrix
has size 3 for the componentes Red, Green, Blue. Be careful with this constructor, the memory is shared between the ImageRGB
object and the given matrix
object.
> img_rgb = ImageRGB(matrix(100,100,3):linear())
> = img_rgb
# ImageRGB 100x100+0+0 [0x2535700 data= 0x251fc40]
Image
classThe following methods are shared between both classes, and their has the same interface:
ImageRGB = img_rgb:crop(string)
ImageRGB = img_rgb:crop(width, height, offsetx, offsety)
width,height,offsetx,offsety = img_rgb:geometry()
r,g,b = img_rgb:getpixel(x,y)
img_rgb:putpixel(x,y, r,g,b)
ImageRGB = img_rgb:clone()
ImageRGB = img_rgb:shear_h(angle, [, units="rad" [, WHITE=0.0 ] ] )
img_rgb:shear_h_inplace(angle, [, units="rad" [, WHITE=0.0 ] ] )
img_rgb:copy(ImageRGB, dest_x, dest_y)
ImageRGB = img_rgb:rotate90cw(param)
ImageRGB = img_rgb:invert_colors()
ImageRGB = img_rgb:convolution5x5(table [, r,g,b ])
ImageRGB = img_rgb:resize(x,y)
ImageRGB,x,y = img_rgb:affine_transform(AffineTransform2D, r,g,b)
matrix = img_rgb:matrix()
This method returns the underlying matrix
. Be careful with this method, the memory is shared between the ImageRGB
object and the returned matrix
object.
Image = img_rgb:to_grayscale()
This method returns an instance of Image
, transforming the 3 color components into a gray component.
A usual situation is to load a matrix
from a PNG image (in RGB color, with 0=BLACK, and 1=WHITE), but transform it to train ANNs in grayscale with 1=BLACK and 0=WHITE. This could be done by the following code:
> img_matrix = ImageIO.read(filename):to_grayscale():invert_colors():matrix()
ImageIO
packageThis package is avaialable as require("aprilann.ImageIO")
.
ImageIO package implement functions for read/write of images in any supported format. Currently PNG and TIFF formats are supported.
ImageRGB = ImageIO.read( filename [, img_format] )
This function allows to read an image from a given filename
. It returns an instance of Image
class. The img_format
is optional, if not given, it will be extracted from the extension of the given filename
. If given, it must be a string like png
, tiff
or tif
. More formats will be supported in the future.
ImageIO.write( ImageImageRGB, filename [, img_format] )
This function allows to write an image into a given filename
. The img_format
is optional, if not given, it will be extracted from the extension of the given filename
. If given, it must be a string like png
, tiff
or tif
. More formats will be supported in the future.
AffineTransform2D
packageThis package is avaialable as require("aprilann.AffineTransform2D")
.
AffineTransform2D
class> AffineTransform2D()
> AffineTransform2D(matrix)
obj = obj:accumulate(other)
obj = obj:rotate(angle [, center_x, center_y ])
obj = obj:scale(sx, sy)
obj = obj:translate(x, y)
obj = obj:shear(angle_x, angle_y)
dstx,dsty = obj:transform(x, y)
class
packagePackage class
is a copy of the code avaiable at Lua OOPiter for deploying OOP to Lua. It could be loaded by using require "aprilann.class"
, or if you have installed the Lua OOPiter module, by doing class = require "oopiter.class"
.
See testclass.lua file for an example of how to use this package. More updated version of this documentation is in Lua OOPiter README.md, at class
module section.
The class module implements OOP for Lua in a similar way as luabind does for C++ classes. So, the class module functions are compatible with it. The OOP is implemented by defining several tables for each desired class. Every class has a name, which allow to store it into a weak table in order to retrieve the class by its name in any moment. A class is defined by doing:
class_table, methods_table = class('myClassName'[, parent_class_table[, class_table]])
Two tables are returned as result of this call, the class_table
which allows to construct instances by using the class_table
as a function (it has implemented the __call
metamethod), and a methods_table
where instance methods must be defined. Class methods will be defined into class_table
, and special names constructor/destructor allow to define the behavior of the object at these specific stages. So, the first is to define a constructor and a destructor (NOTE: both are optional):
class_table:constructor(whatever) self.blah = whatever end
class_table:destructor() free_resource(self.blah) end
In the same way, instance methods will be defined in methods_table
:
methods_table:my_method() return self.blah end
Additionally, instance metamethods can be defined using the class.extend_metamethod
function. Be carefule, __gc
and __index
metamethods are defined by default and them cannot be modified, any change will produce an unexpected behavior:
class.extend_metamethod(class_table, "__tostring", function() print("foo") end)
Looking with more detail inside the architecture, the class(...)
function call defines the following hierarchy of tables:
class_table = {
constructor = default_constructor,  it does nothing
destructor = default_destructor,  it does nothing
 the meta_instance table contains the metatable of instance objects
meta_instance = {
id = class_name_string,
cls = class_table_reference,
__tostring = default_tostring_metamethod,  it is safe to be overwritten
__index = methods_table,  the table where instance methods are defined
__gc = default_gc_metamethod,
}
}
 class_table metatable contains:
{
id = class_name .. " class",
parent = parentclass,  if given any
__tostring = default_tostring_metamethod,
__concat = default_concat_metamethod,
__call = constructor_call,
}
The class(...)
function call returns first the class_table
and second the class_table.meta_instance.__index
field, letting the user to define class methods and instance methods there. By default constructor and destructor functions does nothing, and they are implemented at class_table.constructor
and class_table.destructor
fields. class_table.meta_instance
table can be safety modified by calling to class.extend_metamethod(...)
, or writing nonsafety manual changes into class_table.meta_instance
.
Simple inheritance has been implemented by defining a metatable for the class_table.meta_instance.__index
table. Having a class table myClass1
, you can define the class myClass2
as a child of previous one by writing:
>  parent class
> myClass1,myClass1Methods = class("myClass1")
> myClass1:constructor(...) whatever stuff here end
>  derived or child class
> myClass2,myClass2Methods = class("myClass2", myClass1)
> myClass2:constructor(...) myClass1.constructor(self, ...) more stuff here end
Note that parent constructor call is not made by default, and myClass2:constructor
calls explicitly myClass1.constructor
function passing the self
reference. In this way, whatever construction stuff done in myClass1
will be done for myClass2
. It is not mandatory to do this, but in many cases it will be helpful. However, you can build myClass2
instances in whatever way you want if the result is compatible with the methods inherited from myClass1
.
myClass2Methods
can overwrite or not methods defined at myClass1Methods
. Non overwritten methods will be delegated calling myClass1
implementation, so be careful to ensure both objects are compatible.
Destructors are called following the hierarchy, first child destructor and after the parent class.
The following public functions are available:
Creates a class table with a given class_name. It receives an optional parent class to implement simple inheritance. It returns the class table; another table which will contain the methods of the object. Constructor and destructor methods will be declared into the class table as class_name:constructor(...)
and class_name:destructor()
. Additionally, a third optional argument is given, which allows to give a predefined class_table
, useful is you want to make global instead of local variables, or to convert into a class an existent table.
>  a simple class with name cls1
> cls1,cls1_methods = class("cls1")
>  a derived class from cls1
> cls2,cls2_methods = class("cls2")
>  a nested class defined into cls2 table
> cls2.nested1 = {}
> nested1,nested1_methods = class("cls2.nested1", nil, cls2.nested1)
>  a derived nested class
> cls2.nested2 = {}
> nested2,nested2_methods = class("cls2.nested2", cls2.nested1, cls2.nested2)
A class_name cannot be used two times, that is, a class can't be redefined. If you need to redefine a class, use class.forget(class_name)
before. Otherwise the following error message will be displayed:
> class("cls1")
> class("cls1")
./oopiter/class.lua:40: cls1 class name exists
stack traceback:
[C]: in function 'assert'
./oopiter/class.lua:40: in function 'register_class_table'
./oopiter/class.lua:289: in function 'class'
stdin:1: in main chunk
[C]: in ?
Predicate which returns true if a given object instance is a subclass of a given Lua class table.
> cls1,cls1_methods = class("cls1")
> cls2,cls2_methods = class("cls2")
> cls3,cls3_methods = class("cls3", cls1)
> cls3 = cls3()
> = class.is_a(o1, cls1)
true
> = class.is_a(o1, cls2)
false
> = class.is_a(o1, cls3)
true
Returns the super class table of a given derived class table. Throws an error if the given class has not a super class.
> cls1,cls1_methods = class("cls1")
> cls2,cls2_methods = class("cls2", cls1)
> = ( class.super(cls2) == cls1 )
true
Returns the class table of the given object instance. In case the given parameter is a Lua value but not an object, it returns nil
. So, this method can be used also to ask if a Lua value is or not an object.
> cls1,cls1_methods = class("cls1")
> o = cls1()
> = ( class.of(o) == cls1 )
true
> = class.of( {} )
nil
> = class.of( 5 )
nil
Extends the given class table with the addition of a new key=value pair into the object instance table. It throws an error if the 1st parameter is not a class table.
> cls1,cls1_methods = class("cls1")
> foo = function() end
> class.extend(cls1, "foo", foo)
> ( cls1_methods.foo == foo )
true
Extends the given class table with the addition of a new key=value pair into the object meta_instance
table, where metamethods are stored. It throws an error if the 1st parameter is not a class table. Be careful, several metamethods (__index
, __gc
) and keys (id
, cls
) are defined by default in order to implement OOP, overwritten them will produce unexpected errors. The call will throw an error if you try to overwrite any of them. However, __tostring
metamethod is also defined but it is totally safe to overwrite it.
> cls1,cls1_methods = class("cls1")
> foo = function() return "Hello world!" end
> class.extend_metamethod(cls1, "__concat", foo)
> o = cls1()
> = o .. o
Hello world!
Returns the value associated with the given key at the given class_table. Throws an error if the 1st parameter is not a class table.
> cls1,cls1_methods = class("cls1")
> cls1_methods.foo = function() end
> = ( class.consult(cls1, "foo") == cls1_methods.foo )
true
Equivalent to previous one.
> cls1,cls1_methods = class("cls1")
> cls1_methods.foo = function() end
> = ( cls1.."foo" == cls1_methods.foo )
true
Returns the value associated with the given key at the given class_table meta_instance (instance metatable). Throws an error if the 1st parameter is not a class table.
> cls1,cls1_methods = class("cls1")
> foo = function() return "Hello world!" end
> class.extend_metamethod(cls1, "__concat", foo)
> = ( class.consult_metamethod(cls1, "__concat") == foo )
true
Calls a method in a given class_table using the given vararg arguments. It throws an error if the 1st parameter is not a class table or if the given method doesn't exist.
> cls1,cls1_methods = class("cls1")
> cls1_methods.foo = function(self) print(self.n) end
> class.call(cls1, "foo", { n=5 })
5
Returns true/false if the given Lua value is a class table.
> cls1,cls1_methods = class("cls1")
> = class.is_class(cls1)
true
Returns true/false if the given instance object is an instance of a derived class.
> cls1,cls1_methods = class("cls1")
> cls2,cls2_methods = class("cls2", cls1)
> o1 = cls1()
> o2 = cls2()
> = class.is_derived(o1)
false
> = class.is_derived(o2)
true
Returns the class table associated with the given class_name.
> cls1,cls1_methods = class("cls1")
> aux_cls1,aux_cls1_methods = class.find("cls1")
> = ( cls1 == aux_cls1 and cls1_methods == aux_cls1_methods )
Removes the given class_name from the auxiliary table of classes, allowing to redifine this class. Notice that the class can't be removed at all because your scripts can have taken the class tables as upvalue, and the instantiated objects will continue working as expected.
> cls1 = class("cls1")
> cls1 = class("cls1")
./oopiter/class.lua:40: cls1 class name exists
stack traceback:
[C]: in function 'assert'
./oopiter/class.lua:40: in function 'register_class_table'
./oopiter/class.lua:289: in function 'class'
stdin:1: in main chunk
[C]: in ?
> class.forget("cls1")
> second_cls1 = class("cls1")
> = ( cls1 == second_cls1 )
false
clustering
packagePackage clustering.kmeans.matrix
, available in Lua with require("aprilann.clustering.kmeans.matrix")
.
The package clustering
is developed to contain different clustering implementations. Currently, only one is available.
clustering.kmeans.matrix
This package contains an implementation of kmeans clustering designed to be very efficient with large databases (as large as it could be contained in your main memory), but with a small number of clusters.
distortion,centroids = clustering.kmeans.matrix{ ... }
This is the main function of the clustering algorithm. It receives a table with different input arguments, and depending on them, the algorithm could be specialized. The function returns a number with the distortion of the clustering result, and a matrix
with the centroids (ordered by rows).
In this case, the function receives a matrix
with the initial set of centroids. The given table argument must contain the following fields:
data
: it is a matrix
where the data points are ordered by rows.
centroids
: it is a matrix
with the initial value of the centroids, oredered by rows.
max_iter=100
: the maximum number of clustering iterations, by default it is max_iter=100
.
threshold=1e05
: the threshold for stopping clustering algorithm, by default it is threshold=1e05
.
verbose=false
: a boolean indicating verbosity.
The algorithm is executed and the given centroids matrix
will be updated with the newer centroids, resulting from the clustering algorithm.
> data = matrix(5,2):linear()
> clusters = matrix(2,2,{0,0, 1,1})
> = clusters
0 0
1 1
# Matrix of size [2,2] in row_major [0x1131c30 data= 0x1131d00]
> res, C = clustering.kmeans.matrix{
data = data,
centroids = clusters
}
> = res
3.999998196261
> = C
1 2
6 7
# Matrix of size [2,2] in row_major [0x1131c30 data= 0x1131d00]
In this case, the algorithm is implemented in two parts, first the refine algorithm published by P.S. Bradley and U.M. Fayyad is used to initialize the centroids matrix
. After that, the standard clustering algorithm will be used. The given table argument must contain the following fields:
data
: it is a matrix
where the data points are ordered by rows.
K
: a number indicating how many clusters you want to compute using refine algorithm.
random
: a random
object used by the refine algorithm.
subsamples=10
: how many random subsamples of the data will be used in the refine algorithm, by default it is subsamples=10
.
percentage=0.01
: a percentage of the data used by refine algorithm, by default it is percentage=0.01
, that is, a 1%.
max_iter=100
: the maximum number of clustering iterations, by default it is max_iter=100
.
threshold=1e05
: the threshold for stopping clustering algorithm, by default it is threshold=1e05
.
verbose=false
: a boolean indicating verbosity.
The algorithm is executed and a centroids matrix
will be returned.
> data = matrix(5,2):linear()
> res,C = clustering.kmeans.matrix{
data = data,
K = 2,
random = random(1234),
}
> = res
3.999998196261
> = C
2 3
7 8
# Matrix of size [2,2] in row_major [0x25d1180 data= 0x25d1250]
score,T=clustering.kmeans.matrix.find_clusters(X,C [,T [,verbose]])
This function classifies X
(a matrix
with data) in the closest centroid C
(a matrix
with the centroids), and returns the score
of the classification and the tags T
(a matrixInt32
). The function receives positional arguments:
X
: a matrix
with the data ordered by rows (N rows, D columns).
C
: a matrix
with the centroids ordered by rows (K centroids).
T
: a matrixInt32
with size Nx1, which contains for every row of X
the number of its closest centroid. This argument is optional, if not given, a new martrixInt32
will be allocated.
verbose=false
: a boolean indicating if verbosity is desired. By default it is false
> = data
0 1
2 3
4 5
6 7
8 9
# Matrix of size [5,2] in row_major [0x1717960 data= 0x1714f10]
> score,T = clustering.kmeans.matrix.find_clusters(data,C)
> = score
51.4
> = T
1
1
1
2
2
# MatrixInt32 of size [5,1] in row_major [0x2862a70 data= 0x282e830]
knn
packageThe package knn
contains algorithms to deal with KNearestNeighbors algorithm. Currently, an implementation based on kd tree is available, allowing to work with large databases (as far as it could be loaded into main memory), but with low dimensionality.
knn.kdtree
This class is the basic implementation of the kd tree for KNN classification. It allows to work with matrices of data (as many matrices as you want). After all the data is given, the tree is built. No insertion or remove operations are implemented, but it is possible to insert new data and build it again if needed. After the tree is ready, it is possible to query for the nearestneighbor or the Knearestneighbors.
kdt = knn.kdtree(D,random)
The constructor receives two values, a number D
with the number of dimensions (columns) of your data, and a random
object (used at build method).
> D = 6
> kdt = knn.kdtree(D,random(9248))
> = kdt
instance 0x214ad70 of knn.kdtree
kdt = kdt:push(matrix)
This method receives a matrix
with data ordered by rows, and returns the caller kdtree
object. The matrix
pointer is retained by the kdtree
, but it is not inserted into the structure, the build method is who will perform the insertion.
> rnd = random(1234)
> m1 = matrix(100,D):uniformf(1,1,rnd)
> m2 = matrix(200,D):uniformf(1,1,rnd)
> kdt:push(m1)
> kdt:push(m2)
kdt = kdt:build()
This method processes all the given data matrices, and builds the kd tree structure.
> kdt:build()  after that, it is ready to queries
id,distance = kdt:searchNN(matrix)
This method allows to query a built kdtree
object for the nearestneighbor. It receives a bidimensional matrix
with size 1xD
, and returns two values:
id
is the position of the nearestneighbor. If you take all the pushed matrices ordered by rows, this number is the row
corresponding to the sample in the concatenated data.
distance
is a number with the square of the euclidean distance.
> id,dist = kdt:searchNN(matrix(1,D):uniformf(1,1,rnd))
> = id
26
>  the 26 is located at the first matrix
> = dist
0.49883383064235
> id,dist = kdt:searchNN(matrix(1,D):uniformf(1,1,rnd))
> = id
178
>  the 178 is located at the second matrix
> = dist
0.3402419188674
point,matrix = kdt:get_point_matrix(id)
This method receives a number id
of a point in the kdtree
object (as returned by kdt:searchNN(...)
method), and returns a point
which is a matrix
of size 1xD
with the corresponding point data, and the matrix
object where the point is contained. Be careful, this method returns a reference to the original data, any change in the data will led to unexpected behavior of the kdtree
object.
> NN = kdt:get_point_matrix(id)
> = NN
0.657501 0.604099 0.426221 0.421949 0.32904 0.75809
# Matrix of size [1,6] in row_major [0xc80410 data= 0xc754b0]
> = m2(id100,':')
0.657501 0.604099 0.426221 0.421949 0.32904 0.75809
# Matrix of size [1,6] in row_major [0xc7ceb0 data= 0xc754b0]
result = kdt:searchKNN(K,matrix)
This method performs the KNearestNeighbors search, using the given K
as number of neighbors and the given matrix
(with size 1xD
) as data point. The method returns a table with pairs id,distance
.
> result = kdt:searchKNN(4,matrix(1,D):uniformf(1,1,rnd))
> = result
table: 0x197caa0
> for i=1,#result do print(result[i][1], result[i][2]) end
152 0.42534565841526
40 0.54756417013329
101 0.5931407024824
166 0.66157509210318
class = knn.kdtree.classifyKNN(result, get_class_function)
This method receives the result of kdt:searchKNN(...)
method and a function which transforms a pattern id
in a class. All the result pairs are traversed, computing the corresponding class from each id
. The majority vote class will be returned. Normally, the get_class_function
will be a function which looks into a dataset
, a Lua table, or a matrix
, looking for the class of the corresponding id
number. In some tasks, because of the order of the data, it is possible to compute the class with a math operation. It depends on your data and your experiment framework how to implement this function.
> best_class = knn.kdtree.classifyKNN(result,
function(id) return id%10 end)
> = best_class
2
table,cls,best = knn.kdtree.posteriorKNN(result, get_cls_func)
This method receives the result of kdt:searchKNN(...)
method and a function get_cls_func
which transforms a pattern id
in a class. All the result pairs are traversed, computing the class posterior probability. A table with pairs of {class,log posterior}
is returned. The posterior is computed considering the negative of euclidean squared distances as logscores, and normalizing over all the logscores in the result
table. In theory, as more K
neighbors were taken, the better posterior will be obtained. However, in practice, with values of K
between 10 and 20 could be enough. Note that the posteriors table is traversed using pairs
, not ipairs
, because two reasons: first, the class identifier could be anything, not only a number, it depends in your get_class_function
; and second, even with numeric class, identifiers not all the classes has to be present in the result
table, so the posteriors table is not an array, it could contains gaps.
Additionally to the posteriors
table, this function returns the maximum posterior cls
class and its value best
.
> result = kdt:searchKNN(20,matrix(1,D):uniformf(1,1,rnd))
> posteriors,bestcls,best = knn.kdtree.posteriorKNN(result,
function(id) return id%10 end)
> for c,p in pairs(posteriors) do print(c,p) end
1 3.3052420168408
2 2.7092774252334
4 2.289865236587
5 2.4045060888367
6 1.8979527591223
7 2.0151971414884
8 2.0497985519464
0 2.2390630830692
9 1.6785405659992
>  in this example, the best class is 9, has the maximum posterior
> print(bestcls,best)
9 1.6785405659992
predicted,logp = knn.kdtree.regressionKNN(result, get_tgt_func)
This function receives a result
table and a get_tgt_func
, and computes a prediction which is a weighted mean of the neighbors in result
, weighted by the posteriors computed in a similar fashion as posteriorsKNN
function, but ignoring the marginalization over classes.
The get_tgt_func
is a Lua function which receives a training pattern id
and returns the target (predicted) value associated with it. The returned value must be an instance of matrix
, a Lua table
or a Lua number
. In any case, the predicted
value will be a matrix
.
Additionally, this functions returns the the logp
with the logposterior of every neighbor.
> function price(id)
 DO_STUFF
return price_of_id
end
> predicted = knn.kdtree.regressionKNN(result, price)
Currently, the most widely used hyperparameter optimization technique is grid search. Recently, random search is proposed as an easy method which could obtain interesting good results (competitive with grid search, even better in some tasks) 2012 Bergstra and Bengio.
In APRILANN it is possible to do random search hyperparameter optimization using the script located at:
tools/trainable/randomsearchhyperparemeteroptimization.lua
This scripts receives a configuration Lua file like this:
return {
hyperparams = {
{ option="o1=", value=10, tag="o1", sampling="fixed", hidden=true },
{ option="o2=", value=20, tag="o2", sampling="fixed" },
{ option="r1", tag="r1", sampling = "loguniform",
type="real", prec=3,
values= { { min=0.001, max=1 }, },
filter = function(hyperparams) hyperparams["r1"] = "200" return true end },
{ option="r2=", tag="r2", sampling = "uniform", values= { 1, 2, 3, 4, 5 } },
{ option="r3=", tag="r3", prec=3,
sampling = "gaussian", values= { mean=0, variance=0.1 } },
{ option="r4=", tag="r4", sampling = "random",
filter = function(hyperparams)
if hyperparams["r2"] == "1" then hyperparams["r4"] = "0" end return true
end },
{ option=nil, tag="r5", sampling="random" }
},
filter = function(hyperparams) hyperparams['r5'] = '0.4' return true end,
script = "",
exec = "echo",
working_dir = "/tmp/",
 seed = ANY_SEED_VALUE (if not given, take random from bash)
n = 50 }
The configuration file returns a Lua table which contain some prior knowledge about each hyperparameter (a fully random optimization is unreliable). The table has this major fields:
hyperparams
: a table which describes the prior knowledge of each random searched hyperparameter (note that some of them could be 'fixed' instead of random). Each random hyperparemter is identified by a tag
string, a unique option
and fields which describe different prior distributions of hyperparameters. The sampling="fixeduniformloguniformgaussianrandom"
field indicates if the sampling distribution will be fixed (always the same value), uniform, loguniform, gaussian, or totally random (this last one is not contrained). The fixed distribution needs a value=SOMETHING
field which contains the value of this hyperparameter. The uniform distribution needs a values
field which contains a table of values (values={1, 4, 8}
) or an array of tables with min/max/step constrains (values={ {min=0, max=10, step=2}, {min=20, max=30, step=4} }
). The loguniform distribution needs a table with min/max constrains (not step). The field type="realinteger"
is only useful for min/max/step values. The field prec=3
indicates the number of precission digits needed. All of them could be hidden=true
, indicating that this hyperparameter won't be at the output filename string, but yes at the arguments list. Besides, the 'option' field could be option=nil
, indicating that this hyperparameter is a metaparameter which won't be passed as argument to the script, but yes to the filter functions of each hyperparameter and the global filter function. The filter
field is a function which returns true or false indicating if this set of hyperparameters is valid, and receives a table indexed by TAGs which contains all top hyperparameter values (it is possible to modify any value at this table).
filter
: is a function which received a dictionary table which associates each tag
with its value (a string in all cases, even for integer or real numbers). This function is called just before run an experiment. It checks the validity of hyperparameters returning true, otherwise, the experiment won't be executed, and it modifies any hyperparameter value. NOTE that is recommended to write filter functions which use 'string' type for their modified hyperparameters.
exec
: the executable file, normally an aprilann binary file, but others are possible.
script
: it will be an script given as first argument of the executable.
working_dir
: where to store stdout of each experiment. Each experiment is stored at a filename "WORKING_DIR/outputTAG1:VALUE1_TAG2:VALUE2_TAG3:VALUE3_..._TAGM:VALUEM.log". Hyperparamteres marked as hidden=true
won't be used to form this filename.
seed
: an optional random number generator seed.
n
: the number of experiments which will be executed.
The random search executes a script which receives non positional command line options. The option
field indicates the string which will be concatenated as prefix of the value. So, if the script needs an option like this: blah=NUMBER
, the field may be: option="blah="
.
An option
field could be nil
, indicating that this hyperparameter is not used at the script, but it is needed in filter
functions.
WARNING!!! variable params of each filter funtion always has string type, in order to ensure that the required number of precission digits is correct. So, you need to use tonumber(hyperparam[TAG])
in order to compare two numbers, and also is recommended to modify hyperaparams using string type values.
The execution of the procedure follows this syntax:
aprilann tools/trainable/randomsearchhyperparemeteroptimization.lua configurationscript.lua [ARGS]
where ARGS
follows this syntax:
ARGS : ARGS [ "all_hyperparams.TAG.value='blah'"  "global_vars.working_dir='blah'"  "global_vars.n=blah" ... ]
where all_hyperparams
is a Lua table (associates tag names with hyperparmeter fields) which contains the fixed and randomized parameters of configurationscript.lua
, so it is possible to modify any parameter field (option, value/s, sampling, prec, tag, values.min, values.max, ...) from the command line, and global_vars
is a Lua table which contains the rest parameters of configurationscript.lua
(seed, n, exec, script, working_dir, filter). All this command line arguments must be valid Lua code.
bunch_size
at validation step?Yes, it is. A field bunch_size
could be defined at the table received by train_dataset
and validate_dataset
methods of trainable.supervised_trainer
objects:
trainer:train_dataset{
input_dataset = in_ds,
output_dataset = out_ds,
shuffle = random_object,
bunch_size = 32,  TRAINING BUNCH SIZE
}
trainer:validate_dataset{
input_dataset = in_ds,
output_dataset = out_ds,
bunch_size = 1024,  VALIDATION BUNCH SIZE
}
It is a common mistake, probably you forget to use the parameter which is received by noise_pipeline
functions. See this example:
INPUT_DATASET = whatever...
...
noise_pipeline = { function(GIVEN_DS)
return dataset.salt_noise{
ds=INPUT_DATASET, ....
}
end }
...
This example will produce the error, because the INPUT_DATASET is used inside the function defined for noise_pipeline
table, and this variable is taken as closure of the function. However, the SDAE procedure exepcts that you use the GIVEN ARGUMENT ds
, which has been prepared to contain the data after training the first AutoEncoder. So, the code must be like this:
...
noise_pipeline = { function(GIVEN_DS)
return dataset.salt_noise{
ds=GIVEN_DS, ....
}
end }
...
APRILANN, Copyright (c) 20122016, ESET, Universidad CEUCardenal Herrera, (F. Zamora)
APRILANN, Copyright (c) 20122016, DSIC, Universitat Politècncia de València (S. España, J. Pastor, A. Palacios)
April, Copyright (c) 20062012, DSIC, Universitat Politècnica de València (S. España, J. Gorbe, F. Zamora)
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. http://fsf.org/ Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
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A compilation of a covered work with other separate and independent works, which are not by their nature extensions of the covered work, and which are not combined with it such as to form a larger program, in or on a volume of a storage or distribution medium, is called an "aggregate" if the compilation and its resulting copyright are not used to limit the access or legal rights of the compilation's users beyond what the individual works permit. Inclusion of a covered work in an aggregate does not cause this License to apply to the other parts of the aggregate.
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"Additional permissions" are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:
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All other nonpermissive additional terms are considered "further restrictions" within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.
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Additional terms, permissive or nonpermissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way.
You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).
However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.
Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.
You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peertopeer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.
Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License.
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You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a crossclaim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it.
A "contributor" is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, "control" includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.
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If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. "Knowingly relying" means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it.
A patent license is "discriminatory" if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the nonexercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.
If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.
Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.
The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.
Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.
If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program.
Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands show w' and
show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see http://www.gnu.org/licenses/.
The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read http://www.gnu.org/philosophy/whynotlgpl.html.
Lua originaly is under the terms of MIT license. However the version used here has minimal modifications and is sublicensed as GPL v3.
Lua is licensed under the terms of the MIT license reproduced below. This means that Lua is free software and can be used for both academic and commercial purposes at absolutely no cost.
For details and rationale, see http://www.lua.org/license.html .
Copyright © 1994–2013 Lua.org, PUCRio.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.